Abstract:
keep_len="250">Reliable medium-range weather forecasts are crucial for both science and society. Although weather predictions primarily rely on numerical weather models, the artificial intelligence (AI) weather big models have shown potential for accurate weather forecasts with reduced computational costs. However, prediction skills of big models remain uncertain, particularly in East Asia, which limits further application of weather AI models. To systematically evaluate predictive capabilities of Pangu, FuXi, and GraphCast models over East Asia, their prediction results are focusing on 500 hPa geopotential height, 2 m air temperature, 10 m wind speed, precipitation, and track of tropical cyclones.ECMWF reanalysis V5 (ERA5) datasets are utilized to provide the initial conditions for big models, and to assess their predictive skill. Additionally, precipitation observations and China Meteorological Administration tropical cyclone datasets are utilized to access big models as well. FuXi shows the highest forecasting skills among 3 big models for 500 hPa geopotential height. The forecast from FuXi is reliable for up to 9.75 days, while the forecasts from Pangu and GraphCast are reliable for 8.75 days and 8.5 days, respectively. For 2 m air temperature forecasting, FuXi presents higher skills with an averaged temporal anomaly correlation coefficient (TCC) ranging from 0.48 to 0.91, while TCCs of Pangu and GraphCast are 0.43-0.91 and 0.38-0.83, respectively. Among 3 models, only FuXi and GraphCast provide precipitation forecasts. FuXi shows higher prediction skill compared to GraphCast in forecasting precipitation, light rain, and moderate rain; however, GraphCast has advantage in heavy rain forecast. As the lead time increases, the threat scores (TSs) of FuXi for rainfall, light rainfall and moderate rainfall are 0.22-0.41, 0.15-0.24 and 0.06-0.22, respectively. The model demonstrates higher skill in the northern and southeastern regions of China. For predicting the track of cyclones, Pangu model demonstrates superior predictive skill. As the lead time increases from 6 hours to 240 hours, biases of Pangu's prediction track increase from 17.5 km to 1850 km.The study focuses on the prediction skill of various AI big models through TCC, spatial anomaly correlation coefficient, and TS. Generally, the performance of FuXi is superior for most elements. And reasonable evaluation of AI model is helpful for the development of AI models. Reliable medium-range weather forecasts are crucial for both science and society. Although weather predictions primarily rely on numerical weather models, the artificial intelligence (AI) weather big models have shown potential for accurate weather forecasts with reduced computational costs. However, prediction skills of big models remain uncertain, particularly in East Asia, which limits further application of weather AI models. To systematically evaluate predictive capabilities of Pangu, FuXi, and GraphCast models over East Asia, their prediction results are focusing on 500 hPa geopotential height, 2 m air temperature, 10 m wind speed, precipitation, and track of tropical cyclones.ECMWF reanalysis V5 (ERA5) datasets are utilized to provide the initial conditions for big models, and to assess their predictive skill. Additionally, precipitation observations and China Meteorological Administration tropical cyclone datasets are utilized to access big models as well. FuXi shows the highest forecasting skills among 3 big models for 500 hPa geopotential height. The forecast from FuXi is reliable for up to 9.75 days, while the forecasts from Pangu and GraphCast are reliable for 8.75 days and 8.5 days, respectively. For 2 m air temperature forecasting, FuXi presents higher skills with an averaged temporal anomaly correlation coefficient (TCC) ranging from 0.48 to 0.91, while TCCs of Pangu and GraphCast are 0.43-0.91 and 0.38-0.83, respectively. Among 3 models, only FuXi and GraphCast provide precipitation forecasts. FuXi shows higher prediction skill compared to GraphCast in forecasting precipitation, light rain, and moderate rain; however, GraphCast has advantage in heavy rain forecast. As the lead time increases, the threat scores (TSs) of FuXi for rainfall, light rainfall and moderate rainfall are 0.22-0.41, 0.15-0.24 and 0.06-0.22, respectively. The model demonstrates higher skill in the northern and southeastern regions of China. For predicting the track of cyclones, Pangu model demonstrates superior predictive skill. As the lead time increases from 6 hours to 240 hours, biases of Pangu's prediction track increase from 17.5 km to 1850 km.The study focuses on the prediction skill of various AI big models through TCC, spatial anomaly correlation coefficient, and TS. Generally, the performance of FuXi is superior for most elements. And reasonable evaluation of AI model is helpful for the development of AI models.

