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Peatland fires in Indonesia tend to be more active during El Niño-related droughts, with the exception of fires in North Sumatra. As North Sumatra is located north of the equator and is affected by the winter and summer monsoons, fires tend to be more active not only during the dry main season from January to March, but also in June and August due to short-term droughts. Due to these complex fire trends, no appropriate fire-related indices have been found in North Sumatra. In this paper, 20 years of fire (hotspot (HS) data from 2003 to 2022, weather data (hourly and daily), and various satellite data were used to analyze fire weather conditions in Dumai plantation areas. Analysis results of 20 fire incidents (largest fires (HSs) of each year) showed the following fire weather conditions: high wind speeds (>19 km h−1), high temperatures (>33 °C), and low relative humidity (<50%). Based on the results of fire and weather analyses, several fire-related indices selected from various satellite-measured data were examined. Precipitable water vapor has the highest negative correlation with fires. It is hoped that this new fire index will be used for fire prevention not only Sumatra but also in other areas in Indonesia. [ABSTRACT FROM AUTHOR]

Tropical cyclone (TC) size is an important parameter for estimating TC risks, such as precipitation distribution, gale-force wind damage, and storm surge. This paper uses the TC size dataset compiled by the Shanghai Typhoon Institute of China Meteorological Administration (STI/CMA) to investigate the interannual, monthly variation in TC size, and the relationships between TC size and intensity in the WNP basin from 1981 to 2009. The results show that the annual mean TC size oscillated within the range of 175–210 km from 1981 to 2002, then decreased following 2003. For the monthly average TC size, there are two peaks in September and October. The TC size, overall, becomes larger with increasing intensity; the samples with an unusually large size are mainly concentrated near a 40 m s−1 intensity. After the TC intensity exceeds 40 m s−1, the number of unusually large size samples gradually decreases. About 60% of the TCs reach their maximum size after reaching the peak intensity, and the average lag time is 8.3 h. [ABSTRACT FROM AUTHOR]

The wind microclimate plays an important role in architectural design, and computational fluid dynamics is a method commonly used for analyzing the issue. However, due to its high technical difficulty and time-consuming nature, it limits the interaction and exploration between designers and environment performance analyses. To address the issue, scholars have proposed a series of approximation models based on machine learning that have partially improved computational efficiency. However, these methods face challenges in terms of balancing applicability, prediction accuracy, and sample size. In this paper, we propose a method based on the classic Vggnet deep convolutional neural network as the backbone to construct an approximate model for predicting steady-state flow fields in urban areas. The method is trained on a small amount of sample data and can be extended to calculate the wind environment performance. Furthermore, we investigated the differences between geometric representation methods, such as the Boolean network representation and signed distance function, as well as different structure models, such as Vgg-CFD-11, Vgg-CFD-13, Vgg-CFD-16, and Vgg-CFD-19. The results indicate that the model can be trained using a small amount of sample data, and all models generally possess the ability to predict the wind environment. The best performance on the validation set and test set was achieved with an RMSE (Root Mean Square Error) of 0.7966 m/s and 2.2345 m/s, respectively, and an R-Squared score of 0.9776 and 0.8455. Finally, we embedded the best-performing model into an architect-friendly urban comprehensive analysis platform, URBAN NEURAL-CFD. [ABSTRACT FROM AUTHOR]

The Philippines is highly vulnerable to multiple climate-related hazards due to its geographical location and weak adaptation measures. Floods are the most catastrophic hazards that impact lives, livelihoods, and, consequently, the economy at large. Understanding the ability of the general circulation models to simulate the observed rainfall using the latest state-of-the-art model is essential for reliable forecasting. Based on this background, this paper objectively aims at assessing and ranking the capabilities of the recent Coupled Model Intercomparison Project Phase 6 (CMIP6) models in simulating the observed rainfall over the Philippines. The Global Precipitation Climatology Project (GPCP) v2.3 was used as a proxy to gauge the performance of 11 CMIP6 models in simulating the annual and rainy-season rainfall during 1980–2014. Several statistical metrics (mean, standard deviation, normalized root means square error, percentage bias, Pearson correlation coefficient, Mann–Kendall test, Theil–Sen slope estimator, and skill score) and geospatial measures were assessed. The results show that that CMIP6 historical simulations exhibit satisfactory effectiveness in simulating the annual cycle, though some models display wet/dry biases. The CMIP6 models generally underestimate rainfall on the land but overestimate it over the ocean. The trend analysis shows that rainfall over the country is insignificantly increasing both annually and during the rainy seasons. Notably, most of the models could correctly simulate the trend sign but over/underestimate the magnitude. The CMIP6 historical rainfall simulating models significantly agree on simulating the mean annual cycle but diverge in temporal ability simulation. The performance of the models remarkably differs from one metric to another and among different time scales. Nevertheless, the models may be ranked from the best to the least best at simulating the Philippines' rainfall in the order GFDL, NOR, ACCESS, ENS, MRI, CMCC, NESM, FIO, MIROC, CESM, TAI, and CAN. The findings of this study form a good basis for the selection of models to be used in robust future climate projection and impact studies regarding the Philippines. The climate model developers may use the documented shortcoming of these models and improve their physical parametrization for better performance in the future. [ABSTRACT FROM AUTHOR]

Sub-hourly heavy precipitation events (SHHPs) associated with regional low-pressure (RegL) systems in Portugal are a natural hazard that may have significant socioeconomic implications, namely in agriculture. Therefore, in this paper, their dynamic and thermodynamic drivers are analysed. Three weather stations were used to isolate SHHPs from 2000 to 2022. Higher precipitation variability is found in southern Portugal, with a higher ratio of extreme events on fewer rainy days. This study shows that these SHHP events are associated with low-pressure systems located just to the west of the Iberian Peninsula. These systems exhibit a cold core, particularly strong at mid-levels, and a positive vorticity anomaly, which is stronger in the upper troposphere, extending downward to low levels. These conditions drive differential positive vorticity advection and, therefore, rising motion to the east of the low-pressure systems. Moreover, at low levels, these systems promote moisture advection over western Iberia, also generating instability conditions, which are assessed by instability indices (Convective available potential energy, the Total-Totals index, and the K-index). The combination of these conditions drives heavy precipitation events. Lastly, the total column cloud ice water revealed higher values for the heavier precipitation events, suggesting that it may be a useful predictor of such events. [ABSTRACT FROM AUTHOR]

Through analyzing the triggering factors and activity characteristics of avalanches in Aerxiangou in the Western Tianshan Mountains, the formation and disaster-causing process of avalanches were studied to provide theoretical support and a scientific basis for avalanche disaster prevention. In this paper, based on remote sensing interpretation and field investigation, a spatial distribution map of avalanches was established, and the induced and triggering factors in disaster-prone environments were analyzed using the certainty factor model. The degree of influence (E) of the disaster-causing factors on avalanche triggering was quantified, and the main control conditions conducive to avalanche occurrence in different periods were obtained. The RAMMS-avalanche model was used to analyze the activity characteristics at points where multiple avalanches occurred. Research results: (1) The E values of the average temperature, average snowfall, and surface roughness in February were significantly higher than those of other hazard-causing factors, reaching 1.83 and 1.71, respectively, indicating strong control. The E values of the surface cutting degree, average temperature, and average snow depth in March were all higher than 1.8, indicating that these control factors were more prominent than the other factors. In contrast, there were four hazard-causing factors with E values higher than 1.5 in April: the mean temperature, slope, surface roughness, and mean wind speed, with clear control. (2) Under the influence of the different hazard-causing factors, the types of avalanches from February–April mainly included new full-layer avalanches, surface avalanches, and full-layer wet avalanches. (3) In the RAMMS-avalanche simulation test, considering the deposition effect, compared to the previous avalanche movement path, the secondary avalanche flow accumulation area impact range changes were slight, while the movement area within the avalanche path changes was large, as were the different categories of avalanches and their different movement characteristic values. Overall, wet snow avalanches are more hazardous, and the impact force is larger. The new snow avalanches start in a short period, the sliding rate is fast, and the avalanche sliding surface (full-snow surface and face-snow) of the difference is mainly manifested in the differences in the value of the flow height. [ABSTRACT FROM AUTHOR]

