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The risk of water erosion in mainland China is intensifying due to climate change. A high-precision rainfall erosivity dataset is crucial for revealing the spatiotemporal patterns of rainfall erosivity and identifying key areas of water erosion. However, due to the insufficient spatiotemporal resolution of historical precipitation data, there are certain biases in the estimation of rainfall erosivity in China, especially in regions with complex terrain and climatic conditions. Over the past decade, the China Meteorological Administration has continuously improved its ground-based meteorological observation capabilities, forming a dense network of ground-based observation stations. These high-precision precipitation data provide a solid foundation for quantifying the patterns of rainfall erosivity in China. In this study, we first performed rigorous quality control on the 1-minute ground observation precipitation data from nearly 70,000 stations nationwide from 2014 to 2022, ultimately selecting 60,129 available stations. Using the precipitation data from these stations, we calculated event rainfall erosivity and generated a national mean annual rainfall erosivity dataset with a spatial resolution of 0.25°. This dataset shows that the mean annual rainfall erosivity in mainland China is approximately 1241 MJ·mm·ha−1·h−1·yr−1, with areas exceeding 4000 MJ·mm·ha−1·h−1·yr−1 mainly concentrated in the southern China and southern Tibetan Plateau. Compared to our study, previously released datasets overestimate China's mean annual rainfall erosivity by 31 %~65 %, and there are significant differences in performance across different river basins. In summary, the release of this dataset facilitates a more accurate assessment of the current water erosion intensity in China. The dataset is available from the National Tibetan Plateau/Third Pole Environment Data Center (https://doi.org/10.11888/Terre.tpdc.301206 ; Chen, 2024). [ABSTRACT FROM AUTHOR]

The vertical profiles of specific humidity in the troposphere measured by the millimetre-wave humidity sounder (MHS) onboard of the Earth Observation Satellite (EOS)-07 (Microsoft 2B) are validated using ground-based radiosonde observations during July-August 2023. MHS onboard EOS-07 (hereafter referred as EOS-07/MHS) is a 6-channel cross-track scanning radiometer operating in the 183.31 ± 16.25 GHz band. The EOS-07 observations are used to retrieve three-dimensional specific humidity profiling from 1000 hPa (surface) to 100 hPa (16 km) atmospheric pressure levels with a spatial resolution of 10 km at nadir. The EOS-07/MHS measurements over India have a high correlation coefficient of 0.98 with the radiosonde observations. The mean relative bias is found to be 0.44 ± 0.55 g kg−1 below 350hPa and 2.8 ± 2.3 g kg−1 above 350 hPa. It is also found that above the 350 hPa level, MHS seems to be systematically overestimating the radiosonde measurements. After that, the EOS-07/MHS measurements are used to construct the diurnal variation of humidity during the active phases of the Indian summer monsoon. The observations are consistent with the present understanding of the Indian summer monsoon system. The present results are very encouraging and demonstrate the great potential of EOS-07/MHS observations of humidity for meteorological application, especially in understanding the hydrological cycle. [ABSTRACT FROM AUTHOR]

The evaluation and monitoring of drought, a complex disaster, are essential issues for the planning of agricultural activities and the operation and management of water resources. In this study, the meteorological drought of the Black Sea region in northern Turkey is evaluated with the Standardized Precipitation Index (SPI) and Reconnaissance Drought Index (RDI) methods at 3-, 6-, and 12-month time scales. Meteorological parameters obtained between 1965 and 2019 from 28 meteorological observation stations are used to analyze the drought. Drought-wet period percentages, percentages of occurrence of drought classes, critical period, and negative peak index values are investigated. As a result of the study, it is observed that the percentage of drought occurrence at all time scales is higher than 50% at two stations in the study region, which received precipitation above the average of Turkey. The sum of the extreme drought (ED) and severe drought (SD) values throughout the region is approximately 15% for the whole area. There is a high correlation coefficient, 97.8, 97.6, and 97.3 for the 3-, 6-, and 12-month time scales, respectively, between the SPI and RDIst methods in the evaluation of meteorological drought in the Black Sea region, which is generally a rainy region. It is determined that with the increase in the time scale, the percentage of ED occurrence increased and the risk level of the region around RDI and 17089 station is the highest. It has been determined that the correlation between oscillation indices and meteorological drought indices for the Black Sea region is almost non-existent. The highest correlation value between the oscillation and meteorological drought indices in the region is obtained with the Atlantic Multidecadal Oscillation (AMO). [ABSTRACT FROM AUTHOR]

If the interplanetary magnetic field (IMF) has a northward component, a portion of the solar wind ions flowing in the high-latitude magnetosheath drastically alters the flow direction owing to lobe reconnection and follows the Earth's magnetic field lines toward the low-altitude cusp. Partly because of this folded path, the relationship between the speed of ion flow in the magnetosheath and the energy of the precipitating ions in the low-altitude cusp remains unclarified. Herein, we examined particle data from 11 years of observations by Defense Meteorological Satellite Program satellites to obtain 1990 cases through an automated event identification of the cusp. This corresponds to the largest number of cusp ion events reported to date for a stable northward IMF. The results revealed a positive correlation between the energy of the precipitating ions and the estimated flow speed in the magnetosheath, which answers the question that has remained unexplained for decades: why is the brightness of the cusp proton aurora more strongly correlated with the solar wind dynamic pressure than with the solar wind number density? We suggest additional energizations of the exhausted ions from lobe reconnection in the outflow region because of the turbulent electric field formed by the opposite flow of two ion populations—one in the tailward-directed magnetosheath flow and the other in the earthward-directed outflow jet. [ABSTRACT FROM AUTHOR]

Satellite-based remote sensing enables the quantification of tropospheric NO2 concentrations, offering insights into their environmental and health impacts. However, remote sensing measurements are often impeded by extensive cloud cover and precipitation. The scarcity of valid NO2 observations in such meteorological conditions increases data gaps and thus hinders accurate characterization and variability of concentration across geographical regions. This study utilizes the Empirical Orthogonal Function interpolation in conjunction with the Extreme Gradient Boosting (XGBoost) algorithm and dense urban atmospheric observed station data to reconstruct continuous daily tropospheric NO2 column concentration data in cloudy and rainy areas and thereby improve the accuracy of NO2 concentration mapping in meteorologically obscured regions. Using Chengdu City as a case study, multiple datasets from satellite observations (TROPOspheric Monitoring Instrument, TROPOMI), near-surface NO2 measurements, meteorology, and ancillary data are leveraged to train models. The results showed that the integration of reconstructed satellite observations with provincial and municipal control surface measurements enables the XGBoost model to achieve heightened predictive accuracy (R2 = 0.87) and precision (RMSE = 5.36 μg/m3). Spatially, this approach effectively mitigates the problem of missing values in estimation results due to absent satellite data while simultaneously ensuring increased consistency with ground monitoring station data, yielding images with more continuous and refined details. These results underscore the potential for reconstructing satellite remote sensing information and combining it with dense ground observations to greatly improve NO2 mapping in cloudy and rainy areas. [ABSTRACT FROM AUTHOR]

Lakes on the Qinghai Tibet Plateau (QTP) are widely distributed spatially, and they are mostly seasonally frozen. Due to global warming, the thickness and phenology of the lake ice has been changing, which profoundly affects the regional climate evolution. There are a few studies about lake ice in alpine regions, but the understanding of climatological characteristics of lake ice on the QTP is still limited. Based on a field experiment in the winter of 2022, the thermal conductivity of Qinghai Lake ice was determined as 1.64 W·m−1·°C−1. Airborne radar ice thickness data, meteorological observations, and remote sensing images were used to evaluate a quasi-steady ice model (Leppäranta model) performance of the lake. This is an analytic model of lake ice thickness and phenology. The long-term (1979–2017) ice history of the lake was simulated. The results showed that the modeled mean ice thickness was 0.35 m with a trend of −0.002 m·a−1, and the average freeze-up start (FUS) and break-up end (BUE) were 30 December and 5 April, respectively, which are close to the field and satellite observations. The simulated trend of the maximum ice thickness from 1979 to 2017 (0.004 m·a−1) was slightly higher than the observed result (0.003 m·a−1). The simulated trend was 0.20 d·a−1 for the FUS, −0.34 d·a−1 for the BUE, and −0.54 d·a−1 for the ice duration (ID). Correlation and detrending analysis were adopted for the contribution of meteorological factors. In the winters of 1979–2017, downward longwave radiation and air temperature were the two main factors that had the best correlation with lake ice thickness. In a detrending analysis, air temperature, downward longwave radiation, and solar radiation contributed the most to the average thickness variability, with contributions of 42%, 49%, and −48%, respectively, and to the maximum thickness variability, with contributions of 41%, 45%, and −48%, respectively. If the six meteorological factors (air temperature, downward longwave radiation, solar radiation, wind speed, pressure, and specific humidity) are detrending, ice thickness variability will increase 83% on average and 87% at maximum. Specific humidity, wind, and air pressure had a poor correlation with ice thickness. The findings in this study give insights into the long-term evolutionary trajectory of Qinghai Lake ice cover and serve as a point of reference for investigating other lakes in the QTP during cold seasons. [ABSTRACT FROM AUTHOR]

