Abstract:
keep_len="250">As a main economic crop in China, tea plant is prone to frost disaster when it germinates in early spring. The research progress and main achievements of tea plant spring frost disaster are systematically summarized. In southern Yangtze River, the research on disaster index and risk assessment of tea plant spring frost disaster is abundant. As for the spatio-temporal distribution characteristics of the disaster, most of the existing studies are based on regional scales and provincial scales, focusing on Southern Yangtze River. There are few reports on the spatio-temporal distribution on the national scale. Tea plant spring frost disaster index can be divided into three categories based on classification criteria. According to data acquisition method, it can be classified into morphological index and laboratory physiological morphological index. According to data category, it can be determined only by meteorological data, or by both meteorological data and tea plant disaster symptoms. According to the time scale of meteorological data, it can be further subdivided into daily scale and hourly scale. In the context of global warming, although the frequency of tea plant spring frost is declining, its harm cannot be ignored. The frequency of tea plant spring frost in Southern Yangtze River shows latitudinal distribution characteristic that gradually increases from south to north. And the topographic distribution characteristic is also revealed that the frequency of tea plant spring frost increases with elevated altitude. For the impact assessment of tea plant spring frost, the areas, including Jiangsu, Zhejiang, Anhui, and Jiangxi provinces, are paid close attention, and the research method is gradually developing from qualitative assessment to quantitative assessment. The risk assessment methods of tea plant spring frost mainly include fuzzy mathematics method and information diffusion method. In recently researches, risk levels are determined by models based on the natural disaster risk formation mechanism. In the future, the construction of tea spring frost disaster index based on the micro-climate factors in tea gardens will become hotspot. Furthermore, the meteorological index of tea spring frost should be improved, and the spatiotemporal distribution characteristics on national scale, and the refined risk assessment will also be paid more attention. As a main economic crop in China, tea plant is prone to frost disaster when it germinates in early spring. The research progress and main achievements of tea plant spring frost disaster are systematically summarized. In southern Yangtze River, the research on disaster index and risk assessment of tea plant spring frost disaster is abundant. As for the spatio-temporal distribution characteristics of the disaster, most of the existing studies are based on regional scales and provincial scales, focusing on Southern Yangtze River. There are few reports on the spatio-temporal distribution on the national scale. Tea plant spring frost disaster index can be divided into three categories based on classification criteria. According to data acquisition method, it can be classified into morphological index and laboratory physiological morphological index. According to data category, it can be determined only by meteorological data, or by both meteorological data and tea plant disaster symptoms. According to the time scale of meteorological data, it can be further subdivided into daily scale and hourly scale. In the context of global warming, although the frequency of tea plant spring frost is declining, its harm cannot be ignored. The frequency of tea plant spring frost in Southern Yangtze River shows latitudinal distribution characteristic that gradually increases from south to north. And the topographic distribution characteristic is also revealed that the frequency of tea plant spring frost increases with elevated altitude. For the impact assessment of tea plant spring frost, the areas, including Jiangsu, Zhejiang, Anhui, and Jiangxi provinces, are paid close attention, and the research method is gradually developing from qualitative assessment to quantitative assessment. The risk assessment methods of tea plant spring frost mainly include fuzzy mathematics method and information diffusion method. In recently researches, risk levels are determined by models based on the natural disaster risk formation mechanism. In the future, the construction of tea spring frost disaster index based on the micro-climate factors in tea gardens will become hotspot. Furthermore, the meteorological index of tea spring frost should be improved, and the spatiotemporal distribution characteristics on national scale, and the refined risk assessment will also be paid more attention.