Abstract:
keep_len="250">Boundary layer convergence lines are recognized as one of the critical mesoscale weather systems triggered convection, and also affect low-altitude flight safety. The accurate and detailed identification of these lines is considered essential for revealing their formation, evolution, and interaction mechanisms with other weather systems. However, existing automatic identification technologies are limited in their ability to adapt the diverse characteristics of these lines, such as scale, intensity, and shape. The rotational region-based convolutional neural network (R2CNN) is employed to enhance the accuracy, robustness, and generalization of the identification process. A comprehensive identification dataset has been constructed for model training, considering the diversity of weather radar models and resolutions. Relevant parameters are adjusted to derive the optimized recognition model. The intersection over union (IoU) with confidence levels are employed to comprehensively assess and validate the identification results. Results indicate that the boundary layer convergence line recognition algorithm developed achieves a higher hit rate and a lower false alarm rate at lower IoU thresholds. At a confidence level of 0.7, the threat score (TS) reaches its maximum value.Compared to the existing Machine Intelligence Gust Front Algorithm (MIGFA), the model proposed in this study demonstrates significant advantages in reducing false alarms, improving hit rates, and achieving a balanced recognition frequency. Therefore, it is more suitable for operational applications and dissemination. This research not only provides a more effective method for identifying boundary layer convergence lines but also contributes to the improvement of low-altitude flight safety and advances meteorological detection technologies. The proposed method addresses limitations of existing technologies by effectively managing the diverse characteristics of boundary layer convergence lines. By incorporating rotational bounding boxes in the detection process, R2CNN model enhances the detection accuracy for objects with arbitrary orientations, which is particularly beneficial for meteorological phenomena that do not align with the standard axis. The constructed dataset includes a diverse collection of radar images from various models and resolutions, ensuring that the model is trained on a wide range of data and can generalize effectively to new, unseen data. Extensive experiments are conducted to evaluate the model's performance under different IoU thresholds and confidence levels. Findings demonstrate that at lower IoU thresholds, the model maintains high detection performance, indicating its robustness in practical applications where precise localization may be challenging. Furthermore, the superior performance of the proposed model compared to MIGFA indicates its potential for widespread adoption by meteorological agencies for better monitoring and forecasting. Boundary layer convergence lines are recognized as one of the critical mesoscale weather systems triggered convection, and also affect low-altitude flight safety. The accurate and detailed identification of these lines is considered essential for revealing their formation, evolution, and interaction mechanisms with other weather systems. However, existing automatic identification technologies are limited in their ability to adapt the diverse characteristics of these lines, such as scale, intensity, and shape. The rotational region-based convolutional neural network (R2CNN) is employed to enhance the accuracy, robustness, and generalization of the identification process. A comprehensive identification dataset has been constructed for model training, considering the diversity of weather radar models and resolutions. Relevant parameters are adjusted to derive the optimized recognition model. The intersection over union (IoU) with confidence levels are employed to comprehensively assess and validate the identification results. Results indicate that the boundary layer convergence line recognition algorithm developed achieves a higher hit rate and a lower false alarm rate at lower IoU thresholds. At a confidence level of 0.7, the threat score (TS) reaches its maximum value.Compared to the existing Machine Intelligence Gust Front Algorithm (MIGFA), the model proposed in this study demonstrates significant advantages in reducing false alarms, improving hit rates, and achieving a balanced recognition frequency. Therefore, it is more suitable for operational applications and dissemination. This research not only provides a more effective method for identifying boundary layer convergence lines but also contributes to the improvement of low-altitude flight safety and advances meteorological detection technologies. The proposed method addresses limitations of existing technologies by effectively managing the diverse characteristics of boundary layer convergence lines. By incorporating rotational bounding boxes in the detection process, R2CNN model enhances the detection accuracy for objects with arbitrary orientations, which is particularly beneficial for meteorological phenomena that do not align with the standard axis. The constructed dataset includes a diverse collection of radar images from various models and resolutions, ensuring that the model is trained on a wide range of data and can generalize effectively to new, unseen data. Extensive experiments are conducted to evaluate the model's performance under different IoU thresholds and confidence levels. Findings demonstrate that at lower IoU thresholds, the model maintains high detection performance, indicating its robustness in practical applications where precise localization may be challenging. Furthermore, the superior performance of the proposed model compared to MIGFA indicates its potential for widespread adoption by meteorological agencies for better monitoring and forecasting.

Abstract:
keep_len="250">NWP (numerical weather prediction) and statistical methods still have limitations in forecasting low visibility. Therefore, enhancing nowcasting techniques is crucial for ensuring the safety of daily life and industrial activities. A short-term visibility forecast model in Jiangsu Province PhyDNet-ATT-VIS is established based on PhyDNet-ATT (physical dynamics network with attention) deep learning method, using high spatial and temporal resolution ground observations and NWP data. Ground observations contain 1 min average horizontal visibility from automatic weather stations in Jiangsu Province, as well as its upstream regions of Anhui Province, Henan Province, and Shandong Province. NWP data are derived from PWAFS (Precision Weather Analysis and Forecast System) gridded meteorological forecast data. Temporal resolution and spatial resolutions of PWAFS forecast products are 1 h and 3 km, respectively. Due to PhyDNet-ATT-VIS's outstanding ability to handle nonlinear problems, visibility forecast from 6 h to 18 h with a spatial resolution of 3 km and a temporal resolution of 1 h is achieved, and model results are tested and evaluated. The visibility forecasting product of ECMWF (European Centre for Medium-Range Weather Forecasts) is selected to evaluate and compare the forecasting skills with that of PhyDNet-ATT-VIS. The initial forecast time of ECMWF is 0800 BT and 2000 BT every day, with the time interval of 3 h and the spatial resolution of 0.125°×0.125°. Compared to ECMWF visibility product, the rootmean square error (RMSE) and mean absolute error (MAE) of PhyDNet-ATT-VIS are reduced by 201% and 310%, respectively. Across different visibility levels, the probability of detection (POD) significantly improves, while the false alarm ratio (FAR) substantially decreases. Threat score (TS) of the forecast demonstrates a clear advantage, although the model's ability to predict low visibility (defined as visibility less than 0.2 km) in 15 h to 18 h range still requires further improvement. In terms of spatial distribution, the forecast error for visibility in lake and coastal areas is significantly lower than that in other regions for NWP. For PhyDNet-ATT-VIS, the error in areas with dense observation sites is significantly lower than that in regions with sparse observation sites. Compared to ECMWF model, PhyDNet-ATT-VIS can more accurately predict key characteristic parameters, such as affected areas, intensity, onset, and dissipation, in both regional and local fog processes. This study can provide a reliable and operationally replicable