Abstract:
keep_len="250">The disaster of wire icing is one important natural disaster that causes accidents in the power system. Its research progress is summarized from the related concepts and classification of wire icing, the influence and hazard of wire icing, temporal and spatial distribution characteristics, causes, influence factors, forecast models, risk assessment, and preventive measures are integrated. Besides, the further research directions of wire icing are also investigated. In China, wire icing can be classified as two major kinds, including glaze ice and rime ice. The hazards caused by wire icing mainly include line overload accidents, adjacent uneven ice coating or deicing accidents in different periods, insulator string ice flash accidents, and ice-coated wire galloping accidents. The most important conditions for ice accretion are the presence of cold air and sufficient water vapor conditions, which are closely related to the atmospheric circulation situation. The environmental causes of wire icing also include quasi-stationary fronts, vertical atmospheric structure, and temperature inversion. In addition, it is also affected by meteorological factors, terrain, height, the characteristics of the wires themselves and so on. In general, the disaster of wire icing presents a distribution pattern of rime ice in the north and glaze ice in the south. In the north, the areas with frequent wire icing are scattered, and in the south are distributed in strips. The disaster of wire icing mainly occurs in winter, and occasionally occurs in autumn and spring. The earliest start date is usually in October and the last finish date is in the next April, and the dates vary depending on the latitudes. The disaster of wire icing appears more frequently in the winter months of December, January, and February. Under historical climate warming condition, the number of ice accretions has a change in the 1980s and 1990s, which declines after the 1990s. Since the 1950s, various ice accretion prediction models have been developed. In the early days, some fixed models are proposed, and later, many regional models are established using regression methods, artificial neural networks, support vector machines and other methods. The current risk assessment of disaster of wire icing mainly focuses on risks and vulnerabilities. Future research directions include the comprehensive indicators of wire icing based on multidisciplinary indicators, comprehensive risk assessment based on catastrophic processes, and the impact of climate change on wire icing. The disaster of wire icing is one important natural disaster that causes accidents in the power system. Its research progress is summarized from the related concepts and classification of wire icing, the influence and hazard of wire icing, temporal and spatial distribution characteristics, causes, influence factors, forecast models, risk assessment, and preventive measures are integrated. Besides, the further research directions of wire icing are also investigated. In China, wire icing can be classified as two major kinds, including glaze ice and rime ice. The hazards caused by wire icing mainly include line overload accidents, adjacent uneven ice coating or deicing accidents in different periods, insulator string ice flash accidents, and ice-coated wire galloping accidents. The most important conditions for ice accretion are the presence of cold air and sufficient water vapor conditions, which are closely related to the atmospheric circulation situation. The environmental causes of wire icing also include quasi-stationary fronts, vertical atmospheric structure, and temperature inversion. In addition, it is also affected by meteorological factors, terrain, height, the characteristics of the wires themselves and so on. In general, the disaster of wire icing presents a distribution pattern of rime ice in the north and glaze ice in the south. In the north, the areas with frequent wire icing are scattered, and in the south are distributed in strips. The disaster of wire icing mainly occurs in winter, and occasionally occurs in autumn and spring. The earliest start date is usually in October and the last finish date is in the next April, and the dates vary depending on the latitudes. The disaster of wire icing appears more frequently in the winter months of December, January, and February. Under historical climate warming condition, the number of ice accretions has a change in the 1980s and 1990s, which declines after the 1990s. Since the 1950s, various ice accretion prediction models have been developed. In the early days, some fixed models are proposed, and later, many regional models are established using regression methods, artificial neural networks, support vector machines and other methods. The current risk assessment of disaster of wire icing mainly focuses on risks and vulnerabilities. Future research directions include the comprehensive indicators of wire icing based on multidisciplinary indicators, comprehensive risk assessment based on catastrophic processes, and the impact of climate change on wire icing.