Abstract:
keep_len="250">Silver iodide (AgI) is the most widely-used seeding agent in field experiment and operations of weather modification. There are several nucleation modes for AgI seeding agents, and the nucleation process is affected by many factors including atmospheric temperature, humidity, particle size, composition of seeding agent and its particle generation method. The nucleation efficiency, nucleation modes affect the number of nucleated crystals, and thus affect the seeding effect.Through laboratory research of cloud chamber and theoretical calculation, the critical embryo sizes required for different nucleation mechanisms, nucleation threshold temperature, nucleation mechanisms, factors effecting nucleation for AgI and with some other added materials are analyzed. It is currently accepted that there are four nucleation mechanisms, including deposition, condensation freezing, contact freezing and immersion freezing nucleation. And it is recognized that nucleation processes depend on the varied temperature, humidity, and cloud conditions that can be encountered in the atmosphere. Immersion nucleation refers to nucleation of freezing by a particle immersed in water, and deposition nucleation refers to nucleation of the ice phase from the vapor, contact nucleation refers to the nucleation of the freezing induced by a particle during first contact with supercooled water, and condensation freezing is the nucleation of the freezing from the condensation of vapor to liquid droplet.The ice-nucleating properties and nucleation effectiveness of cloud seeding materials produced by burning acetone solutions or pyrotechnics of AgI and other materials are tested in a wind-tunnel cloud chamber test facility along with isothermal cloud chamber and dynamic cloud chamber. Through laboratory test, different formulations are compared, and high nucleation efficiency of seeding materials are selected, and nucleation features for four nucleation modes separately are obtained.Silver iodide seeding cloud model are based on a combination of theory and laboratory results. The ice nucleation schemes employed in cloud models vary widely. Hsie's seeding scheme simulated both contact-freezing and deposition nucleation on the laboratory measured effectiveness spectra of nucleus. Meyers seeding scheme considers all the four processes on cloud chamber results of ice-forming processes by AgI. In China, AgI seeding models are developed since the 1990s, either similar with Hsie's seeding scheme on three nucleation mechanisms, or similar with Meyers seeding scheme on four nucleation mechanisms. Silver iodide (AgI) is the most widely-used seeding agent in field experiment and operations of weather modification. There are several nucleation modes for AgI seeding agents, and the nucleation process is affected by many factors including atmospheric temperature, humidity, particle size, composition of seeding agent and its particle generation method. The nucleation efficiency, nucleation modes affect the number of nucleated crystals, and thus affect the seeding effect.Through laboratory research of cloud chamber and theoretical calculation, the critical embryo sizes required for different nucleation mechanisms, nucleation threshold temperature, nucleation mechanisms, factors effecting nucleation for AgI and with some other added materials are analyzed. It is currently accepted that there are four nucleation mechanisms, including deposition, condensation freezing, contact freezing and immersion freezing nucleation. And it is recognized that nucleation processes depend on the varied temperature, humidity, and cloud conditions that can be encountered in the atmosphere. Immersion nucleation refers to nucleation of freezing by a particle immersed in water, and deposition nucleation refers to nucleation of the ice phase from the vapor, contact nucleation refers to the nucleation of the freezing induced by a particle during first contact with supercooled water, and condensation freezing is the nucleation of the freezing from the condensation of vapor to liquid droplet.The ice-nucleating properties and nucleation effectiveness of cloud seeding materials produced by burning acetone solutions or pyrotechnics of AgI and other materials are tested in a wind-tunnel cloud chamber test facility along with isothermal cloud chamber and dynamic cloud chamber. Through laboratory test, different formulations are compared, and high nucleation efficiency of seeding materials are selected, and nucleation features for four nucleation modes separately are obtained.Silver iodide seeding cloud model are based on a combination of theory and laboratory results. The ice nucleation schemes employed in cloud models vary widely. Hsie's seeding scheme simulated both contact-freezing and deposition nucleation on the laboratory measured effectiveness spectra of nucleus. Meyers seeding scheme considers all the four processes on cloud chamber results of ice-forming processes by AgI. In China, AgI seeding models are developed since the 1990s, either similar with Hsie's seeding scheme on three nucleation mechanisms, or similar with Meyers seeding scheme on four nucleation mechanisms.