reference for improving short-term forecasting skills in visibility. NWP (numerical weather prediction) and statistical methods still have limitations in forecasting low visibility. Therefore, enhancing nowcasting techniques is crucial for ensuring the safety of daily life and industrial activities. A short-term visibility forecast model in Jiangsu Province PhyDNet-ATT-VIS is established based on PhyDNet-ATT (physical dynamics network with attention) deep learning method, using high spatial and temporal resolution ground observations and NWP data. Ground observations contain 1 min average horizontal visibility from automatic weather stations in Jiangsu Province, as well as its upstream regions of Anhui Province, Henan Province, and Shandong Province. NWP data are derived from PWAFS (Precision Weather Analysis and Forecast System) gridded meteorological forecast data. Temporal resolution and spatial resolutions of PWAFS forecast products are 1 h and 3 km, respectively. Due to PhyDNet-ATT-VIS's outstanding ability to handle nonlinear problems, visibility forecast from 6 h to 18 h with a spatial resolution of 3 km and a temporal resolution of 1 h is achieved, and model results are tested and evaluated. The visibility forecasting product of ECMWF (European Centre for Medium-Range Weather Forecasts) is selected to evaluate and compare the forecasting skills with that of PhyDNet-ATT-VIS. The initial forecast time of ECMWF is 0800 BT and 2000 BT every day, with the time interval of 3 h and the spatial resolution of 0.125°×0.125°. Compared to ECMWF visibility product, the rootmean square error (RMSE) and mean absolute error (MAE) of PhyDNet-ATT-VIS are reduced by 201% and 310%, respectively. Across different visibility levels, the probability of detection (POD) significantly improves, while the false alarm ratio (FAR) substantially decreases. Threat score (TS) of the forecast demonstrates a clear advantage, although the model's ability to predict low visibility (defined as visibility less than 0.2 km) in 15 h to 18 h range still requires further improvement. In terms of spatial distribution, the forecast error for visibility in lake and coastal areas is significantly lower than that in other regions for NWP. For PhyDNet-ATT-VIS, the error in areas with dense observation sites is significantly lower than that in regions with sparse observation sites. Compared to ECMWF model, PhyDNet-ATT-VIS can more accurately predict key characteristic parameters, such as affected areas, intensity, onset, and dissipation, in both regional and local fog processes. This study can provide a reliable and operationally replicable reference for improving short-term forecasting skills in visibility.

Abstract:
keep_len="250">High spatial-temporal resolution observations of precipitation from automatic weather stations (AWSs)serve as a vital data source, extensively utilized in research activities such as severe weather monitoring, model evaluation, and forecast analysis. Influenced by factors such as observation environments and equipment performance, precipitation observations inevitably contain various forms of random and systematic errors. A quality control method (multi-source data collaborative quality control, MDC) has been established for hourly precipitation data from AWSs in China, based on high spatial-temporal resolution radar data and weather phenomena. The MDC includes three modules: Precipitation self-detection, multi-source data collaborative detection, and dynamic blacklisting. The MDC has been applied to quality control of hourly precipitation data from AWSs from 2021 to 2023. A comprehensive effectiveness assessment of the method has been conducted using a combination of quantitative indicators and case analyses of detection effects on various types of erroneous data. Results indicate that the correct identification rate of the MDC reaches 99.92%, with a false exclusion rate of 0.08%. The majority of falsely excluded data consists of weak precipitation amounts ranging from 0.1-1 mm, accounting for 60.72%. While ensuring a high correct rate, the MDC also demonstrates a high capability in identifying erroneous data. The average error data hit rate of the MDC in China is 39.8%, which represents an improvement of 39.3% over the existing Meteorological Ddata Operatioin System (MDOS) real-time quality control system. The ability MDC to identify erroneous data between 0-50 mm is approximately 40%, and this hit rate significantly increases with higher precipitation values. When precipitation amounts exceed 100 mm, the hit rate achieves 100%. MDOS real-time quality control system has an almost zero hit rate for erroneous data with precipitation amounts less than 20 mm but possesses some identification capability for abnormal precipitation of more than 20 mm.The hit rate of the MDC shows significant spatial variation due to the coverage of radar and national station observations. In the eastern region, where observation stations are densely distributed, most stations have an error data hit rate of over 90%. However, in the western and northeastern regions, where observation stations are sparse and do not meet the conditions for multi-source collaborative detection, the hit rate of the MDC decreases significantly, approaching that of the MDOS real-time quality control. Case analyses of the quality control effects on different types of erroneous data reveal that the MDC significantly the identification ability of abnormal data such as clear sky precipitation, snowmelt precipitation, and false zero value precipitation, effectively making up for the deficiencies of traditional methods. High spatial-temporal resolution observations of precipitation from automatic weather stations (AWSs)serve as a vital data source, extensively utilized in research activities such as severe weather monitoring, model evaluation, and forecast analysis. Influenced by factors such as observation environments and equipment performance, precipitation observations inevitably contain various forms of random and systematic errors. A quality control method (multi-source data collaborative quality control, MDC) has been established for hourly precipitation data from AWSs in China, based on high spatial-temporal resolution radar data and weather phenomena. The MDC includes three modules: Precipitation self-detection, multi-source data collaborative detection, and dynamic blacklisting. The MDC has been applied to quality control of hourly precipitation data from AWSs from 2021 to 2023. A comprehensive effectiveness assessment of the method has been conducted using a combination of quantitative indicators and case analyses of detection effects on various types of erroneous data. Results indicate that the correct identification rate of the MDC reaches 99.92%, with a false exclusion rate of 0.08%. The majority of falsely excluded data consists of weak precipitation amounts ranging from 0.1-1 mm, accounting for 60.72%. While ensuring a high correct rate, the MDC also demonstrates a high capability in identifying erroneous data. The average error data hit rate of the MDC in China is 39.8%, which represents an improvement of 39.3% over the existing Meteorological Ddata Operatioin System (MDOS) real-time quality control system. The ability MDC to identify erroneous data between 0-50 mm is approximately 40%, and this hit rate significantly increases with higher precipitation values. When precipitation amounts exceed 100 mm, the hit rate achieves 100%. MDOS real-time quality control system has an almost zero hit rate for erroneous data with precipitation amounts less than 20 mm but possesses some identification capability for abnormal precipitation of more than 20 mm.The hit rate of the MDC shows significant spatial variation due to the coverage of radar and national station observations. In the eastern region, where observation stations are densely distributed, most stations have an error data hit rate of over 90%. However, in the western and northeastern regions, where observation stations are sparse and do not meet the conditions for multi-source collaborative detection, the hit rate of the MDC decreases significantly, approaching that of the MDOS real-time quality control. Case analyses of the quality control effects on different types of erroneous data reveal that the MDC significantly the identification ability of abnormal data such as clear sky precipitation, snowmelt precipitation, and false zero value precipitation, effectively making up for the deficiencies of traditional methods.