Ground-based cloud images contain a wealth of cloud information and are an important part of meteorological research. However, in practice, ground cloud images must be segmented and classified to obtain the cloud volume, cloud type and cloud coverage. Existing methods ignore the relationship between cloud segmentation and classification, and usually only one of these is studied. Accordingly, our paper proposes a novel method for the joint classification and segmentation of cloud images, called CloudY-Net. Compared to the basic Y-Net framework, which extracts feature maps from the central layer, we extract feature maps from four different layers to obtain more useful information to improve the classification accuracy. These feature maps are combined to produce a feature vector to train the classifier. Additionally, the multi-head self-attention mechanism is implemented during the fusion process to enhance the information interaction among features further. A new module called Cloud Mixture-of-Experts (C-MoE) is proposed to enable the weights of each feature layer to be automatically learned by the model, thus improving the quality of the fused feature representation. Correspondingly, experiments are conducted on the open multi-modal ground-based cloud dataset (MGCD). The results demonstrate that the proposed model significantly improves the classification accuracy compared to classical networks and state-of-the-art algorithms, with classification accuracy of 88.58%. In addition, we annotate 4000 images in the MGCD for cloud segmentation and produce a cloud segmentation dataset called MGCD-Seg. Then, we obtain a 96.55 mIoU on MGCD-Seg, validating the efficacy of our method in ground-based cloud imagery segmentation and classification. [ABSTRACT FROM AUTHOR]

It remains challenging to accurately classify complicated clouds owing to the various types of clouds and their distribution on multiple layers. In this paper, multi-band radiation information from the geostationary satellite Himawari-8 and the cloud classification product of the polar orbit satellite CloudSat from June to September 2018 are investigated. Based on sample sets matched by two types of satellite data, a random forest (RF) algorithm was applied to train a model, and a retrieval method was developed for cloud classification. With the use of this method, the sample sets were inverted and classified as clear sky, low clouds, middle clouds, thin cirrus, thick cirrus, multi-layer clouds and deep convection (cumulonimbus) clouds. The results indicate that the average accuracy for all cloud types during the day is 88.4%, and misclassifications mainly occur between low and middle clouds, thick cirrus clouds and cumulonimbus clouds. The average accuracy is 79.1% at night, with more misclassifications occurring between middle clouds, multi-layer clouds and cumulonimbus clouds. Moreover, Typhoon Muifa from 2022 was selected as a sample case, and the cloud type (CLT) product of an FY-4A satellite was used to examine the classification method. In the cloud system of Typhoon Muifa, a cumulonimbus area classified using the method corresponded well with a mesoscale convective system (MCS). Compared to the FY-4A CLT product, the classifications of ice-type (thick cirrus) and multi-layer clouds are effective, and the location, shape and size of these two varieties of cloud are similar. [ABSTRACT FROM AUTHOR]

The factors influencing precipitation in western China are quite complex, which increases the difficulty in determining accurate predictors. Hence, this paper models the monthly measured precipitation data from 240 meteorological stations in mainland China and the precipitation data from the European Centre for Medium-Range Weather Forecasts and the National Climate Centre and employs 88 atmospheric circulation indices to develop a precipitation prediction scheme. Specifically, a high-quality grid-point field is created by fusing and revising the precipitation data from multiple sources. This field is combined with the Empirical Orthogonal Function decomposition and the causal information flow. Next, the best predictors are screened through Empirical Orthogonal Function decomposition and causal information flow, and a data-driven precipitation prediction model is established using a Back Propagation Neural Network and a Random Forest algorithm to conduct the 1-month, 3-month, and 6-month precipitation predictions. The results show that: The machine learning-based precipitation prediction model has high accuracy and is generally able to predict the precipitation trend in the western region better. The Random Forest algorithm significantly outperforms the Back Propagation Neural Network algorithm in the prediction of the three starting times, and the prediction ability of both models gradually decreases as the starting time increases. Compared with the 2022 flood season prediction scores of the Institute of Atmospheric Sciences of the Chinese Academy of Sciences, the model improves the prediction of 1-month and 3-month precipitation in the western region and provides a new idea for the short-term climate prediction of precipitation in western China. [ABSTRACT FROM AUTHOR]

Prolonged exposure to high concentrations of suspended particulate matter (SPM), especially aerodynamic fine particulate matter that is ≤2.5 μm in diameter (PM2.5), can cause serious harm to human health and life via the induction of respiratory diseases and lung cancer. Therefore, accurate prediction of PM2.5 concentrations is important for human health management and governmental environmental management decisions. However, the time-series processing of PM2.5 concentration based only on a single region and a special time period is less explanatory, and thus, the spatial-temporal applicability of the model is more restricted. To address this problem, this paper constructs a PM2.5 concentration prediction optimization model based on Convolutional Neural Networks-Long Short-Term Memory (CNN-LSTM). Hourly data of atmospheric pollutants, meteorological parameters, and Precipitable Water Vapor (PWV) of 10 cities in the Beijing-Tianjin-Hebei metropolitan area during the period of 1–30 September 2021/2022 were used as the training set, and the PM2.5 data of 1–7 October 2021/2022 were used for validation. The experimental results show that the CNN-LSTM model optimizes the average root mean square error (RMSE) by 25.52% and 14.30%, the average mean absolute error (MAE) by 26.23% and 15.01%, and the average mean absolute percentage error (MAPE) by 35.64% and 16.98%, as compared to the widely used Back Propagation Neural Network (BPNN) and Long Short-Term Memory (LSTM) models. In summary, the CNN-LSTM model is superior in terms of applicability and has the highest prediction accuracy in the Beijing-Tianjin-Hebei metropolitan area. The results of this study can provide a reference for the relevant departments in the Beijing-Tianjin-Hebei metropolitan area to predict PM2.5 concentration and its trend in specific time periods. [ABSTRACT FROM AUTHOR]

The measurement of cloud microphysical parameters plays an important role in describing characteristics of liquid phase clouds and investigating mutual relationships between clouds and precipitation. In this paper, cloud microphysical parameters at Liupan Mountain Weather Station in Ningxia are measured with a high-resolution coaxial digital holographic imager and a fog monitor 120. There are differences in the measurement results between the two instruments. The number concentration measured by the digital holographic imager is about 1.5 times that of the fog monitor 120. However, their Pearson correlation coefficient is above 0.9. Through analysis, we found that the measurement results of the digital holographic imager and fog monitor 120 are differences in 2–4 µm and 7–50µm. For the droplets with the diameters of 4–7 µm, their measurement results have good consistency. By analyzing the influence of wind field and detection sensitivity on the measurement principle, the reasons which caused the difference are proposed. Advice is given to observe topographic clouds by using the above two instruments. In addition, the differences in liquid water content and visibility are analyzed due to the absence of small and large droplets. The study provides data support for improving the accuracy of instruments in measuring cloud droplets and is useful for research in the field of cloud microphysical processes. [ABSTRACT FROM AUTHOR]

From December 2021 to May 2022, MeteoSwiss and Meteomatics conducted a proof of concept to demonstrate the capability of automatic drones to provide data of sufficient quality and reliability on a routine operational basis. Over 6 months, Meteodrones MM-670 were operated automatically eight times per night at Payerne, Switzerland. In total, 864 meteorological profiles were measured and compared to co-located standard measurements, including radiosoundings and remote sensing instruments. To our knowledge, this is the first time that Meteodrone's atmospheric profiles have been evaluated in such an extensive campaign. The paper highlights two case studies that showcase the performance and challenges of measuring temperature, humidity, and wind with a Meteodrone. It also focuses on the overall quality of the drone measurements. Throughout the campaign, the availability of Meteodrone measurements was 75.7%, with 82.2% of the flights reaching the nominal altitude of 2000 m above sea level. The quality of the measurements was assessed against the WMO's (World Meteorological Organization) requirements. The temperature measurements gathered by the Meteodrone met the "breakthrough" target, while the humidity and wind profiles met the "threshold" target for high-resolution numerical weather prediction. The temperature measurement quality was comparable to that of a microwave radiometer, and the humidity quality was similar to that obtained from a Raman LiDAR. However, the wind measurements gathered by a Doppler LiDAR were more accurate than the estimation provided by the Meteodrone. This campaign marks a significant step towards the operational use of automatic drones for meteorological applications. [ABSTRACT FROM AUTHOR]