Abstract:
keep_len="250">Deep learning shows great potential in severe convective weather nowcasting. The establishment of deep learning model is inseparable from a large number of training and learning, which is in terms of large capacity and high-quality dataset. Based on multi-source observations of CMA(China Meteorological Administration), disaster reports and internet media information, a dataset of severe convective weather for artificial intelligence training (SCWDS) is established. SCWDS is organized by severe convective weather events. It includes 184865 cases and each case is composed of several samples in the spatiotemporal window of the event. There are 9256405 samples including thunderstorm, gale, short-term heavy rain, hail and tornado in China from 2012 to 2019 in SCWDS. Each sample includes severe weather event annotation and corresponding spatiotemporal window of surface observations of temperature, precipitation, pressure, humidity, winds (average wind speed and maximum wind speed), radiosonde observations of temperature, dew point temperature, geopotential height and winds from 1000 to 1 hPa, lightning observations of intensity, radar volume scan data, visible, long wave infrared, water vapor and mid infrared channels of FY-2E, FY-2G and FY-2D nominal disk data, and environmental factors of ERA5 reanalysis data. Quality control and data cleaning are carried out, and all cases of time discontinuity, wrong logical relationship or caused by non-convective factors are eliminated. It shows that the thunderstorm, the short-term heavy rain and the hail mainly occur from April to September, especially from June to August in summer. However, the thunderstorm and the gale occur most frequently from April to May. The tornado occurs frequently from June to August and April. The thunderstorm, the gale and the hail show the same diurnal variation, and the high frequency period is concentrated between afternoon and evening. The daily cycle of the occurrence frequency of the short-term heavy rain presents a bimodal feature, and the high value period is in 0300-0400 BT and 1500-1600 BT. The occurrence of severe convective weather presents large spatial variability. The thunderstorm mainly distributes in South China, Jiangnan, the Tibet Plateau and the Yunnan-Guizhou Plateau where the frequency generally exceeds 40 times. The gale mainly distributes in the northern part of North China and Xinjiang, coastal areas in the south of the Yangtze with frequency of more than 10 times. The short-time heavy rain is mainly concentrated in southwest, South China, Jiangnan and Huanghuai Regions with frequency of more than 100 times. The hail is mainly distributed in the Tibet Plateau, the Yunnan-Guizhou Plateau and the northern part of North China where the frequency generally exceeds 6 times. The tornado mainly distributes in Jiangsu, Guangdong and Qiongzhou Straits. Deep learning shows great potential in severe convective weather nowcasting. The establishment of deep learning model is inseparable from a large number of training and learning, which is in terms of large capacity and high-quality dataset. Based on multi-source observations of CMA(China Meteorological Administration), disaster reports and internet media information, a dataset of severe convective weather for artificial intelligence training (SCWDS) is established. SCWDS is organized by severe convective weather events. It includes 184865 cases and each case is composed of several samples in the spatiotemporal window of the event. There are 9256405 samples including thunderstorm, gale, short-term heavy rain, hail and tornado in China from 2012 to 2019 in SCWDS. Each sample includes severe weather event annotation and corresponding spatiotemporal window of surface observations of temperature, precipitation, pressure, humidity, winds (average wind speed and maximum wind speed), radiosonde observations of temperature, dew point temperature, geopotential height and winds from 1000 to 1 hPa, lightning observations of intensity, radar volume scan data, visible, long wave infrared, water vapor and mid infrared channels of FY-2E, FY-2G and FY-2D nominal disk data, and environmental factors of ERA5 reanalysis data. Quality control and data cleaning are carried out, and all cases of time discontinuity, wrong logical relationship or caused by non-convective factors are eliminated. It shows that the thunderstorm, the short-term heavy rain and the hail mainly occur from April to September, especially from June to August in summer. However, the thunderstorm and the gale occur most frequently from April to May. The tornado occurs frequently from June to August and April. The thunderstorm, the gale and the hail show the same diurnal variation, and the high frequency period is concentrated between afternoon and evening. The daily cycle of the occurrence frequency of the short-term heavy rain presents a bimodal feature, and the high value period is in 0300-0400 BT and 1500-1600 BT. The occurrence of severe convective weather presents large spatial variability. The thunderstorm mainly distributes in South China, Jiangnan, the Tibet Plateau and the Yunnan-Guizhou Plateau where the frequency generally exceeds 40 times. The gale mainly distributes in the northern part of North China and Xinjiang, coastal areas in the south of the Yangtze with frequency of more than 10 times. The short-time heavy rain is mainly concentrated in southwest, South China, Jiangnan and Huanghuai Regions with frequency of more than 100 times. The hail is mainly distributed in the Tibet Plateau, the Yunnan-Guizhou Plateau and the northern part of North China where the frequency generally exceeds 6 times. The tornado mainly distributes in Jiangsu, Guangdong and Qiongzhou Straits.