Abstract:
keep_len="250">The slope angle (or top angle) of the building significantly influences the formation of upward leader. To simulate the lightning strike process on tall and sloping building using three-dimensional variable grid leader model, the influence of slope angle (θ) on the initiation of stable upward leaders is analyzed, focusing on various building heights (H_b) and peak values of lightning current (I_p). It can be concluded from data that, when the peak lightning current is held constant, reducing the building height and increasing the building width result in an enhanced influence of the slope angle on stable upward leader inception (η), which in turn makes it increasingly challenging to incept. When the height of building is held constant, a reduction in the peak value of lightning current enhances the influence of slope angle on the inception of stable upward leaders (η). This, in turn, makes the inception process increasingly challenging. Changes in building width have a lesser impact on the initiation of the upward leader compared to building height. As heights of buildings and peak values of lightning currents increase, the influence of slope angle on the initiation of stable upward leader becomes less significant. By conducting a multiple linear regression analysis with η as the dependent variable and slope angle (θ), building height (H_b), and peak lightning current (I_p) as independent variables. Results indicate that I_p and H_b have a significant negative effect on η, whereas θ has a positive effect on η. The degree of influence on η is as follows: I_p has the greatest influence, followed by θ, while Hb has the least influence. The influence of slope angle on the inception of stable upward leaders, represented by the parameter η, is significant for building heights below 100 m and peak lightning current values below 40 kA, with the estimated effect exceeding 23.32%. In contrast, for building heights exceeding 500 m and peak values of lightning current above 100 kA, the impact of slope angle on stable upward leader inception is relatively minimal, with an estimated effect of less than 15.88%, in the case, the distinction between the impact of sloped buildings and rectangular buildings on the inception of stable upward leader is sufficiently marginal to enable an approximate analytical approach. The slope angle (or top angle) of the building significantly influences the formation of upward leader. To simulate the lightning strike process on tall and sloping building using three-dimensional variable grid leader model, the influence of slope angle (θ) on the initiation of stable upward leaders is analyzed, focusing on various building heights (H_b) and peak values of lightning current (I_p). It can be concluded from data that, when the peak lightning current is held constant, reducing the building height and increasing the building width result in an enhanced influence of the slope angle on stable upward leader inception (η), which in turn makes it increasingly challenging to incept. When the height of building is held constant, a reduction in the peak value of lightning current enhances the influence of slope angle on the inception of stable upward leaders (η). This, in turn, makes the inception process increasingly challenging. Changes in building width have a lesser impact on the initiation of the upward leader compared to building height. As heights of buildings and peak values of lightning currents increase, the influence of slope angle on the initiation of stable upward leader becomes less significant. By conducting a multiple linear regression analysis with η as the dependent variable and slope angle (θ), building height (H_b), and peak lightning current (I_p) as independent variables. Results indicate that I_p and H_b have a significant negative effect on η, whereas θ has a positive effect on η. The degree of influence on η is as follows: I_p has the greatest influence, followed by θ, while Hb has the least influence. The influence of slope angle on the inception of stable upward leaders, represented by the parameter η, is significant for building heights below 100 m and peak lightning current values below 40 kA, with the estimated effect exceeding 23.32%. In contrast, for building heights exceeding 500 m and peak values of lightning current above 100 kA, the impact of slope angle on stable upward leader inception is relatively minimal, with an estimated effect of less than 15.88%, in the case, the distinction between the impact of sloped buildings and rectangular buildings on the inception of stable upward leader is sufficiently marginal to enable an approximate analytical approach.