The majority of available climate data in global digital archives consist of data only from the 1940s or 1950s onwards, and many of these series have gaps and/or are available for only a subset of the variables which were actually observed. However, there exist billions of historical weather observations from the 1700s, 1800s, and early 1900s that are still in hard‐copy form and are at risk of being lost forever due to deterioration. An assessment of changes in climate extremes in several IPCC regions was not possible in IPCC AR6 WGI owing, in many cases, to the lack of available data. One such region is Africa, where the climate impact research and the ability to predict climate change impacts are hindered by the paucity of access to consistent good‐quality historical observational data. The aim of this innovative project was to use classroom‐based participatory learning to help transcribe some of the many meteorological observations from Africa that are thus far unavailable to researchers. This project transcribed quickly and effectively station series by enrolling the help of second‐year undergraduate students at Maynooth University in Ireland. The newly digitized African data will increase the temporal and spatial coverage of data in this important data‐sparse region. Students gained new skills while helping the global scientific community unearth new insight into past African climate. The project managed to transcribe 79 months of data at Andapa in Madagascar and 56 months of data for Macenta in Guinea. The digitized data will be openly and freely shared with the scientific and wider community via the Pangaea data repository, the C3S Climate Data Store, and the National Oceanic and Atmospheric Administration's (NOAA) National Centers for Environmental Information (NCEI) data centre in the US. The project model has the potential for a broader roll‐out to other educational contexts and there is no shortage of data to be rescued. This paper provides details of the project, and all supporting information such as project guidelines and templates to enable other organizations to instigate similar programs. [ABSTRACT FROM AUTHOR]

Accurate long-term daily cloud-gap-filled fractional snow cover products are essential for climate change and snow hydrological studies in the Asian Water Tower (AWT) region, but existing Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products are not sufficient. In this study, the multiple-endmember spectral mixture analysis algorithm based on automatic endmember extraction (MESMA-AGE) and the multistep spatiotemporal interpolation algorithm (MSTI) are used to produce the MODIS daily cloud-gap-filled fractional snow cover product over the AWT region (AWT MODIS FSC). The AWT MODIS FSC products have a spatial resolution of 0.005° and span from 2000 to 2022. The 2745 scenes of Landsat-8 images are used for the areal-scale accuracy assessment. The fractional snow cover accuracy metrics, including the coefficient of determination (R2), root mean squared error (RMSE) and mean absolute error (MAE), are 0.80, 0.16 and 0.10, respectively. The binarized identification accuracy metrics, including overall accuracy (OA), producer's accuracy (PA) and user's accuracy (UA), are 95.17 %, 97.34 % and 97.59 %, respectively. Snow depth data observed at 175 meteorological stations are used to evaluate accuracy at the point scale, yielding the following accuracy metrics: an OA of 93.26 %, a PA of 84.41 %, a UA of 82.14 % and a Cohen kappa (CK) value of 0.79. Snow depth observations from meteorological stations are also used to assess the fractional snow cover resulting from different weather conditions, with an OA of 95.36 % (88.96 %), a PA of 87.75 % (82.26 %), a UA of 86.86 % (78.86 %) and a CK of 0.84 (0.72) under the MODIS clear-sky observations (spatiotemporal reconstruction based on the MSTI algorithm). The AWT MODIS FSC product can provide quantitative spatial distribution information on snowpacks for mountain hydrological models, land surface models and numerical weather prediction in the Asian Water Tower region. This dataset is freely available from the National Tibetan Plateau Data Center at 10.11888/Cryos.tpdc.272503 (Jiang et al., 2022) or from the Zenodo platform at 10.5281/zenodo.10005826 (Jiang et al., 2023a). [ABSTRACT FROM AUTHOR]

Over the past decades, drought has been considered as one of the most catastrophic natural disasters due to the devastating implications associated with this phenomenon. This research examines drought characteristics, severity, intensity, and duration in Sudan from 1980 to 2017 using meteorological observations and remote sensing datasets. According to analyses of the standardized precipitation index (SPI) provided by 29 sites, 72% of all dry occurrences appear to have occurred during the 1980s. The wettest occurrences, on the other hand, occurred after the mid-1990s timeframe and accounted for 82% of the total. SPI-3 research over Sudan revealed dry conditions during the 1980s. Early datasets indicate extreme drought periods occurred between 1982 and 1987, while fewer occurrences were observed during the 1990s and 2000s. SPI-3 shows that rainfall increased dramatically in the 1990s. Among the understudied time scales, 1982, 1983, 1984, 1987, 1990, 1992, and 2004 regimes recorded the driest events, whereas 1988, 1998, 1999, 2000, 2003, 2005, 2006, 2007, and 2016 had the wettest. Recent seasonal vegetation conditions from the normalized difference vegetation index (NDVI) and soil moisture were examined. The intensity of severe droughts declined for 2000, 2005, 2010, 2015, and 2020 periods. Conversely, a discernible upward trend in wet conditions with corresponding increases can be observed. Findings depict a graphical illustration of changes in the vegetation condition index (VCI), both geographically and chronologically in Sudan between 2000 and 2020. Generally, a positive correlation between the SPI-12 and AMO was obtained across Sudan's different regions. Significant power association peaked between 1 and 2 and 6 and 10 years from 1980 to 1990 and 6 and 8 years from 2005 to 2017. This study lays the groundwork for future drought studies, which further provides the technical basis for policymakers to address the consequences of drought on agriculture and the economy. [ABSTRACT FROM AUTHOR]

Based on the hourly surface ozone (O3) observations and meteorological data from Zhuzhou in 2021, the pollution characteristics and influencing factors of O3 in Zhuzhou were investigated in the study. In addition, the Potential Source Contribution Function (PSCF) and Concentration Weighted Trajectory (CWT) analysis methods were employed to analyze the transmission paths and potential pollution sources of O3 pollution in Zhuzhou. The results showed that the total number of days with O3 exceeding the standard at all monitoring stations in Zhuzhou was 142 days in 2021. The overall air quality was less affected by SO2, NO2, and CO, and the trend of O3 pollution was still increasing. The concentrations of O3, CO, and NO2 varied significantly in different months, and the variation of O3 exhibited a "double-peak" pattern, with the peak value occurring in September. The O3 concentration in urban areas was significantly higher than that in suburban areas. Meteorological conditions had a significant impact on the degree of O3 pollution in Zhuzhou. The average wind speed in Zhuzhou throughout the year was 1.7 m/s, and the prevailing wind direction in summer was southeast, with a frequency of 16%. O3 pollution was mainly transported by short-distance airflow during the over-standard periods in 2021, accounting for 37.64%. The main source of O3 pollutant was from Jiangxi Province in the east, with the shortest distance of regional transport and the highest O3 concentration. In addition, transportation from central Guangdong Province, western Jiangxi Province, and central Hubei Province also had a significant impact. [ABSTRACT FROM AUTHOR]

Meteorological fields calculated by numerical weather prediction (NWP) models drive offline chemical transport models (CTMs) to solve the transport, chemical reactions, and atmospheric interaction over the geographical domain of interest. HARMONIE (HIRLAM ALADIN Research on Mesoscale Operational NWP in Euromed) is a state-of-the-art non-hydrostatic NWP community model used at several European weather agencies to forecast weather at the local and/or regional scale. In this work, the HARMONIE WINS50 (cycle 43 cy43) reanalysis dataset at a resolution of 0.025° × 0.025° covering an area surrounding the North Sea for the years 2019–2021 was coupled offline to the LOTOS-EUROS (LOng-Term Ozone Simulation-EURopean Operational Smog model, v2.2.002) CTM. The impact of using either meteorological fields from HARMONIE or from ECMWF on LOTOS-EUROS simulations of NO2 has been evaluated against ground-level observations and TROPOMI tropospheric NO2 vertical columns. Furthermore, the difference between crucial meteorological input parameters such as the boundary layer height and the vertical diffusion coefficient between the hydrostatic ECMWF and non-hydrostatic HARMONIE data has been studied, and the vertical profiles of temperature, humidity, and wind are evaluated against meteorological observations at Cabauw in The Netherlands. The results of these first evaluations of the LOTOS-EUROS model performance in both configurations are used to investigate current uncertainties in air quality forecasting in relation to driving meteorological parameters and to assess the potential for improvements in forecasting pollution episodes at high resolutions based on the HARMONIE NWP model. [ABSTRACT FROM AUTHOR]