Abstract:
keep_len="250">Northern China is a main winter wheat production area and plays an important role in ensuring food security. Wheat aphids, as one kind of main agricultural pest, threaten wheat production. Based on wheat aphids disaster and prevention data, planting area and yield loss of wheat, growth period of winter wheat, and daily meteorological data at 561 observation stations from 1958 to 2018 in 8 main wheat production provinces of northern China, relationships between surface meteorological factors and the occurrence area of wheat aphids for every province in North China and Huanghuai area are fully analyzed using methods of correlation analysis, principal component analysis and stepwise regression analysis in various time-periods from last December to 10 June. Eight key meteorological factors which affect the occurrence area of wheat aphids in North China and 6 key meteorological factors for Huanghuai area are determined. The climate disaster indices of wheat aphids are established based on the normalized key meteorological factors and validated in 8 provinces. Furthermore, climatic risk aspects are assessed to explore the occurrence tendency of winter wheat aphids in northern China. The frequencies of different-level decadal climate disasters are taken as hazard index, the ratio of occurrence area of wheat aphids to the wheat-sown area is defined as vulnerability index, and the ratio of controlled area to occurrence area is calculated to measure the disaster prevention and mitigation capability.Comprehensive risk index is built by integrating hazard, vulnerability, disaster prevention and mitigation capability indexes to assess risk development trend in decades. The results show that the climatic hazard for wheat aphids tends to increase gradually and there are significant differences in different decades. The vulnerability for wheat aphids tends to be severe over time.The disaster prevention and mitigation capability for wheat aphids tends to improve gradually especially in the 1990s, and the trend slows down since 2001. The comprehensive climatic risk has been more severe and their scopes of the highest risk have been larger since the 1990s. The climatic risk is highest in Beijing, Tianjin, central-south Hebei, part of north Shandong. And it's the second highest in most of Shandong, north Henan, eastern and southern Shanxi, and north Jiangsu area, where effective measures should be taken to reduce the detriment of wheat aphids. Northern China is a main winter wheat production area and plays an important role in ensuring food security. Wheat aphids, as one kind of main agricultural pest, threaten wheat production. Based on wheat aphids disaster and prevention data, planting area and yield loss of wheat, growth period of winter wheat, and daily meteorological data at 561 observation stations from 1958 to 2018 in 8 main wheat production provinces of northern China, relationships between surface meteorological factors and the occurrence area of wheat aphids for every province in North China and Huanghuai area are fully analyzed using methods of correlation analysis, principal component analysis and stepwise regression analysis in various time-periods from last December to 10 June. Eight key meteorological factors which affect the occurrence area of wheat aphids in North China and 6 key meteorological factors for Huanghuai area are determined. The climate disaster indices of wheat aphids are established based on the normalized key meteorological factors and validated in 8 provinces. Furthermore, climatic risk aspects are assessed to explore the occurrence tendency of winter wheat aphids in northern China. The frequencies of different-level decadal climate disasters are taken as hazard index, the ratio of occurrence area of wheat aphids to the wheat-sown area is defined as vulnerability index, and the ratio of controlled area to occurrence area is calculated to measure the disaster prevention and mitigation capability.Comprehensive risk index is built by integrating hazard, vulnerability, disaster prevention and mitigation capability indexes to assess risk development trend in decades. The results show that the climatic hazard for wheat aphids tends to increase gradually and there are significant differences in different decades. The vulnerability for wheat aphids tends to be severe over time.The disaster prevention and mitigation capability for wheat aphids tends to improve gradually especially in the 1990s, and the trend slows down since 2001. The comprehensive climatic risk has been more severe and their scopes of the highest risk have been larger since the 1990s. The climatic risk is highest in Beijing, Tianjin, central-south Hebei, part of north Shandong. And it's the second highest in most of Shandong, north Henan, eastern and southern Shanxi, and north Jiangsu area, where effective measures should be taken to reduce the detriment of wheat aphids.

Abstract:
keep_len="250">Precipitation is the main water source of apple production in northern China. Under the background of climate change, it is of great significance for apple planning to study the apple water requirement and precipitation suitability. Based on daily meteorological data and 1 km resolution digital elevation data of 210 meteorological stations in the study area and its surrounding area, the minimum humidity method is used to estimate the daily crop coefficient (K_c), and the spatial interpolation software ANUSPLIN based on spline function interpolation theory is applied. Then the water requirement and precipitation suitability of apple are calculated. According to the geographical distribution of the main planting area and percentile method, the threshold value range of precipitation suitability and optimal critical value of precipitation suitability are determined respectively. Using geographic information system (GIS) software ArcGIS, coefficient of