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Satellite infrared detectors cannot penetrate clouds, especially precipitating clouds. Improving precipitation estimation accuracy based on infrared brightness temperature has always been important but challenging. In this paper, based on the infrared brightness temperature of the Advanced Geosynchronous Radiation Imager (AGRI) onboard China's Feng‐Yun 4A satellite, we develop and evaluate a new precipitation estimation method. First, using static data, physical characteristics of clouds, cloud image texture features, temporal motion features, and AGRI infrared channel brightness temperature, we construct features for a machine learning model. Then, we develop precipitation estimation methods. Precipitation is estimated in two steps: classification and regression. We employ a random forest classification model to identify whether there is precipitation in a given field of view. If there is precipitation, a multi‐model ensemble regression learning method is used to estimate the areas with this precipitation. The ensemble learning method uses convex optimization to integrate prediction results based on the optimization of hyperparameters of five basic models (i.e., those of random forest, XGBoost, LightGBM, decision tree, and extra tree models). Furthermore, two regression stacking ensemble models—the Least Absolute Shrinkage and Selection Operator (herein referred to as Stacking1‐LASSO) and K‐nearest neighbor (herein referred to as Stacking2‐KNN)—are used to predict the results of the aforementioned basic models. The results of basic models are used as inputs of these two stacking models. Finally, based on the Integrated Multi‐satellitE Retrievals for GPM (IMERG) precipitation product and rain gauge precipitation data, we conduct precipitation estimation experiments and evaluate our methods. The results show that ensemble learning models have greater accuracy in estimating precipitation than the basic models. When using IMERG precipitation as the target precipitation, ensemble learning models can estimate the central area of heavy precipitation during typhoons Ampil and Maria. The ensemble learning estimation effect is better than that of Stacking2‐KNN. Moreover, when rain gauge data is used as the target precipitation, ensemble learning can also estimate the center of heavy precipitation and with good consistency with recorded satellite brightness temperature data. Key Points: A convex optimization weighted and stacked ensemble learning method for satellite data estimation of precipitation is developedNear real‐time precipitation estimates can be obtained throughout the day using only geographic information and Advanced Geosynchronous Radiation Imager infrared brightness temperature dataThe proposed ensemble learning method can estimate the central areas of typhoons and short‐term heavy rainfall [ABSTRACT FROM AUTHOR]

Accurate rainfall measurement with a precise spatial and temporal resolution is essential for making informed decisions during disasters and conducting scientific studies, particularly in regions characterized by intricate terrain and limited coverage of automated weather stations. Retrieval of precipitation with satellite is currently the most effective means to obtain precipitation over large‐scale areas. The key to enhancing the accuracy of precipitation estimation and forecasting in regions with complex terrain lies in effectively integrating satellite data with topographic information. This paper introduces a deep learning approach called AttUnet_R_SFT that utilizes high temporal, spatial, and spectral resolution data obtained from the Fengyun 4A satellite, and incorporates the Deep Spatial Feature Transform (SFT) layer to incorporate geographical data for estimating half‐hourly precipitation in northeastern China. We assess it by compared to operational near‐real‐time satellite precipitation products demonstrated to be successful in estimating precipitation and baseline deep learning models. According to the experimental findings, the AttUnet_R_SFT model outperforms practical precipitation products and baseline deep learning models in both identifying and estimating precipitation. The main enhancement of the model performance is shown in the windward slope of the Greater Khingan Mountains as a result of the successful incorporation of geographical data. Hence, the suggested framework holds the capability to function as a superior and dependable satellite‐derived precipitation estimation solution in regions characterized by intricate terrain and infrequent rainfall. The findings of this study indicate that the utilization of deep learning algorithms for satellite precipitation estimation shows potential as a fruitful avenue for further research. Plain Language Summary: A deep learning model named AttUnet_R_SFT is proposed, which use high temporal, spatial and spectral resolution data from the Fengyun 4A satellite, and combines with the Deep Spatial Feature Transform (SFT) layer to input geographic information for half‐hourly precipitation estimation in the complex terrain region represented by northeast China. The model can provide a reference for improving the performance of precipitation estimation in areas with complex topography. Key Points: A deep‐learning model is proposed to effectively fuse satellite multispectral data of the Fengyun 4A satellite, with topographic informationHalf‐hourly precipitation is estimated with higher temporal resolution, which is closer to the operational needs of weather forecastingAs precipitation in the study area is a non‐high‐frequency event, data enhancement is attempted to use and obtain effective results [ABSTRACT FROM AUTHOR]

Currently, the opportunistic method to estimate rainfall using commercial microwave links (CMLs) has been shown as an efficient way to complement traditional instruments in terms of spatial‐temporal resolution and coverage. In this paper, we collected data from 26 CMLs in Jiangyin City, Jiangsu Province, and conducted experiments on rainfall field reconstruction and nowcasting. First, the raw CML data were processed to invert the path‐averaged rainfall intensity. Second, the algorithms of inverse distance weighting (IDW) and ordinary kriging (OK) interpolation were employed to reconstruct the rainfall field. Then a 10‐min prediction of the rainfall field was achieved using a nowcasting model based on the long short‐term memory neural network and a setup window was introduced to improve the prediction performance of the first few minutes. The reconstruction results show that the average correlation coefficient (ACC) and the average root mean square error (ARMSE) between the IDW‐based results and daily cumulative rainfall from rain gauges (RGs) are 0.89 and 8.69 mm, respectively, while the ACC and ARMSE between the OK‐based results and RG data are 0.89 and 9.13 mm, respectively. The nowcasting results show that the ACC between the prediction results with a 5‐min setup window and the IDW‐retrieved rainfall fields can reach 0.91 at the first minute and gradually decrease to 0.20 within 10 min. Furthermore, the model has better nowcasting performance for stratiform precipitation and mixed precipitation compared to convective precipitation. Plain Language Summary: Accurate and real‐time rainfall monitoring and forecasting are of great significance for disaster prevention and control, agriculture, meteorological research, and related fields. However, traditional rainfall measurement methods are insufficient for meeting the demands of comprehensive precipitation observations because of poor spatial‐temporal resolution and limited coverage. Currently, measuring rainfall using additional microwave attenuation caused by raindrops has been shown as an efficient way to complement traditional instruments. Based on commercial microwave link (CML) rainfall measurement technology, this paper carries out rainfall field reconstruction and nowcasting experiments in Jiangyin City, China. The results show the CML network enables accurate rainfall field reconstruction and nowcasting. Key Points: Based on the commercial microwave link (CML) rainfall monitoring network in Jiangyin, China, accurate rainfall inversion by CMLs is achievedThe two‐dimensional rainfall fields are accurately reconstructed using the inverse distance weighting and ordinary kriging interpolation algorithmsA long short‐term memory‐based rainfall field nowcasting model is proposed to achieve 10‐min continuous predictions of rainfall fields [ABSTRACT FROM AUTHOR]

In network real‐time kinematic positioning of multi‐reference station, the spatial and temporal distribution of tropospheric delay is affected by both horizontal and elevation. The traditional modeling strategy of regional troposphere takes more consideration of the horizontal factor, and the incomplete consideration of the elevation factor will lead to the problem of reduced modeling accuracy, especially in the face of the scene with large regional height deviation. Based on the traditional linear interpolation method (LIM), a simple and effective height‐deviation compensation‐based linear interpolation method (HCLIM) for regional tropospheric is proposed. The modeling accuracy of troposphere and the positioning accuracy of user RTK in large height deviation region are significantly improved. The method was verified based on six experimental subnets with large height deviations from a provincial continuously operating GNSS reference stations network in central China. The results showed that: For GPS satellite modeling, compared with the traditional LIM method, the average modeling accuracy improvement rate of HCLIM method is (84.5%, 75.5%, 59.3%, 26.7%) in the elevation angle range of (10–30°/30–40°/40–50°/50–90°). For BDS satellite, the average modeling accuracy improvement rate of HCLIM method in the above four elevation angles is (83.3%, 70%, 50%, 23.5%). For the positioning performance of user RTK, The horizontal positioning accuracy and RTK fixing rate were similar under the two methods, while HCLIM method showed only slight improvement. However, in the U direction, LIM method showed obvious systematic bias, while HCLIM method showed consistent positioning accuracy, which was improved to 82.8% compared with LIM method. Plain Language Summary: In GNSS relative positioning, the positioning performance of user largely depends on the extent to which atmospheric deviations (ionospheric delay or tropospheric delay) are eliminated or weakened. Compared with the ionosphere, the temporal and spatial characteristics of troposphere are significantly different. It is part of the lower atmosphere, extends from the Earth's surface to an altitude of about 20 km, and contains most of the atmospheric water vapor. The delay of satellite signals through the troposphere is affected by both horizontal and elevation. The traditional tropospheric regional modeling strategy only uses plane coordinates for interpolation, ignoring the influence of elevation factors or the consideration of height‐deviation is not perfect. When the height difference between the reference station and the rover station is large, the modeling accuracy will obviously be reduced. The large modeling residual may bring systematic deviation to the user coordinates, especially the elevation direction component. In this paper, we propose a regional tropospheric linear interpolation method (HCLIM) considering height deviation compensation. This method is simple and effective, The modeling accuracy of troposphere and the positioning accuracy of user RTK in large height difference region are significantly improved. Key Points: A simple and effective height‐deviation compensation‐based linear interpolation method (HCLIM) for regional tropospheric is proposedThe HCLIM method greatly improves the tropospheric modeling accuracy of each elevation angle range in the large height difference site areaThe HCLIM method significantly improves the systematic bias in elevation direction of user RTK positioning due to modeling residuals [ABSTRACT FROM AUTHOR]

This study proposes a novel method, which is a U‐shaped convolutional neural network that combines non‐local attention mechanisms, Res2net residual modules, and terrain information (UNR‐Net). The original U‐Net method and the linear regression (LR) method are conducted as benchmarks. Generally, the UNR‐Net has demonstrated promise in performing a 10× downscaling for daily 2‐m temperature over North China with lead times of 1–7 days and shows superiority to the U‐Net and LR methods. To be specific, U‐Net and UNR‐Net demonstrate higher Nash‐Sutcliffe Efficiency coefficient values compared to LR by 0.052 and 0.077, respectively. The corresponding improvements in pattern correlation coefficient are 0.013 and 0.016, while the root mean square error values are higher by 0.22 and 0.338, respectively. Additionally, the structural similarity index metric is higher by 0.033 and lower by 0.015. Furthermore, regions with significant errors are primarily distributed in complex terrain areas such as the Taihang Mountains, where UNR‐Net exhibits noticeable improvements. In addition, the 12 components‐based error decomposition method is proposed to analyze the error source of different models. Generally, the smallest errors are observed during the summer season and the sequence error component is proven to be the main source error of 2‐m temperature forecasts. Furthermore, UNR‐Net consistently demonstrates the lowest errors among all 12 error components. Therefore, combining the numerical weather prediction model and deep learning method is very promising in downscaling temperature forecasts and can be applied to routine forecasting of other atmospheric variables in the future. Plain Language Summary: This research proposes a new method for downscaling using deep learning. The method uses a specific type of neural network called UNR‐Net, which combines attention mechanisms, residual modules, and terrain information. The performance of UNR‐Net is compared to two other methods: U‐Net and LR. In the study, UNR‐Net shows promise in performing a 10× downscaling of the daily 2‐m temperature in North China. The UNR‐Net demonstrates the best overall performance among all the comprehensive indicators (NSE, pattern correlation coefficient, root mean square error, and structural similarity index metric). Errors in the predictions are mainly found in complex terrain areas like the Taihang Mountains, but UNR‐Net shows noticeable improvements in these regions. The study also proposes a 12 components‐based error decomposition method to analyze the error sources of different models. All in all, it is found that the smallest errors are observed during the summer season and the main source error is the sequence error component. Additionally, when considering lead times of 1–7 days, UNR‐Net consistently shows the lowest errors among all 12 error components. Based on these findings, combining numerical weather prediction models with deep learning methods holds great promise for generating high‐resolution temperature forecasts. Key Points: This paper presents a novel deep learning downscaling method, UNR‐Net, capable of downscaling daily 2‐m temperature by a factor of 10The overall performance of the UNR‐Net method surpasses the U‐Net method and linear regression methodThe 12 components‐based error decomposition method is proposed to analyze the error source of different models [ABSTRACT FROM AUTHOR]