Stable boundary layer height (SBLH) is an important parameter to characterize the characteristics and vertical structure of the nocturnal lower atmosphere at night. The distribution of SBLH has obvious spatial and temporal differences, and there are many meteorological factors affecting the SBLH, but at present, there are few quantitative studies on the effects of near-surface meteorological factors on the SBLH in the desert hinterland. This study was based on GPS sounding balloon data, near-surface meteorological observation data, and ERA5 data from Tazhong Station (TZ) in the Taklamakan Desert (TD) collected in July 2017, 2019, and 2021. The variation characteristics of the SBLH and its relationship with near-surface meteorological factors are described. We quantitatively analyzed the degree of influence of near-surface meteorological factors affecting the SBLH and verified it using a model. The study also elucidates the possible formation mechanism of the SBLH in the TD hinterland. The SBLH in the TD hinterland trended upward in July 2017, 2019, and 2021, which is consistent with the changes in meteorological factors, according to the near-surface meteorological observation and ERA5 data. Therefore, we think that an inherent connection exists between near-surface meteorological factors and the SBLH. The results of correlation analysis show that complex internal connections and interactions exist among the meteorological factors near the ground; some thermal, dynamic, and other meteorological factors strongly correlate with the SBLH. Having established the change in SBLH (ΔSBLH) and in major thermal, dynamic, and other meteorological factors (Δ), the linear regression equation between them revealed that near-surface meteorological factors can affect the SBLH. The dynamic factors have a stronger influence on the ΔSBLH than thermal and other factors. The results of model validation based on the variable importance projection (VIP) also confirmed that the SBLH in the TD hinterland is jointly affected by dynamic and thermal factors, but the dynamic factors have a stronger impact. The mechanism through which the SBLH forms is relatively complex. At night, surface radiative cooling promotes the formation of a surface inversion layer, and low-level jets strengthen wind shear, reducing atmospheric stability. The combined effects of heat and dynamics play an important role in dynamically shaping the SBLH. This study helps us with accurately predicting and understanding the characteristics of the changes in and the factors influencing the SBLH in the TD hinterland, providing a reference for understanding the mechanism through which the SBLH forms in this area. At the same time, it provides a scientific basis for regional weather and climate simulation, meteorological disaster defense, air quality forecasting, and model parameterization improvement. [ABSTRACT FROM AUTHOR]

Based on the observation data of meteorological stations, Doppler radar observation data of Ulanqab City, and ERA-5 reanalysis data, a snowstorm process in Ulanqab City from March 17 to 18, 2022 was analyzed. The results show that this was a type II snowstorm process generated under the joint influence of upper trough and ground low inverted trough and frontal cyclone. The main period of snowfall can be divided into two time stages, and the total snowfall was more in the south and less in the north, which was consistent with that of average specific humidity field. Water vapor conditions provided by strong water vapor transport and convergence, strong upward movement shown by large vertical velocity field, and the suction action of highand low-layer divergence and convergence were the reasons for the hourly heavy snowfall on the 18"'. During the process, radar echoes were mainly sheetshaped, and composite reflectivity was 15 -25 dBZ in most areas. The zero speed line in the first period was positively "S" -shaped, and there was warm advection and southwest wind. On the morning of the 18"', after the cold front transited the city, Ulanqab City was gradually controlled by northwest wind, and the snow tended to end. [ABSTRACT FROM AUTHOR]

Based on the daily meteorological observation data of seven meteorological stations in southern Tibet from 1980 to 2021 (April October), the temporal and spatial variation characteristics and influencing factors of aridity index (AI) in the growing season of major grain producing areas in Tibet were studied by using climate tendency rate, Mann-Kendal test, Morlet wavelet analysis, GIS hybrid interpolation method, Pearson correlation coefficient, contribution rate analysis and other methods. The results showed that the average Al in the main grain producing areas of Tibet was 1.7, which belonged to the semi-arid area, and the overall trend was decreasing (humidifying) (-0.036/10 a). The linear decreasing trend was different in different regions, and the area around Lhatse County was the most significant (-0.26/10 a). Al had no obvious abrupt change, and had long- and medium-term fluctuation characteristics of 24 years, 6 years. The spatial dis- tribution was uneven, and had the characteristics of 'shrinking arid area and expanding humid area'. The contribution rates of the main climate influencing factors of Al varied in different regions. In general, the contribution rates after quantification was as follows: precipitation (34.9%) > relative humidity (28.4%) > sun- shine (19.9%) > maximum temperature (12.4%). [ABSTRACT FROM AUTHOR]

Daily weather observations measured by students and staff at Halkali Agricultural School (a school opened in 1892 on agriculture and animal husbandry during the Ottoman period) from 1896 to 1917 in Istanbul, Turkiye have been transcribed from the original publications into digital form and translated from Ottoman Turkish (the Perso‐Arabic script) to English (Latin alphabet). Over 55,000 observations of daily maximum, minimum and average temperature; rainfall, soil and under soil (0.25 m) temperature; humidity, pressure and wind speed were recovered. In addition, weather observations taken in Kandilli Observatory and Earthquake Research Institute from 1911 to 1936 and taken in Florya Meteorological Station from 1937 to 2022 could inform long‐term temperature changes in Istanbul. The publication of a new historical data set that includes, for the first time, digitized and quality‐controlled daily meteorological observations in Istanbul will enhance the understanding of weather changes in Turkiye back to the late 19th century. These observations will be used to fill gaps in existing temperature and pressure records and to the improvement of the accuracy of reanalysis products prior to the 1950s. It will be the first data set publication of other parameters such as soil temperature, wind speed and humidity for that period in that region. Data are available on the CEDA Archive in csv file format. [ABSTRACT FROM AUTHOR]

Integrated water vapor (IWV) has been widely perceived through machine learning (ML) strategies. During this investigation, we employed IWV time series from weather stations to determine the oscillations and patterns with IWV across two latitudes namely VBIT, Hyderabad (India) and PWVUO station, Oregon (US). The GPS derived IWV and meteorological data such as pressure (P), temperature (T) and relative humidity (RH) dataset for the year 2014 has been taken from VBIT station and from PWVUO station for 2020. Five machine learning algorithms namely Optimized Ensemble (OE) model, Rational Quadratic Gaussian Process Regression model (RQ-GPR), Neural Networks model (NN), Cubic Support Vector Machine (CSVM) and Quadratic Support Vector Machine (QSVM) algorithms are used. The GPS derived IWV data revealed the maximum variation during summer monsoon period specifically in the month of July. The correlation analysis between GPS-IWV and optimized ensemble technique showed the highest correlation for the VBIT station with correlation coefficient as (ρ) = 99 % and at PWVUO station as (ρ) = 88 % for two different datasets. The residual analysis has also showed less variation to the optimized ensemble model. The performance metrics obtained for OE at VBIT station are mean absolute error (MAE) as 0.64 kg/m2, mean absolute percentage error (MAPE) as 3.80 % and root mean squared error (RMSE) as 0.94 kg/m2 and at PWVUO station the values are MAE = 1.91 kg/m2, MAPE = 11.76 % and RMSE as 1.97 kg/m2, respectively. The results explained that the OE method has shown a better performance compared to the remaining models. [ABSTRACT FROM AUTHOR]

Yunnan plays a pivotal role in transmitting electricity from west to east within China's Southern Power Grid. During 7–13 January 2021, a large-scale continuous ice-covering event of ultra-high voltage (UHV) transmission lines occurred in the Qujing area of eastern Yunnan Province. Based on ERA5 reanalysis data and meteorological observation data of UHV transmission line icing in China's Southern Power Grid, the synoptic causes of the icing are comprehensively analyzed from various perspectives, including weather situations, vertical stratification of temperature and humidity, local meteorological elements, and atmospheric circulation indices. The results indicate a strong East Asian trough and a blocking high directing northern airflow southward ahead of the ridge. Cold air enters the Qujing area and combines with warm and moist air from the subtropical high pressure of 50–110° E. As warm and cold air masses form a quasi-stationary front over the northern mountainous area of Qujing due to topographic uplift, the mechanism of "supercooling and warm rain" caused by the "warm–cold" temperature profile structure leads to freezing rain events. Large-scale circulation indices in the Siberian High, East Asian Trough, and 50–110° E Subtropical High regions provided clear precursor signals within 0–2 days before the icing events. [ABSTRACT FROM AUTHOR]

Due to the scarcity of meteorological observation stations in some areas, there is not enough data available for hydrological simulation as one of the main subjects of hydrology and environmental subjects. This can be partially solved by extracting the required data from global climate centers. The essential data for modeling rainfall-runoff of Dez watershed (16,000 km2), Khuzestan province, Iran, for a 20-year period (1990–2010) were obtained from NCEP CFSR climate center. The simulation (calibration and validation) was done by Soil and Water Assessment Tool (SWAT) and Identification of Unit Hydrographs and Component Flows from Rainfall, Evapotranspiration and Streamflow (IHACRES) models. The SWAT-CUP software has been used for optimization, calibrating and analyzing the uncertainty of the SWAT model by communicating with the model. The ability of the models to simulate the runoff of the basin was determined using the coefficient of determination (R2) and Nash–Sutcliffe efficiency (NSE). R2 and NSE for the SWAT model were 0.75, and 0.78 (calibration), and 0.59 and 0.72 (validation); for the IHACRES model, they were equal to 0.63 and 0.69 (calibration), and 0.54 and 0.66 (validation), respectively. The SWAT model performed better than the IHACRES model by efficiently using the climatic database of NCEP CFSR center. The IHACRES model was also able to simulate the runoff of the watershed with a relatively high correlation with the observational data. It is possible to simulate and predict rainfall-runoff of the Dez watershed using the tested models for taking the measures, which may avoid natural phenomenon including flooding. [ABSTRACT FROM AUTHOR]