variation, climate tendency rate and other related mathematical statistical methods, the spatial and temporal variation characteristics of apple water requirement and precipitation suitability in the study area are analyzed, and the precipitation suitability index model of apple in whole growth period is constructed to evaluate the precipitation suitability. Results show that, the average annual water requirement in most areas is 500-800 mm, accounting for 87.1% of the study area. The average water requirement in the main planting areas is basically 500-800 mm.The ratios of the average water requirement in the germination and young fruit stage, fruit expanding stage and coloring and maturity stage to the annual average water demand are 0.186-0.282, 0.392-0.562 and 0.159-0.282, respectively. The threshold ranges of precipitation suitability of apple in the annual, germination and young fruit stage, fruit expanding stage and coloring and maturity stage are 0.49-2.07, 0.25-1.70, 0.54-2.25 and 0.46-2.65, respectively. The optimal threshold values are 0.71, 0.55, 0.82 and 0.56, respectively. Therefore 85.4%, 87.4%, 85.6% and 84.9% of the study area are suitable during each stage. The area from the most suitable critical value to the balance of water supply and demand account for 39.8%, 36.9%, 20.4% and 47.1% of the study area. The most suitable, sub suitable and unsuitable areas in the whole growth stage of apple account for 31.9%, 50.6% and 17.5% of the study area, respectively. Precipitation is the main water source of apple production in northern China. Under the background of climate change, it is of great significance for apple planning to study the apple water requirement and precipitation suitability. Based on daily meteorological data and 1 km resolution digital elevation data of 210 meteorological stations in the study area and its surrounding area, the minimum humidity method is used to estimate the daily crop coefficient (K_c), and the spatial interpolation software ANUSPLIN based on spline function interpolation theory is applied. Then the water requirement and precipitation suitability of apple are calculated. According to the geographical distribution of the main planting area and percentile method, the threshold value range of precipitation suitability and optimal critical value of precipitation suitability are determined respectively. Using geographic information system (GIS) software ArcGIS, coefficient of variation, climate tendency rate and other related mathematical statistical methods, the spatial and temporal variation characteristics of apple water requirement and precipitation suitability in the study area are analyzed, and the precipitation suitability index model of apple in whole growth period is constructed to evaluate the precipitation suitability. Results show that, the average annual water requirement in most areas is 500-800 mm, accounting for 87.1% of the study area. The average water requirement in the main planting areas is basically 500-800 mm.The ratios of the average water requirement in the germination and young fruit stage, fruit expanding stage and coloring and maturity stage to the annual average water demand are 0.186-0.282, 0.392-0.562 and 0.159-0.282, respectively. The threshold ranges of precipitation suitability of apple in the annual, germination and young fruit stage, fruit expanding stage and coloring and maturity stage are 0.49-2.07, 0.25-1.70, 0.54-2.25 and 0.46-2.65, respectively. The optimal threshold values are 0.71, 0.55, 0.82 and 0.56, respectively. Therefore 85.4%, 87.4%, 85.6% and 84.9% of the study area are suitable during each stage. The area from the most suitable critical value to the balance of water supply and demand account for 39.8%, 36.9%, 20.4% and 47.1% of the study area. The most suitable, sub suitable and unsuitable areas in the whole growth stage of apple account for 31.9%, 50.6% and 17.5% of the study area, respectively.

Abstract:
keep_len="250">Sounding analysis is one important method for short-term heavy rainfall event forecasting. By using sounding data of 119 stations at 0800 BT of 1 June -30 September during 2015-2019, based on XGBoost integrated learning framework, a prediction model for short-term heavy rainfall events (not less than 20 mm·h^-1) is proposed. Sounding data and derivative physical elements are used as characteristics parameters. The model can forecast whether short-term heavy rainfall occurs around the sounding station in following 12 h. Then an optimization method of high-risk weather is proposed. Using piecewise cost function as a loss function, different weighting factors are used to make the model more sensitive. This will ensure the total number of false prediction samples do not increase, but more false alarms rather than missing ones, leading to a slight increase on threat score (TS), a great improvement on probability of detection (POD) and the false alarm rate (FAR) will not exceed the threshold such as 0.5. After that, two tests are designed including a weighted sensitivity test for the piecewise loss function and a comparison test of the loss function using 12 datasets of 7 regional center sounding stations. The efficiency of the model optimization method is verified and the prediction ability before and after the improvement are compared. At last, a test of national short-term heavy precipitation forecast is designed, by using sounding data from 1 June to 30 September in 2019 as independent test set. Results show that reducing w_TP will decrease the number of hits and false alarm of the model's forecast; reducing w_FN will increase the number of hits and false alarms; w_TN and w_FP have little influence on the prediction. Compare with other commonly used cost function, the model with piecewise weight cost function has better forecasting skills, in which the TS is improved by 0.05-0.1, the POD is increased by more than 0.15, and the FAR