The China‐France ocean satellite (CFOSAT) has obtained synchronized observations of sea winds and sea waves for the first time in the world, providing accurate data support for the study of swells. In this paper, the accuracy of key parameters and the wave spectrum of CFOSAT surface waves investigation and monitoring (SWIM) L2 data are analyzed and evaluated using a spatial‐temporal matching criterion. The accuracies of the significant wave height (SWH), spectrum peak period (Tp) and dominant azimuth (Azi) are 0.76 m, 2.89 s and −24.02°, respectively, in the coastal area of the South China Sea (SCS) and 0.30 m, 0.97 s and 13.02° in the offshore area of the SCS. Calculated based on CFOSAT SWIM data, the annual average SWH of mixed waves in the SCS is 1.47 m, and 80% of them are between 0.5 and 3 m. There are high value areas in the central, northeastern and southwestern parts of the SCS. The average annual SWH of swells is 0.93 m, and 99% of swells are less than 2 m. A high swell SWH also exists in the central part of the SCS. Plain Language Summary: Swells are an important marine resource. Due to their long propagation distance and fast propagation speed, they can effectively forecast storm surge. They also have large energy and good stability, which are conducive to acquisition and conversion, and they have become a new method for offshore power generation. China‐France ocean satellite provides a convenient means to observe waves and to verify the accuracy of wave parameters, which is an important step for wave detection. The South China Sea, with its vast sea area and abundant swell energy reserves, is an ideal area for offshore power generation. Understanding the distribution of swell energy in the South China Sea (SCS) provides an opportunity to explore a sustainable and reliable renewable energy source. The SCS is also important and significant to the development and utilization of swell energy and for the site selection of offshore power stations. Key Points: Precision valuation of surface waves investigation and monitoring L2 data such as significant wave height (SWH), spectrum peak period (Tp), dominant azimuth (Azi) were carried out and it was determined that the data were stableThe annual average SWH in the South China Sea is 1.47 m and 80% of them are between 0.5 and 3 mWind‐swell separation was carried out, and the distribution of swell SWH shows a high value in the central part of the South China Sea [ABSTRACT FROM AUTHOR]

Historically, the long wavelength side (6–7.5 μm) of the mid‐infrared water vapor absorption band has been used for imaging from the geostationary perspective. This began with the 6.4 μm band on Europe's Meteosat‐1. While geostationary sounders for moisture profiling, including China's hyperspectral resolution infrared sounder and a planned sounder from Europe, are or will be measuring the short wavelength side of the water vapor band, this is not the case for geostationary imagers. Shorter wavelength (5–6 μm) spectral bands for imaging applications should be considered for observing moisture in the mid and lower troposphere because of several potential advantages offered by this spectral range. The short wavelength side of the water vapor band contains fewer additional absorbing gases that overlap the water vapor absorption lines. In addition, the shorter wavelengths would show less diffraction blurring which could enable finer spatial resolution for turbulence detection. This study considers some of the differences and potential advantages of the spectral information in the short wavelength side of the water vapor absorption band from the perspective of geostationary imaging. For dry conditions land heating could impact the qualitative use of observed brightness temperatures at 5.1 μm, while the solar reflection component over most clear‐sky scenes is small for a 5.1 μm band and essentially zero for a 5.6 μm band. Plain Language Summary: Long before images are sent back to Earth from satellites, the needed spectral bands have to be specified. The band specification includes attributes such as the wavelength centers, widths, and signal‐to‐noise ratio. This paper investigates the rationale for including data from a region of the electromagnetic spectrum that has not been used for imaging from geostationary orbit. If implemented, these short wavelength water vapor bands would provide unique information regarding low‐level moisture and mid‐level turbulence. This paper quantifies the expected reflected component from the sun and the influence of land heating on observations, both of which are relatively larger at shorter wavelengths. Key Points: Historically, the long wavelength side of the mid‐infrared water vapor absorption band has been used for imagingShort wavelength (near 5 μm) spectral bands should be considered, especially with finer spatial resolutionsOver most clear‐sky surfaces, the reflective component ranges from small to zero for 5.1 and 5.6 μm bands [ABSTRACT FROM AUTHOR]

Both long‐term but small number of national stations and short‐term but large number of regional stations have been frequently used to study the extreme hourly precipitation (EXHP) in China. However, few studies focus on the differences of the two for revealing the features of EXHP. In this study, the characteristics of EXHP in Zhejiang Province are investigated using three rainfall data sets at three threshold criteria. The comparison between different data sets shows that increasing the station density can better reflect the climatic spatial distribution of EXHP thresholds if long‐term data is absent. The majority of EXHP can be classified into four weather types: the southwesterly wind type (30.7%–48.5%), the trough type (12.2%–23.6%), the tropical cyclone (TC) type (11.4%–17.5%) and the easterly wind type (4.9%–17.9%). The selection of stations is more sensitive to the proportions of the four weather types than the statistical years and threshold criteria. The monthly and diurnal variations of EXHP, as well as their differences revealed by the three data sets, are varied by weather type. Only using national stations cannot distinguish the spatial differences between the TC type and the easterly wind type, and there is an underestimation for southwesterly wind type and trough type in the mountainous area of southwest Zhejiang. The statistical year and station height are the main reasons for the differences in the duration of EXHP events calculated by different data sets, with the TC type having the largest effect and the southwesterly wind type the smallest. Plain Language Summary: Station data is the most common and reliable data for studying the characteristics of precipitation in China. There are two sources of station data available, that is, national station data with long time series but sparse and regional stations with short time series but numerous. However, few studies have compared the differences between these two sources of data for revealing the characteristics of extreme hourly precipitation (EXHP). In this study, the possible impact of different data sets for revealing the characteristics of EXHP under four weather types are investigated in Zhejiang Province. Zhejiang is located on the southeast coast of China with a long coastline and many mountains. Results suggest that increasing the station density can better reflect the spatial distribution of the EXHP thresholds if long‐term data is absent. EXHP events with strong synoptic‐scale forcing are more sensitive to the results obtained by different data sets. As the national stations are mostly located in the plains, the high frequency centers of EXHP are biased in each weather type if only using national stations, especially in southwest Zhejiang and near the coast. In summary, this paper provides additional perspectives for understanding the characteristics of regional EXHP by using the most common observations. Key Points: The selection of stations is sensitive to the proportions of the four main weather types in Zhejiang where extreme hourly rainfall occursExtreme hourly precipitation events with strong synoptic‐scale forcing are more sensitive to results obtained using different data setsThe sparse national stations located on the plains could hardly reflect the spatial distribution of extreme rainfall in mountainous Zhejiang [ABSTRACT FROM AUTHOR]

In the search for precursors to earthquakes, correlation has been found between geochemical characteristics of soil gases and seismic activity. In this paper we present evidence that seismic waves can trigger emission of soil radon (Rn) and carbon dioxide (CO2). An active experiment was performed in two fault zones in China, the Annighe fault in Sichuan province and the Xiadian fault in Heibei province. An active seismic source was used to generate seismic waves at 10 m depth in wells within bedrock. Rn and CO2 detectors were placed around the wells at a distance of ∼1 m for observing the effects of the seismic waves on the emission of the gases. The observations confirm that the seismic waves have a significant and direct effect on the concentration and flux of soil radon and carbon dioxide. When the seismic events were triggered, the observed concentrations of Rn and CO2 immediately increased and reached peak values within 5–50 min and 30–60 min, with corresponding increases of Rn and CO2 concentrations by 10.5%–238.7% and 3.1%–54.1%, respectively. The measured concentrations and flux of CO2 and Rn after the passage of the seismic waves showed strong correlation, confirming the suggestion that CO2 is the carrier gas for Rn. To the best of our knowledge this is the first direct, in‐situ measurement of gas emission caused by the passage of seismic waves and provides important constraints for better understanding of geochemical earthquake precursors. Plain Language Summary: Observed geochemical properties of soil gases migrated from the deep Earth can be used to survey seismicity, volcanic activity and emission of greenhouse gases. However, due to difficulty in natural earthquake prediction, most observations on the geochemical effects linked to seismic activity are conducted after or away from naturally occurring events, with the effects normally inferred from seismic parameters. In this study, an active seismic source based on methane gaseous detonation was employed to artificially produce seismic events along two fault zones, and in‐situ measurements of concentrations and flux of radon (Rn) and carbon dioxide (CO2) were conducted. These observations showed strong correlation between concentrations and flux of CO2 and Rn after the events, in agreement with the hypothesis that CO2 is the carrier gas for Rn in tectonically active settings. Key Points: Soil CO2 and Rn emission triggered by a new type active source that excited at 10 m deep well in bedrock are observed at first timeQuantitative effect of seismicity on the concentration and flux of soil CO2 and Rn are presentedEmission of CO2 and Rn after the seismicity show strong correlation and proved that CO2 is the carrier gas for Rn [ABSTRACT FROM AUTHOR]

Reconstructing the tectonic architecture of the southern Altaids is of great importance to piece together the continental growth history of the Central Asian continent. In this paper, we address the subduction‐accretion process of the Paleo‐Tianshan Ocean, one branch of the South Tianshan Ocean, in the southern Altaids, through a detailed geological and geochemical investigation of the Wulanmoren Accretionary Complex (WAC). The WAC is composed of ophiolitic mélanges, ocean plate stratigraphy mélanges, and turbiditic coherent units. In this study, we present key geological, petrological, and geochemical evidence indicating back‐arc and ocean island affinity of the igneous rocks of the WAC. Zircon U‐Pb dates of these rocks ages range between 380.2 ± 1.3 and 302.0 ± 5.3 Ma. The sedimentary matrix units of the mélanges were deposited in the Early Devonian (∼405 Ma) to the Late Triassic (∼222 Ma), rather than Silurian strata as previously thought. However, the ages of some detrital zircons in the sedimentary matrix are Precambrian, pointing to a complex evolution involving multiple ocean island terrains and island arcs of different ages that accreted from the mid‐Paleozoic to the Early Mesozoic. Plain Language Summary: Well‐exposed rock outcrops of the Wulanmoren Accretionary Complex (WAC) offer an ideal opportunity to solve the enigma of the evolutionary history of the Paleo‐Tianshan Ocean (PTO) of Central Asia. We report field observations, geochemistry, and geochronology of the WAC in the southern Altaids, pointing to multiple tectonic settings of ages spanning from the Early Devonian (∼405 Ma) to the Late Triassic (∼222 Ma). These reconstructions suggest closure time of the PTO occurred post‐Triassic. Based on detrital zircons provenance analysis, we propose a multi‐island ocean model to explain the tectonic evolution of the PTO in the time period studied. Key Points: The Wulanmoren Accretionary Complex was composed of ophiolitic mélanges, ocean plate stratigraphy mélanges, and coherent unitsThe Late Paleozoic MORB‐like and OIB‐type basalts, and ∼222 Ma coherent units indicate closure of the Paleo‐Tianshan Ocean occurred post‐TriassicThe southern Paleo‐Asian Ocean was an archipelago with several subduction systems which is similar to the modern southwest Pacific [ABSTRACT FROM AUTHOR]

The reduction of the runoff to lake has become a common phenomenon of the inland lakes in northwest China, and the quantitative calculation of the driving factors affecting the runoff change has always been a challenge in lake research. In this pursuit, the present study was performed on Lake Daihai as an example, based on the relevant data from 1960 to 2018. The quantitative calculations of the runoff driving factors of Lake Daihai from 1982 to 2004 and from 2004 to 2017 were based on the slope change ratio of cumulative quantity (SCRCQ). It was concluded that human activities were the main factors affecting runoff change after 1982. The SCRCQ method was improved, which could carry out the quantitative calculations on the driving factors affecting the runoff change of the Lake Daihai from 1961 to 2017 on a yearly basis. The results show that the dominant factor of runoff changes in Lake Daihai was found to be the climate change before 1975, and it changed into human activities after 1975. Finally, based on the comparative analysis of the results of SCRCQ and improved SCRCQ, the advantages of improved SCRCQ were discussed. In addition, the reasons for the variation of the runoff to lake dominated by different factors in different periods for Lake Daihai were analyzed. Key Points: This paper analyzes the influence of climate change and human activities on the change in runoff volume of Lake Daihai according to the slope change ratio of cumulative quantity (SCRCQ)The SCRCQ method was improved, which could carry out the quantitative calculations on the driving factors affecting the runoff change of the Lake Daihai from 1961 to 2017 on a yearly basisThe reasons for the variation of the runoff to lake dominated by different factors in different periods for Lake Daihai were analyzed [ABSTRACT FROM AUTHOR]

When many cities need quantitative forecasts of air quality to adjust industrial production plans and urbanization development, how to build an efficient forecast model remain a challenge. Methodology for quantitative prediction of air quality can no longer rely on single‐site observations, and thus approaches that require fewer input data and are more efficient and more reliable need to be explored. This paper proposes the principles and steps of a new model using the empirical orthogonal function (EOF) and principal component regression (PCR), which is a hybrid EOF‐PCR approach that decomposes the panel data of PM2.5 and predictors into spatial structures (EOFs) and time expansion coefficients (ECs) by EOF analysis, establishes the PCR of the ECs of PM2.5, and simulates the PM2.5 concentrations in each city by projecting the fitted ECs through EOFs. The very heart of the new model is the PCR modeling and projection. The results are presented for PM2.5 concentrations over Jiangsu Province in eastern China. The results show that this EOF‐PCR model, which is based on EC1s with a cumulative variance contribution rate above 90%, has an average prediction accuracy of 65%. The model performs best in spring and autumn, better in summer and worst in winter. Most predictors have a maximum lag of a week, and they are quite different among seasons. Considering the influence of the spatial distributions of predictors, rather than covariates at a single site, this model can reflect regional influences and effectively improve the simulation effect. Plain Language Summary: This study aims to develop a hybrid statistical model for regional forecasting of PM2.5 concentrations using EOF analysis and principal component regression, based on daily observations of PM2.5 concentrations and meteorological predictors. In the study area, the emission sources are relatively fixed, and meteorological conditions represent the main factor affecting short‐term air quality. The panel data of PM2.5 concentration and predictors are decomposed into spatial structures (EOFs) and temporal expansion coefficients (ECs) by EOF analysis. A principal component regression model was built to simulate the ECs of the PM2.5 concentrations, with the ECs of the predictors. Finally, through the inverse operation of the EOF analysis, the fitting values of the PM2.5 ECs are projected as PM2.5 concentrations at each site. The proposed model can reduce input requirements, improve computational efficiency, and emphasize the synergistic effect of meteorological factor distributional modes, rather than a point influence. Key Points: Proposed new regional forecast approach has less demand for input data, especially emissions dataForecasting accuracy was improved and reached nearly 70%, and computational efficiency and speed also increasedPM2.5 concentrations are sensitive to regional distributions of different meteorological predictors, that is, EOFs [ABSTRACT FROM AUTHOR]