The Bay of Bengal Storm (BBS) frequently occurs during the "double-peak periods" in (1) May (pre-peak period) and (2) October to November (post-peak period). Exploring precipitation variation and its anomalies during these periods is crucial for understanding climate change in the Qinghai-Tibet Plateau (QTP). Precipitation products of MSWEP v2 (Multi-Source Weighted Ensemble Precipitation Version 2) along with precipitation observations from meteorological stations and BBS data from the Guam Joint Typhoon Warning Center from 1979 to 2020 were employed for the analysis. The objective of the present study was to investigate the anomalous characteristics and multi-temporal variability of regional precipitation over the QTP and its relationship with the BBS using the empirical orthogonal function (EOF), rotated empirical orthogonal function (REOF), and percentage calculation method. There was a consistent spatial distribution of precipitation on the QTP during double-peak periods based on the first mode of EOF. However, the second mode of EOF identified a northeast-southwest reverse in precipitation distribution. The QTP was categorized into five precipitation anomalous subregions during double-peak periods using REOF. Precipitation showed significant increases (12.6 mm/10a in May and 5.1 mm/10a in October to November) in specific regions. Precipitation anomalies on the QTP during double-peak periods exhibited positive anomalies at various timescales (monthly, ten-day, and pentad), with stronger BBS influence in the pre-peak period. From 1979 to 2020, all monthly precipitation anomalies on the QTP during double-peak periods were accompanied by BBS, with an 8% probability. The rates of precipitation anomalies under the influence of BBS were about 18% and 28% in the ten-day and pentad timescales, respectively, with BBS contributing the most to pentad precipitation anomalies. Daily precipitation anomalies during the pre-peak and post-peak periods were influenced by BBS to varying extents (30%, 20%, and 25% in May, October, and November, respectively). Extreme daily precipitation days affected by BBS represented 19%, 16%, and 21% of the statistical period extreme precipitation days in May, October, and November, respectively, with higher rates in the post-peak period. [ABSTRACT FROM AUTHOR]

Rice production is sensitive to climate change and significantly affected by warming in recent years. To what extent climate warming shifted rice phenology and varied thermal resource condition were explored across five agro-ecological zones in China, based on up-to-date observations of meteorology and rice cultivation in 1981–2020. It was clearly signaled that there was a general advance of 0.3–3.8 days in observed sowing date and a delay of 0.4–3.5 days in observed maturity date in 2001–2020 relative to 1981–2000 in major zones. A vacant time slice of 2.6–28.8 days between observed sowing date and potential sowing date, and a lag of 15.4–56.7 days in potential maturity date compared to observed maturity date were identified in 2001–2020. Within longer growing season, useful accumulated temperature increased by 76.7–117.6 °C·d in 2001–2020 relative to 1981–2000, while disactive accumulated temperature also increased. In Northeast China, actual rice cultivation was undergoing earlier sowing date than potential sowing date and longer growing duration than potential duration, yet leading to upward disactive accumulated temperature. The decrease in the thermal resource utilization in 2001–2020 relative to 1981–2000 was highlighted at 55.3–78.3% stations in major zones, with a decrement of 0.006–0.018 in average magnitude. The changes in thermal resource utilization unveiled that the shifts in actual rice cultivation still could not compensate for the suitability in thermal resource utilization benefited from climate warming. [ABSTRACT FROM AUTHOR]

Weather observations and forecasts are crucial for aviation safety and efficiency, particularly in the presence of hazards such as fog. Hence, climatological studies of this phenomenon are standard information for the aviation industry. Aeronautical stations combined with synoptic stations have to provide local and current weather conditions in two different types of reports by using the same instrumentation: SYNOP for synoptic purposes and METAR for aeronautical purposes. When climatological studies have to be carried out, the question of which type of report is more appropriate for the task arises. In this work, a comparison between these two types of hourly meteorological observation reports is made, with the aim of identifying the conditions under which differences can have a significant impact on the final results. For this purpose, SYNOP and METAR reports for the period 2000–2019 from 13 Argentine airports were selected from different databases. Results show that level of agreement depends strongly on the variable of interest. A key difference is found in "present weather" mainly due to different reporting rules. In this context, six different selection criteria using visibility, dew point depression, and present weather are used to evaluate the impact of the differences found in the fog and mist climatology. In conclusion, SYNOP data could be used instead of METAR in statistical studies if the need arises, but with the caveat that the frequency of fog occurrence should not be interpreted too strictly. [ABSTRACT FROM AUTHOR]

The turbulent exchange in boundary layer models is usually characterized by a scalar eddy viscosity coefficient assumed to be a positive function of the vertical variable. We introduce a more general form for the turbulence exchange description, which includes two functions that describe the turbulence without any assumption about their positivity. We construct a model of the Akerblom–Ekman type, but with a complex coefficient of turbulent exchange. The basic quality criterion for these models and algorithms is the maximal agreement with meteorological observations. We optimize the agreement between the global meteorological archive of high-resolution wind observations that are provided by World Meteorological Organization (WMO) in Binary Universal Form for the Representation (BUFR). The main result of our work is that agreement between model solutions and observations will be much better if the turbulent exchange coefficient is optimized in the space of all complex-valued functions, and not limited to the cone of real positive functions. [ABSTRACT FROM AUTHOR]

Little is known about the mechanism of climate–vegetation coverage coupled changes in the Tibetan Plateau (TP) region, which is the most climatically sensitive and ecologically fragile region with the highest terrain in the world. This study, using multisource datasets (including satellite data and meteorological observations and reanalysis data) revealed the mutual feedback mechanisms between changes in climate (temperature and precipitation) and vegetation coverage in recent decades in the Hengduan Mountains Area (HMA) of the southeastern TP and their influences on climate in the downstream region, the Sichuan Basin (SCB). There is mutual facilitation between rising air temperature and increasing vegetation coverage in the HMA, which is most significant during winter, and then during spring, but insignificant during summer and autumn. Rising temperature significantly enhances local vegetation coverage, and vegetation greening in turn heats the atmosphere via enhancing net heat flux from the surface to the atmosphere. The atmospheric heating anomaly over the HMA thickens the atmospheric column and increases upper air pressure. The high pressure anomaly disperses downstream via the westerly flow, expands across the SCB, and eventually increases the SCB temperature. This effect lasts from winter to the following spring, which may cause the maximum increasing trend of the SCB temperature and vegetation coverage in spring. These results are helpful for estimating future trends in climate and eco-environmental variations in the HMA and SCB under warming scenarios, as well as seasonal forecasting based on the connection between the HMA eco-environment and SCB climate. [ABSTRACT FROM AUTHOR]

This study designs an automatic monitoring and alarm system for integrated meteorological observation based on the cloud platform to improve the automatic monitoring ability for integrated meteorological observation and the operation and maintenance management level of meteorological departments. A meteorological data collector was used to collect comprehensive meteorological observation data, transmit the collected comprehensive meteorological observation data to the main control processing module, preprocess the comprehensive meteorological observation data, and finally transmit the pre-processed comprehensive meteorological observation data to the cloud service module through the data transmission module. In the cloud service module, cloud computing and an improved K-means algorithm were used to analyze the comprehensive meteorological observation data, mine abnormal comprehensive meteorological observation data, and send alarm information to the application module. Users can view the comprehensive meteorological observation data in the cloud server on the client in real-time, thus realizing automatic monitoring and alarm for comprehensive meteorological observation. Results show that the average transmission rate of various meteorological comprehensive observation data transmitted by the system is as high as 99.48%, thereby achieving clustering of abnormal meteorological comprehensive observation data. Upon detecting abnormal meteorological data, the system sends alarm information to the mobile phone client to complete the monitoring of abnormal meteorological information. [ABSTRACT FROM AUTHOR]

Drought, defined as the period when water availability considerably remains below normal levels for a certain time period, has devastating social, economic and environmental impacts. It is significant to closely monitor the temporal and spatial variability of drought in order to implement policies to mitigate the drought risk and ensure the sustainable management of water resources. In this study, three different drought indices, standard precipitation index (SPI), standardized precipitation‐evapotranspiration index (SPEI) and reconnaissance drought index (RDI), are calculated for 3‐, 6‐ and 12‐month time scales from the precipitation and temperature data measured in 11 meteorological observation stations within the Mediterranean coastal region of Türkiye during 1972–2020. Initially, two absolute homogeneity tests were applied, namely the standard normal homogeneity test (SNHT) and the Pettitt test, to detect abrupt changes in drought index time series. Then, trend analysis was carried out with the innovative trend analysis (ITA) and improved visualization of ITA (IV‐ITA) methods to determine the temporal variation of the drought in the region. A graphical representation is developed for the drought classifications based on IV‐ITA. As a result of the SNHT and Pettitt test, significant change points were found at a 5% significance level in 91.92% and 85.86% of all indices, respectively. In both tests, breaks were detected at similar times (between 1972 and 1974) at Marmaris station, while at Alanya station, changes were found between 1972 and 1973 for the SNHT and in 2008 for the Pettitt test. The trend differences between extreme conditions (extremely dry and wet) decreased with an increase in time scale, distinctively. The ITA trends were almost identical for 3‐month time scales, and they started to differ with time scale increases. Particularly, the presence of negative differences in data points contributes to the augmentation of drought periods, despite a few instances of extreme cases and minor fluctuations that can also be observed in IV‐ITA. The findings obtained from the study are expected to provide valuable information about the recent shifts in drought in the Mediterranean basin. [ABSTRACT FROM AUTHOR]