is improved by 0.05-0.1. The model shows a clear tendency of forecasting positive instead of missing. In addition, the model shows similar results in all independent experiments, indicating that the optimization method has consistent effects on the results. The independent test of the national short-term heavy rainfall forecast experiment shows that the improved model has a certain short-term heavy rainfall forecast ability, with POD of 0.66, FAR of 0.37, and TS of 0.47. Above all, a short-term heavy rain prediction model is constructed based on the integrated decision tree and sounding data. The optimization method which could enhance the forecast skill of model is also proposed and verified. Sounding analysis is one important method for short-term heavy rainfall event forecasting. By using sounding data of 119 stations at 0800 BT of 1 June -30 September during 2015-2019, based on XGBoost integrated learning framework, a prediction model for short-term heavy rainfall events (not less than 20 mm·h^-1) is proposed. Sounding data and derivative physical elements are used as characteristics parameters. The model can forecast whether short-term heavy rainfall occurs around the sounding station in following 12 h. Then an optimization method of high-risk weather is proposed. Using piecewise cost function as a loss function, different weighting factors are used to make the model more sensitive. This will ensure the total number of false prediction samples do not increase, but more false alarms rather than missing ones, leading to a slight increase on threat score (TS), a great improvement on probability of detection (POD) and the false alarm rate (FAR) will not exceed the threshold such as 0.5. After that, two tests are designed including a weighted sensitivity test for the piecewise loss function and a comparison test of the loss function using 12 datasets of 7 regional center sounding stations. The efficiency of the model optimization method is verified and the prediction ability before and after the improvement are compared. At last, a test of national short-term heavy precipitation forecast is designed, by using sounding data from 1 June to 30 September in 2019 as independent test set. Results show that reducing w_TP will decrease the number of hits and false alarm of the model's forecast; reducing w_FN will increase the number of hits and false alarms; w_TN and w_FP have little influence on the prediction. Compare with other commonly used cost function, the model with piecewise weight cost function has better forecasting skills, in which the TS is improved by 0.05-0.1, the POD is increased by more than 0.15, and the FAR is improved by 0.05-0.1. The model shows a clear tendency of forecasting positive instead of missing. In addition, the model shows similar results in all independent experiments, indicating that the optimization method has consistent effects on the results. The independent test of the national short-term heavy rainfall forecast experiment shows that the improved model has a certain short-term heavy rainfall forecast ability, with POD of 0.66, FAR of 0.37, and TS of 0.47. Above all, a short-term heavy rain prediction model is constructed based on the integrated decision tree and sounding data. The optimization method which could enhance the forecast skill of model is also proposed and verified.

Abstract:
keep_len="250">The advantages of X-band polarimetric weather radar focus on its high spatio-temporal resolution and capability of multi-radar networking. However, the previously designed hydrometeor classification algorithm (HCA) for S-band weather radar is unsuitable for X-band weather radar due to the difference of backscattering characteristics and heavy precipitation attenuation. Therefore, the key technologies of hydrometeor classification algorithm and multi-radar mosaic algorithm for X-band polarimetric weather radar are proposed. First, it is found that the melting layer detection algorithm designed for S-band polarimetric weather radar is not suitable for X-band weather radar through analysis on the data collected by Beijing X-band radar network. A melting layer detection method based on quasi-vertical profile is proposed, which greatly improves the accuracy of obtaining the melting information. Second, a confidence threshold adjustment method is proposed to accurately estimate the data quality in the case of precipitation and clutter superposition. Third, an optimization method of membership functions based on data statistics is proposed to reconstruct the classification parameters suitable for Beijing X-band radar network. Finally, a multi-radar mosaic method based on rainfall attenuation is proposed, in which the reflectivity factors of networking radars are weighted and averaged by the data quality factor. Compared with the traditional method, it is found that the structural inhomogeneity of X-band radar mosaic result is effectively reduced. Those modifications effectively enhance the reliability of classification mosaic results of X-band weather radar network and provide technical support for the rapid deployment of X-band radar in China. Three typical precipitation cases in Beijing during the flood season in 2016 are used to compare the observational efficiency between X-band weather radar network and S-band operational radar. For the cases of convective rainfall, fine echo structures and reasonable hydrometeor distributions are found in X-band radar mosaic results. Especially for convective rainfall with short duration and small spatial scale, the advantage of X-band radar is more obvious, which alleviates the limited detection ability of S-band operational radar in urban areas. In addition, the hail falling area identified by X-band radar can be verified by manual observation in national weather stations. The performance of X-band weather radar network in large-scale stratiform precipitation, however, is not