This paper presents our efforts to characterize the candidate landing region (109°–133°E, 23°–30°N) for Tianwen‐1, China's first mission to Mars, in terms of engineering safety and scientific significance. Topographic analysis reveals that the region has a low elevation around −4,230 m, and 98% of the region have slopes smaller than 8°. The geomorphological mapping and analysis show that the region has an average crater density of about 28 craters (≥200 m in diameter) per 100 square kilometers, with several clusters of high crater densities distributed around the center of the region. There are also pitted cones distributed mainly in the southern part of the region, with a density of approximately 6.6 cones per 100 square kilometers in specific local areas. The region has rock abundances ranging from 1% to 23%, with local clusters of low and high rock abundances. The region comprises four main geological units, including a lowland unit formed in the Late Hesperian and a volcanic unit formed in the Amazonian and Hesperian period. Their specific surface ages are estimated through the analysis of crater size‐frequency distribution. Combining the engineering constraints on surface slopes, crater density, cone density, and rock abundance, a hazard map of the candidate landing region is generated for landing site evaluation and safety assessment. Based on the results, we further discuss the potential scientific outcomes from the exploration in this region. The findings will be helpful for the mission planning and maximization of the scientific return from Tianwen‐1, and complement existing Martian scientific research. Key Points: Topographic, geomorphological, and geologic characterization of the candidate landing region of Tianwen‐1A hazard map of the region combining engineering constraints on slopes, crater density, cone density, and rock abundanceTwo local areas in the region are highlighted as potential landing sites for their low hazard and scientific significance [ABSTRACT FROM AUTHOR]

China has lots of funds, materials, and human resources in cloud seeding operations to help with water shortage problems across the country. But there is little guide about when and where the operations would obtain better benefits. To fill this gap, the modified Water Utilization Level (WUL) map of China mainland was plotted with the acquirable water utilization data on sub‐watershed scales in this study. Then the agricultural drought levels map of different counties were integrated with the modified WUL Map under preset rules to generate an integrated water scarcity map. Lastly, the cloud seeding demand maps of different seasons were drawn by integrating the agricultural drought seasons map of different counties with the integrated water scarcity map and the modified WUL map. Results showed that the middle north of China should have the highest priorities for cloud seeding operations during spring and summer, and some regions at the south during winter and spring, a few scattered regions at the northwest during spring and summer and a few regions at the middle of southeast China during summer and autumn should also have high priorities of cloud seeding operations. This research can help rain enhancement operations to obtain better effects on easing water scarcity problems and deepen the cooperation of water resources department and weather modification department in China. The designed intersecting rules of different maps in this paper also might inspire other relevant researches. Key Points: The results gave a clear demand from water resources field to weather modification department in China mainlandThe middle north areas of China have the severest water scarcity problems, especially during spring and summerSome less developed regions at the south, the northwest and the southeast have high water scarcity levels in different seasons [ABSTRACT FROM AUTHOR]

Ice clouds are mostly composed of different ice crystal habits. It is of great importance to classify ice crystal habits seeing as they could greatly impact single‐scattering properties of ice crystal particles. The single‐scattering properties play an important role in the study of cloud remote sensing and the Earth's atmospheric radiation budget. However, there are countless ice crystals with different shapes in ice clouds, and the task of empirical classification based on naked‐eye observations is unreliable, time consuming and subjective, which leads to classification results having obvious uncertainties and biases. In this paper, the images of ice crystals observed from airborne Cloud Particle Imager in China are used to establish an ice crystal data set called Ice Crystals Database in China, which consists of 10 habit categories containing over 7,000 images. We propose an automatic classification model of ice crystal habits, called TL‐ResNet152, which is a deep convolutional neural network based on the newly developed method of transfer learning. The results show that the TL‐ResNet152 model could achieve reliable performance in ice crystal habits classification with the accuracy of 96%, which is far more accurate than traditional classification methods. Achieving high‐precision automatic classification of ice crystal habits will help us better understand the radiation characteristics of ice clouds. Plain Language Summary: In recent years, Convolutional Neural Networks (CNNs), as one of the representative algorithms used in deep learning, have been widely applied in the field of image classification and have achieved remarkable results. However, to the best of our knowledge, there are few studies regarding the application of deep CNNs to ice crystal image classification. In this paper, we propose an automatic classification model of ice crystal habits called TL‐ResNet152, which is a deep CNN based on a newly developed method of transfer learning. Using the TL‐ResNet152 model, high precision and automatic classification of ice crystal habits could be achieved, furthering our understanding of radiation characteristics of ice clouds. We have set up an ice crystal data set, called Ice Crystals Database in China, which consists of 10 habit categories with 7,282 images. As far as we know, it is the first publicly available ice crystals data set in China, pertaining to ice crystals observed in natural ice clouds. The publication of this database will promote more and more researches into understanding the physical process of ice clouds based on ice crystal habits classification. Key Points: An ice crystal data set called Ice Crystals Database in China (ICDC) containing over 7,000 images has been first establishedWe propose an automatic classification model of ice crystal habits, TL‐ResNet152, which is a convolutional neural network created using transfer learningThe high‐precision automatic classification of ice crystal habits is achieved by using this classification model [ABSTRACT FROM AUTHOR]

A coupled soil temperature (ST) and moisture (SM) balance reflects a synthetic climate regime, having huge ecological impacts. This paper used ST and SM data from the European Center for Medium-Range Weather Forecasts climate reanalysis-Land and the Coupled Model Intercomparison Project Phase 6 and leaf area index (LAI) data from the Global Land Surface Satellite Product Suite. The focus was on understanding joint ST-SM changes and the resulting ecological response across China. The results show that during 2000--2020, 24.5% of the land area in China experienced a warming-drying trend resulting in a 9.7% LAI decrease, while 6.4% of the area experienced a warming-wetting trend leading to an 8.6% LAI increase. During 2015--2100, 30.6% of the land area in China will be warmer and drier, while 55.2% of the area will be warmer but wetter across three shared socioeconomic pathways (SSP126, 245, and 585). Superimposed on the long-term trends, there are also significant spatiotemporal variabilities in ST and SM on annual to decadal timescales. The LAI also showed substantial short-term fluctuations in both typical regions and ecosystems despite consistent long-term increases. Our findings suggest that ecosystems could be impaired on annual to decadal scales by adverse soil conditions in the twenty-first century, but in terms of long-term trends, ecosystems may be resilient partly because of the compensating effects of global warming and regional hydrological changes. Impact studies should thus focus more on annual to decadal soil-ecosystem anomalous events. [ABSTRACT FROM AUTHOR]

The observation errors in precipitation gauges contribute to diminished precision in precipitation data sets. To reduce the impact of these errors, the World Meteorological Organization Solid Precipitation Intercomparison Experiments recommended the Double Fence Intercomparison Reference as a reference standard for precipitation measurements. This study proposed a new rain, snow, and mixed precipitation adjustment method for national standard precipitation gauges, using DFIR‐measured precipitation as the standard values. This method was used to adjust for systematic errors, including wind‐induced errors, wetting loss, and trace precipitation, in precipitation data collected by 785 stations in China from 1961 to 2020. After bias adjustment, the annual precipitation increased by 6.1–177.9 mm (with an average of 52.7 mm), accounting for 3.3%–52.1% (8.9%) of the total precipitation. The average annual error‐adjustment amounts for wind‐induced error, wetting loss, and trace precipitation were 21.9 (3.6% of total precipitation), 26.6 (4.7%), and 4.2 mm (1.3%), respectively. The adjustment percentage in winter was higher than that in summer, with the high‐adjusted‐percentage regions predominantly located in areas with drought, high proportion of snowfall, and strong wind speeds. Additionally, the annual average error‐adjustment amounts for rain, snow, and mixed precipitation respectively accounted for 5.2%, 38.2%, and 17.1% of their corresponding total amounts, indicating the significance of bias adjustment, particularly for snow and mixed precipitation, in the northern and Qinghai‐Tibet Plateau regions. Therefore, bias adjustment is necessary to enhance the accuracy of the precipitation data set in China. Key Points: Propose a new adjustment method using the Double Fence Intercomparison Reference as a reference standard to improve precipitation accuracyThe wind‐induced error was smaller than wetting loss, which may be attributed to the decrease in surface wind speed across ChinaThe bias adjustments for snow and mixed precipitation were important for the cold and arid northern and Qinghai‐Tibet Plateau regions [ABSTRACT FROM AUTHOR]

China's first Mars sampling return mission (Tianwen‐3) is designed to launch and retrieve samples around 2030. Three tentative landing areas (TLAs) (Amazonis, Chryse and Utopia Planitiae, i.e., TLA‐A, TLA‐C and TLA‐U) are selected based on elevation <−2,000 m and latitude between 17° and 30°N. As a dominant feature of Martian meteorology, dust storms manifest in all seasons and affect the accuracy and safety of Mars exploration missions. Tianwen‐3's landing, sampling and ascent phases are in the dust storm season. Therefore, analyzing dust storm activity around landing areas is significant for the Tianwen‐3 mission. According to Mars Daily Global Maps taken by Mars Orbiter Camera spanning Martian years 24–28, 2,476 dust storm events around the three TLAs were identified in this research. Dust storm temporal probabilities within TLA‐A, TLA‐C and TLA‐U were calculated as 0%–44.69%, 0%–66.29% and 0%–33.64%, separately. Dust storm spatial probabilities around the TLA‐A, TLA‐C and TLA‐U were computed, with ranges of 0%–10.71%, 0%–14.55% and 0%–19.96% during the T1 period (Ls = 161–309°), and 0%–6.75%, 0%–7.65% and 0%–8.26% during the T2 period (Ls = 342‐55°), respectively. Finally, considering the temporal and spatial distribution of dust storms, we recommend the T2 period as the launch scenario. Three safe periods (Ls = 2–18°, 4–12°, and 356–4°) were assigned for the entry‐descent‐landing (EDL) phase, along with one period (Ls = 45–55°) for the take‐off and ascent phase. Five circular landing zones with dust storm spatial probability <3% were selected for the Tianwen‐3 mission. Plain Language Summary: Tianwen‐3 is the first Mars sampling return mission by China, scheduled to launch and return samples around 2030. Three tentative landing areas (TLAs), Amazonis Planitia (TLA‐A), Chryse Planitia (TLA‐C), and Utopia Planitia (TLA‐U), located at elevations of <−2,000 m and within the latitude range of 17°–30°N. Three TLAs are situated along the trajectories of Martian dust storm sequences. The harsh dusty conditions significantly impact the success of the Mars exploration mission. Therefore, it is meaningful to analyze the dust storm occurrence probability within and around three TLAs. In this study, we utilized Mars Global Surveyor (MGS) Mars Daily Global Maps to (a) detect the dust storm events around three TLAs in five consecutive Mars years (MY 24–28); (b) calculate and analyze the temporal and spatial probabilities of dust storm occurrence; and (c) designate the safe landing areas, the preferred take‐off and ascent and entry‐descent‐landing (EDL) periods for the Tianwen‐3 mission. Key Points: Dust storms within three tentative landing areas exhibit distinct seasonality and heterogeneity throughout a Mars year, with clear solstitial pause periodsDust storm spatial‐temporal analysis reveals that the T2 period is more suitable as the launch scenario for the Tianwen‐3The preferred entry‐descent‐landing period, preferred take‐off and ascent period, and safe landing areas are chosen for the Tianwen‐3 [ABSTRACT FROM AUTHOR]

Three scatterometers have been launched in China including Haiyang‐2A scatterometer (HY‐2A/SCAT), Haiyang‐2B scatterometer (HY‐2B/SCAT), and China French Oceanography Satellite scatterometer (CFOSCAT). Both of HY‐2A/SCAT and HY‐2B/SCAT are rotating pencil‐beam scatterometers while CFOSCAT is the first rotating fan‐beam scatterometer (RFSCAT) in the world. In this study, we focus on the evaluation of Chinese scatterometer operational wind products from January 2019 to April 2019. Scatterometer wind products are collocated with buoy measurements. Error varieties under different atmospheric stratification conditions are also taken into consideration. The average biases and root mean square errors (RMSEs) between buoy and scatterometer data are basically satisfied with the scatterometer mission requirements (2 m/s and 20°). HY‐2B/SCAT wind products have the best data quality. However, HY‐2A/SCAT wind products have the largest deviations compared with buoy measurements and the wind direction RMSE was about 24.19°. The biases change a lot between different atmospheric stratification conditions. HY‐2A/SCAT winds have abnormal large deviations when wind speeds are higher than 10 m/s while HY‐2B/SCAT and CFOSCAT winds are stable under neutral conditions. In brief, the wind data quality of HY‐2B/SCAT is the best and then is CFOSCAT while HY‐2A/SCAT winds have the largest deviations and are unstable under different atmospheric stratification conditions. Atmospheric stratification impacts should be considered in wind retrieval and marine application. Key Points: We evaluated operational wind products of Chinese scatterometers from January 2019 to April 2019The qualities of wind products under different atmospheric stratification conditions are also evaluatedThe results reveal that the order of retrieved wind quality from high to low can be ranked as Haiyang‐2B scatterometer (HY‐2B/SCAT), China French Oceanography Satellite scatterometer (CFOSCAT), and Haiyang‐2A scatterometer (HY‐2A/SCAT) [ABSTRACT FROM AUTHOR]