The fallow agricultural soils of Northwest Russia represent an evolutionary model of the development of ecosystem components in time and space with multidirectional dynamics of agrogenic impact during the long history of agricultural land development. There has been both large-scale land development and uncontrolled conversion of arable lands to a fallow state along with their removal in recent times. All this has led to the formation of a chrono-series of different-age soils with varying degrees of exposure of agrogenic factors. This paper presents a current review of the humus state of fallow soils in Northwest Russia, and examines the main factors (self-restoration, humus transformation, acidification) influencing the transformation of the soil cover under the process of post-agrogenesis. Effective farming techniques aimed at fixing carbon in soils as part of increasing the sequestration potential to mitigate the impact of climate change are considered. The ongoing process of the transition of lands into a fallow state could lead to organic carbon losses and changes in the main physical and chemical parameters, which negatively affects the self-restoration of fallow lands. We offer some recommendations for the effective rewetting of fallow lands in Northwest Russia with the purpose of carbon sequestration in the soil cover. [ABSTRACT FROM AUTHOR]

This paper studies the three-dimensional (3-D) ionospheric electron density variation over the continental US and adjacent regions during the August 2017 Great American Solar Eclipse event, using Millstone Hill incoherent scatter radar observations, ionosonde data, the Swarm satellite measurements, and a new TEC-based ionospheric data assimilation system (TIDAS). The TIDAS data assimilation system can reconstruct a 3-D electron density distribution over continental US and adjacent regions, with a spatial–temporal resolution of 1 ∘ × 1 ∘ in latitude and longitude, 20 km in altitude, and 5 min in universal time. The combination of multi-instrumental observations and the high-resolution TIDAS data assimilation products can well represent the dynamic 3-D ionospheric electron density response to the solar eclipse, providing important altitude information and fine-scale details. Results show that the eclipse-induced ionospheric electron density depletion can exceed 50% around the F2-layer peak height between 200 and 300 km. The recovery of electron density following the maximum depletion exhibits an altitude-dependent feature, with lower altitudes exhibiting a faster recovery than the F2 peak region and above. The recovery feature was also characterized by a post-eclipse electron density enhancement of 15–30%, which is particularly prominent in the topside ionosphere at altitudes above 300 km. [ABSTRACT FROM AUTHOR]

Theoretical predictions of laser beam interactions with jet-engine-exhaust-induced turbulence are of importance for optimization of various optical systems, including LIDARs and airborne laser measurement systems. This paper is based on the extended Huygens–Fresnel diffraction integral and the Wigner distribution function, combined with the recently proposed power spectral density function of atmospheric turbulence induced by jet engine exhaust, to derive the formula for the quality factor of a partially coherent Airy beam in turbulent media induced by jet engine exhaust. Numerical calculations based on the analytical formula show that the smaller the structure constant and outer scale of the atmospheric turbulence induced by jet engine exhaust, the smaller the quality factor of a partially coherent Airy beam, indicating better laser beam quality. At the same time, the larger the generalized parameter and inner scale of the jet-engine-exhaust-induced turbulence and the longer the wavelength of the laser beam, the smaller the corresponding quality factor. The numerical results also show that reducing the coherence length of a partially coherent Airy beam and increasing its characteristic width are beneficial for reducing the quality factor in the atmospheric turbulence induced by jet engines. However, the numerical results show that increasing the coherence length of a partially coherent Airy beam and reducing its characteristic width are beneficial for reducing the quality factor in vacuum. This situation demonstrates significant differences between the interaction mechanisms of an Airy beam in vacuum and in the jet-engine-exhaust-induced turbulence. The results of our discussion contribute to clarifying the mechanism of interaction between an Airy laser beam and random media, as well as the application of a curved Airy laser beam in practical situations. [ABSTRACT FROM AUTHOR]

During the second half of August 2022, a dust intrusion event occurred when dust that originated in the dry regions of the Kalmyk steppe (located in Russia, northeast of the Black Sea, north of Georgia, and northwest of the Caspian Sea) and the Precaspian plain was transported over the eastern region of Romania. The arid soil found in these areas can be attributed to an extended period of intense drought, with notable instances occurring in 2002, 2003, 2015, and 2018. This situation was further intensified by heatwaves experienced in May and June of 2022. The dust event was captured in MODIS images. In addition, smoke trains originating from fires in the north of the Azov were detected, but these did not reach Romania. Optical parameters from AERONET were used to confirm the dust event. To determine the trajectory of the particles, the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model was used in this paper. The ensemble median model was used to highlight the presence and concentration of dust in the eastern part of Romania. Aerosols were detected between 0 and 4 km, according to radar and ceilometer data from the REXDAN cloud remote sensing facility in Galați, Romania. This dust intrusion event was the result of the dominant easterly circulation caused by the extension of the East European High to the northeast of the continent, which transported the dust towards the eastern part of Romania for more than 2 days. Moreover, the torrential rains between 22 and 24 August did not clear the atmosphere of dust, since the intense easterly circulation kept carrying the dust into the Moldavian area. [ABSTRACT FROM AUTHOR]

Evapotranspiration is a key part of the water cycle between the atmosphere and the land surface, and it is an important parameter for studying the land–atmosphere system. Change and evolution have important implications. Therefore, the understanding and research of actual evapotranspiration (AET) can profoundly affect water use, ecological environment, temperature, and precipitation. In this paper, the single-layer monthly average reanalysis meteorological data of the Qinghai–Tibet Plateau from 1981 to 2020 was used to study and calculate the actual evapotranspiration in the Qinghai–Tibet Plateau, and the temporal and spatial variation characteristics, variation laws, and changes were analyzed by methods such as cumulative anomalies and the Mann–Kendall trend test. The results showed the following: (1) The evapotranspiration gradually decreased from southeast to northwest. The evapotranspiration in the southeastern region is strong, and the maximum value appears in the Hengduan Mountains. (2) The evapotranspiration was the largest in summer and gradually decreased from southeast to northwest; the evapotranspiration in spring and autumn was relatively uniform, with little overall difference, and the evapotranspiration was the lowest in winter. (3) There were mainly three spatial distribution modes of evapotranspiration in the Qinghai–Tibet Plateau, which were characterized by a significant and consistent change centered on the Tibet region, an east–west reverse type, and an east–west "negative-positive-negative" of the distributed three-pole space. The corresponding time coefficients characterized the interdecadal and interannual variation, and the decadal variation characteristics are more significant than the annual variation characteristics. (4) The actual evapotranspiration had step change; the step change years were 1989, 2002, 2011, and 2015, and there was an interval of about 5 years. [ABSTRACT FROM AUTHOR]

This paper analyzed the variations of two air temperature indices, diurnal temperature range (DTR) and annual temperature range (ATR), calculated based on observations at the Zagreb Grič Observatory over a period of 133 years (1887–2019). In intense climate changes strongly manifested by the increased air temperature, these two climate indices were determined to significantly impact human health and the environment. This effect is especially evident in urban areas. The Zagreb Grič Observatory is located in the center of Zagreb and has not changed its location during the observed period. It has a long homogeneous series of climatological observations, enabling a detailed study of climate variation in the city, which is strongly influenced by various urbanization processes. In 133 years, both of the analyzed indicators showed a statistically insignificant downward trend. The Rescaled Adjusted Partial Sums (RAPS) method revealed statistically significant differences in DTR's time series between three sub-periods: 1887–1953, 1954–1989, and 1990–2019. The time series of ATR during 133 years behaved statistically differently in four sub-periods: 1887–1905; 1906–1926; 1927–1964; and 1965–2019. The analysis of monthly values of DTR showed that the DTR values are the highest in the warm part of the year, from May to August, when they are twice as high as those during the cold period from November to December. With an increase in precipitation, the DTR values decrease, while they increase as the mean annual temperature increases. [ABSTRACT FROM AUTHOR]