as good as S-band weather radar. The advantages of X-band polarimetric weather radar focus on its high spatio-temporal resolution and capability of multi-radar networking. However, the previously designed hydrometeor classification algorithm (HCA) for S-band weather radar is unsuitable for X-band weather radar due to the difference of backscattering characteristics and heavy precipitation attenuation. Therefore, the key technologies of hydrometeor classification algorithm and multi-radar mosaic algorithm for X-band polarimetric weather radar are proposed. First, it is found that the melting layer detection algorithm designed for S-band polarimetric weather radar is not suitable for X-band weather radar through analysis on the data collected by Beijing X-band radar network. A melting layer detection method based on quasi-vertical profile is proposed, which greatly improves the accuracy of obtaining the melting information. Second, a confidence threshold adjustment method is proposed to accurately estimate the data quality in the case of precipitation and clutter superposition. Third, an optimization method of membership functions based on data statistics is proposed to reconstruct the classification parameters suitable for Beijing X-band radar network. Finally, a multi-radar mosaic method based on rainfall attenuation is proposed, in which the reflectivity factors of networking radars are weighted and averaged by the data quality factor. Compared with the traditional method, it is found that the structural inhomogeneity of X-band radar mosaic result is effectively reduced. Those modifications effectively enhance the reliability of classification mosaic results of X-band weather radar network and provide technical support for the rapid deployment of X-band radar in China. Three typical precipitation cases in Beijing during the flood season in 2016 are used to compare the observational efficiency between X-band weather radar network and S-band operational radar. For the cases of convective rainfall, fine echo structures and reasonable hydrometeor distributions are found in X-band radar mosaic results. Especially for convective rainfall with short duration and small spatial scale, the advantage of X-band radar is more obvious, which alleviates the limited detection ability of S-band operational radar in urban areas. In addition, the hail falling area identified by X-band radar can be verified by manual observation in national weather stations. The performance of X-band weather radar network in large-scale stratiform precipitation, however, is not as good as S-band weather radar.

Abstract:
keep_len="250">Based on S-band Doppler weather radar raw data, the total lightning location data of Beijing Lightning Network (BLNET) and hailfall reports from Beijing Meteorological Service, two lightning jump algorithms, Gatlin algorithm and σ algorithm, with different configurations are compared and analyzed for early warning performance of 177 hailfall events in Beijing from 2015 to 2018. The results show that, the early warnings of different multiples of σ algorithm are quite different, 2σ (in this situation, the current changing rate of total flash exceeds two times of the standard deviation of total flash rate in previous time) has the best performance in the σ algorithm. That is to say, 2σ algorithm has the highest probability of detection (POD) and critical success index (CSI), as well as the lowest false alarm rate (FAR), comparing with σ, 3σ and 4σ algorithms. The early warnings of Gatlin algorithm under different N (which is the amount of samples before current time used to calculate the mean and standard deviation) configurations have little difference, and the early warning efficiency is best when N=6, comparing with N=7, 8, 9 and 10. The POD, FAR and CSI of 2σ algorithm are 80.2%, 41.6%, and 51.1%, respectively. The corresponding results of the Gatlin algorithm with N=6 are 82.5%, 62.0%, and 35.2%, respectively. In addition, the applications of these two algorithms to a multi-cell thunderstorm process and a squall line process are analyzed in detail. The results also show that Gatlin algorithm with N=6 has a slightly higher POD than 2σ algorithm, but its FAR is much higher and its CSI is lower. Considering the evaluation of hail nowcast results by Gatlin algorithm and 2σ algorithm, 2σ lightning jump algorithm is more suitable for hail nowcasting in Beijing. And it is beneficial to improving the application of lightning data to hail nowcasting in Beijing Based on S-band Doppler weather radar raw data, the total lightning location data of Beijing Lightning Network (BLNET) and hailfall reports from Beijing Meteorological Service, two lightning jump algorithms, Gatlin algorithm and σ algorithm, with different configurations are compared and analyzed for early warning performance of 177 hailfall events in Beijing from 2015 to 2018. The results show that, the early warnings of different multiples of σ algorithm are quite different, 2σ (in this situation, the current changing rate of total flash exceeds two times of the standard deviation of total flash rate in previous time) has the best performance in the σ algorithm. That is to say, 2σ algorithm has the highest probability of detection (POD) and critical success index (CSI), as well as the lowest false alarm rate (FAR), comparing with σ, 3σ and 4σ algorithms. The early warnings of Gatlin algorithm under different N (which is the amount of samples before current time used to calculate the mean and standard deviation) configurations have little difference, and the early warning efficiency is best when N=6, comparing with N=7, 8, 9 and 10. The POD, FAR and CSI of 2σ algorithm are 80.2%, 41.6%, and 51.1%, respectively. The corresponding results of the Gatlin algorithm with N=6 are 82.5%, 62.0%, and 35.2%, respectively. In addition, the applications of these two algorithms to a multi-cell thunderstorm process and a squall line process are analyzed in detail. The results also show that Gatlin algorithm with N=6 has a slightly higher POD than 2σ algorithm, but its FAR is much higher and its CSI is lower. Considering the evaluation of hail nowcast results by Gatlin algorithm and 2σ algorithm, 2σ lightning jump algorithm is more suitable for hail nowcasting in Beijing. And it is beneficial to improving the application of lightning data to hail nowcasting in Beijing