Atmospheric wet delay caused by Precipitable Water Vapor (PWV) significantly impacts the performance of many geodetic surveying systems such as Global Navigation Satellite System (GNSS). In this study, we use wet delay corrections forecast by the Weather Research and Forecasting (WRF) model to enhance GNSS Precise Point Positioning (PPP) during two observation periods with two different weather conditions, that is, period 1: March 01 to 14, 2020 (average PWV: 23.5 kg/m2) and period 2: June 02 to 15, 2020 (flooding weather with average PWV: 55.6 kg/m2), over the South China. PWV data from 277 to 263 GNSS stations are assimilated into WRF model to enhance the WRF water vapor forecasting capability for period 1 and period 2, respectively. Wet delay corrections from two different WRF configurations, that is, WRF no data assimilation and WRF with assimilation of GNSS PWV, are used to augment the PPP. Totally, eight WRF‐enhanced PPP schemes are tested. The results show that WRF‐enhanced PPP schemes generally have a better positioning performance in the up component than traditional PPP. After using WRF wet delay corrections, for static mode, the vertical positioning accuracy is improved by 14.6% and 33.7% for period 1 and period 2, respectively. The corresponding convergence time are reduced by 41.8% and 25.0% for period 1 and period 2, respectively. For kinematic mode, the positioning accuracy improvements in the up component reach 13.8% and 19.0% for period 1 and period 2, respectively. The kinematic PPP convergence time is reduced by up to 8.2% for period 1. Plain Language Summary: Global Navigation Satellite System (GNSS) signal is significantly delayed by the water vapor in the Earth's troposphere. How to mitigate this so‐called wet delay has long been a challenging problem in GNSS Precise Point Positioning (PPP) community. In this study, we use the Weather Research and Forecasting (WRF) model to simulate the distribution and variation of water vapor over the South China during two periods with different water vapor conditions, that is, period 1: March 01 to 14, 2020 (average PWV: 23.5 kg/m2) and period 2: June 02 to 15, 2020 (flooding weather with average PWV: 55.6 kg/m2). In addition, water vapor data from 277 and 263 GNSS stations have been assimilated into the WRF model for period 1 and period 2, respectively, to further improve WRF water vapor forecasting accuracy. The high‐quality water vapor forecasts are then used to correct the wet delay for GNSS PPP. Our results show that, with use of the wet delay corrections forecast by the WRF model, GNSS PPP can achieve a better performance in positioning accuracy and convergence speed during both two periods. Key Points: Weather Research and Forecasting (WRF)‐enhanced Precise Point Positioning (PPP) strategies improve positioning accuracy in the up component for both static and kinematic modesWRF‐enhanced PPP strategies accelerate static positioning convergence speed in the up componentWRF‐enhanced PPP strategies show a larger positioning accuracy improvement in wet period [ABSTRACT FROM AUTHOR]

China's first Mars exploration mission (Tianwen‐1), launched on July 23, 2020 with the goal of "orbiting, landing, and roving," and its tentative landing areas (TLAs) are Chryse Planitia (TLA 1) and Isidis‐Elysium Planitia (TLA 2). Dust storm is a typical activity on Mars; it has strong interaction with atmospheric circulation and will influence the visibility of the Martian surface. To ensure the safety and accuracy for landing, it is meaningful to analyze the probability of dust storm activity during Entry‐Descent‐Landing (EDL) season in TLAs. First, based on Mars Orbiter Camera (MOC) and Mars Color Imager (MARCI) Daily Global Maps (MDGMs) from Mars Years 24–31, we identified 882 dust storms within the 2,000 km radius monitor circle of TLA 2 (short for TLA 2‐C). Second, the average daily probability of dust storm activity (Adp_ds) in TLA 2‐C was calculated, with a range of 0% to 14.13% showing obvious seasonality and discontinuity. Third, we divided TLA 2‐C into 0.5° longitude × 0.5° latitude square grids and calculated their spatial probability of dust storm activity. The spatial probability of dust storm in TLA 2‐C ranged from 0% to 11.87% and generally reduced from north to south. Finally, according to the temporal and spatial dust storm probability in TLA 2 during EDL season, we suggested the optimal landing time of China's Tianwen‐1 mission was in Ls = 20–41° and selected five preferred landing areas with the spatial probability of dust storm <3% for this mission. Plain Language Summary: The first China's Mars exploration mission (Tianwen‐1), will achieve the targets of "orbiting, landing, and roving." There are two tentative landing areas (TLAs) in the latitude range of 5–30°, Chryse Planitia (TLA 1) and Isidis‐Elysium Planitia (TLA 2). TLA 2 is on the south of Utopia which is an important origination area of dust storm sequences in the northern hemisphere. The probability of dust storm activity is an important criterion of atmospheric risk assessment for a Martian surface mission. Thus, it is crucial to evaluate the probabilities and characteristics of dust storm activity in and around TLA 2. In this paper, we used Mars Daily Global Maps (MDGMs) of Mars Orbiter Camera (MOC) and Mars Color Imager (MARCI) to (1) identify and catalog the dust storms in and around TLA 2; (2) calculate and analyze the temporal and spatial probability of dust storms in and around TLA 2; and (3) select the preferred landing time and areas for Tianwen‐1 mission based on the temporal and spatial probability of dust storm activity in TLA 2. Key Points: The temporal probability of dust storm activity in Isidis‐Elysium Planitia shows obvious seasonal and inconsecutive activityThe spatial probability of dust storm activity in Isidis‐Elysium Planitia is generally reduced from north to southThe optimal landing time was in Ls = 20–41°, and five preferred landing areas were selected for Tianwen‐1 [ABSTRACT FROM AUTHOR]

Heat flow is a geothermal parameter for indicating the heat source distribution and evaluating geothermal reservoirs. Only 1,230 heat flow points are distributed unevenly in China, mainly concentrated in high‐temperature geothermal and southeast regions. The Songliao Basin is a potential geothermal field in China. Still, only 20 measurement points are known, making evaluating the geothermal genetic mechanism difficult. Sparse data interpolation using deep learning methods is highly accurate and widely used in fields such as image processing. In this work, we propose a deep neural network for predicting heat flow in the Songliao Basin. More than 4,000 global heat flows and 23 geological and geophysical parameters are used as reference constraints for training. The uncertainty error of the prediction is estimated based on the correlation and distance‐based generalized sensitivity analysis. The results show that the maximum heat flow is 85 mW/m2, the average is 67.1 mW/m2, and the error with the measured data is 10.64%. The previous geophysical and geological interpretation results indicate that the heat flow is higher in the west and lower in the east, with high anomalies in the central region, which may be related to the uplift of the deep mantle and the depression of the shallow low‐velocity sedimentary layer. Some high‐temperature melt bodies are in the deep layers, forming the current potential geothermal field. The measured data validates that the DNN is an effective method for predicting regional‐scale heat flow, providing reliable heat source information for evaluating geothermal resources. Plain Language Summary: Geothermal has received extensive global attention as a new renewable energy resource. Geothermal heat flow (GHF) is crucial among various indicators for assessing potential geothermal fields, with over 20,000 measurement points worldwide. However, due to the expensive cost of drilling measurements, the spatial distribution of heat flow points could be more balanced, posing challenges to geothermal exploration and investigation. Traditional heat flow prediction methods rely on interpolation based on sparse measurement points to estimate the regional heat flow distribution. However, spatial variations in the measurement points often affect the prediction results, leading to lower accuracy. Therefore, this study proposes a deep learning approach to establish the complex relationship between heat flow and geological/geophysical features, aiming to estimate the heat flow distribution in the target area. The proposed method has been successfully applied to heat flow prediction in the Songliao Basin, accompanied by an uncertainty analysis technique that provides confidence intervals for the prediction results. These findings offer direct evidence for the geothermal formation mechanisms and thermal evolution laws in the Songliao Basin. Key Points: We propose a deep neural network (DNN) method for predicting terrestrial heat flowCorrelation and Distance‐based Generalized Sensitivity Analysis (DGSA) methods are used to evaluate the uncertainty of DNN predicted heat flow resultsThe prediction of terrestrial heat flow in the Songliao Basin provides direct evidence for deep heat source channels and geothermal genesis mechanisms [ABSTRACT FROM AUTHOR]

Skilled seasonal forecasting will effectively reduce the economic losses caused by droughts and floods. Because of the powerful data mining capability of deep learning networks, it is increasingly applied in studies of seasonal rainfall prediction. However, there remain two prominent issues in the modeling process: the lack of enough training samples and the effect of a small number of extreme values on the model optimization. To tackle these deficiencies, we combine strategies such as principal component analysis, reduction of model hidden layers, and early‐stopping with Attention U‐Net to construct a rainfall classification forecasting model. These steps reduced the model outfitting and improved the model generalization. The results show that the prediction accuracy of this network with leads of 1–3 months is obviously better than that of the numerical model. Further analysis also supports that the spatial features of precipitation predicted by the network are very close to the observations. Plain Language Summary: Accurate summer precipitation forecasts can effectively reduce economic losses and casualties caused by heavy precipitation and flooding in China. Nowadays, seasonal forecasting still mainly relies on numerical models based on physical knowledge. However, the predictors of rainfall are so broad and complex that some physical processes are not yet fully understood. As a result, the improvement of the model skills encountered a bottleneck. Recently, deep learning (DL) network can achieve complex function approximations of data by automatically extracting and learning data features. Yet the existing DL seasonal prediction models suffer from over‐fitting and interference from noise and extreme values. Here, we use Attention U‐Net to predict rainfall levels and combine strategies such as principal component analysis, reduced model depth, and early‐stopping. This network not only solves the two drawbacks mentioned above. It also shows the superiority in the prediction results. First, the rainfall level prediction of this network is more accurate than the numerical model at 1–3 months ahead. Second, the network captures the spatial features with excellent performance. The predicted distributions are largely consistent with the true values. Key Points: The Attention U‐Net model outperforms the numerical model in predicting summer precipitation levels with a forecast lead of 1 monthConverting a regression task into a classification task can eliminate the bad effects of noises and extremesThe principal component analysis and the early‐stopping strategy facilitate the model's generalization [ABSTRACT FROM AUTHOR]

The reconstruction models of hourly 10‐m wind speeds were developed for each of 2384 stations over China using stepwise regression, random forest and XGBoost machine learning approaches based on hourly observed and ERA5 reanalysis data from 2005 to 2021. The reconstruction procedures applied observed hourly data to reduce the systematic biases of reanalysis data sets. Furthermore, the procedures employed the past dynamically consistent states of the atmosphere simulated by ERA5 reanalysis techniques to reduce/remove wind‐observed data impacts of long‐term non‐meteorological condition changes over time (e.g., urbanization around weather stations), which provide homogenous hourly wind speed data sets from 1959 to 2021. The systematic errors of the models' simulations derived from three approaches are similarly small, with almost two orders of magnitude smaller than ERA5 original data sets. The systematic errors of reconstructed data sets derived from stepwise regression are similar to its simulation; however, the biases from two machine learning methods are even much greater than ERA5 original data sets. This result implies that machine learning methods are not suitable for such typical time‐series predictions using the previous‐hour wind speed as a predictor to reconstruct wind speed data for the next hour. Therefore, stepwise regression was selected to reconstruct hourly wind speed data sets, which have much better quality than ERA5 reanalysis data with the median correctness increased by >50% and the median rRMSE decreased by 25%–50%. Consequently, the reconstructed wind speed data sets have great potential to be useful for more precisely assessing the characteristics/trends of wind energy resources in the past 60 years over China. Plain Language Summary: The reliable wind speed data with great spatial and temporal coverages are essential meteorological information for more accurately evaluating and forecasting wind power characteristics and variabilities. The observed historical wind speed data sets have various limitations: insufficient spatial/temporal coverages, non‐meteorological condition changes over time (e.g., urbanization around weather stations, measurement instruments, station locations). The reanalysis products cannot accurately reproduce surface wind fields since the systematic biases are usually caused by the reanalysis data assimilation processing/modeling. To overcome the limitations of both data sets, the reconstruction models of hourly wind speeds were developed based on hourly observed and reanalysis data from 2005 to 2021. The reconstruction procedures applied observed hourly data to reduce the systematic biases of reanalysis data sets and employed the past dynamically consistent states of the atmosphere simulated by reanalysis techniques to reduce/remove wind‐observed data impacts of non‐meteorological condition changes over time. Therefore, the reconstruction models are able to provide homogenous hourly wind speed data sets from 1959 to 2021 for available stations over China, which have much better quality than the reanalysis data. Consequently, the reconstructed wind speed data sets have great potential to be useful for more precisely assessing the characteristics/trends of wind energy resources in the past 60 years over China. Key Points: The reconstruction models of hourly wind speed data sets for available stations over China were developed using three different approachesThe hourly wind speed data from 1959 to 2021 for 2384 stations over China were reconstructed based on ERA5 reanalysis/observed data setsThe reconstructed historical homogeneous hourly wind speed data have advantages of being better quality than reanalysis data [ABSTRACT FROM AUTHOR]