Abstract:
keep_len="250">Catalysts for warm cloud seeding have significant potential for applications in warm cloud catalytic precipitation and fog elimination. In recent years, numerous innovative materials have been developed, each with the potential to be used in warm cloud catalysis. However, a universally recognized ideal formulation has not yet been established. It is necessary to conduct a scientific analysis on the performance of various hygroscopic catalysts under consistent experimental conditions.Therefore, 9 types of typical powdered hygroscopic catalysts are collected, and experiments involving fog chamber and static testing of catalysts are conducted at CMA Key Laboratory of Cloud-precipitation Physics and Weather Modification in May 2023. Fog elimination capabilities and hygroscopic characteristics of various catalysts are comprehensively evaluated and compared. Results indicate that, under the same fog conditions, salt type catalysts demonstrate the shortest time to eliminate fog, and porous composite materials (PCM-100 and PCM-10) are also effective, while fog elimination effects of modified starch, molecular sieves, organic bentonite and sodium bentonite are not obvious after seeding. In static detection, under normal temperature and humidity conditions, CaCl₂ exhibits the strongest static hygroscopicity, followed by porous composite materials such as PCM-100 and PCM-10. Composite salt catalysts exhibit strong hygroscopic absorption, whereas the static hygroscopic absorption capacity of other catalysts is not as pronounced. In high humidity conditions, bentonite and molecular sieve catalysts still do not exhibit moisture absorption characteristics. The hygroscopic abilities of CaCl₂, PCM-100, and PCM-10 are significantly higher than those of other catalysts. The performance of various catalysts in the fog chamber experiment and static detection is basically consistent.Microstructures of various catalysts with strong hygroscopic properties do not show significant changes after 30 minutes. PCM-10 primarily exists in the form of liquid droplets after standing for 5 minutes and can continue to provide hydration. PCM-100 remains in irregular crystal form after 5 minutes and transforms into droplets after 30 minutes. CaCl₂ absorbs moisture rapidly under the microscope, initially existing as liquid droplets for 5 minutes. Subsequently, there are no significant changes, primarily small droplets. Complex salts always form crystals, while the size of liquid droplets formed is larger.It should be noted that, although the hygroscopicity and the ability of fog elimination of various catalysts are important indicators, the dispersibility, corrosiveness and ease of preparation and storage of the catalyst are also important parameters of whether they can be used as efficient warm cloud catalysts. Follow-up research and evaluation of various catalysts will be conducted. Catalysts for warm cloud seeding have significant potential for applications in warm cloud catalytic precipitation and fog elimination. In recent years, numerous innovative materials have been developed, each with the potential to be used in warm cloud catalysis. However, a universally recognized ideal formulation has not yet been established. It is necessary to conduct a scientific analysis on the performance of various hygroscopic catalysts under consistent experimental conditions.Therefore, 9 types of typical powdered hygroscopic catalysts are collected, and experiments involving fog chamber and static testing of catalysts are conducted at CMA Key Laboratory of Cloud-precipitation Physics and Weather Modification in May 2023. Fog elimination capabilities and hygroscopic characteristics of various catalysts are comprehensively evaluated and compared. Results indicate that, under the same fog conditions, salt type catalysts demonstrate the shortest time to eliminate fog, and porous composite materials (PCM-100 and PCM-10) are also effective, while fog elimination effects of modified starch, molecular sieves, organic bentonite and sodium bentonite are not obvious after seeding. In static detection, under normal temperature and humidity conditions, CaCl₂ exhibits the strongest static hygroscopicity, followed by porous composite materials such as PCM-100 and PCM-10. Composite salt catalysts exhibit strong hygroscopic absorption, whereas the static hygroscopic absorption capacity of other catalysts is not as pronounced. In high humidity conditions, bentonite and molecular sieve catalysts still do not exhibit moisture absorption characteristics. The hygroscopic abilities of CaCl₂, PCM-100, and PCM-10 are significantly higher than those of other catalysts. The performance of various catalysts in the fog chamber experiment and static detection is basically consistent.Microstructures of various catalysts with strong hygroscopic properties do not show significant changes after 30 minutes. PCM-10 primarily exists in the form of liquid droplets after standing for 5 minutes and can continue to provide hydration. PCM-100 remains in irregular crystal form after 5 minutes and transforms into droplets after 30 minutes. CaCl₂ absorbs moisture rapidly under the microscope, initially existing as liquid droplets for 5 minutes. Subsequently, there are no significant changes, primarily small droplets. Complex salts always form crystals, while the size of liquid droplets formed is larger.It should be noted that, although the hygroscopicity and the ability of fog elimination of various catalysts are important indicators, the dispersibility, corrosiveness and ease of preparation and storage of the catalyst are also important parameters of whether they can be used as efficient warm cloud catalysts. Follow-up research and evaluation of various catalysts will be conducted.

Abstract:
keep_len="250">Under the harsh environmental conditions characterized by intense ultraviolet radiation and elevated ozone (O₃) background, the temporal dynamics of atmospheric O₃ concentrations and their associated environmental ramifications in the densely populated and emission-concentrated urban regions of the Tibetan Plateau have garnered considerable scientific interest. This comprehensive study meticulously compiles O₃ concentration data spanning 2015 to 2023 from 7 cities of Xizang, conducting rigorous trend analyses and employing a robust suite of 13 risk assessment indicators to gauge the implications for human health and ecological vegetation. It shows that O₃ concentrations of these cities demonstrate significant geographical variations, with the central city of Lhasa recording the highest O₃ mass concentration, while those in the southern cities of Shannan and Rikaze are relatively lower. O₃ concentrations of Nagqu, located in the north, are comparable to those of Lhasa and are significantly higher than those of Ali in the west, as well as those of Linzhi and Changdu in the east Plateau. O₃ concentrations of Changdu and Linzhi peak in June and March-April, respectively, while the other cities reach their peaks in May. Since 2015, interannual variations in O₃ concentrations of Ali, Nagqu, Lhasa, and Linzhi do not show statistically significant trends. In contrast, Shannan, Rikaze, and Changdu experience significant increases in concentration. Specifically, AMDA8_max and AMDA8_4th in Rikaze and Changdu increase significantly, whereas the other cities show decreasing trends. Similarly, both NDGT90 and NDGT70 exhibit comparable trends. SOMO35 indicator, which indicates human health risks, and AOT40 and W126 indicators, which are closely related to ecological vegetation and crop growth, show a high degree of consistency in their trends relative to diurnal O₃ concentration changes. In Lhasa, values of these indicators exceed safety thresholds, particularly during spring and summer, highlighting the combined effect of high background O₃ concentrations in the Plateau and intensified O₃ photochemical formation due to anthropogenic emissions, posing potential threats to human health and ecosystems. Although the current O₃-related risk indicators of Rikaze and Changdu have not yet reached critical levels, their significant upward trends should not be overlooked. With the continuous rise in anthropogenic pollutant emissions in the region, adverse effects of O₃ photochemical formation are anticipated to intensify. Therefore, there is an urgent need to enhance monitoring and assessment in these cities and to implement effective measures to mitigate or control O₃ pollution, thereby safeguarding regional environmental security and promoting sustainable development. Under the harsh environmental conditions characterized by intense ultraviolet radiation and elevated ozone (O₃) background, the temporal dynamics of atmospheric O₃ concentrations and their associated environmental ramifications in the densely