Abstract:
keep_len="250">Newly reconstructed dataset of regional persistent historical heavy rain events in 1981-2018, corresponding daily rainfall data of 2474 observational stations in China, and NCEP/NCAR global reanalysis data of daily geopotential height field are used to study the persistent heavy rain events in Jianghuai Region.Based on 72 persistent heavy rainfall cases, typical rain patterns and circulation fields are refined by empirical orthogonal function(EOF). And the corresponding time coefficient is obtained by projecting rainfall of individual days to the typical rain patterns, and the training and test dataset samples are determined by the time coefficient. Using residual neural network(CNN), a transfer learning CNN classification model of Jianghuai persistent heavy rainfall is established by three transfer learning processes. Compared with the analog quantity(R) and Cosine similarity coefficient(COS) methods, the transfer learning CNN model has the highest classification accuracy on the test dataset.CNN, R and COS methods are used to objectively classify the circulation of all persistent heavy rain cases and to synthesize the distribution of various types of rainfall and circulation during 1981-2015. The statistical analysis shows that the transfer learning CNN model is better at classification. By comparing the correlation coefficients between rain distribution of each type and typical rain patterns, it shows that the transfer learning CNN model performs better than the R and COS methods. The variance between different types of geopotential height fields at 500 hPa obtained by the CNN model is the largest and the CNN model can better distinguish the circulation fields of different types of heavy rainfall.The analysis of samples with inconsistent objective classification of three methods shows that the correlation coefficients of various patterns of rainfall of the transfer learning CNN model are significantly higher than those of R typing and COS typing methods. The spatial distribution of various rainfall patterns of CNN model can clearly show the characteristics of the three typical heavy rain patterns, while the results obtained by R typing and COS typing methods are almost opposite to the typical rain patterns except for type Ⅱ. Considering classification of independent samples in 2016-2018, the correlation coefficients between the rain distribution of each type and typical rain patterns obtained by the transfer learning CNN model are much higher than the R and COS methods. The transfer learning CNN model has certain advantages over R typing and COS typing methods in classification and also has a certain ability to distinguish the non-continuous heavy rainfall circulation pattern. Newly reconstructed dataset of regional persistent historical heavy rain events in 1981-2018, corresponding daily rainfall data of 2474 observational stations in China, and NCEP/NCAR global reanalysis data of daily geopotential height field are used to study the persistent heavy rain events in Jianghuai Region.Based on 72 persistent heavy rainfall cases, typical rain patterns and circulation fields are refined by empirical orthogonal function(EOF). And the corresponding time coefficient is obtained by projecting rainfall of individual days to the typical rain patterns, and the training and test dataset samples are determined by the time coefficient. Using residual neural network(CNN), a transfer learning CNN classification model of Jianghuai persistent heavy rainfall is established by three transfer learning processes. Compared with the analog quantity(R) and Cosine similarity coefficient(COS) methods, the transfer learning CNN model has the highest classification accuracy on the test dataset.CNN, R and COS methods are used to objectively classify the circulation of all persistent heavy rain cases and to synthesize the distribution of various types of rainfall and circulation during 1981-2015. The statistical analysis shows that the transfer learning CNN model is better at classification. By comparing the correlation coefficients between rain distribution of each type and typical rain patterns, it shows that the transfer learning CNN model performs better than the R and COS methods. The variance between different types of geopotential height fields at 500 hPa obtained by the CNN model is the largest and the CNN model can better distinguish the circulation fields of different types of heavy rainfall.The analysis of samples with inconsistent objective classification of three methods shows that the correlation coefficients of various patterns of rainfall of the transfer learning CNN model are significantly higher than those of R typing and COS typing methods. The spatial distribution of various rainfall patterns of CNN model can clearly show the characteristics of the three typical heavy rain patterns, while the results obtained by R typing and COS typing methods are almost opposite to the typical rain patterns except for type Ⅱ. Considering classification of independent samples in 2016-2018, the correlation coefficients between the rain distribution of each type and typical rain patterns obtained by the transfer learning CNN model are much higher than the R and COS methods. The transfer learning CNN model has certain advantages over R typing and COS typing methods in classification and also has a certain ability to distinguish the non-continuous heavy rainfall circulation pattern.