Using a 10‐year data set of cloud‐to‐ground lightning (CG) flashes, the climatological characteristics of ±CG flashes with a large peak current (>75 kA or <−75 kA) (±LPCCGs) are for the first time obtained in China's most populous areas in terms of density distribution, seasonal variation, and monthly and diurnal evolution. The results show that the distribution of +LPCCG flashes has evident seasonal variations, while the activity center of −LPCCG flashes always remains in Southwest China. The diurnal evolution of +LPCCG flash percentages in southern China's Mainland shows an opposite trend to that in the north. Furthermore, the peak current distributions of +LPCCG flashes in most of China's mainland are found to be distinct from those shown in other regions of the world. Additionally, Southwest China is found to be an important and special area for LPCCG flash activity in China. Not only is it the density center of ±LPCCG flashes, but the peak current distribution of −LPCCG flashes in this region also shows an evidently different pattern from those in other regions. The diurnal evolution of the ‐LPCCG/+LPCCG flash frequencies in Southwest China also shows a different pattern from that in other regions. Plain Language Summary: This study exhibits the climatological characteristics of CG with a large peak current (LPCCG) in China's most populous areas for the first time. The results show that LPCCG flashes in northern and southern China have some significantly differences in dominant polarity, seasonal and diurnal variations. Furthermore, the peak current distributions of +LPCCG flashes in most of China's mainland are found to be distinct from those shown in other regions of the world. Southwestern China is an important and special area for LPCCG flash activity in China because not only is it the density center of both negative and positive LPCCG flashes, but the peak current distribution of −LPCCG flashes and diurnal evolution of ±LPCCG flashes in this area are different from that in other areas of China. Key Points: For the first time, the climatology of large‐peak‐current CG (LPCCG) flashes in China's population concentration area was analyzedSignificant numbers and unique diurnal trends of LPCCG flashes in Southwest China were revealedLPCCG flash activities in northern and southern China showed some significant differences in seasonal and diurnal variations [ABSTRACT FROM AUTHOR]

The current study aims at assessing the capabilities of five machine learning models in terms of mapping tungsten polymetallic prospectivity in the Gannan region, China. The five models include logistic regression (LR), support vector machine (SVM), random forest, convolutional neural network (CNN), and light gradient boosting machine (LGBM) models. Three types of data sets including geochemical data, lithostratigraphic contacts, and faults were employed to generate 16 evidential maps that were used to build the machine learning models. Tungsten polymetallic deposits were randomly separated into two parts: 80% for training and 20% for validating. Performance of the models was evaluated through receiver operating characteristic and K‐fold cross validation, with an emphasis on the variable influence within different machine learning methods. The results show that the models are especially sensitive to the chemical elements: Be, Bi, Pb, and Cd, implying that they are closely related to tungsten polymetallic mineralization. Compared to other models, the LGBM and CNN models performed best, while the LR model was the most stable. The results also indicated that the CNN model can predict the maximum number of tungsten polymetallic deposits within the minimum area based on the prediction‐area plot analysis, while the SVM model can capture the least amount of tungsten polymetallic deposits within the largest area. Finally, 18 prospective areas were delineated according to the prediction results of the machine learning models, which provided important guidance for tungsten polymetallic exploration in the study area. Key Points: Five machine learning models were used and compared for tungsten polymetallic prospectivity analysis in the Gannan region, ChinaPerformances of the machine learning models were evaluated through receiver operating characteristic, P‐A plot, and K‐fold cross validationConvolutional neural network model is most efficient in mapping favorable areas for tungsten polymetallic exploration [ABSTRACT FROM AUTHOR]

This study analyzes the change characteristics of compound extreme events (CEEs) of temperature and precipitation (including warm‐wet, warm‐dry, cold‐wet and cold‐dry) in China on interannual and interdecadal scales between 1901 and 2019. The results demonstrate a long‐term increasing trend and interdecadal oscillations in CEEs total frequency. However, the frequency of each type of CEEs changes in a different manner compared with total CEEs frequency. There are fewer CEEs but increasing warm‐dry during 1901–1950. The period 1951–1995 are characterized by frequent cold CEEs (cold‐wet and cold‐dry), cold‐wet are largely distributed in most areas except for northeast and coastal areas of China, while cold‐dry are distributed in most areas except for the northwest regions of China. There are frequent warm CEEs (warm‐wet and warm‐dry) and fewer cold CEEs during 1996–2019. Warm‐wet frequently occurs in the Tibetan Plateau and northwest China, and warm‐dry mainly concentrates in southwest and northern China during this period. The frequency of warm‐dry and cold‐wet were higher than that of warm‐wet and cold‐dry over the past 119 years, whereas warm‐wet increased fastest in the northwest region after 1996, consistent with the warming and wetting characteristics in the northwest region of China. Further study show that long‐term change and low frequency oscillations have the greatest impact on CEEs among different time scale factors. Furthermore, the temperature rise caused by climate change affects the interdecadal characteristics of CEEs in China through the changes of circulation fields such as East Asian trough and subtropical high and the configuration between them. Plain Language Summary: An isolated analysis of univariate extreme events may seriously underestimate the losses caused by multivariable compound extreme events (CEEs). Evolution characteristics of CEEs of temperature and precipitation in China during the past 119 years were investigated. The CEEs in China over the past 119 years experienced three stages: 1901–1950 with less CEEs, 1951–1995 with frequent cold CEEs and 1996–2019 with frequent warm CEEs. Total frequency of CEEs showed an increasing trend but each type of CEEs had an obvious interdecadal variation. The frequency of warm‐dry and cold‐wet was higher than that of warm‐wet and cold‐dry, whereas warm‐wet increased fastest among the four CEEs after 1996. Long‐term change and low frequency oscillations have the greatest impact on CEEs among long‐term change and low frequency oscillations, changes in the variability of higher frequencies and coupling changes between temperature and precipitation. Key Points: Total frequency of compound extreme events (CEEs) shows long‐term increasing trends and interdecadal oscillationWarm CEEs increased rapidly in the past three decades, while the cold CEEs were less during this periodClimate change is the dominant factor of CEEs relative to other scale factors [ABSTRACT FROM AUTHOR]

Tropical cyclones (TCs) constitute one of the major types of meteorological disasters in China, causing extensive damage. Based on the latest quality‐controlled provincial‐level TC disaster losses data over China from 2001 to 2020, the spatiotemporal variation in TC disaster and its link with TC activity in the form of prevailing track, precipitation, and intensity were investigated. Results showed that the areas impacted by TCs expanded significantly in the past decade (2011–2020) compared with the previous decade (2001–2010) throughout China, while the direct economic losses resulting from TCs did not increase significantly. Regionally, TC disaster losses increased in Northeast China but decreased in Fujian province. Causal analysis indicated that the increased influential TC (ITC) frequency and TC precipitation together led to the increase in damage for northern China. Also, more westward ITCs resulted in the increase in TC disasters for southwestern China. For southeastern China, the reduction in TC disasters was related to the northward shift in the northwestward ITC track. Although TC disasters increase with exposure, they are heavily influenced by TC track, precipitation, and intensity. Our findings suggest that more attention should be paid to TC prediction and early warning in northern China, especially Northeast China, where TC disasters are growing but there is greater vulnerability and insufficient capacity for TC prevention and mitigation. Plain Language Summary: China is one of the most frequently affected countries by tropical cyclones (TCs) in the world, suffering huge losses in term of human life and property. The average number of influential TCs varies little from year to year, but TC disasters vary broadly. These TC disasters involve not only the features of TCs themselves, such as precipitation, winds, and storm surges, but also socioeconomic development, the capacity of disaster prevention and preparedness. Utilizing the latest data on TC disasters, this study's results reveal that TC disasters have been aggravated in low‐impact areas, such as Northeast China. Understanding these changes in the characteristics of TC disasters is essential for TC disaster mitigation and climate change adaptation. Key Points: Tropical cyclone (TC) impacted areas expanded significantly in the past decade (2011–2020) compared with the previous decade (2001–2010) throughout ChinaTC disasters increased in Northeast China, where there is greater vulnerability and insufficient capacity for TC prevention and mitigationTC disasters are heavily influenced by TC track, precipitation, and intensity [ABSTRACT FROM AUTHOR]

The genesis and timing of formation of the giant Bayan Obo deposit, the world's largest rare earth element (REE) deposit in the western part of the late Paleozoic northern North China continental arc (NCA), are highly controversial due to complex mineral assemblages and reported ages of mineralization. We conducted new zircon U‐Pb and 40Ar/39Ar dating of metamorphic and igneous Neoarchean to Permian mid‐to upper‐crustal rocks exhumed along a north−south corridor across the western NCA and its retroarc foreland. The results show that the mid‐ to upper‐crust of the western part of the NCA has been strongly affected by thermal modifications during arc construction in the late Paleozoic, while the retroarc foreland remained thermally stable. Our results first reveal an unusually warm upper‐crust around Bayan Obo during the late Paleozoic with high geothermal gradients of 50.0 ± 8.3 to 88.3 ± 8.3°C/km and strong thermal modification of the upper‐crust during arc construction. This unusually warm upper‐crust and high geothermal gradients resulted in intensive thermal perturbations and recrystallization of REE‐bearing minerals in the Bayan Obo deposit, as well as formation of high‐grade REE ores and complete or partial resetting (U‐)Th‐Pb isotopic systems of REE minerals. Our identification of the unusually warm upper‐crust and high geothermal gradients in the western part of the NCA provides important constraints on genesis, timing and thermal modification of the giant Bayan Obo deposit, as well as other REE deposits with complex isotopic ages. Plain Language Summary: The Bayan Obo deposit is the largest rare earth element (REE) deposit in the world. Located in North China, its formation and timing of mineralization have been debated for over 60 years. We conducted thermochronometric investigations on metamorphic and igneous minerals (hornblende, muscovite and biotite) of the mid‐to upper‐crusal rocks around Bayan Obo and concluded that anomalously high geothermal gradients led to extensive crustal mobilization and enrichment of the REE minerals to form the ore deposits. We suggest that these thermal perturbation events were also responsible for resetting isotopic clocks (e.g., (U‐)Th‐Pb systems) of REE minerals, ultimately causing uncertainties in the mineralization ages of the Bayan Obo deposit and geological reconstructions of the region. Key Points: Archean‐Paleoproterozoic basement rocks in the northern North China continental arc have young 40Ar/39Ar ages of 290−220 MaAn unusually warm upper‐crust around Bayan Obo had high geothermal gradients of 50.0 ± 8.3°C/km to 88.3 ± 8.3°C/km during the late PaleozoicThe world's first largest Bayan Obo rare earth element deposit was strongly modified during arc construction in the late Paleozoic [ABSTRACT FROM AUTHOR]

The salinity of groundwater in Qinzhou Bay has been increasing since 2013. This phenomenon affects livelihoods and the local agricultural economy, specifically in the Longmengang area. In this work, we investigate the causes of water salinization to provide evidence for effectively managing the local water resources and improving water quality. We collected 110 samples of water from spatially distributed local wells, rivers, reservoirs, and the sea, and conducted a hydrochemical study complemented by numerical modeling. We found that water pumping and mariculture practices are the principal contributors to seawater intrusion into the groundwater table, which leads to increased water salinization. Plain Language Summary: Groundwater salinity in Qinzhou Bay is increasing due to excessive well water extraction and mariculture practices, which promote seawater intrusion into the local groundwater table. Based on the hydrochemical analyses, the spatially distributed trend of ions content in groundwater shows low concentrations inland and increasing concentrations seaward. However, several localities of Qinzhou Bay are out of this scenario because of artificial activities requiring specific monitoring and regulation. Key Points: Field investigations and hydrochemical analyses show salinization of groundwater in Qinzhou BayHydrogeological modeling indicates that different processes contribute to water salinizationGroundwater salinization in Longmen Island is caused by excessive groundwater use and mariculture practices [ABSTRACT FROM AUTHOR]