populated and emission-concentrated urban regions of the Tibetan Plateau have garnered considerable scientific interest. This comprehensive study meticulously compiles O₃ concentration data spanning 2015 to 2023 from 7 cities of Xizang, conducting rigorous trend analyses and employing a robust suite of 13 risk assessment indicators to gauge the implications for human health and ecological vegetation. It shows that O₃ concentrations of these cities demonstrate significant geographical variations, with the central city of Lhasa recording the highest O₃ mass concentration, while those in the southern cities of Shannan and Rikaze are relatively lower. O₃ concentrations of Nagqu, located in the north, are comparable to those of Lhasa and are significantly higher than those of Ali in the west, as well as those of Linzhi and Changdu in the east Plateau. O₃ concentrations of Changdu and Linzhi peak in June and March-April, respectively, while the other cities reach their peaks in May. Since 2015, interannual variations in O₃ concentrations of Ali, Nagqu, Lhasa, and Linzhi do not show statistically significant trends. In contrast, Shannan, Rikaze, and Changdu experience significant increases in concentration. Specifically, AMDA8_max and AMDA8_4th in Rikaze and Changdu increase significantly, whereas the other cities show decreasing trends. Similarly, both NDGT90 and NDGT70 exhibit comparable trends. SOMO35 indicator, which indicates human health risks, and AOT40 and W126 indicators, which are closely related to ecological vegetation and crop growth, show a high degree of consistency in their trends relative to diurnal O₃ concentration changes. In Lhasa, values of these indicators exceed safety thresholds, particularly during spring and summer, highlighting the combined effect of high background O₃ concentrations in the Plateau and intensified O₃ photochemical formation due to anthropogenic emissions, posing potential threats to human health and ecosystems. Although the current O₃-related risk indicators of Rikaze and Changdu have not yet reached critical levels, their significant upward trends should not be overlooked. With the continuous rise in anthropogenic pollutant emissions in the region, adverse effects of O₃ photochemical formation are anticipated to intensify. Therefore, there is an urgent need to enhance monitoring and assessment in these cities and to implement effective measures to mitigate or control O₃ pollution, thereby safeguarding regional environmental security and promoting sustainable development.

Abstract:
keep_len="250">The Tibetan Plateau, located in the mid-latitude region of the Asian continent, is commonly referred to as the third pole and the water tower of Asia. The high-altitude terrain and distinct circulation systems contribute to the formation of an ozone valley in the atmosphere above the Plateau. The discovery of this ozone valley has garnered significant attention from the international scientific community regarding the ozone levels over the Tibetan Plateau. At the same time, in the context of global warming, the increase of surface ozone concentrations in various regions of the world has posed significant threats to both human health and ecological environment. However, due to limited observational data and satellite data of short time scales, past studies on the Tibetan Plateau's ozone primarily focused on total column ozone, the upper troposphere and lower stratosphere, or surface ozone, while fewer studies have examined the tropospheric ozone column over the region. Therefore, longer-term data are needed to investigate the interannual variability and influencing factors of the tropospheric ozone column over the Tibetan Plateau. Based on AIRS (atmospheric infrared sounder) satellite data from 2003 to 2022, the tropospheric ozone column over the Tibetan Plateau during summer seasons from 2003 to 2022, as well as its interannual variation characteristics are analyzed. Additionally, using ERA5 reanalysis data from 2003 to 2022 and surface station data from China's Ministry of Ecology and Environment from 2015 to 2022, the study employs composite and correlation analyses to explore the factors influencing the tropospheric ozone column over the region. Results show that the tropospheric ozone column over the Tibetan Plateau during the summer exhibits significant interannual variability, increasing at a rate of approximately 0.08 DU per year. The difference of total tropospheric ozone column in high and low years is not only directly related to the difference of vertical distribution of ozone in the upper and lower troposphere, but also related to the difference of dynamic and chemical processes in the upper and lower troposphere. When the total tropospheric ozone column over the Tibetan Plateau is elevated, the tropopause on the northern side of the Plateau is lower, and the subtropical westerly jet is weak and fragmented. The weak transmission barrier enhances stratospheric tropospheric exchange, which is beneficial to the downward transmission of stratospheric high-concentration ozone air, while the vertical circulation in the lower troposphere affects the ozone concentration in the whole troposphere by upward transmission of low-concentration ozone air in the lower troposphere. The tropospheric ozone column anomaly in the northern Plateau is primarily associated with tropopause folding, while the lower ozone concentration across the entire troposphere in the southwestern Plateau is linked to anomalies in the South Asian High. The elevated tropospheric ozone column over the central Plateau may be associated with unusually high levels of surface solar radiation and emissions from surface pollutants. The Tibetan Plateau, located in the mid-latitude region of the Asian continent, is commonly referred to as the third pole and the water tower of Asia. The high-altitude terrain and distinct circulation systems contribute to the formation of an ozone valley in the atmosphere above the Plateau. The discovery of this ozone valley has garnered significant attention from the international scientific community regarding the ozone levels over the Tibetan Plateau. At the same time, in the context of global warming, the increase of surface ozone concentrations in various regions of the world has posed significant threats to both human health and ecological environment. However, due to limited observational data and satellite data of short time scales, past studies on the Tibetan Plateau's ozone primarily focused on total column ozone, the upper troposphere and lower stratosphere, or surface ozone, while fewer studies have examined the tropospheric ozone column over the region. Therefore, longer-term data are needed to investigate the interannual variability and influencing factors of the tropospheric ozone column over the Tibetan Plateau. Based on AIRS (atmospheric infrared sounder) satellite data from 2003 to 2022, the tropospheric ozone column over the Tibetan Plateau during summer seasons from 2003 to 2022, as well as its interannual variation characteristics are analyzed. Additionally, using ERA5 reanalysis data from 2003 to 2022 and surface station data from China's Ministry of Ecology and Environment from 2015 to 2022, the study employs composite and correlation analyses to explore the factors influencing the tropospheric ozone column over the region. Results show that the tropospheric ozone column over the Tibetan Plateau during the summer exhibits significant interannual variability, increasing at a rate of approximately 0.08 DU per year. The difference of total tropospheric ozone column in high and low years is not only directly related to the difference of vertical distribution of ozone in the upper and lower troposphere, but also related to the difference of dynamic and chemical processes in the upper and lower troposphere. When the total tropospheric ozone column over the Tibetan Plateau is elevated, the tropopause on the northern side of the Plateau is lower, and the subtropical westerly jet is weak and fragmented. The weak transmission barrier enhances stratospheric tropospheric exchange, which is beneficial to the downward transmission of stratospheric high-concentration ozone air, while the vertical circulation in the lower troposphere affects the ozone concentration in the whole troposphere by upward transmission of low-concentration ozone air in the lower troposphere. The tropospheric ozone column anomaly in the northern Plateau is primarily associated with tropopause folding, while the lower ozone concentration across the entire troposphere in the southwestern Plateau is linked to anomalies in the South Asian High. The elevated tropospheric ozone column over the central Plateau may be associated with unusually high levels of surface solar radiation and emissions from surface pollutants.