Glaciers in the Alps are losing mass at unprecedented and accelerating rates. Monitoring of glacier mass change as well as relevant atmospheric parameters plays an important role in improving understanding of local and downstream impacts. We present subseasonal, seasonal, and annual glaciological mass balance data and meteorological observations from Mullwitzkees and Venedigerkees, two glacier monitoring sites in the Hohe Tauern range of the Austrian Alps. Ablation stake networks were established on Mullwitzkees in 2006/07 and on Venedigerkees in 2011/12. Monitoring is ongoing. In addition to stake readings at subseasonal intervals, accumulation measurements (snow pits and probing) are carried out seasonally. The glaciological data set consists of subseasonal floating date measurements as well as fixed date seasonal and annual values. Fixed date glacier wide mass balance was derived from annual point mass balance values. Automatic weather stations measuring standard meteorological parameters were installed near Mullwitzkees and Venedigerkees in 2020 and 2019, respectively. Meteorological data is provided in 10 minute intervals. Uncertainties for individual point mass balance measurements were computed following the approach of the Swiss glacier monitoring service (GLAMOS), taking into account estimated density and reading errors. The subseasonal mass balance records highlight shorter term variability in mass loss and the linkage with meteorological conditions. The most negative annual point mass balance recorded in the period of record was -5.8 ± 0.66 m w.e. at an elevation of 2536 on Venedigerkees. 2022 stands out as the most negative mass balance year to date in both time series, particularly at higher elevations. The cumulative specific mass balance (glacier wide) over the period of record was -14.68 m w.e. at Mullwitzkees and -8.79 m w.e. at Venedigerkees. Data is available in PANGAEA publication series and the associated datasets. The main publication series are updated annually. The Mullwitzkees mass balance datasets can be found at: doi: 10.1594/PANGAEA.965660 and doi: 10.1594/PANGAEA.965719. The Venedigerkees data can be found at doi: 10.1594/PANGAEA.965648 and doi: 10.1594/PANGAEA.965729. [ABSTRACT FROM AUTHOR]

Two severe dust storm (DS) events (15–17 March and 28–29 March) hit northern China in 2021 consecutively. The lower atmospheric vertical dynamic and thermal structures during the two cases were compared using the ground-based sensor data from the microwave radiometer and radar wind profiler, combined with the environmental and meteorological observations data in Jinan, China. It was found that both cases occurred under the background of cold vortexes over northeastern China. The dust was transported through the cold air on the northwest route. During the dust period, 2–3 km was the west or northwest airflow, and below 2 km was the northeast wind. The variation in the dynamic structure determined the duration of the DS. During the DS maintenance phase, the vertical wind shear (VWS) below 3 km measured approximately 10 m∙(s∙km)−1. The increased VWS during the dust intrusion period facilitated the transportation of dust. In contrast, the more significant VWS was not conducive to the maintenance of DS, and the shift to south wind control in the upper middle layer indicated the weakening of DS. In both cases, we observed a cliff-like decrease in relative humidity as a prominent indicator of dust outbreaks, occurring approximately 2–5 h beforehand. The diurnal difference between the vertical temperature and relative humidity during the dust maintenance period was found to be insignificant. [ABSTRACT FROM AUTHOR]

Severe Typhoon Koinu passed south of Hong Kong on 8 and 9 October 2023, triggering the issuance of the Increasing Gale or Storm Signal No. 9, the second highest tropical cyclone (TC) warning signal in Hong Kong. Koinu was a difficult case for TC warning service due to its compact size and rather erratic movement over the coastal waters of Guangdong. To monitor Koinu's movement and wind structure, the Hong Kong Observatory utilized various observational platforms, including meteorological aircraft, ocean radar, and synthetic aperture radar on polar orbiting satellites. The paper presents major observations derived from these measurements. The aircraft probe and dropsonde data suggested boundary layer inflow, warm core structure, eyewall updraft, and high turbulence in the eyewall of the typhoon. The weather radar observations indicated the occurrence of a waterspout in the vicinity of the typhoon. Additionally, the study highlights the forecasting performance of the AI‐based Pangu‐Weather model, which could outperform the conventional global numerical weather prediction models in forecasting TC track in the region. The documentation of these observations aims to provide valuable references for weather forecasters and stimulate further research on forecasting this type of tropical cyclone. Key Points: Observations from meteorological aircraft, ocean radar, and synthetic aperture radar show the structure of Severe Typhoon KoinuWeather radar observations suggest the occurrence of a waterspout in the vicinity of KoinuAI‐based Pangu‐Weather model could outperform the conventional global numerical weather prediction models in forecasting Koinu's track [ABSTRACT FROM AUTHOR]

Vegetation greening is time-dependent and region-specific. The uncertainty of vegetation greening under global warming has been highlighted. Thus, it is crucial to investigate vegetation greening and its response to climate change at the regional scale. The Yellow River Basin (YRB) is a vital ecological barrier in China with high ecological vulnerability and climatic sensitivity. The relationship between vegetation greening and climate change in the YRB and the relative contribution of climate change remain to be explored. Using the Enhanced Vegetation Index (EVI) and meteorological observation data, the spatiotemporal patterns of vegetation greening across the YRB in response to climate change at the basin and vegetation sub-regional scales from 2000 to 2020 were analyzed. The impact of human activities on regional greening was further quantified. Results showed that approximately 92% of the basin had experienced greening, at average annual and growing season rates of 0.0024 and 0.0034 year–1, respectively. Greening was particularly prominent in the central and eastern YRB. Browning was more prevalent in urban areas with a high intensity of human activities, occupying less than 6.3% of the total basin, but this proportion increased significantly at seasonal scales, especially in spring. Regional greening was positively correlated with the overall warmer and wetter climate, and the partial correlation coefficients between EVI and precipitation were higher than those between EVI and temperature. However, this response varied among different seasonal scales and vegetation sub-regions. The combined effects of climate change and human activities were conducive to vegetation greening in 84.5% of the YRB during the growing season, while human activities had a stronger impact than climate change. The relative contributions of human activities to greening and browning were 65.15% and 70.30%, respectively, mainly due to the promotion of ecological rehabilitation programs and the inhibition of urbanization and construction projects. [ABSTRACT FROM AUTHOR]

The Hengduan Mountains Region (HMR) is essential for the future ecological protection, clean energy production, Sichuan-Xizang and Yunnan-Xizang railways, and other major infrastructure projects in China. The distributions of climate and vegetation exhibit significant regional differentiation and vertical zonality due to the rugged longitudinal ranges and gorges and the complex disaster-prone environments in HMR. Therefore, it is urgent to develop the climate-vegetation regionalization in HMR to effectively satisfy the national requirements such as agricultural production and ecological protection, mountain disaster risk prevention, and major project construction. We here develop a new scheme of climate-vegetation regionalization with the latest demarcation outcome of HMR, the ground observation from 122 meteorological stations in HMR and its surrounding areas during 1990–2019, and the high-precision remote sensing data of land cover types. The new scheme first constructs the regionalization index system, fully considering the extraordinarily complicated geomorphic pattern ofmountains and valleys, the scarcity of meteorological observations, and the remarkable differentiation of climate and vegetation in HMR. The system consists of three primary regionalization indices (i.e., days with daily average temperature steady above 10°C, aridity index, and main vegetation types, dividing the temperature zones, moisture regions, and vegetation subregions, respectively) and three auxiliary indices of the accumulated temperature above 10°C, and the temperatures in January and July. Then, the HMR is divided into five temperature zones, 20 moisture regions, and 55 vegetation subregions. Compared with previous regionalization schemes, the new scheme optimizes the climate spatial interpolation model of thin plate smoothing spline suitable for the unique terrain in HMR. Moreover, the disputed division index threshold between different climatic zones (regions) is scientifically clarified using geographical detectors. Specifically, the stepwise downscaling pane division method is initially proposed to determine the zoning boundary, alleviating the excessive dependence of the traditional zoning method on subjective experience. Besides, the scheme considers the typical regional characteristics of the complex underlying surface and the high gradient zone of climate-vegetation distribution types in HMR. Consequently, the transition zone with quick climate changes between the plateau temperate and mid-subtropical zones is divided into mountainous subtropics, taking into account the spatial distribution characteristics of climate-vegetation regionalization indices. The regionalization scheme will provide practically theoretical support for agricultural production, ecological protection, major project construction, disaster prevention and relief efforts, and other socioeconomic activities in HMR, serving as a classic case of climate-vegetation regionalization for the alpine and canyon regions with intricate underlying surface, striking regional differences, and lack of ground observations. [ABSTRACT FROM AUTHOR]