The Kuroshio volume transport (KVT) in the East China Sea has enormous impacts on navigation, circulation structure, ecological environment, and local climate. In this study, we aim to forecast the daily variability of the KVT at three different sections using the deep learning method. We train the deep learning model using data from 1982 to 2008 and validate the model with data of 2009–2010 and subsequently test it with data of 2011–2015. Four deep learning models, including Artificial Neural Network, Temporal Convolutional Network, Gated Recurrent Unit, and Long Short‐Term Memory (LSTM) models, are first tested to choose the best prediction model. As a result, the LSTM has the best performance for the KVT prediction at each section. We then employ a multivariate causal analysis method to identify the factors affecting the KVT at the current section, such as upstream and downstream KVT, regional mean wind stress, sea surface height and temperature and combine this method with the LSTM model to construct an information flow causality‐based LSTM (IFC‐LSTM) model for predicting daily KVT variability. The results indicate that IFC‐LSTM has the highest forecast skill compared to the standard LSTM (only input KVT at the current section into the LSTM), ALL‐LSTM (input all nine variables into the LSTM), multiple linear regression, and persistence model, which can forecast the KVT variability from 23 to 27 days in advance at the three sections with relative improvement rates of 12.5%–50%. Plain Language Summary: The Kuroshio, a well‐known western boundary warm current of the North Pacific, has enormous impacts on navigation, circulation structure, ecological environment, and local climate. Therefore, predicting the Kuroshio volume transport (KVT) in the East China Sea is vital to provide valuable decision support for policymakers. Recently, the deep learning method has developed rapidly in the atmosphere and ocean fields. Meanwhile, considering that the multivariate causal analysis method can reasonably identify the causal factors affecting the KVT, we combine this method and the Long Short‐Term Memory (LSTM) model to establish an information flow causality‐based LSTM (IFC‐LSTM) model for predicting the daily variability of the KVT. The IFC‐LSTM model achieves more accurate predictions of the daily variability of the KVT than other state‐of‐the‐art methods. Key Points: Long short‐term memory (LSTM) has the potential to forecast the daily variability of Kuroshio volume transport (KVT) in the East China SeaLSTM based on information flow causal method (IFC‐LSTM) is constructed for predicting KVTIFC‐LSTM can significantly enhance the KVT forecast skills [ABSTRACT FROM AUTHOR]

The Zhurong rover of the China's first Mars Exploration Mission successfully landed on the northern plain of Mars and measured near‐infrared (NIR) reflectance spectra with the Mars Surface Composition Detector (MarSCoDe). There was only orbital NIR hyperspectral data before the success of the Zhurong and Perseverance rovers in 2021. The in situ spectral measurements by the rover face different problems when compared with orbital measurements in data analysis, and thus need a new process pipeline. We analyzed the factors affecting the quality of the NIR spectra acquired by the Zhurong rover, including temperature variation of the detector, non‐homogeneous properties of the calibration panels, the atmosphere transmission and Martian dust buildup on the calibration panels. We then identified that the Martian dust buildup on the calibration panels is the main factor affecting the spectra, causing significant discrepancies between the reflectance calibrated with the white and gray panels, respectively, and the systematically higher reflectance values of the Martian soils measured by the Zhurong rover than those by other missions. We proposed a spectral unmixing strategy to restore the radiance of clean calibration panels on the rover and then derived the reflectance spectra with an uncertainty of ∼14%. The method has been tested by simulation experiments in laboratory. The data quality of the in situ NIR spectra of the Zhurong rover is significantly improved after the calibrations based on our pipeline, which is critical for mineral identification on small scales and calibrations of the orbital remote sensing data. Plain Language Summary: The Zhurong rover successfully landed on the shoreline‐like boundaries in the northern lowlands of Mars, and has the capability of exploring the compositions there using near‐infrared reflectance spectra. This is the first attempt to obtain the in situ hyperspectral reflectance data of surface in the northern lowlands. So, the accurate data is very critical for compositional analysis, which provide an unprecedented opportunity to explore if the ancient ocean existed or not. We systematically analyzed the factors on the quality of in situ measured spectra and built a data process pipeline, which lays the foundation for the massive data analysis of the Zhurong rover. Key Points: The Zhurong rover in situ measured the near‐infrared hyperspectral reflectance data of surface in the northern lowlands of Mars for the first timeThe Martian dust buildup on the calibration panels significantly affect the shapes and intensities of reflectance spectra of Martian surfaceA data process pipeline for near‐infrared reflectance spectra acquired by the Zhurong rover is built [ABSTRACT FROM AUTHOR]

Historical changes and possible future projections of temperature extremes in China, in terms of return values of annual extreme temperatures, are examined based on daily maximum and minimum temperatures from station observations and multiple models of the fifth and sixth phases of the Coupled Model Intercomparison Project (CMIP). The observations suggest that increases in temperature extremes are largely attributable to the changing mean climate, while the varying natural variability also has an important impact, which depends on the index of the variability. The models simulate warm extremes reasonably well but underestimate the spatial heterogeneity and temporal trend of cold extremes in China. In comparison, Coupled Model Intercomparison Project Phase 6 (CMIP6) models have higher skill in simulating temperature extremes in China, showing smaller biases and intermodel variability. MRI‐ESM2‐0 and NorESM2‐LM from CMIP6 and GFDL‐ESM2M and NorESM1‐M from CMIP5 are selected as reference models based on the better performance in reproducing observed temperature extremes in China. In the future, projections from CMIP6 multimodel ensemble (MME, represented as the multimodel median) and reference models all show a continued uptick in temperature extremes, with statistically significant increases in warm extremes mainly in the north and increases in cold extremes prominent in most parts of China. Different individual models, which have similar historical simulations, yield divergent future trends of temperature extremes, which may be associated with different climate sensitivities of models. In addition, MME usage should be treated with caution since its smoothing on spatial heterogeneity and possible information from poor models. Plain Language Summary: Given the serious hazards of extreme temperature events, understanding changes in extreme temperatures and effectively predicting their future changes are critical to climate mitigation and adaptation. According to station observations and model data from the Coupled Model Intercomparison Project Phase 5 (CMIP5) and 6 (CMIP6), we find besides the varying temperature mean, the varying temperature variance also has an important impact on changes in extreme temperatures, and CMIP6 models have overall better performance than CMIP5 models in reproducing changes in extreme temperatures in China. Our results also emphasize that good performances in historical simulation cannot guarantee better projections in the future and projections of extreme temperatures from the multimodel ensemble sacrifice the spatial heterogeneity relative to individual reference models. Key Points: The impact of the varying natural variability on changes in temperature extremes depends on the variability indexCMIP6 models perform better than CMIP5 in simulating temperature extremes changes in ChinaMultimodel ensemble projections of extreme temperatures sacrifice spatial heterogeneity [ABSTRACT FROM AUTHOR]

By harmonically fitting the arrival times of the P‐to‐S converted phases from the Moho, we systematically investigate the crustal anisotropy beneath the Cathaysia Block (CaB) and the surrounding areas in South China for the first time. The measured anisotropy parameters show strong lateral variations with an average splitting time of 0.35 ± 0.12 s. The resulting fast directions exhibit clear geographic variations with two possible boundaries that separate the CaB into three parts. The inconsistent fast direction of crustal anisotropy among each part, and the comparisons with teleseismic XKS splitting measurements indicate that the mechanisms of crustal deformation vary between distinct regions. The crust deforms coherently with the upper mantle in the western CaB, which is possibly driven by mantle flow. The fast directions of crustal anisotropy in the central CaB exhibit a radial pattern and show large discrepancies with XKS splitting measurements. We suggest that the crust has a complex deformation mechanism in this region. We propose a new scenario that emphasizes the interaction of the crust and upper mantle for interpreting the radial‐patterned crustal anisotropy, which involves the processes of subduction plate rollback, asthenospheric upwelling, crustal melting and lithospheric mantle intrusion. The fast directions of crustal anisotropy in the eastern CaB are dominantly parallel to the continental margin, suggesting subduction‐controlled crustal deformation in this region. Plain Language Summary: The Cathaysia Block (CaB) is an important part of the South China Block, which has experienced multistage tectono‐thermal events and a complex tectonic evolution. Previous studies focused on upper mantle deformation and rarely involved crustal deformation. We systematically measure the crustal anisotropy from receiver function analysis beneath the CaB for the first time. The results show that crust at different parts of the CaB have different deformation mechanisms. The crust of the western CaB deforms coherently with lithospheric mantle, which may be driven by deep mantle flow. The central CaB has a complex crustal deformation mechanism involving a series of processes like plate rollback, asthenospheric upwelling, crustal melting and lithospheric mantle intrusion. The crust deformation of the eastern CaB is likely controlled by oceanic plate subduction. Key Points: We investigate the crustal anisotropy beneath the Cathaysia Block (CaB) from receiver function analysis for the first timeThe fast directions of crustal anisotropy exhibit geographic variations with two clear tectonic boundariesThe complex features of crustal anisotropy reveal different deformation mechanisms beneath different parts of the CaB [ABSTRACT FROM AUTHOR]

The Gao Fen 7 (GF‐7) satellite is China's first submeter, high‐resolution Earth observation and remote sensing satellite for natural resource monitoring, land surveying, and other industrial applications in China. The GF‐7 laser altimeter system is the successor of the ZiYuan3‐02 (ZY3‐02 satellite) laser altimeter. The key objective of the GF‐7 laser altimetry system is to provide high‐precision ground elevation control points that assist in optical imaging to achieve 1:10000 mapping in China. This paper introduces the GF‐7 laser altimeter system payload specifications, mission objectives, in‐orbit calibration plan, data products, and service policy. In the future, the GF‐7 laser altimeter system will also serve multidisciplinary and professional applications through its precise elevation measurement capabilities. In addition, it will play a vital role in Earth observation, climate change monitoring, and environmental protection. Plain Language Summary: Spaceborne laser altimeters are widely used for planet surface measurements due to their high range accuracy. This paper details the GF‐7 laser altimeter system, which is China's first formal spaceborne laser altimeter system for Earth observations. This laser altimeter system will be used in scientific experiments for the first time in China. GF‐7 laser altimeter system is the first to be equipped with laser footprint cameras and a laser optical axis surveillance camera. The GF‐7 satellite will be launched in October 2019, and the scientific experiments will begin in November. All the data it gets will be released globally. In the future, these data will help researchers from different disciplines around the world to conduct scientific experiments and monitor the Earth's resources. The GF‐7 laser altimeter system will play a vital role in global climate change monitoring, environmental protection, disaster warning, etc. The content presented in this article will help students, researchers, and engineers in the fields of geography, oceanography, and glaciology understand and use GF‐7 satellite laser data, which will be of great significance. Key Points: A new laser altimeter system design consisting of a laser altimeter, two laser footprint cameras, and a laser optical axis surveillance camera equipped with the Gao Fen 7 satellite is introducedThe application of the Gao Fen 7 laser altimeter system including ground control point measurement, polar ice sheet monitoring, and forest biomass estimation is presentedScientific experiments on the Gao Fen 7 laser altimeter system to be performed in November 2019 are given. Six levels of data products are listed and will be provided to users worldwide [ABSTRACT FROM AUTHOR]

The disruption of the mantle roots of cratons is common after cratonization. Craton destruction, which is characterized by severe lithospheric thinning, extensive thrust and extensional deformation, basin filling, and intense thermal activities, is relatively rare and is generally attributed to intensely reduced viscosity contrasts between the lithospheric mantle root and the underlying asthenosphere. However, the extent of the required viscosity contrast remains unclear. The North China craton (NCC) is a typical example of a partially destroyed craton, with its eastern part experiencing destruction in the Early Cretaceous. In this study, we measured the water content of clinopyroxene phenocrysts in Middle Jurassic lithospheric mantle-derived, weakly alkaline volcanic rocks from the Shanhaiguan area. Our data and those of previous studies show that the lithospheric mantle in the eastern NCC contained substantial amounts of water (650--2,900 ppm) before craton destruction. In addition, the continuous supply of water by the subducted Paleo-Pacific slab resulted in a more hydrous lithospheric mantle (up to ca. 9,000 ppm) during the craton destruction. The viscosity contrasts between the lithospheric mantle root and the asthenosphere (with an average viscosity of 3.7 x 1018 Pa s) were 2--8 and 0.3--2 before and during the craton destruction, respectively. Our study indicates that a drastic drop in the lithospheric mantle viscosity, which is controlled by the synergic effects of a high water content and elevated temperature, is required to induce craton destruction. [ABSTRACT FROM AUTHOR]

This study investigated the regional transport of particulate matter (PM2.5) leading to air pollution in Fuzhou, in the west bank of the Taiwan Strait (WBTS), which has a relatively good air quality with a lower aerosol optical depth than that of East China. The continental polluted aerosol zone over the coast of East China was identified as an aerosol transport belt, and the daily aerosol dynamics observed by MODIS remote sensing indicated the southward transport of fine particulate matter to the WBTS along the coast. The external air pollutants from regional transport contributed 84% of the PM2.5 detected in Fuzhou, while only 16% due to local emissions of air pollutants. The northerly winds of the cold front drove the southward transport of polluted air masses along the coast of the northwest Pacific from the Yangtze River Delta (YRD) to the WBTS. The vertical mixing of air pollutants from the upper boundary layer to the surface layer could be conducive to regional‐transport‐induced haze pollution in this coastal area of the WBTS. Fuzhou is considered a receptor region of PM2.5 transport in Eastern China, and the source region is mainly from the YRD. The results of this study have significant implications on determining the source of pollution and controlling local air quality for good air quality regions. This study also shed some light on using multi‐satellites to fill the surface observation gaps in coast or nearshore areas, and to confirm the characteristics and passways of aerosol transport. Key Points: The southward transport of air pollutants built up a source‐receptor relationship in East Aisa Monsoon regionThe transport of exogenous PM2.5 accounts for more than 80% during a severe haze pollution in FuzhouThe unstable atmospheric boundary layer leads to the downward mixing of PM2.5 to the surface in clean areas [ABSTRACT FROM AUTHOR]