Abstract:
keep_len="250">The concentration of greenhouse gases in the atmosphere has increased continuously since industrial revolution and significantly impacted the global climate, of which global warming is the most direct and prominent manifestation. The Community Atmosphere Model V5.1 (CAM5.1) is examined and used to simulate multiple meteorological elements of Asian summer monsoon using the reanalysis data of NCEP/NCAR (National Center for Environmental Prediction/National Center for Atmospheric Research), and the results show that it could reproduce the main features of Asian summer monsoon well. Sensitivity experiments are then carried out to study the response mechanism of Asian summer monsoon to greenhouse gas increase in terms of energy transformation, which adopt greenhouse gases emission scenarios of 2000 and 1850 respectively. The models are run for 20 years from 1991 to 2010, and the results of the latter 10 years in summer (June to August) are analyzed.With increasing greenhouse gases concentration, the surface air temperature in the Asian continent is mostly increasing, except for the Arabian Peninsula and northwestern Indian Peninsula. The monsoon is strengthened in central Indian Peninsula, Indo-China Peninsula and eastern China. In addition, monsoon precipitation increases in the central and northern Indian Peninsula, northern and central Indo-China Peninsula, and eastern China, while decreases in southern Indian Peninsula, southern Tibetan plateau, central and western China, the Philippines and Japan. Correlation analysis of atmospheric energy budget and conversion shows that increased greenhouse gases concentration enhances the atmospheric heat sources by means of increasing the convective condensational latent heat. The increase in atmospheric heat sources results in an increase of full potential energy. Thus, there are positive transformations of full potential energy to kinetic energy of divergent wind, and the transformation of kinetic energy from divergent wind to non-divergent wind also increases, which ultimately enhances the summer monsoon over central Indian Peninsula, Indo-China Peninsula and eastern China. Further analysis shows that the increase of convective condensational latent heat is the result of the decrease of atmospheric stability, the enhancement of convective activity, the increase of cloud thickness and the increase of convective precipitation caused by the increase of greenhouse gases concentration. Meanwhile, the increase of convective precipitation is the main cause for the increase of total precipitation. The concentration of greenhouse gases in the atmosphere has increased continuously since industrial revolution and significantly impacted the global climate, of which global warming is the most direct and prominent manifestation. The Community Atmosphere Model V5.1 (CAM5.1) is examined and used to simulate multiple meteorological elements of Asian summer monsoon using the reanalysis data of NCEP/NCAR (National Center for Environmental Prediction/National Center for Atmospheric Research), and the results show that it could reproduce the main features of Asian summer monsoon well. Sensitivity experiments are then carried out to study the response mechanism of Asian summer monsoon to greenhouse gas increase in terms of energy transformation, which adopt greenhouse gases emission scenarios of 2000 and 1850 respectively. The models are run for 20 years from 1991 to 2010, and the results of the latter 10 years in summer (June to August) are analyzed.With increasing greenhouse gases concentration, the surface air temperature in the Asian continent is mostly increasing, except for the Arabian Peninsula and northwestern Indian Peninsula. The monsoon is strengthened in central Indian Peninsula, Indo-China Peninsula and eastern China. In addition, monsoon precipitation increases in the central and northern Indian Peninsula, northern and central Indo-China Peninsula, and eastern China, while decreases in southern Indian Peninsula, southern Tibetan plateau, central and western China, the Philippines and Japan. Correlation analysis of atmospheric energy budget and conversion shows that increased greenhouse gases concentration enhances the atmospheric heat sources by means of increasing the convective condensational latent heat. The increase in atmospheric heat sources results in an increase of full potential energy. Thus, there are positive transformations of full potential energy to kinetic energy of divergent wind, and the transformation of kinetic energy from divergent wind to non-divergent wind also increases, which ultimately enhances the summer monsoon over central Indian Peninsula, Indo-China Peninsula and eastern China. Further analysis shows that the increase of convective condensational latent heat is the result of the decrease of atmospheric stability, the enhancement of convective activity, the increase of cloud thickness and the increase of convective precipitation caused by the increase of greenhouse gases concentration. Meanwhile, the increase of convective precipitation is the main cause for the increase of total precipitation.