Abstract:
keep_len="250">In the context of China's commitment to improve air quality enhancement, cities within severe cold climate zones, exemplified by Harbin continue to confront the exigent issue of fine particulate matter (PM_2.5) pollution, notably during winter. Despite nationwide initiatives aimed at mitigating air pollution, Harbin's unique geographical and climatic conditions, combined with its predominant economic activities, have led to consistently high levels of PM_2.5 during winter. An episode of severe PM_2.5 pollution is observed in January 2020, with the monthly average mass concentration of PM_2.5 peaking at 155.0 μg·m^-3, significantly surpassing national standards. To explore the mechanism of this severe PM_2.5 pollution episode, an integrated analysis of chemical compositions and influencing factors is conducted during this period. In the meantime, methods of backward trajectory clustering and the weighted potential source contribution function (WPSCF) are employed to investigate source areas and transport pathways of air pollutants. Results indicate that the severe PM_2.5 pollution in Harbin mainly originates from primary emissions, with biomass burning contributing significantly. During the observation period, the concentration of levoglucosan in PM_2.5, a common tracer of biomass burning, reaches as high as 1.1 μg·m^-3, which is 3.7 to 5.5 times higher than that in other regions experiencing severe biomass burning pollution during winter. Furthermore, research findings indicate that meteorological conditions play a significant role in exacerbating PM_2.5 pollution in Harbin. High relative humidity (averaging at 80.0%) combined with extremely low temperatures (averaging at -18.0 ℃) provided favorable conditions for secondary aerosol formation. Under such low-temperature and high-humidity conditions, the average sulfur oxidation rate reaches as high as 25.6%, and the nitrogen oxidation rate reaches 10.8%. It significantly increases the contribution of secondary aerosols to PM_2.5 in Harbin. Additionally, this study also reveals the impact of regional transportation on the air quality of Harbin. It indicates that the air quality of Harbin is influenced not only by local emissions but also by the transportation of pollutants from neighboring cities such as Suihua, Daqing, Changchun, and Songyuan. The inter-city transfer of pollution highlights the close connection of air pollution issues within the region. Through the comprehensive analysis of causes of a severe PM_2.5 pollution event in Harbin during winter from multiple perspectives, a scientific basis is provided for understanding causes for air pollution in similar cold climate. In the context of China's commitment to improve air quality enhancement, cities within severe cold climate zones, exemplified by Harbin continue to confront the exigent issue of fine particulate matter (PM_2.5) pollution, notably during winter. Despite nationwide initiatives aimed at mitigating air pollution, Harbin's unique geographical and climatic conditions, combined with its predominant economic activities, have led to consistently high levels of PM_2.5 during winter. An episode of severe PM_2.5 pollution is observed in January 2020, with the monthly average mass concentration of PM_2.5 peaking at 155.0 μg·m^-3, significantly surpassing national standards. To explore the mechanism of this severe PM_2.5 pollution episode, an integrated analysis of chemical compositions and influencing factors is conducted during this period. In the meantime, methods of backward trajectory clustering and the weighted potential source contribution function (WPSCF) are employed to investigate source areas and transport pathways of air pollutants. Results indicate that the severe PM_2.5 pollution in Harbin mainly originates from primary emissions, with biomass burning contributing significantly. During the observation period, the concentration of levoglucosan in PM_2.5, a common tracer of biomass burning, reaches as high as 1.1 μg·m^-3, which is 3.7 to 5.5 times higher than that in other regions experiencing severe biomass burning pollution during winter. Furthermore, research findings indicate that meteorological conditions play a significant role in exacerbating PM_2.5 pollution in Harbin. High relative humidity (averaging at 80.0%) combined with extremely low temperatures (averaging at -18.0 ℃) provided favorable conditions for secondary aerosol formation. Under such low-temperature and high-humidity conditions, the average sulfur oxidation rate reaches as high as 25.6%, and the nitrogen oxidation rate reaches 10.8%. It significantly increases the contribution of secondary aerosols to PM_2.5 in Harbin. Additionally, this study also reveals the impact of regional transportation on the air quality of Harbin. It indicates that the air quality of Harbin is influenced not only by local emissions but also by the transportation of pollutants from neighboring cities such as Suihua, Daqing, Changchun, and Songyuan. The inter-city transfer of pollution highlights the close connection of air pollution issues within the region. Through the comprehensive analysis of causes of a severe PM_2.5 pollution event in Harbin during winter from multiple perspectives, a scientific basis is provided for understanding causes for air pollution in similar cold climate.