Background: This study by design, approach, duration and number of respondents is the first of its kind for Suceava and Romania. The first objective of the study was to assess the average level of thermal perceptions of the population in the researched area. Methods: The respondents appreciated and wrote the level of comfort/discomfort felt daily, the interdiurnal, interlunar and intertemporal regime of thermal perceptions by reference to the meteorological elements and biometeorological indices was also assessed. The biometeorological survey test was conducted from January 1, 2019 to February 28, 2021, and 9321 valid responses were received (on average 11.8 answers/day-1). Results: The overall average of the answers (of -0.55) shows that the respondents perceived the atmosphere of the urban agglomeration as cool, the winters were cold and moderately cold (the answers average = -1.43), whereas the summers were more thermally comfortable than warm (the average of the answers = 0.73). The Pearson correlation coefficients between the averages of the daily answers and the average or extreme values of temperature, respectively of the biometeorological indices, ranged between 0.65 and 0.88. Conclusions: The PCA analysis reconfirmed high degrees of correlation between the average responses, the examined meteorological and biometeorological factors. [ABSTRACT FROM AUTHOR]

Visibility is the primary criterion for the landing and takeoff of an aircraft. At all major airports, a procedure called the low visibility procedure (LVP) is adopted in cases of marginal visibility, in which aircraft are permitted to land or take off based on the observed value of visibility, or specifically the runway visual range (RVR), and the forecast on the tendency of the RVR. Since the observed and forecasted value of the RVR is crucial for critical decisions on the landing or takeoff of an aircraft at airports, particularly at Patna airport, where landing is not permitted below the RVR of 1000 m, reliable prediction of the RVR is of great importance. As the predominant factors for the reduction of visibility at Patna Airport are fog, haze, and thunderstorms, this article proposes a novel, fully connected network architecture of a hybrid CNN-LSTM (convolutional neural network-long-short-term memory) framework to predict short-term RVR based on past instrumentally derived RVR values while considering exogenous meteorological factors that support the formation and intensification of fog, haze, etc. The proposed hybrid CNN-LSTM framework to function reliably and precisely, time series hourly observation data of meteorological elements, and the observed RVR values were used for the two forecasting horizons for a thorough examination. Extensive experimentation with the proposed hybrid CNN-LSTM framework architecture has been carried out along with the benchmark models of CNN, GRU (gated current unit), BiLSTM (bidirectional LSTM), and LSTM. Compared with CNN, GRU, BiLSTM, and LSTMs, the experimental findings reveal that the proposed hybrid CNN-LSTM framework achieves the best prediction performance in two random datasets with two different forecasting horizons, totaling four assessment criteria. Also, we look into how CNN, LSTM, and their hybrid network combinations might be used to make such predictions with reliable accuracy. We improve upon earlier models for short-term RVR prediction by optimising the loss function and network structure of the original CNN and LSTM models, making them more amenable to being used in actual operational environments. [ABSTRACT FROM AUTHOR]

Using 22‐year ground observations on meteorological variables, we conducted a statistical analysis to reveal fog characteristics at Shanghai Pudong International Airport (SPIA). Fog events were classified by fog types using an objective method. Two types of advection fogs are dominant in fog events at SPIA, followed by radiation fogs. Different fogs have evident annual and monthly variations in frequency. Advection fog mostly appear from February to April, while radiation fog is mainly between November and February. Six synoptic patterns associated with fogs were determine by a self‐organizing maps cluster method, two of which, namely 'west of marine high pressure' and 'inverted trough', are most favourable for the formation of advection fogs, whereas 'bottom of weak cold high‐pressure' is favourable for radiation fogs. The frequency of advection fog occurrence exhibits temporal fluctuations with distinct peaks occurring 2–3 h after sunset, around midnight, and 1–2 h before sunrise, while almost all radiation fog occur in the second half of the night, and favourable conditions for fog onset become more effective with the length of the night. Radiation fog exhibits a longer duration than advection fog and tends to result in lower visibility. The prevalent wind direction for the formation of advection fog is generally from the east to the southeast, whereas radiation fog is typically associated with westerly winds. The temperature and air pressure during the formation of both advection and radiation fogs follow a normal distribution in most seasons. More fog characteristics were categorized by fog types and seasons. [ABSTRACT FROM AUTHOR]

Scanning micro-X-ray fluorescence analysis with synchrotron radiation was applied to obtain the depth-variation profiles of element compositions of banded clays in Peyungda Lake (Krasnoyarsk krai, Evenkia) located 30 km southwest of the epicenter of the Tunguska event (1908). The age model was confirmed by a dated layer of anomalous thickness related to the fall and explosion of the Tunguska Cosmic Body (TCB). The variations in the element content in dated core layers were compared with the instrumental meteorological observation data over the last century. The regional average annual temperature and element compositions of the coeval bottom sediments (transfer function) were found to be interrelated. The approximation of the transfer function to the sampling depth of the sedimentary section made it possible to reconstruct the air temperature in the studied region over the past 1000 years. The average annual temperature reconstructions in the study area compared with the global temperature reconstructions in the Arctic region for the same period made it possible to reveal general trends and extreme values. [ABSTRACT FROM AUTHOR]

The micro-physical characteristics of a typical sea of clouds process in Jiuxian Mountain are investigated by comprehensively analyzing parameters that delineate the micro-physical characteristics of clouds and atmospheric stratification based on data from a cloud radar, wind profiler, meteorological gradient observation in high mountains, and other observations. The results show that water vapor condenses into cloud particles via an entrained and mixing process accompanied by an updraft originating from orographic uplift. During the thickening stage of the sea of clouds, atmospheric motion within the clouds is featured as "downdraft on the top—updraft on the bottom". The zero vertical velocity area is located closely to the maximum of liquid water content. The thermal inversion layer is formed during the maintenance stage; however, the enhancement of inversion on the cloud top could suppress updraft in areas with a high liquid water content. The values mainly concentrate on the cloud top, and repetitively lifting and falling processes caused by the atmospheric upward and downward motion are in favor of the coalescence growth of cloud particles, which result in the persistence of strong radar echo. At the dissipation stage, warming on the cloud top is greater than that on the cloud bottom due to the short-wave absorption of clouds as the solar radiation enhances. As a result, the inversion layer thickens and elevates, evaporation caused by heating outweighs the condensation caused by cooling, a strong radar echo band descends from the top to the middle part of clouds, a sea of clouds dissipates gradually as cloud particles evaporates, and the particle size and concentration number of cloud particles decrease simultaneously. [ABSTRACT FROM AUTHOR]

Between 15 and 19 March 2022, East Antarctica experienced an exceptional heat wave with widespread 30°–40°C temperature anomalies across the ice sheet. This record-shattering event saw numerous monthly temperature records being broken including a new all-time temperature record of −9.4°C on 18 March at Concordia Station despite March typically being a transition month to the Antarctic coreless winter. The driver for these temperature extremes was an intense atmospheric river advecting subtropical/midlatitude heat and moisture deep into the Antarctic interior. The scope of the temperature records spurred a large, diverse collaborative effort to study the heat wave's meteorological drivers, impacts, and historical climate context. Here we focus on describing those temperature records along with the intricate meteorological drivers that led to the most intense atmospheric river observed over East Antarctica. These efforts describe the Rossby wave activity forced from intense tropical convection over the Indian Ocean. This led to an atmospheric river and warm conveyor belt intensification near the coastline, which reinforced atmospheric blocking deep into East Antarctica. The resulting moisture flux and upper-level warm-air advection eroded the typical surface temperature inversions over the ice sheet. At the peak of the heat wave, an area of 3.3 million km2 in East Antarctica exceeded previous March monthly temperature records. Despite a temperature anomaly return time of about 100 years, a closer recurrence of such an event is possible under future climate projections. In Part II we describe the various impacts this extreme event had on the East Antarctic cryosphere. Significance Statement: In March 2022, a heat wave and atmospheric river caused some of the highest temperature anomalies ever observed globally and captured the attention of the Antarctic science community. Using our diverse collective expertise, we explored the causes of the event and have placed it within a historical climate context. One key takeaway is that Antarctic climate extremes are highly sensitive to perturbations in the midlatitudes and subtropics. This heat wave redefined our expectations of the Antarctic climate. Despite the rare chance of occurrence based on past climate, a future temperature extreme event of similar magnitude is possible, especially given anthropogenic climate change. [ABSTRACT FROM AUTHOR]

Based on the monitoring data of PM2.5 concentration in Bengbu Environmental Monitoring Station and precipitation observation data of Bengbu National Meteorological Observation Station from 2016 to 2019, the influence of precipitation on PM2.5 mass concentration in Bengbu City was analyzed. The results show that precipitation had a washing and removal effect on PM2.5 in the air, and the removal effect was related to precipitation level, precipitation intensity, precipitation duration and PM2J concentration. The removal effect of precipitation on PM2J increased with the increase of precipitation level, and the seasonal difference was obvious. Precipitation intensity was positively correlated with the removal effect of PM2.5, but the average removal rate began to decline when precipitation intensity exceeded 10 mm. With the increase of precipitation intensity, the proportion of positive removal showed an overall upward trend, but there was a low-value area as precipitation intensity was 3--10 mm. Precipitation duration was also positively correlated with the removal effect of PM2.5, and there was a low-value area when precipitation duration was 10 - 15 h. When PM2.5 concentration was low before the precipitation process began, the removal effect was not good, and the average removal rate was negative. As PM2Î concentration was high before the precipitation process started, the removal effect was obvious. [ABSTRACT FROM AUTHOR]