Landfill gas generated by municipal solid waste (MSW) landfills is the world's third largest source of greenhouse gas emissions. Additionally, the accumulation of landfill gas in waste piles can trigger instability in landfill piles. Based on the exponential distribution pattern of the variation of gas permeability coefficients with burial depth measured in situ, this paper presents an analytical solution for landfill gas-pressure distribution that is more in line with on-site conditions and has been verified by numerical calculations. Compared with cases where the gas permeability coefficient of landfill piles remains constant, the consideration that the gas permeability coefficient of MSW decreases exponentially with increasing burial depth is more likely to cause the accumulation of landfill gas at the landfill bottom, leading to higher gas pressure that can be more than five times higher than that in the former case. Based on a numerical analysis of gas extraction simulations, constant pressure gas extraction is relatively more effective in that a relative pressure of −0.1 kPa can lower the gas pressure in almost the entire pile, while bottom drainage fails to completely collect landfill gas even using a flux of 10–30 times ML. [ABSTRACT FROM AUTHOR]

Radon (Rn) is a radioactive, colourless, odourless, noble gas that decays rapidly. It's most stable isotope, 222Rn, has a half-life of around 3.8 days. Atmospheric radon measurements play an important role in understanding our atmospheric environments. Naturally occurring radon can be used as an atmospheric tracer for airmass tracking, to assist in modelling boundary layer development, and is important for understanding background radiation levels and personal exposure to natural radiation. The daughter products from radon decay also play an important role when measuring fine particle pollution using beta-attenuation monitors (BAM). Beta radiation from the 222Rn decay chain interferes with BAM measurements of fine particles; thus, some BAMs incorporate radon measurements into their sampling systems. BAMs are ubiquitous in air quality monitoring networks globally and present a hitherto unexplored source of dense, continuous radon measurements. In this paper, we compare in situ real world 222Rn measurements from a high quality ANSTO dual flow loop, dual filter radon detector, and the radon measurements made by a commercial BAM instrument (Thermo 5014i). We find strong correlations between systems for hourly measurements (R2 = 0.91), daily means (R2 = 0.95), hour of day (R2 = 0.72–0.94), and by month (R2 = 0.83–0.94). The BAM underestimates radon by 22–39%; however, the linear response of the BAM measurements implies that they could be corrected to reflect the ANSTO standard measurements. Regardless, the radon measurements from BAMs could be used with correction to estimate local mixed layer development. Though only a 12-month study at a single location, our results suggest that radon measurements from BAMs can complement more robust measurements from standard monitors, augment radon measurements across broad regions of the world, and provide useful information for studies using radon as a tracer, particularly for boundary layer development and airmass identification. [ABSTRACT FROM AUTHOR]

This paper presents a novel mapping prediction method for surface solar radiation with linear regression models. The dataset for surface solar radiation prediction is the daily surface incoming shortwave radiation (SIS) product from CM SAF SARAH-E. The spatial resolution is 0.05° × 0.05° and the temporal coverage is from 2007 to 2016. The first five years (2007–2011) are used as training data, and the remaining five years (2012–2016) are used as test data in the prediction model. Datasets were detrended, de-seasonalized, and normalized before being applied to multiple linear regression (MLR), principal component regression (PCR), stepwise regression (SR), and partial least squares regression (PLSR), which are used to perform prediction mapping. The statistical analysis using MAE, MSE, and RMSE shows that the PCR model had the smallest MAE, MSE, and RMSE as compared to the other three models. The PCR model seems better for SSR mapping prediction over Reunion Island. Although the PCR model provides better prediction results, its MAE, MSE, and RMSE are quite large. [ABSTRACT FROM AUTHOR]

The serious and frequent typhoon activities can easily cause extreme precipitation weather in the eastern coastal area of China, which is affected by land and sea differences. To explore the temporal and spatial characteristics of Precipitable Water Vapor (PWV) and rainfall during the typhoon period, the data of the conspicuous case named 'Meihua' in 2022 is adopted in analysis. In this paper, firstly, the accuracy of the PWV retrieved by ERA5 was evaluated, which met the experimental analysis requirements, compared with the conference value of the Radiosonde (RS). Secondly, the correlation between PWV, rainfall and the typhoon path were analyzed qualitatively and quantitatively, using 16 meteorological stations in the typhoon path. The results indicated that PWV reached its peak value 2–6 h than rainfall, which was an important reference for rainfall forecasting. Then, the 'Pearson correlation coefficient' method was used for the quantitative evaluation of the correlation between PWV and the distance of the 'weather station-typhoon'. The results showed that PWV had an obvious upward trend, with a decrease in the distance between the 'weather station-typhoon'. The variation in PWV is intense at a reduced distance, and can reach its peak 16 h before the arrival of the typhoon. A strong negative correlation was demonstrated, with an average value of −0.73 for the Pearson correlation coefficient. Analyzing the temporal and spatial changes of the typhoon track, PWV and rainfall, the results show that before the typhoon passes through the region, both the PWV and rainfall certainly reach their maximum. The variation trends of PWV and rainfall in the period of the typhoon are significantly consistent. The center of PWV and rainfall is mainly located on the northwest side of the typhoon center, which showed obvious asymmetry. [ABSTRACT FROM AUTHOR]

The phase-field model (PFM) is gaining increasing attention in the application of multiphase flows due to its advantages, in which the phase interface is treated as a narrow layer and phase parameters change smoothly and continually at this thin layer. Thus, the construction or tracking of the phase interface can be avoided, and the bulk phase and phase interface can be simulated integrally. PFM provides a useful alternative that does not suffer from problems with either the mass conservation or the accurate computation of surface tension. In this paper, the state of the art of PFM in the numerical modeling and simulation of multiphase flows is comprehensively reviewed. Starting with a brief description of historical developments in the PFM, we continue to take a tour into the basic concepts, fundamental theory, and mathematical models. Then, the commonly used numerical schemes and algorithms for solving the governing systems of PFM in the application of multiphase flows are presented. The various applications and representative results, especially in non-match density scenarios of multiphase flows, are reviewed. The primary challenges and research focus of PFM are analyzed and summarized as well. This review is expected to provide a valuable reference for PFM in the application of multiphase flows. [ABSTRACT FROM AUTHOR]

This paper presents the extent to which the combination of extra-atmospheric and hydroclimatic factors can be deciphered to record their contribution to the evolution and forecasting of the Danube discharge (Q) in the lower basin. A combination of methods such as wavelet filtering and deep learning (DL) constitutes the basic method for discriminating the external factors (solar activity through Wolf numbers) that significantly contribute to the evolution and prediction of the lower Danube discharge. An ensemble of some of the most important factors, namely, those representing the atmospheric components, i.e., the Greenland-Balkan Oscillation Index (GBOI) and the North Atlantic Oscillation Index (NAOI); the hydroclimatic indicator, the Palmer Hydrological Drought Index (PHDI); and the extra-atmospheric factor, constitutes the set of predictors by means of which the predictand, Q, in the summer season, is estimated. The external factor has to be discriminated in the Schwabe and Hale spectra to make its convolutional contribution to the Q estimation in the lower Danube basin. An interesting finding is that adding two solar predictors (associated with the Schwabe and Hale cycles) to the terrestrial ones give a better estimation of the Danube discharge in summer, compared to using only terrestrial predictors. Based on the Nash–Sutcliffe (NS) index, a measure of performance given by the extreme learning machine (ELM), it is shown that, in association with certain terrestrial predictors, the contribution of the Hale cycle is more significant than the contribution of the Schwabe cycle to the estimation of the Danube discharge in the lower basin. [ABSTRACT FROM AUTHOR]