In this study, multi‐source data, especially weather radar data, were used in the analysis of structural features and evolution of a squall line‐like rainband (SLLR) of a linear mesoscale convective system (MCS) in the eastern slope of the Taihang Mountains on 12 and 13 August 2018 and reached the following conclusions: (a) The SLLR, located in north of the linear MCS, exhibited a significant horizontal gradient of reflectivity and large vertical extension. The front‐to‐rear flow that appeared between the surface and the stratosphere was lifted upward immediately in front of a cold pool that formed rearward‐tilting updrafts. (b) As the SLLR passed over, surface meteorological quantities changed drastically, coinciding with the heavy precipitation. (c) The SLLR, when propagating northeastward, showed a complex convective development of sequential intensification, weakening, and re‐intensification within a 2.5‐hr period. The temporal intensification was related not only to the development of a low‐level rear‐inflow jet but also to the dynamic interaction between the vertical environmental wind shear and the cold pool. The lifted rear‐inflow jet, which formed over a vertically stacked horizontal vorticity couplet behind the leading edge, provides a reasonable explanation for the intensification of the SLLR, resulting in the reduction of the cold pool circulation and enhancement of the convective updraft. Topographic forcing might play a crucial role in the re‐intensification of the SLLR, which suggests the importance of the cold pool and topography in structural features and convective evolution of the squall line‐like rain bands. Key Points: The squall line‐like rainband (SLLR) in a linear mesoscale convective system exhibited the characteristics of the typical squall lineThe SLLR was associated with a complex convective development of sequential intensification, weakening, and re‐intensificationThe cold pool and topography play crucial roles in structural features and convective evolution of the SLLR [ABSTRACT FROM AUTHOR]

Evaluating the spatial‐temporal dynamics in ecological risk and understanding its impact on water quality in reservoirs could optimize watershed land use and protect reservoir water quality. However, this impact remains elusive due to the lack of long‐term field data, the heterogeneity of land use, and scale effects. Therefore, Danjiangkou Reservoir area was selected as the study area, where rapid urban expansion and ecological conservation and restoration measures have significantly changed the ecological environment, altering the water quality. We investigated the spatial‐temporal changes of land use from 1990 to 2020 and evaluated how landscape ecological risk changed, as well as explored the impact of landscape ecological risk changes on water quality. The landscape ecological risk was calculated by landscape vulnerability and landscape disturbance (based on fragmentation, separation, and fractal dimension). The results indicated that the growth rate of water bodies surged (7.65 km2/a) and cropland experienced an apparent reduction (14.39%) over the past 30 years. These landscape changes decreased ecological risk, especially after the water transfer. The results revealed that the impacts of ecological risk on water quality were better explained at the riparian scale than at the reach and catchment scales. Specifically, the ecological risk was strongly relevant to dissolved oxygen, Turbidity, Cl− and NO3− ${{\text{NO}}_{3}}^{-}$, and moderately correlated to Ca2+ and pH. While it was not correlated with total nitrogen, total phosphorus, and F−, possibly due to the irregular reservoir operations, occasional excessive use of fertilizers, and rock weathering. This study preliminarily discloses the impact of landscape ecological risk on water quality in a basin reservoir and provides a theoretical basis to take measures in advance for the sustainable development of a reservoir basin. Plain Language Summary: Assessing the spatial and temporal dynamics of ecological conditions (denoted by ecological risk) and understanding their impact on reservoir water quality is not only important for optimizing land use in the basin, but also for protecting reservoir water quality. However, this impact remains elusive due to the limitations of existing studies and data. Therefore, the Danjiangkou Reservoir area was selected as the study area, where rapid urban expansion and major ecological conservation and restoration measures around the reservoir have significantly altered the ecological environment and water quality. We evaluated the changes in landscape ecological risk based on an investigation of spatial and temporal changes in land use between 1990 and 2020, and explored the impact of changes in landscape ecological risk on water quality. The results show that the rate of growth of water bodies has increased dramatically and the area of cropland has decreased significantly following the implementation of the dam raising project and water transfer. These landscape changes reduced ecological risk, improved ecological conditions and ultimately improved water quality. This study provides preliminary insights into the effects of landscape ecological risk on water quality and provides a theoretical basis for sustainable development of reservoir basins. Key Points: The ecological conditions and water quality in the rainy season were generally better than in the dry season during the same yearThe effect of the water transfer project on local ecological quality and water quality has been positiveThe improvement in eco‐environment quality reduced pollution sources and improved water self‐purification capacity, improved water quality [ABSTRACT FROM AUTHOR]

Extreme torrential rainfall events are low‐probability events. A "China‐Record Extremely Heavy Rainfall" (CREHR) event with rain rate of 201.9 mm/hr occurred on 20 July in Zhengzhou in North China. Using high‐density meteorological observations, ERA5 reanalysis data, remote sensing data from China's FY‐4A satellite, and numerical simulation, we revealed that sufficient warm–humid airflows were continuously transported to the Zhengzhou area via multiple pathways that were mainly modulated by the large‐scale western Pacific subtropical high to the north along with the anomalies of the westerly belt and a meso‐scale binary typhoon system. At the local scale, under the combined action of the vertical circulation that caused low‐level convergence and high‐level divergence, and the rapid uplift of water vapor related to the blocking by mountainous terrain, this CREHR event was eventually triggered. Particularly, our present work shows the roles of both typhoon Cempaka and meso‐scale convective systems to south of Henan province can not be ignored, producing a strong super‐thick water vapor transport layer. The existence of the binary typhoon raises the level of uncertainty involved in the water vapor transport of this CREHR event. Plain Language Summary: Extreme torrential rainfall events are low‐probability events. The rainfall from 16:00 to 17:00 Beijing time on 20 July in Zhengzhou in North China reached 201.9 mm, which was a record in mainland China—a so‐called "China‐Record Extremely Heavy Rainfall" (CREHR) event. By using high‐density meteorological observations, ERA5 reanalysis data, remote sensing data from China's FY‐4A satellite, and numerical simulation, we found that sufficient warm–humid airflows were continuously transported to the Zhengzhou area via multiple pathways that were mainly modulated by the large‐scale western Pacific subtropical high to the north along with the anomalies of the westerly belt and a meso‐scale binary typhoon system. At the local scale, under the combined action of the vertical circulation that caused low‐level convergence and high‐level divergence, and the rapid uplift of water vapor related to the blocking by mountainous terrain, this CREHR event was eventually triggered over northern mainland China. Our findings reveal that the existence of the binary typhoon circulation raises the level of uncertainty involved in the water vapor transport of this CREHR event, which has implications for improving our ability to predict and provide early warnings for severe rainstorms and related disasters in cities with such a complex context. Key Points: Sufficient warm‐humid airflows were continuously transported to the Zhengzhou area via multiple water vapor pathwaysMultiple pathways were mainly modulated by the large‐scale western Pacific subtropical high, the westerly belt and mesoscale binary typhoonsNorthward movement of multiple meso‐scale convective systems caused a strong super‐thick water vapor transport layer [ABSTRACT FROM AUTHOR]

Geological processes alter pore spaces over time, and their analysis can shed light on the dynamic fractal structure and fluid flow of rocks over time. This study presents experimental evidence to illustrate that the pore fractal structure evolves with sedimentation, carbonate cementation, clay growth, and dissolution. It examines, describes and characterizes a suite of core samples from the Gaotaizi oil layer of the second and third members of the Qingshankou Formation, Songliao Basin, China. The effects of mechanical compaction and other diagenesis effects on fractal pore structure on sedimentary rocks are discussed. A schematic diagram is proposed that describes the impacts of these diagenetic processes on fractal pore structure at the microscopic scale in sedimentary rocks. This work links the state of diagenetic alteration and fractal pore structure, which can guide practical applications such as predicting the permeability of sedimentary rocks. Plain Language Summary: Mechanical compaction or chemical alteration process will change the pore space of the rock, including pore size and grain‐pore interface properties. We present the evidence that geological processes alter the "roughness" amplitude of grain‐pore interface (fractal pore structure) in sedimentary rock, and discuss the evolutionary mechanism of the "roughness" amplitude of grain‐pore interface. This work links the state of diagenetic alteration and fractal properties of rocks, which can guide practical applications such as predicting permeability of sedimentary rocks for any historical period. Key Points: Evolution of fractal dimension with diagenesis was revealedEffects of diagenesis on fractal upper and lower limits were discussedEffect mechanism of fractal pore structure was revealed in sedimentary rocks [ABSTRACT FROM AUTHOR]

To support future solar energy deployment in China, long‐term changes in solar energy resources over China were investigated based on high‐resolution dynamical downscaling simulations under three emission scenarios. First, an evaluation of model performance was conducted through comparison with station and ERA5 data, which indicated that although consistent overestimation was found, the spatial distribution and intra‐annual variation of solar radiation were captured well by ensemble simulation, showing added value over regions with complex topography as well. Then, the trends of the solar power output from photovoltaic (PV) systems during 2020–2099 were projected, characterized by an increase in east and central China, and a consistent decrease in the solar‐energy‐abundant regions (e.g., northeast China, the Tibetan Plateau, and northwest China) under the three scenarios. Except for under the low emission scenario, the regional averaged trends of PV were projected to decrease consistently over China in all four seasons, with significant magnitude apparent in both spring and winter. In addition, a more intense magnitude of decline was found under the higher emission scenario, with more subregions with a significant trend and higher intra‐annual and inter‐annual variability. However, it seems that the emission scenarios impact the spatial pattern only remarkably in the late century, however strengthened the decreasing magnitude in all future periods. In addition, results indicate decline in the future PV potential over most of subregions of China due to reduced radiation and increased temperature, but for the decrease over Tibetan Plateau the reduced wind speed also act as an important factor. Plain Language Summary: The long‐term goal of carbon neutrality in China involves new requirements for the development and utilization of solar energy. The present study was motivated by supporting future solar energy deployment at local scales. However, due to data limitations, relatively little effort has been made to project long‐term changes in solar energy over different subregions of China. Accordingly, we investigated this based on a set of high‐resolution dynamical downscaling simulations under different climate scenarios, and found that the higher emission scenarios reinforce the magnitude of the decline in solar energy and result in higher inter‐annual variability. These findings suggest an increased need for constraining emissions for the benefit of solar energy. In addition, we found that radiation will likely be the most important factor related to future changes in solar resources over most of subregions under RCP2.6 scenario, however the effect of wind speed should not be ignored over Tibetan Plateau. Key Points: Despite some overestimation, the spatial distribution and intra‐annual variation of solar radiation were captured well, showing added valuesFuture solar power were projected to generally increase in east and central China but decrease in solar‐energy‐abundant regionsRadiation was the most robust factor for future solar energy trend over China, however wind speed can not be ignored over Tibetan Plateau [ABSTRACT FROM AUTHOR]

Extremely persistent heavy rainfall (EPHR) generated by landfalling tropical cyclones (LTCs) can cause destructive natural disasters. In this study, hourly precipitation data and best‐track data set for 2009–2019 are used to investigate the basic characteristics and preliminary causes of LTC‐generated EPHR over China. Using the relative threshold method, heavy precipitation lasting more than 7 hr is defined as EPHR. The result shows that most of the EPHR events occur in the south and southeast China, especially in the northwest of Hainan Island and the coastal areas of Zhejiang. The maximum duration of EPHR can reach 13 hr with a maximum cumulative rainfall of 668.9 mm. EPHR in the intermediate region (150–300 km away from the tropical cyclone (TC) center) of the LTCs tends to have a higher frequency and greater intensity, and the development process of the EPHR events exhibits a significant asymmetrical feature. Although EPHR accounts for a relatively small proportion (29.4%) of duration in EPHR events, it accounts for a large proportion (61.0%) of cumulative precipitation. The combination effects of topographic forcing and tracks of the TCs directly determines the high incidence areas of EPHR. And the preliminary causes of EPHR are mainly due to the upliftment or blocking effects of the topography. In addition, the translation speed and intensity of the TC can also restrict the generation of EPHR. In general, a stronger and slower TC is more likely to cause EPHR events when it passes through north of Hainan Island or makes landfall at Zhejiang‐Fujian area. Key Points: Heavy rainfall lasting more than 7 hr is defined as extremely persistent heavy rainfall (EPHR) and its maximum duration can reach 13 hrEPHR events mainly occur in the northwest of Hainan Island and the coastal areas of ZhejiangThe terrain and the tracks of tropical cyclones directly affect the distribution of EPHR events [ABSTRACT FROM AUTHOR]