Abstract:
keep_len="250">Simulating growth processes of greenhouse crops under different environmental factors is one of the important means for planning cultivation and predicting yield in greenhouse production. Tomatoes are main greenhouse plants in northern China, characterized by high nutritional value and strong adaptability to cultivation. Clarifying the quantitative relationship between the growth indicators of greenhouse tomatoes and microclimate environmental factors is of great significance for improving the economic benefits. Utilizing environmental factors and various growth indicators of tomatoes, Logistic growth models are constructed by taking accumulated radiation, effective accumulated temperature, and suitability index as independent variables, and different growth indicators of tomatoes as dependent variables. Subsequently, models are validated using independent data. By comparing the precision of three models in simulating different tomato growth indicators, advantages and disadvantages of each model are analyzed to select the optimal model for different stages of tomato development. It provides a more precise theoretical basis for meteorological services and tomato yield prediction. Results show that greenhouse tomatoes are not sensitive to light during the flowering period. Therefore, choosing the accumulated temperature method to establish a logistic model yields the best simulation of the number of flowers. In the second inflorescence of tomatoes, the limit value of the number of flowers is 5.4; The accumulated radiation required to reach this limit is 146.6 mol·m^-2, the effective accumulated temperature is 73.3 ℃, and the suitability index is 15.1. Main meteorological factors affecting the number of fruit sets in tomatoes are light, temperature, and humidity. Therefore, using the suitability method to establish a logistic model achieves the highest accuracy in simulating this. The maximum number of fruit sets in the second inflorescence of tomatoes is 5.0; the accumulated radiation required to reach this limit is 146.9 mol·m^-2, the effective accumulated temperature is 47.1 ℃ and the suitability index is 14.6. Tomato fruit growth is mainly related to photosynthetically active radiation and temperature; therefore, choosing the accumulated radiation method provides the highest precision in simulating tomato fruit growth. The maximum transverse diameter of the tomato fruit is 51.6 mm, requiring accumulated radiation, effective accumulated temperature, and suitability index of 230.0 mol·m^-2, 69.6 ℃, and 18.8. The maximum longitudinal diameter of the tomato fruit is 74.9 mm, requiring accumulated radiation, effective accumulated temperature, and suitability index of 252.0 mol·m^-2, 69.6 ℃, and 18.8, respectively. Overall, the effective accumulated temperature model has fewer parameters and is simple and convenient to calculate, showing significant effectiveness in simulating the non-light-sensitive developmental stages of crops. The accumulated radiation method has higher accuracy, but involves a complex calculation process and greater difficulty in data acquisition. On the contrary, selecting the suitability method, which involves relatively simple data acquisition and incorporates more environmental factors, for simulation can also achieve relatively accurate results, making it more cost-effective in practical applications. Simulating growth processes of greenhouse crops under different environmental factors is one of the important means for planning cultivation and predicting yield in greenhouse production. Tomatoes are main greenhouse plants in northern China, characterized by high nutritional value and strong adaptability to cultivation. Clarifying the quantitative relationship between the growth indicators of greenhouse tomatoes and microclimate environmental factors is of great significance for improving the economic benefits. Utilizing environmental factors and various growth indicators of tomatoes, Logistic growth models are constructed by taking accumulated radiation, effective accumulated temperature, and suitability index as independent variables, and different growth indicators of tomatoes as dependent variables. Subsequently, models are validated using independent data. By comparing the precision of three models in simulating different tomato growth indicators, advantages and disadvantages of each model are analyzed to select the optimal model for different stages of tomato development. It provides a more precise theoretical basis for meteorological services and tomato yield prediction. Results show that greenhouse tomatoes are not sensitive to light during the flowering period. Therefore, choosing the accumulated temperature method to establish a logistic model yields the best simulation of the number of flowers. In the second inflorescence of tomatoes, the limit value of the number of flowers is 5.4; The accumulated radiation required to reach this limit is 146.6 mol·m^-2, the effective accumulated temperature is 73.3 ℃, and the suitability index is 15.1. Main meteorological factors affecting the number of fruit sets in tomatoes are light, temperature, and humidity. Therefore, using the suitability method to establish a logistic model achieves the highest accuracy in simulating this. The maximum number of fruit sets in the second inflorescence of tomatoes is 5.0; the accumulated radiation required to reach this limit is 146.9 mol·m^-2, the effective accumulated temperature is 47.1 ℃ and the suitability index is 14.6. Tomato fruit growth is mainly related to photosynthetically active radiation and temperature; therefore, choosing the accumulated radiation method provides the highest precision in simulating tomato fruit growth. The maximum transverse diameter of the tomato fruit is 51.6 mm, requiring accumulated radiation, effective accumulated temperature, and suitability index of 230.0 mol·m^-2, 69.6 ℃, and 18.8. The maximum longitudinal diameter of the tomato fruit is 74.9 mm, requiring accumulated radiation, effective accumulated temperature, and suitability index of 252.0 mol·m^-2, 69.6 ℃, and 18.8, respectively. Overall, the effective accumulated temperature model has fewer parameters and is simple and convenient to calculate, showing significant effectiveness in simulating the non-light-sensitive developmental stages of crops. The accumulated radiation method has higher accuracy, but involves a complex calculation process and greater difficulty in data acquisition. On the contrary, selecting the suitability method, which involves relatively simple data acquisition and incorporates more environmental factors, for simulation can also achieve relatively accurate results, making it more cost-effective in practical applications.