China's largest high-latitude permafrost distribution zone is in Northeast China. With the intensification of global warming and engineering construction, the carbon stored in permafrost will gradually thaw and be released in the form of methane gas. However, research on the changes in methane concentration and emission sources in this area is still unclear. In this paper, the AIRS (Atmospheric Infrared Sounder) data carried by the Aqua satellite were used to analyze the distribution and change trends in the overall methane concentration in the near-surface troposphere in Northeast China from 2003 to 2022. These data, combined with national meteorological and on-site monitoring data, were used to study the methane emission characteristics and sources in the permafrost area in Northeast China. The results show that the methane concentration in the near-surface troposphere of Northeast China is mainly concentrated in the permafrost area of the Da and Xiao Xing'an Mountains. From 2003 to 2022, the methane concentration in the near-surface troposphere of the permafrost area in Northeast China showed a rapid growth trend, with an average linear trend growth rate of 4.787 ppbv/a. In addition, the methane concentration in the near-surface troposphere of the permafrost area shows a significant bimodal seasonal variation pattern. The first peak appears in summer (June–August), with its maximum value appearing in August, and the second peak appears in winter (December–February), with its maximum value appearing in December. Combined with ground surface methane concentration monitoring, it was found that the maximum annual ground surface methane concentration in degraded permafrost areas occurred in spring, causing the maximum average growth rate in methane concentration, also in spring, in the near-surface troposphere of permafrost areas in Northeast China (with an average value of 6.05 ppbv/a). The growth rate of methane concentration in the southern permafrost degradation zone is higher than that in the northern permafrost stable zone. In addition, with the degradation of permafrost, the geological methane stored deep underground (methane hydrate, coal seam, etc., mainly derived from the accumulation of ancient microbial origin) in the frozen layer will become an important source of near-surface troposphere methane in the permafrost degradation area. Due to the influence of high-permeability channels after permafrost degradation, the release rate of methane gas in spring is faster than predicted, and the growth rate of methane concentration in the near-surface troposphere of permafrost areas can be increased by more than twice. These conclusions can provide a data supplement for the study of the carbon cycle in permafrost areas in Northeast China. [ABSTRACT FROM AUTHOR]

Pulse signals refer to electromagnetic waveforms with short duration and high peak energy in the time domain. Spatial electromagnetic pulse interference signals can be caused by various factors such as lightning, arc discharge, solar disturbances, and electromagnetic disturbances in space. Pulse disturbance signals appear as instantaneous, high-energy vertical-line pulse trains (VLPTs) on the spectrogram. This paper uses computer vision techniques and unsupervised clustering algorithms to process and analyze VLPT on very-low-frequency (VLF) waveform spectrograms collected by the China Seismo-Electromagnetic Satellite (CSES) electric field detector. First, the waveform data are transformed into time–frequency spectrograms with a duration of 8 s using the short-time Fourier transform. Then, the spectrograms are subjected to grayscale transformation, vertical line feature extraction, and binarization preprocessing. In the third step, the preprocessed data are dimensionally reduced and fed into an unsupervised K-means++ clustering model to achieve automatic recognition and labeling of VLPTs. By recognizing and studying VLPT, not only can interference be recognized, but the temporal and spatial locations of these interferences can also be determined. This lays the foundation for identifying VLPT sources and gaining deeper insights into the generation, propagation, and characteristics of electromagnetic radiation. [ABSTRACT FROM AUTHOR]

Prediction of air pollutant concentrations is currently one of the most important methods for the prevention and control of urban air pollution in most countries, and accurate and timely prediction of pollutant concentrations is of great significance for urban pollution control. Using Taiyuan, China, as a case study, this study examines how to predict hourly air pollutant concentrations over longer periods of time while ensuring their accuracy. In this paper, an air pollutant concentration prediction method based on improved inverse distance interpolation and Informer model (EIDW-Informer), and hour-by-hour prediction of PM2.5, NO2, and O3 concentrations in Taiyuan, China is carried out. In this study, historical data from seven environmental monitoring stations in Taiyuan City were used to build multidimensional environmental vectors and calculate the similarity between sample points. Then, the missing values in the dataset were interpolated according to the similarity and distance weights, and the long series prediction was performed by Informer. The experimental results show that the EIDW-Informer method has advantages in hour-by-hour prediction compared to LSTM, CNN-LSTM, and Attention-LSTM models, which improves by 20%, 27%, and 43% on 1 h, 8 h, and 72 h time scales, respectively. [ABSTRACT FROM AUTHOR]

The paper focuses on the area of experimental measurements for monitoring the impact of decarbonization on the technical and environmental parameters of the ICE. The condition for the implementation of the measurements was the selection of an ICE that had driven more than 300,790 km. During the laboratory tests, several test methods were used to assess the technical and emission conditions of the tested ICE with the code designation ALH, which is used in VOLKSWAGEN BORA vehicles. The technical parameters of the tested ICE (corrected power PNorm and torque MMom), emission parameters (absorption coefficient k and NOx emission) and fuel consumption parameters (specific fuel consumption bsfc) were compared and evaluated in the results. The results show that the decarbonization process on the tested vehicle VOLKSVWAGEN BORA 1.9 TDi affected the emission parameters but did not affect the technical parameters and specific fuel consumption. [ABSTRACT FROM AUTHOR]

Astroclimatic conditions characterize the quality of an astronomical site. The Terskol Observatory was founded over 42 years ago in 1980. The astronomical site (coordinates 43°16′29″ N, 42°30′03″ E) is located about 10 km from Mt. Elbrus in the northern Caucasus Mountains. The paper presents the results of an analysis of long-term variations from 1980 to 2022 of astroclimatic parameters such as total cloud cover, precipitable water vapor, and wind speed at a level of 200 hPa above the Terskol Observatory using atmospheric ERA5 Reanalysis data from the European Center for Medium-Range Weather Forecast. The probability distributions and seasonal variations of the astroclimatic parameters are also presented. Long-term trends of the astroclimatic parameters are studied with statistically significant trend analysis methods using the Mann–Kendall test and the Sen's slope test to estimate the magnitude of the changes. The results show non-significant decreases of annual average 200-hPa wind speed and increases in precipitable water vapor, but significant (99 per cent confidence level) long-term decreasing of total cloud cover above the Terskol Observatory. [ABSTRACT FROM AUTHOR]

The Wasatch Front in Utah, USA is currently a non-attainment area for ozone according to the Environmental Protection Agency's (EPA) National Ambient Air Quality Standards (NAAQS). Nitrogen oxides (NOx = NO2 + NO) and volatile organic compounds (VOCs) in the presence of sunlight lead to ozone formation in the troposphere. When the rate of oxidant production, defined as the sum of O3 and NO2, is faster than the rate of NOx production, a region is said to be NOx-limited and ozone formation will be limited by the concentration of NOx species in the region. The inverse of this situation makes the region VOC-limited. Knowing if a region is NOx-limited or VOC-limited can aid in generating effective mitigation strategies. Understanding the background or regional contributions to ozone in a region, whether it be from the transport of precursors or of ozone, provides information about the lower limit for ozone concentrations that a region can obtain with regulation of local precursors. In this paper, measured oxidant and NOx concentrations are analyzed from 14 counties in the state of Utah to calculate the regional and local contributions to ozone for each region. This analysis is used to determine the nature of the atmosphere in each county by determining if the region is VOC- or NOx-limited. Furthermore, this analysis is performed for each county for the years 2012 and 2022 to determine if there has been a change in the oxidative nature and quantify the regional and local contributions to ozone over a 10-year period. All studied counties—except for Washington County—in Utah were found to be VOC-limited in 2012. This shifted in 2022 to most counties being either in a transitional state or being NOx-limited. Local contributions to ozone increased in two major counties, Cache and Salt Lake Counties, but decreased in Carbon, Davis, Duchesne, Uinta, Utah, Washington, and Weber Counties. Generally, the regional contributions to oxidant concentrations decreased across the state. A summertime spike in both regional and local contributions to oxidants was seen. Smoke from wildfires was seen to increase the regional contributions to oxidants and shift the local regime to be more NOx-limited. [ABSTRACT FROM AUTHOR]