To better calibrate the aerosol lidar network constructed by the China Meteorological Administration, and ensure the data quality observed by the network, the Meteorological Observation Center (China Meteorological Administration) and the University of Naples (Italy) jointly developed a "high quality 532 nm Raman aerosol lidar" (REAL lidar) in 2018. The ability to detect Raman–Mie scattering signals was improved through signal detection in a large dynamic range. This study compared the REAL lidar with the reference lidar (European ACTRIS aerosol lidar network) considering three wavelengths and eight channels. The results show that both the original signals and data products of the two radars exhibited good consistency. In the calibration application of China's domestic lidar network, after REAL calibration, the relative average and standard deviations of the backscattering coefficient of the in-station lidar decreased from 55.4% to 7.9% and from 64% to 9.9%, respectively. The effect was significant, which indicates that REAL is an aerosol lidar with a high-performance index. The results satisfy the demand for calibration of the aerosol lidar network, and the REAL was successfully applied to the calibration of the aerosol lidar network. [ABSTRACT FROM AUTHOR]

Based on the global reanalysis data of the National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research, the surface meteorological observation data, sounding data and satellite observation data, this paper comprehensively analyzes the evolution process and formation mechanism of a persistent severe dense fog process occurred on February 15–17, 2015 in Yancheng, eastern China. Through the numerical simulation experiment of Weather Research and Forecast (WRF) model, we further analyze the impact of sea-land breeze on the formation and burst reinforcement of fog. Results show that the precipitation caused by the southwesterly airflow in front of the upper-level trough and the low-pressure inverted trough are conducive to the formation of early rain fog, while the nighttime clear radiance under the control of surface cold high and the infiltration of weak cold advection are conducive to the formation and development of later radiation-advection fog. The WRF model simulates the fog evolution process, which is basically consistent with the actual fog area, and the simulation results are credible to a certain extent. The simulation results show that the establishment of sea breeze has an advection cooling effect on the near surface layer, which is conducive to the formation and development of the inversion layer on the near surface, providing stable stratification conditions for the formation and burst reinforcement of fog. On one hand, the strengthening of sea breeze circulation can continuously transport water vapor to the study area. On the other, the occurrence of ultra-low level jet is favorable for the accumulation of low-level water vapor. At the same time, the inversion intensity further strengthens, which is in favor of the burst reinforcement and long-term maintenance of fog. [ABSTRACT FROM AUTHOR]

From 2016 to 2022, the flowering phenomenon of a orange Osmanthus tree in the courtyard Nanyang Meteorological Bureau had observed for 7 consecutive years. The observing was according to the "Agricultural Meteorological Observation Code and Natural Phenology Volume" woody plant flowering period observation standard, i.e. when "the petals of one or several flowers on the observation tree begin to fully open, it is the beginning of flowering", and when "the petals on the observation tree wither and fall off, leaving a very small number of flowers" is recorded as the end of flowering. The first flowering period of Osmanthus fragrans in autumn is recorded as the first batch of flowering. The phenomenon of a second flowering after the first batch of orange Osmanthus blossomed is recorded as the second batch of orange Osmanthus blossoming, just like the first flowering period. After the second flowering of orange Osmanthus, it was observed that in some years, one or several flowers on the observed orange Osmanthus tree had fully opened petals and withered and shed was recorded as the third batch of flowering. Seldomly, the fourth batch of flowering was observed and recorded until no further flowering occurred. From 2016 to 2022, the flowering number of batches of orange Osmanthus flowering in the autumn of Nanyang every year and the initial flowering period and attributes of each batch of flowering were totally different. First, from the aspects of number and attributes of flowering batches, only one batch of flowers was bloomed per year from 2016 to 2019, which was completed at one time, and the 1st batch was also the last batch. From 2020 to 2022, 2-4 batches was bloomed every year, of which the 2nd batch in 2020, the 4th batch in 2021, and the 3rd batch in 2022 are the last batch. The 1st batch in 2020, the 1st to 3rd batch in 2021, the 1st and 2nd batches in 2022 are not the last batch. From the beginning of each batch of flowering, the earliest is August 25 and the latest is October 19. According to previous studies, as a plant that blooms in autumn, the flowering of Osmanthus is caused by the gradual decrease in temperature to a certain limit. Then, each batch of Osmanthus flowers has at least 1 day of low-temperature process that temperature below this limit, Osmanthus blooming cannot be completed at one time must be the previous low-temperature process did not meet all the conditions that should be achieved, and there is at least 1 day of low-temperature process that temperature below this limit again, and it blooms again. As long as the temperature values of each batch before flowering are statistically analyzed in turn, we can gradually approach to finding this limit. With this boundary, the low-temperature process can be counted, so as to find out the conditions that should be met when all flowers bloom compared with the flowering situation of Osmanthus. Daily temperature values include daily average temperature, daily minimum temperature and daily maximum temperature, which respectively express the average situation and range of daily temperature fluctuation, and should be analyzed to identify their respective functions. This article is based on years of continuous observation records of the multiple batches flowering phenomenon of orange Osmanthus in Nanyang, the minimum temperature values with daily maximum temperature, daily average temperature, daily minimum temperature before the first batch of flowering each year be counted in sequence, the minimum temperature values with daily maximum temperature, daily average temperature, daily minimum temperature between the previous batch of flowering and the current batch of flowering be calculated in sequence when the second and subsequent batches of flowering each year. Their respective maximum values selected as that daily index of low-temperature in each expression form of temperature, statistics of natural low-temperature process according to daily index of low-temperature, merge the discontinuous natural low-temperature process with short intervals into one combined low-temperature process, after the start date and negative effective accumulated temperature of the combined low-temperature process are calculated, analyze the relationship between the beginning of flowering of orange Osmanthus and the start date of the combined low-temperature process, as well as the relationship between the attributes of each batch of flowering and the negative effective accumulated temperature during the combined low-temperature process, the index of low-temperature before flowering which can be used for forecasting the flowering period of each batch of orange Osmanthus to be determined. Tested by correlation coefficients and univariate linear regression methods, the results showed that:(1)the method of stepwise approach method is used to determine the index of low-temperature before flowering of orange Osmanthus in Nanyang, the best effect is to use the daily maximum temperature, which is better than using the daily average temperature and daily minimum temperature. (2) The daily index of low-temperature before flowering of orange Osmanthus is the daily maximum temperature <22.3 °C. (3) The process index of low-temperature before flowering of orange Osmanthus is the daily maximum temperature <22.3 °C natural low-temperature process interval days ≤1d merged into one combined low-temperature process, when the negative effective accumulated temperature during the daily maximum temperature <22.3 °C combined low-temperature process >3.4°C d, the orange Osmanthus flowering once completed, when the negative effective accumulated temperature during the daily maximum temperature <22.3°C combined low-temperature process ≤3.4°Cd, the 1st batch of orange Osmanthus flowering is not the last batch, if the daily maximum temperature <22.3 °C again, orange Osmanthus will bloom again, until the negative effective accumulated temperature during the daily maximum temperature <22.3 °C combined low-temperature process >3.4°C d, the orange Osmanthus flowering is the last batch. (4)The relationship between the beginning of flowering of orange Osmanthus and the low-temperature process is as follows: the sequence of daily maximum temperature <22.3 °C combined low-temperature process corresponds to the batch sequence of flowering of orange Osmanthus, the beginning of flowering of orange Osmanthus was 4-13d after the start date of the combined low-temperature process, the specific flowering date Y is predicted by the univariate regression equation that the factor X be the start date of each combined low-temperature process with daily maximum temperature <22.3 °C, and Y=1.1255X+1.4683, which is a very significant positive correlation linear relationship. [ABSTRACT FROM AUTHOR]

The sea surface roughness parameterization and the universal stability function are key components of the evaporation duct prediction model based on the Monin–Obukhov similarity theory. They determine the model's performance, which in turn affects the efficiency and accuracy of electromagnetic applications at sea. In this study, we collected layered meteorological and hydrological observation data and preprocessed them to obtain near-surface reference modified refractivity profiles. We then optimized the sea surface roughness parameterization and the universal stability function using particle swarm optimization and simulated annealing algorithms. The results show that the particle swarm optimization algorithm outperforms the simulated annealing algorithm. Compared to the original model, the particle swarm optimization algorithm improved the prediction accuracy of the model by 5.09% under stable conditions and by 9.97% under unstable conditions, demonstrating the feasibility of the proposed method for optimizing the evaporation duct prediction model. Subsequently, we compared the electromagnetic wave propagation path losses under two different evaporation duct heights and modified refractivity profile states, confirming that the modified refractivity profile is more suitable as the accuracy criterion for the evaporation duct prediction model. [ABSTRACT FROM AUTHOR]

Sea breezes penetrate inland more than 100 km. Using 7 years of meteorological observations, we have identified 470 cases of deep inland (>100 km) penetrating sea breezes at the Savannah River Site between March and October (27% of days) of 2015–2021. We compared measurements of temperature, dewpoint temperature, incoming solar radiation, cloud fraction, and lightning on days of sea breeze initiation, the day after the sea breeze passage, and all other nonsea breeze (NSB) days for these 8 months over the 7 years. Days of sea breeze initiation were found to have lower cloud fraction, higher temperature, and greater incoming solar radiation compared with NSB days. Variations occurred by time of year as days after the sea breeze passage were found to have higher dewpoint temperature than NSB days in the spring. Lightning density measurements indicated that residual sea breeze conditions could drive earlier initiation of deep convection on days following the sea breeze than normal non sea breeze days. This data set provides a 7‐year record of sea breezes which can be leveraged for future studies. [ABSTRACT FROM AUTHOR]