The process of continental growth and closure of the Paleo‐Asian Ocean are controversial. To address this problem, we report new geochemical and geochronological data of granitic rocks along a N‐S corridor of the Dananhu arc. These rocks yield 401–270 Ma U‐Pb zircon ages. The arc‐related granites are enriched in the light rare earth elements (LREE), Rb, Ba, Th, Sr and Pb, but depleted in Nb and Ta, and have high zircons values of εHf(t) (+7.7−+19.6) and εNd(t) (+5.4−+7.2); they were possibly generated by partial melting of juvenile intermediate or mafic arc crust. A 356 Ma high‐K calc‐alkaline granite has high Ga/Al (3.28–3.37), low Mg# (14–16), and high zircon εHf(t) (+8.9−+12.0); it can be classified as A2‐type‐granite, suggesting it was a product of substantial fractional crystallization of a mantle‐derived arc‐related magma in an intra‐arc extensional setting. The youngest granite (254 Ma) is an adakite‐like granite which is characterized by high Sr/Y (26–38), depletion in heavy rare earth elements (HREE), and high εNd(t) (+7.0) and εHf(t) (+10.4 to +15.4) values, most likely it was derived from a subducted oceanic slab. Therefore, all the granitic rocks formed in subduction‐related settings. The granitic rocks are generally young southwards from the Kezier area (ca. 401–335 Ma) to the Tuwu area (334–315 Ma), and to the Kanguer area (ca. 280–254 Ma), indicating that the arc grew southwards, and the Paleo‐Asian Ocean closed possibly after 254 Ma. Therefore, our data provide a robust constraint on the prolonged growth of the Altaids. Plain Language Summary: The continental growth mechanism and closure time of the Paleo‐Asian Ocean in the Altaids are global research hotspots. The arcs (especially the intra‐oceanic type) in the Paleo‐Asian Ocean are the essential accretionary component in the Altaids, however, detailed knowledge of the growth processes of the arcs that generated the different types of magmatism is still insufficient. The voluminous, continuous, and well‐exposed granitic rocks in the Dananhu arc provide an ideal opportunity to study the complicated processes of subduction and evolution of the southern Altaids. We report geochemistry and geochronology of granitic rocks along a N‐S corridor of the Dananhu intra‐oceanic arc in the Eastern Tianshan (NW China), southern Altaids. These I‐type, A2‐type and adakite‐like subduction related granitic rocks were emplaced from Devonian (ca. 401 Ma) to Latest Permian (ca. 254 Ma) with a general younging southward, indicating a complex episodic switch of subduction processes from the Devonian to Late Permian. Our results suggest that the arc grew southwards by retreat and rollback of the Kanguer oceanic plates where overall compressional and extensional events alternated, and the closure time of the Paleo‐Asian Ocean was possibly after 254 Ma. Key Points: The subduction related granites are a general southward younging southward from Devonian (ca. 401 Ma) to Latest Permian (ca. 254 Ma)The arc grew southwards by retreat and rollback of the Kanguer oceanic plates where overall compressional and extensional events alternatedThe continental growth accompanied with volumes of granitic rocks and Paleo‐Asian Ocean possibly closed after 254 Ma in the southern Altaids [ABSTRACT FROM AUTHOR]

Utilizing the measurements and retrievals from the Global Precipitation Measurement dual‐frequency precipitation radar data during the summer of 2014–2020, this study analyzes the precipitation microphysics for summer rainfall over the Yangtze‐Huai River Valley (YHRV) region in China with respect to the precipitation efficiency index (PEI). The results show that the mean near‐surface rain rate and PEI for convective precipitation are generally larger than that for stratiform precipitation. There are larger raindrops in near‐surface rainfall for convective precipitation than that for stratiform precipitation. The High PEI precipitation is characterized by the larger ratio of convective precipitation samples compared to that for the other two categories of precipitation. The mean mass‐weighted mean diameter (Dm) of the near‐surface raindrops increase along with the precipitation top height (PTH) in Moderate PEI precipitation for convective and stratiform precipitating clouds. The larger PEI indicates the larger occurrence of the heavy raindrops and strong radar echoes near the ground. The growth process of raindrops falling below the melting layer in the High PEI precipitation is dominated by collision‐coalescence while the collisional break up is the dominant process below the melting layer in Low PEI precipitation. It is further verified that the microphysical characteristics and mechanisms of summer rainfall over the YHRV region are similar to that of precipitation derived from tropical cyclones over the ocean during the monsoon period. Key Points: The near‐surface raindrops increase along with the precipitation top height in the moderate precipitation efficiency index (PEI) precipitationThe higher PEI indicates the larger occurrence of near‐surface heavy raindrops and strong radar echoesThe microphysical mechanisms in the summer precipitation process are analyzed in terms of various PEI [ABSTRACT FROM AUTHOR]

The atmosphere weighted mean temperature, Tm, is an essential parameter for retrieving precipitable water from the ground‐based Global Navigation Satellite Systems (GNSS). The accuracy of high temporal resolution GNSS precipitable water vapor (PWV) estimation requires wideband Tm information and its magnitude. However, existing Tm empirical models use trigonometric functions with only fixed amplitude and low‐frequency for time fitting, which limits real‐time or near real‐time PWV retrieval from GNSS observation. Thus, an improved Tm model for China, LTCm, containing more frequency information of Tm, based on the antileakage least‐squares spectrum analysis by utilizing the ERA5 pressure‐level products during the years 2015–2019, is developed. Both Tm data from ERA5 pressure‐level products and radiosonde stations distributed in China over 2020 are selected as reference values to verify the performance of the LCTm model. The results show that the LCTm model yields significant performance against other models in Tm estimation over China, especially in marine regions and high‐altitude areas. Furthermore, the LCTm model can generally achieve a mean Bias/root mean square (RMS) of −0.33 K/2.06 K in contrast to ERA5 pressure‐level products and 0.03 K/3.47 K in comparison with radiosonde, which corresponds to a 7.2%–13.8% improvement against GPT2w, GTm‐III, and Bevis. Moreover, LCTm has σpwv and σpwv/PWV values of 0.27 mm and 1.25% when used to retrieve GNSS‐PWV, respectively. Consequently, the LCTm model that considers high‐frequency information of Tm can obtain more reliable Tm values. Therefore, the LCTm model can be applied to real‐time or near real‐time GNSS PWV retrieval, which is of great significance for GNSS meteorological research. Key Points: An advanced grid model based on antileakage least‐squares spectral analysis for estimating weighted mean temperature over China is builtThe performances of the new model, GPT2w, GTm‐III, and Bevis models are assessed under different data sources, and the impact of each model on GNSS PWV is evaluatedThe new model can reflect the complex changes of weighted mean temperature in a short time and has a prospective application in real‐time or nowcasting extreme weather forecasting [ABSTRACT FROM AUTHOR]

We propose a dust‐event detection and tracking procedure based on air quality data from the ground monitoring network by detecting temporal and spatial change‐points in PM10 concentration. It supplements the existing remote sensing based approach with high temporal resolution and better weather adaptivity. Applications of the procedure on the labeled data showed its having high discriminating power for dust events, pollution events, and clean periods. Our study finds changing correlation patterns between PM10 and other air pollutants at the start of the dust events, which are utilized to enhance the discriminating power of the dust‐event detection procedure. The detection and tracking procedure allows the construction of transport networks of the dust‐events as well as the identification of the source regions and the transportation pattern, and assess the intensity and severity of the dust‐events in North China. Our analysis find the dust‐events contributed to 23.3%–34.6% for PM10 and 18.2–33.2% for PM2.5 in the source regions and 2.0%–7.3% and 0.8%–4.0%, respectively, in the down‐stream provinces in the spring season from 2015 to 2020. Plain Language Summary: Dust storm can be tracked via the air quality monitoring network via a statistical semi‐supervised learning algorithm. Key Points: Develop a dust event detection procedure based on air quality data with finer spatial‐temporal resolution and better weather adaptationConstruct dust transport networks and find altering dependence between PM10 and CO, SO2 and NO2 at the start of dust eventsDust‐events contributed to 23.3%–34.6% for PM10 and 18.2–33.2% for PM2.5 in the source regions and 2.0%–7.3% and 0.8%–4.0%, respectively, in the down‐stream provinces in spring in North China [ABSTRACT FROM AUTHOR]

GLDAS2.0 provides long‐term fine resolution gridded hydrometeorological data sets, which are necessary for water‐related studies, particularly in some transboundary rivers that are partially without observation. Yet, GLDAS2.0 has only been validated at limited locations, and few studies have been conducted to develop approaches to correct the GLDAS2.0 data for transboundary rivers. This work assessed the GLDAS2.0 data and developed approaches to correct their uncertainties for studies in large transboundary rivers in the Tibetan Plateau and Northeast China (NC). To achieve these goals, observational data from 1982 to 2010 and a water and energy budget‐based distributed hydrological model including biosphere after calibration and validation were employed. We find that the GLDAS2.0 data (except for wind speed) can reasonably replicate observed seasonal variations. However, its specific humidity and wind speed have large uncertainty, and precipitation has large uncertainty in summer. In NC, the trends of its precipitation, air temperature, downward longwave radiation, and wind speed are consistent with the observations. In the Yarlung Tsangpo, Lancang, and Nu Rivers, the trends of all GLDAS2.0 data reproduce the observation very well, that is, wetting, warming, and dimming trends. Validations show that the corrections are effective and the corrected forcing data can be successfully used in hydrological simulation with improved performance than the raw GLDAS2.0 data, which demonstrates the usefulness of the methodology and corrected forcing data to hydrometeorological studies in transboundary rivers in China as well as in other nearby regions/countries. Plain Language Summary: GLDAS2.0 provides long‐term hydrometeorological data sets, which are necessary for water‐related studies, particularly in some transboundary rivers that are partially without observation. This work assessed GLDAS2.0 data and developed approaches to correct their uncertainties for studies in large transboundary rivers in the Tibetan Plateau (TP) and Northeast China (NC). In NC, the trends of GLDAS2.0 precipitation, air temperature, downward longwave radiation, and wind speed are consistent with observations. In TP, the trends of all the GLDAS2.0 data reproduce observation very well. Validations show that the corrections are effective and the corrected data have improved performance than raw GLDAS2.0 data in hydrological simulation. Key Points: Specific humidity and wind speed have large uncertainty, and precipitation has large uncertainty in summerTrends of precipitation, air temperature, downward longwave radiation, and wind speed are consistent with observationEquations are developed to reduce forcing data uncertainty, and validations show that the equations are effective [ABSTRACT FROM AUTHOR]

Accurate prediction of tropical cyclone (TC) induced rainfall is of great importance to disaster prevention but quite challenging to operational forecasters. This study quantifies the spatial characteristics of the hourly rainfall caused by TCs at several individual weather stations over the south China coast. We use the historical TC datasets from 2004 to 2018 and the hourly rainfall data from six weather stations to investigate the stations' rainfall distribution characteristics induced by TCs within a distance of 700 km to the weather stations. The hourly rainfall data during the study period from six coastal stations: Zhuzilin (ZZL), Shekou Ferry Terminal (SFT), and Wutong Village (WTV) in Shenzhen, and Hong Kong Observatory (HKO), Sha Tin (SHA), and Tai Mo Shan (TMS) in Hong Kong are used to investigate the statistical relationship. Results show that the most hazardous areas for TC‐induced rainfall are TCs within a distance of 200 km relative to the stations. High intensity TCs in the southwest quadrant may bring torrential rainfall to stations within 500 km distance, and low intensity TCs are likely to induce torrential rainfall to stations within 300 km distance when they are in the northeast and northwest quadrants. If TCs are over the region with the azimuth of 60–150° and distance out of 200 km to the six stations, there is almost no rainfall influence on those stations. The findings of this study are valuable to provide information for the local operational forecasters when predicting the potential rainfall along the south China coast due to future TCs. Key Points: The most hazardous areas for tropical cyclones (TCs) induced rainfall are TCs within a distance of 200 km to the stationTCs are more likely to bring torrential rainfall to a coastal station when they are in the southwest quadrant of the stationTCs have almost no impact on rainfall when the azimuth is between 60 and 150° and distance is out of 200 km from the station [ABSTRACT FROM AUTHOR]

Novel prediction methods using artificial intelligence have been developed to improve the identification, discovery, and utilization of new types of mineral resources at new depths or using new technologies. However, most artificial intelligence methods require large training data sets that are often unavailable for mineralization prediction models, leading to inaccuracies. To address this issue, we developed a semi‐supervised machine‐learning method to identify metallogenic anomalies using the density‐based spatial clustering of applications with noise method and autoencoder. The outputs of this method show irregularity in distributions inferred from geological, geochemical, and hyperspectral remote sensing data that match known mineralization locations. We focus on the Daqiao mining area of Gansu Province in China to show that the model predictions are highly consistent with known deposits of the Yinmahe and Daqiao gold mines, and two new prospecting areas have been highlighted for further field confirmation. The accuracy of this semi‐supervised learning method was verified by an interdisciplinary intelligent analysis, showing that this method could have wide‐reaching applications for improving regional geological surveys. Plain Language Summary: Mineral resources are irreplaceable and necessary for modernization. Novel prediction methods using artificial intelligence have been developed to improve the prediction of mineral resources. However, the training data sets are still a big problem for the artificial intelligence methods used in this application. To solve this issue, we developed a semi‐supervised machine‐learning method to identify metallogenic anomalies using the DBSCAN and autoencoder. The accuracy of this method was verified by an interdisciplinary intelligent analysis, showing that this method could have wide‐reaching applications for improving regional geological surveys. Key Points: A new artificial intelligence technology to analyze multivariate data for ore prospecting and predictionThe prediction results show that ore bodies in the Daqiao area are controlled by the large fault structureThe improvement and new applications of remote sensing image data to synthesize geological and geochemical data for ore‐forming anomalies [ABSTRACT FROM AUTHOR]