The Solid Oxide Fuel Cell (SOFC) system is a highly intricate system characterized by multiple variables and couplings. Developing an accurate model for the SOFC independent power generation system is of paramount importance. Conducting experimental studies on the SOFC system is costly, and it carries certain risks due to the requirements for pure hydrogen, high-temperature environments, and other factors. To address these challenges, a high-performing model that precisely reflects the inherent characteristics of the SOFC is essential for dynamic static analysis and the identification of optimal operating points. This paper presents a SOFC system model based on current controls, which was implemented in the MATLAB/Simulink environment, and it utilized a nodal approach for modeling. The model incorporated a cold air bypass, which enabled the more precise control of the SOFC reactor's inlet and outlet temperatures. Furthermore, a 3D test and verification method are proposed in order to focus on the influence of input parameters on the four electrical characteristics, and four thermal characteristics, of output parameters. By conducting one-dimensional, two-dimensional, and three-dimensional studies of these output parameters, a more intuitive understanding of the system's response to changes in input parameters was obtained. Under conditions wherein all other variables were kept constant, the entire system attained its maximum efficiency at approximately FU = 0.8, BP = 0, and AR = 6. The outcomes of this study have significant implications for exploring the optimal operating point in the SOFC independent power generation system in an in-depth manner. It provides valuable insights for enhancing the system's efficiency and performance. [ABSTRACT FROM AUTHOR]

In response to the nonlinear fitting difficulty of the traditional weighted average temperature (Tm) modeling, this paper proposed four machine learning (ML)-based Tm models. Based on the seven radiosondes in the Yangtze River Delta region from 2014 to 2019, four forecasting ML-based Tm models were constructed using Light Gradient Boosting Machine (LightGBM), Support Vector Machine (SVM), Random Forest (RF), and Classification and Regression Tree (CART) algorithms. The surface temperature (Ts), water vapor pressure (Es), and atmospheric pressure (Ps) were identified as crucial influencing factors after analyzing their correlations to the Tm. The ML-based Tm models were trained using seven radiosondes from 2014 to 2018. Then, the mean bias and root mean square error (RMSE) of the 2019 dataset were used to evaluate the accuracy of the ML-based Tm models. Experimental results show that the overall accuracy of the LightGBM-based Tm model is superior to the SVM, CART, and RF-based Tm models under different temporal variations. The mean RMSE of the daily LightGBM-based Tm model is reduced by 0.07 K, 0.04 K, and 0.13 K compared to the other three ML-based models, respectively. The mean RMSE of the monthly LightGBM-based Tm model is reduced by 0.09 K, 0.04 K, and 0.11 K, respectively. The mean RMSE of the quarterly LightGBM-based Tm model is reduced by 0.09 K, 0.04 K, and 0.11 K, respectively. The mean bias of the LightGBM-based Tm model is also smaller than that of the other ML-based Tm models. Therefore, the LightGBM-based Tm model can provide more accurate Tm and is more suitable for obtaining GNSS precipitable water vapor in the Yangtze River Delta region. [ABSTRACT FROM AUTHOR]

In recent years, the concentrations of PM2.5 in urban ambient air in China have been declining; however, the strong atmospheric oxidation capacity (AOC) represents challenges to the further reduction of PM2.5 concentration and the continuous improvement of ambient air quality in China in the future, since the overall AOC is still at a high level. For this paper, based on ground observation data recorded in Beijing from 2016 to 2019, the variation in AOC was characterized according to the concentration of odd oxygen (OX = O3 + NO2). The concentrations of the primary and secondary components of PM2.5 were analyzed using empirical formulas, the correlation between AOC and the concentrations of secondary PM2.5 and the secondary inorganic components (SO42−, NO3−, NH4+, and SNA) in Beijing were explored, the impact of atmospheric photochemical reaction activity on the generation of atmospheric secondary particles was evaluated, and the impact of atmospheric oxidation variations on PM2.5 concentrations and SNA in Beijing was investigated. The results revealed that OX concentrations reached their peak in 2016 and reached their lowest point in 2019. The OX concentrations followed a descending seasonal trend of summer, spring, autumn, and winter, along with a spatial descending trend from urban observation stations to suburban stations and background stations. The degree of photochemical activity and the magnitude of the AOC have a large influence on the production of atmospheric secondary particles. When the photochemical reaction was more active and the AOC was stronger, the mass concentrations of the secondary generated PM2.5 fraction were higher and accounted for a higher proportion of the total PM2.5 mass concentrations. In the PM2.5 fraction, SNA accounted for 50.7% to 94.4% of the total mass concentrations of water-soluble inorganic ions in the field observations. Higher concentrations of the atmospheric oxidant OX in ambient air corresponded to a higher sulfur oxidation ratio (SOR) and nitrogen oxidation ratio (NOR), suggesting that the increase in AOC could promote the increase of PM2.5 concentration. Based on a relationship analysis of SOR, NOR, and OX, it was inferred that the relationship between OX and SOR and the relationship between OX and NOR were both nonlinear. Therefore, when establishing PM2.5 control strategies in Beijing in the future, the impact of the AOC on PM2.5 generation should be fully considered, and favorable measures should be taken to properly regulate the AOC, which would be more effective when carrying out further control measures regarding PM2.5 pollution. [ABSTRACT FROM AUTHOR]

The GRAPES (Global/Regional Assimilation and Prediction System) global medium-range ensemble forecast system (GRAPES_GFS) is a new generation numerical weather forecast model developed by the China Meteorological Administration (CMA). However, the forecasts of surface latent heat fluxes and surface air temperature have systematic biases, which affect the forecasts of atmospheric dynamics by modifying the lower boundary conditions and degrading the application of GRAPES_GFS since the 2 m air temperature is one of the key components of weather forecast products. Here, we add a soil resistance term to reduce soil evaporation, which ultimately reduces the positive forecast bias of the land surface latent heat flux. We also reduce the positive forecast bias of the ocean surface latent heat flux by considering the effect of salinity in the calculation of the ocean surface vapor pressure and by adjusting the parameterizations of roughness length for the exchanges in momentum, heat, and moisture between the ocean surface and atmosphere. Moreover, we modify the parameterization of the roughness length for the exchanges in heat and moisture between the land surface and atmosphere to reduce the cold bias of the nighttime 2 m air temperature forecast over areas with lower vegetation height. We also consider the supercooled soil water to reduce the warm forecast bias of the 2 m air temperature over northern China during winter. These modified parameterizations are incorporated into the GRAPES_GFS and show good performance based on a set of evaluation experiments. This paper highlights the importance of the representations of the land/ocean surface and boundary layer processes in the forecasting of surface heat fluxes and 2 m air temperature. [ABSTRACT FROM AUTHOR]

Climate is closely related to human life, food security and ecosystems. Forecasting future climate provides important information for agricultural production, water resources management and so on. In this paper, historical climate data from 1962–2001 was used at three sites in Tianjin Baodi, Tianjin and Tanggu districts as baseline and the model parameters were calibrated by the Long Ashton Research Station Weather Generator (LARS-WG). 2m-temperatures in 2011–2020 were verified under two scenarios, representative concentration pathway (RCP) 4.5 and RCP8.5 in different atmospheric circulation models with optimal minimum 2m-temperatures at the three sites. From 2031–2050, Tianjin will be using more moderate minimum 2m-temperatures in future simulations. Support vector machines (SVM) were used to optimize the simulated data to obtain more accurate future maximum and minimum 2m-temperatures for the three sites. The results showed that the determinant coefficient of LARS-WG simulation was 0.8 and SVM optimized determinant coefficient was 0.9 which greatly improved the prediction accuracy. The minimum and maximum future 2m-temperatures optimized under European Community Earth System Model (EC-EARTH) were relatively low and the same future 2m-temperatures optimized under Hadley Centre Global Environment Model Earth System (Had-GEM2-ES4) were high especially in the RCP8.5 scenario which simulated 2051–2070 climate. The SVM optimization showed that the maximum and minimum 2m-temperatures were in general agreement with the original simulation values. [ABSTRACT FROM AUTHOR]

A substantial body of research has demonstrated the relationships between cardiac arrhythmias and geomagnetic activity. In this work, the idea is centered on finding the relationship between the local magnetic field (LMF) and acute myocardial infarction (AMI). It is hypothesized and demonstrated via a series of statistical analyses that the relationship between the LMF and AMI is maintained over long-term observation windows. The data are collected from the two hospitals and one public institute of health in Lithuania from 2014 till 2019. The data are categorized into (1) daily average of the Schumann resonance for the local magnetic field measured by the Lithuanian magnetometer, which is used as the input variable; and (2) the total number of patients hospitalized in Lithuania per day with the diagnosis of AMI (the output variable). The data are classified both weekly as well as by gender. Following the data categorization and classification, the data were subjected to rigorous statistical analysis to determine the relationship between the input and output variables. This paper shows that only the beta and gamma (S-beta, S-gamma) frequency ranges of the Schumann resonances contribute to maintaining the long-term relationship between the LMF and AMI. [ABSTRACT FROM AUTHOR]