This study utilizes data from national ground meteorological observation stations in Shandong province, Fengyun-4 satellite data, and ERA5 reanalysis data. Through the calculation of atmospheric heat source changes, the role of diabatic heating in the occurrence and development of heavy rainfall is revealed. The widespread heavy-to-torrential rainfall event in Shandong province on 25 June 2018 is analyzed as a case study. It was found that a deep and robust southwest jet stream was the key system for the formation of this rainfall event. Satellite cloud images during the peak rainfall period showed vigorous development in the rainfall cloud region. During the concentrated rainfall period and when the low-altitude jet stream strengthened, there was mostly cold advection overhead at the observation station. The low-altitude jet stream transported moisture, increasing the humidity gradient, thus enhancing frontogenesis. The warm advection in the low-altitude jet stream was not the main energy supplier during heavy rainfall, and local temperature variations were the primary contributors to the thermodynamic conditions during the peak rainfall period. The rate of warming caused by the condensation and release of heat from water vapor significantly increased during the concentrated rainfall period. This warming effect played a heating role in the middle and lower layers, and the positive feedback from the latent heat release of water vapor condensation intensified the weather system affecting the rainfall, providing strong thermodynamic and dynamic conditions for heavy rainfall. [ABSTRACT FROM AUTHOR]

Aerosol optical properties retrieved using polarization Raman lidar observations play an increasingly vital role in meteorology and environmental protection. The quality of the data products directly affects the impact of relevant scientific applications. However, the quality of aerosol optical properties retrieved from polarization Raman lidar signals is difficult to assess. Various factors, such as hardware system performance, retrieval algorithm, and meteorological conditions at the observation site, influence data quality. In this study, we propose a method that allows for assessing the reliability of aerosol optical properties derived from polarization Raman lidar observations. We analyze the factors that affect the reliability of retrieved aerosol optical properties. We use scoring methods combined with a weight-assignment scheme to evaluate the quality of the retrieved aerosol optical properties. The scores and weights of each factor are arranged based on our analysis of a simulation study and the characteristics of each factor. We developed an automatic retrieval algorithm that allows for deriving homogeneous aerosol optical data sets. We also assess with this method the quality of retrieved aerosol optical properties obtained with different polarization Raman lidars under different measurement scenarios. Our results show that the proposed quality assurance method can distinguish the reliability of the retrieved aerosol optical properties. [ABSTRACT FROM AUTHOR]

The global navigation satellite system radio occultation (GNSS-RO) is an important means of space-based meteorological observation. It is necessary to test the Global Navigation Satellite System Occultation signal receiver on the ground before the deployment of space-based occultation detection systems. The current approach of testing the GNSS signal receiver on the ground is mainly the mountaintop-based testing approach, which has problems such as high cost and large simulation error. In order to overcome the limitations of the mountaintop-based test approach, this paper proposes an accurate, repeatable, and controllable GNSS atmospheric occultation simulation system and builds a load performance evaluation approach based on the ground-based GNSS atmospheric occultation simulation system on the basis of it. The GNSS atmospheric occultation simulation system consists of the visualization and interaction module, the GNSS-RO simulation signal generation module, the GNSS-RO simulator module, the GNSS-RO signal receiver module, and the GNSS-RO inversion and evaluation module, combined with the preset atmospheric model to generate GNSS-RO simulation signals with a high degree of simulation, and comparing the atmospheric parameters of the inversion performance of the GNSS-RO signal receiver with the parameters of the preset atmospheric model to obtain the error data. The overall performance of the GNSS-RO signal receiver can be evaluated based on the error information. The novel approach to evaluate the GNSS-RO signal receiver performance proposed in this paper is validated by using the FY-3E (FengYun-3E) receiver qualification parts that have been verified in orbit, and the results confirm that the approach can meet the requirements of the GNSS-RO receiver performance test. This study shows that the novel approach to evaluate the GNSS-RO signal receiver performance in terms of the ground-based atmospheric occultation simulation system can efficiently and accurately be used to carry out the receiver test and provides an effective solution for the ground-based test of GNSS-RO signal receivers. [ABSTRACT FROM AUTHOR]

Exploration of the spatiotemporal variability in precipitation gradients (PGs) is important for understanding the spatial differences in precipitation and optimizing hydrological process simulations. Here, the spatiotemporal distribution of precipitation and PG was analyzed in the Tianshan and Qilian Mountains based on national meteorological station data from 1961 to 2017. The relationships between PG and relative humidity and wind speed were explored. The results show that precipitation exhibits a significant increasing trend during the last decades. Precipitation in the Tianshan Mountains is greater in the north and west, the maximum precipitation height (MPH) occurs at 1942.5 m a.s.l., and the precipitation change rate is higher in the west than in the east. Precipitation in the eastern Qilian Mountains is greater, the MPH is approximately 2850 m a.s.l., and the precipitation change rate decreases from the middle to the western and eastern sections. There are noticeable differences in PG between the two mountain areas. Annually, the PG in the Tianshan and Qilian Mountains, except for the Yili Valley, has increased significantly. The PG change rate in the western Qilian Mountains is higher. Monthly, the PG increases most significantly from June to August. Precipitation is positively correlated with elevation in the Tianshan Mountains from April to October and in the western and eastern sections of the Qilian Mountains every month, while that in the middle Qilian Mountains decreases with increasing elevation from November to January. The PG in both mountain regions experienced abrupt changes around the 1970s–1980s, and most areas increased significantly after the abrupt changes, showing a clear humidification phenomenon. Except for some subregions, PG is directly proportional to relative humidity and inversely proportional to wind speed. [ABSTRACT FROM AUTHOR]

Regional-scale surface energy balance (SEB) models of glacier melt require forcing by coarse-gridded data from reanalysis or global climate models that need to be downscaled to glacier scale. As on-glacier meteorological observations are rare, it generally remains unknown how exact the reanalysis and downscaled data are for local-scale SEB modeling. We address this question by evaluating the performance of reanalysis from the European Centre for Medium-Range Weather Forecasts (ERA5 and ERA5-Land reanalysis), with and without downscaling, at four glaciers in western Canada with available on-glacier meteorological measurements collected over different summer seasons. We dynamically downscale ERA5 with the Weather Research and Forecasting (WRF) model at 3.3 and 1.1 km grid spacing. We find that our SEB model, forced separately with the observations and the two reanalyses, yields less than 10 % difference in simulated total melt energy and shows strong correlations (0.86) in simulated time series of daily melt energy at each site. The good performance of the reanalysis-derived melt energy is partly due to cancellation of biases between overestimated incoming shortwave radiation and substantially underestimated wind speed and subsequently turbulent heat fluxes. Downscaling with WRF improves the simulation of wind speed, while other meteorological variables show similar performance to ERA5 without downscaling. The choice of WRF physics parameterization schemes is shown to have a relatively large impact on the simulations of SEB components but a smaller impact on the modeled total melt energy. The results increase our confidence in dynamical downscaling with WRF for long-term glacier melt modeling in this region. [ABSTRACT FROM AUTHOR]

The three-orbit constellation can comprehensively increase the spatial coverage of polar-orbiting satellites, but the polar-orbiting satellites currently in operation are only mid-morning-orbit and afternoon-orbit satellites. Fengyun-3E (FY-3E) was launched successfully on 5 July 2021 in China. As an early-morning-orbit satellite, FY-3E can help form a complete three-orbit observation system together with the mid-morning and afternoon satellites in the current mainstream operational system. In this study, we investigate the added benefit of FY-3E microwave sounding observations to the mid-morning-orbit Meteorological Operational satellite-B (MetOp-B) and afternoon-orbit Fengyun-3D (FY-3D) microwave observations in the Chinese Meteorological Administration global forecast system (CMA-GFS). The results show that the additional FY-3E microwave temperature sounder-3 (MWTS-3) and microwave humidity sounder-2 (MWHS-2) data can increase the global coverage of microwave temperature and humidity sounding data by 14.8% and 10.6%, respectively. It enables the CMA-GFS to achieve nearly 100% global coverage of microwave-sounding observations at each analysis time. Furthermore, after effective quality control and bias correction, the global biases and standard deviations of the differences between observations and model simulations are also reduced. Based on the Advanced Microwave Sounding Unit A and the Microwave Humidity Sounder onboard MetOp-B, and the MWTS-2 and MWHS-2 onboard FY-3D, adding the microwave sounding data of FY-3E can further reduce the errors of analysis results and improve the global prediction skills of CMA-GFS, especially for the southern-hemisphere forecasts within 96 hours, all of which are significant at the 95% confidence level. [ABSTRACT FROM AUTHOR]

The article offers information on the XXXVI Conference of the Spanish Meteorological Association (AME) and the 22nd Hispano-Portuguese Meteorology Meeting, highlighting the impact of the COVID-19 pandemic on the event's cancellation and subsequent return after six years. Topics include the successful organization of the conference, the diverse participation from meteorological professionals, and the valuable interactions and networking opportunities.