As a radiant light source within the dynamic range of most spacecraft payloads, the Moon provides an excellent reference for on-orbit radiometric calibration. This research hinges on the precise simulation of lunar spectral irradiances and Earth-based Moon observation geometry. The paper leverages the Hapke model to simulate the temporal changes in lunar spectral irradiances, utilizing datasets obtained from the Lunar Reconnaissance Orbiter Camera (LROC). The research also details the transformation process from the lunar geographic coordinate system to the instantaneous projection coordinate system, thereby delineating the necessary observational geometry. The insights offered by this study have the potential to enhance future in-orbit spacecraft calibration procedures, thereby boosting the fidelity of data gathered from satellite observations. [ABSTRACT FROM AUTHOR]

In the context of anomalous global climate change and the frequent occurrence of droughts and floods, studying trends in the conversion rate between precipitable water vapor (PWV) and actual precipitation in a certain region can help in analyzing the causes of these natural disasters. This paper examines the variation trend in the conversion rate between PWV and actual precipitation on a monthly scale in Hubei from 1960 to 2020. To estimate historical PWV data, we propose a new method for estimating PWV using water vapor pressure based on the RF algorithm. The new method was evaluated by radiosonde data and improved the accuracy by 1 mm over the traditional method in Hubei. Based on this method, we extrapolate the monthly average PWV in Hubei from 1960 to 2020 and analyze the conversion rate between PWV and precipitation during this period. Our results showed that there was no obvious cyclical pattern in the conversion rate in either the longitude or latitude directions. In Hubei, where the topography varies significantly in the longitude direction, the conversion rate is influenced by topography, with the smallest conversion rate being in the transition zone between the mountainous region of western Hubei and the Jianghan Plain. In the latitudinal direction, the conversion rate decreases with increasing latitude. [ABSTRACT FROM AUTHOR]

The special terrain of Urumqi (in the valley area) often triggers strong foehn winds, causing huge losses to local people's lives and social economies. By using the surface observation data of the hourly temperature, pressure, humidity, and wind from the downwind Urumqi Meteorological Station and the upwind Dabancheng Meteorological Station in the Middle Tianshan Canyon and the NCEP/NCAR reanalysis data during 2008–2022, this paper establishes the dataset of foehn processes at Urumqi Station in the past 15 years and analyzes the variation rules of the associated meteorological variables during the foehn processes. In addition, based on the physical mechanism of the occurrence of foehn, a three-element identification criterion (i.e., 94° ≤ 2 min average wind direction ≤ 168°, 2 min average wind speed ≥ 2.0 m/s, and Δθ between Urumqi station and Dabancheng station ≥ 0.29 K) for foehn in Urumqi is established by comparing and analyzing the variations of wind direction (WD), wind speed (WS), and the potential temperature difference (Δθ) between the two weather stations during the periods of foehn and non-foehn winds from 2013 to 2022. In addition, the performance of the three-element identification criterion is verified, and the results suggest that this criterion has an accuracy of 82.96% and a hit rate of 89.50% for the 2008–2012 foehn events in Urumqi. Moreover, the hit rate of this criterion for foehn wind of gale or above level (i.e., a 2 min wind ≥ 10.8 m/s on average) is 100%. In addition, combined with two predictors of sea-level pressure difference (ΔP) and Δθ between downwind stations and upwind stations, the foehn forecast can be more accurate than that provided by a single predictor. With ΔP ≤ −12 hPa and Δθ ≥ 5 K, the chances for foehn to occur are over 90%. This finding would have some reference and application values for the foehn forecasting. [ABSTRACT FROM AUTHOR]

In Canada, Indigenous populations are disproportionately threatened by wildfire smoke and the associated adverse health impacts. This paper presents the results of a narrative review of 51 academic and related resources which explored protective action decision making during wildfire smoke events within Indigenous communities in Canada. A search of scholarly articles and other relevant sources yielded resources which were subject to thematic analysis and described in order to present a narrative review of current knowledge and gaps in research. A small and growing literature provides insights into protective actions taken by the general population during wildfire smoke events, but very little is known about protective actions taken by Indigenous peoples in Canada during wildfire smoke events. This lack of understanding hinders the capacity of decision makers to improve emergency management and minimize community health impacts of wildfire smoke. [ABSTRACT FROM AUTHOR]

On 19 September 2021, La Palma volcano (Canarias Archipelagos) started an eruption that persisted until 13 December 2021. Despite the Volcano Explosive Index (VEI) being estimated equal to 3, corresponding to not so powerful eruption, the long eruption activity posed much scientific interest in this natural hazard event. In this paper, we searched for possible effects of this eruption on the atmosphere and ionosphere, investigating the climatological archive and Swarm magnetic satellite data. In particular, we explored Aerosol, Sulphur Dioxide and Carbon Monoxide concentrations in the atmosphere identifying both the direct emissions from the volcano as well as the plume that drifted toward West-South-West and was reinforced during the eruption period. The vertical profile of temperature from the Saber satellite was analysed to search for the possible presence of acoustic gravity waves induced by volcanic activity. Compared with the year before without eruption in the areas, a lot of Saber profiles present an Energy Potential very much higher than the previous year, proposing the presence of Acoustic Gravity Waves (AGW) induced by volcano eruption activity. We also identified Swarm magnetic disturbances on the day of the eruption and in November. The mechanism of coupling could be different for the latter one, as there is no evidence for AGW. They may be due to a more complex of physical and chemical alterations that propagate from the lower atmosphere to the upper one into the ionosphere. [ABSTRACT FROM AUTHOR]

Accurately identifying and predicting droughts can provide local managers with a basis for decision-making. The Xinyang region is prone to droughts and floods, which have a large impact on local agriculture and socio-economics. This paper employs precipitation data from the Xinyang region to provide a scientific basis for drought and flood control measures in this region. The data are first treated with standardized precipitation indices (SPIs) on three-month, six-month, and nine-month time scales. Subsequently, a Morlet wavelet analysis is performed for each of the three time scales analyzed for the SPI. The results show multiple time scales of drought and flood disasters in the Xinyang region. The cycles of drought and flood disasters in the Xinyang region show different fluctuations on different SPI scales. The SPI time series reflect a strong fluctuation period of 17a for drought and flood disasters in the Xinyang region. An analysis of the variance of the wavelet coefficients showed that the first main cycle of drought and flood disasters in the Xinyang region is 7a, and the second and third sub-cycles are 4a and 13a, respectively. We conclude that floods are more frequent than droughts in Xinyang and are more likely to occur from 2017 to 2021, with a subsequent shift to droughts. Local managers should put drought prevention measures in place to deal with droughts after 2021. [ABSTRACT FROM AUTHOR]

Public participation is playing an increasingly important role in environmental governance. This paper uses panel data from Chinese cities to evaluate the causal relationship between public participation, regional carbon emissions, and regional carbon intensity. We obtain the following conclusions: (1) Public participation significantly reduces regional carbon emissions and regional carbon intensity, which remains robust after a series of robustness and endogeneity discussions. (2) The carbon reduction effect of public participation performs better in eastern regions, regions with higher per capita income, and regions with a concentration of tertiary industries and talents. (3) We divided the public participation into resident participation and environment non-governmental organizations (ENGOs) participation. We found an excellent interactive emission reduction effect with resident participation and a good interaction between resident participation and government and environmental organizations. (4) This paper finds that promoting regional green technology innovation is a significant mechanism for public participation in achieving carbon emission reduction. (5) Finally, this paper found an "inverted U-shaped" non-linear relationship between public participation and regional carbon emissions. The results reveal the importance of public participation in regional carbon emissions and provide an empirical basis for promoting informal environmental regulation. [ABSTRACT FROM AUTHOR]

The Tarim Basin is a large inland arid basin in the arid region of northwest China and has been experiencing significant "warming and wetting" since 1987. As a result, the purpose of this paper is to determine whether the climate transition phenomenon occurred in the Tarim Basin as well as the role of atmospheric circulation in this process. We use meteorological data and atmospheric circulation indexes to study the seasonal trends of climate change in this region from 1987 to 2020 to understand how they are affected by atmospheric circulation. The findings show that, from 1987 to 2020, the Tarim Basin experienced significant warming and wetting; with the exception of the winter scale, all other seasonal scales exhibited a clear warming and wetting trend. From the perspective of spatial distribution, most of the areas showed a significant warming trend, and the warming amplitude around the basin is greater than that in the central area of the basin. However, there are significant regional differences in precipitation change rates. Meanwhile, wavelet analysis shows that there is a significant oscillation period of 17–20 years between climate change and the atmospheric circulation index during 1987–2020. The correlation analysis shows that the Pacific decadal oscillation (PDO) and El Niño-Southern Oscillation (ENSO) are the main influencing factors of climate change in the Tarim Basin at different seasonal scales, while the teleconnection of the Arctic Oscillation (AO) and North Atlantic Oscillation (NAO) is low and the PDO dominates the summer and autumn temperature changes in the Tarim Basin. The research results of this paper show that, despite the warming and wetting trends since 1987 in the Tarim Basin, the climate type did not change. From 1987 to 2020, the main teleconnection factors of climate change in the Tarim Basin were PDO and ENSO. [ABSTRACT FROM AUTHOR]

A hybrid machine learning (ML) model is becoming a common trend in predicting reference evapotranspiration (ETo) research. This study aims to systematically review ML models that are integrated with meta-heuristic algorithms (i.e., parameter optimisation-based hybrid models, OBH) for predicting ETo data. Over five years, from 2018–2022, the articles published in three reliable databases, including Web of Science, ScienceDirect, and IEEE Xplore, were considered. According to the protocol search, 1485 papers were selected. After three filters were applied, the final set contained 33 papers related to the nominated topic. The final set of papers was categorised into five groups. The first group, swarm intelligence-based algorithms, had the highest proportion of papers, (23/33) and was superior to all other algorithms. The second group (evolution computation-based algorithms), third group (physics-based algorithms), fourth group (hybrid-based algorithms), and fifth group (reviews and surveys) had (4/33), (1/33), (2/33), and (3/33), respectively. However, researchers have not treated OBH models in much detail, and there is still room for improvement by investigating both newly single and hybrid meta-heuristic algorithms. Finally, this study hopes to assist researchers in understanding the options and gaps in this line of research. [ABSTRACT FROM AUTHOR]

Image-based quantified visibility estimation is an important task for both atmospheric science and computer vision. Traditional methods rely largely on meteorological observation or manual camera calibration, which restricts its performance and generality. In this paper, we propose a new end-to-end pipeline for single image-based quantified visibility estimation by an elaborate integration between meteorological physical constraint and deep learning architecture design. Specifically, the proposed Deep Quantified Visibility Estimation Network (abbreviated as DQVENet) consists of three modules, i.e., the Transmission Estimation Module (TEM), the Depth Estimation Module (DEM), and the Extinction coEfficient Estimation Module (E3M). Casting on these modules, the meteorological prior constraint can be combined with deep learning. To validate the performance of DQVENet, this paper also constructs a traffic image dataset (named QVEData) with accurate visibility calibration. Experimental results compared with many state-of-the-art methods on QVEData demonstrate the effectiveness and superiority of DQVENet. [ABSTRACT FROM AUTHOR]

Under the influence of the dual policies of sustainable economic development and the national dual-carbon target, the establishment of an environmental protection department for the treatment of heavily polluting industries is imminent, and the country has launched pollution control policies and regulations to restrict the emission rights of heavily polluting industries. Therefore, this paper focuses on whether the restriction of emission rights in key industries has reduced carbon emissions. To achieve this, this paper uses panel data of prefecture-level cities in China from 2006 to 2019 and adopts a two-way fixed-effects DID model to systematically analyze the impact of the key pollution industry governance policies launched by the Ministry of Environmental Protection on CO2 emissions in 2017. And further analyze the role of variables such as green technology innovation patents and energy efficiency using this model, while parallel trend tests and placebo tests, and related policies are used to ensure the robustness of the regression results. This paper reveals that: (1) The heavy pollution industry governance policy implemented in 2017 can effectively reduce CO2 emissions in the cities of the treated group, and the effect is more significant in the year of policy implementation; (2) Green utility patents and energy-use efficiency are the effective mediating mechanisms to reduce CO2 emissions; (3) Over time, the effect of heavy pollution industry governance policy on CO2 emissions gradually decreases; (4) The reliability of the baseline regression results of this paper is proved by the use of parallel trend tests, placebo tests, and tests excluding the influence factors such as relevant policies in the same period. Therefore, the key polluting industries treatment policy launched by China's Ministry of Environmental Protection in 2017 under the recent dual-carbon policy development goals formulated by China, can effectively reduce carbon emissions; however, in the future economic development process, the government should give more consideration to the continuity of the policy impact and its coherence on economic development when implementing the policy. [ABSTRACT FROM AUTHOR]

The paper took Guang'an City in Sichuan Province of China as a research region, and it built an evaluation index system based on 14 evaluation indices according to four criteria, namely, ecological background, ecological structure, ecological benefits, and ecological stress. The Delphi algorithm and entropy weight (a combination of subjective and objective methods) were used to calculate the weight value of each evaluation index, and an ideal point model was used to calculate the ideal point value. The ideal point ecological grade was classified, and the principal component analysis method was used to obtain the main control factors of each year, which revealed the relationship between the ecological distribution of ideal points and the environmental impact factor, with the changing characteristics of the ecology in the research region according to the hotspot model of the spatial differentiation of land ecological quality as the main control factor. Lastly, the paper also analyzed the land ecological quality of Guangan City in 2000, 2005, 2010, and 2015. The study results show that the overall land ecological quality in Guang'an City generally increased from 2005 to 2015. The proportion of forest land area and the temperature were the most important main control factors. The air temperature and the land ecological quality were positively correlated. [ABSTRACT FROM AUTHOR]

Accurate prediction of air temperature is of great significance to outdoor activities and daily life. However, it is important and more challenging to predict air temperature in complex terrain areas because of prevailing mountain and valley winds and variable wind directions. The main innovation of this paper is to propose a regional temperature prediction method based on deep spatiotemporal networks, designing a spatiotemporal information processing module to align temperature data with regional grid points and further transforming temperature time series data into image sequences. Long Short-Term Memory network is constructed on the images to extract the depth features of the data to train the model. The experiments demonstrate that the deep learning prediction model containing the spatiotemporal information processing module and the deep learning prediction module is fully feasible in short-term regional temperature prediction. The comparison experiments show that the model proposed in this paper has better prediction results for classical models, such as convolutional neural networks and LSTM networks. The experimental conclusion shows that the method proposed in this paper can predict the distribution and change trend of temperature in the next 3 h and the next 6 h on a regional scale. The experimental result RMSE reached 0.63, showing high stability and accuracy. The model provides a new method for local regional temperature prediction, which can support the planning of production and life in advance and tend to save energy and reduce consumption. [ABSTRACT FROM AUTHOR]

With the development of wireless communication technology, the electromagnetic interference (EMI) of artificial radio to weather radar increases significantly, which has a serious impact on the quality of radar data. Most of the research on detecting and suppressing electromagnetic interference was based on the primary product of the radar. This paper researches the effectiveness of deep convolutional neural networks (DCNN) to identify and suppress electromagnetic interference based on the I/Q data output from the front end of a radar receiver. Firstly, this paper selected UNet, ResNet with UNet structure, and DeepLab V3+ for the semantic segmentation of electromagnetic interference and other signals. After semantic segmentation, this paper used the linear interpolation method to suppress EMI. Finally, this paper selected the prediction precision of the model and compared the quality of primary products before and after EMI suppression to evaluate the effectiveness of DCNN. The results showed that all three models could effectively identify the electromagnetic interference and the quality of the data were improved after suppression. It suggests that the use of DCNN on the I/Q data output from the front end of a radar receiver can play a certain effect on the identification of electromagnetic interference. [ABSTRACT FROM AUTHOR]