Abstract:
keep_len="250">Climate dynamic diagnosis and numerical simulation are important means to understand the rules of climate variability and improve the service efficiency of short-term climate prediction and scientific decision-making. However, the dynamic diagnosis technology based on climate simulation has not been widely used in routine climate service, and almost no platforms can transform scientific research results into the use in climate operation conveniently. Therefore, by integrating various technologies such as modern computer communication protocols, visual editing and meteorological numerical simulation, Climate Dynamic Diagnosis and Analysis System (CDDAS) is developed, which can promote the dynamic diagnosis technology of climate simulation to be more widely used in climate operation. The system has the features with opening structure, high integration of diagnosis methods and high usability. Four functional modules are developed, including data management, climate dynamic diagnosis, multi-model numerical simulation and result analysis. Also, an interactive controlling language is designed, which can provide an easy method for user's further development. In the system, a communication toll among local PC (personal computer) client, remote server and supercomputer is built, which can be managed visually. Visual editing and management functions are provided to users to edit or design the interactive operation interfaces between local terminal and remote server, so as to provide online services according to their own needs. The script language provided by the system can control the visual buttons on the operation interface, the cloud computing in remote server and data network transmission, and it supports four arithmetic operations, logical judgment, numerical circulation and other statements, integrates a variety of network communication protocols, and provides a series of drawing, string processing, and window display control functions. Assisted programming and a fine interface designing tool is also provided. The client of the system can help users to manage interactive pages and graphics, and can make comparative analysis of climate diagnosis results. The system lays a good foundation for the automation of dynamic climate diagnosis. In particular, the establishment of multi-model numerical simulation module and the realization of visualization operation provide an effective way for the dynamic diagnosis and numerical simulation of climate models to be used in climate operation departments. At present, the system has been used in the national climate operation and scientific research units, which has significantly improved the efficiency and convenience of climate operation in the diagnosis of climate anomalies, climate prediction and climate decision-making services. Climate dynamic diagnosis and numerical simulation are important means to understand the rules of climate variability and improve the service efficiency of short-term climate prediction and scientific decision-making. However, the dynamic diagnosis technology based on climate simulation has not been widely used in routine climate service, and almost no platforms can transform scientific research results into the use in climate operation conveniently. Therefore, by integrating various technologies such as modern computer communication protocols, visual editing and meteorological numerical simulation, Climate Dynamic Diagnosis and Analysis System (CDDAS) is developed, which can promote the dynamic diagnosis technology of climate simulation to be more widely used in climate operation. The system has the features with opening structure, high integration of diagnosis methods and high usability. Four functional modules are developed, including data management, climate dynamic diagnosis, multi-model numerical simulation and result analysis. Also, an interactive controlling language is designed, which can provide an easy method for user's further development. In the system, a communication toll among local PC (personal computer) client, remote server and supercomputer is built, which can be managed visually. Visual editing and management functions are provided to users to edit or design the interactive operation interfaces between local terminal and remote server, so as to provide online services according to their own needs. The script language provided by the system can control the visual buttons on the operation interface, the cloud computing in remote server and data network transmission, and it supports four arithmetic operations, logical judgment, numerical circulation and other statements, integrates a variety of network communication protocols, and provides a series of drawing, string processing, and window display control functions. Assisted programming and a fine interface designing tool is also provided. The client of the system can help users to manage interactive pages and graphics, and can make comparative analysis of climate diagnosis results. The system lays a good foundation for the automation of dynamic climate diagnosis. In particular, the establishment of multi-model numerical simulation module and the realization of visualization operation provide an effective way for the dynamic diagnosis and numerical simulation of climate models to be used in climate operation departments. At present, the system has been used in the national climate operation and scientific research units, which has significantly improved the efficiency and convenience of climate operation in the diagnosis of climate anomalies, climate prediction and climate decision-making services.

Abstract:
keep_len="250">The model ability to predict Eurasian snow cover fraction (SCF) is evaluated by using the hindcast data during 1984-2019 from the Beijing Climate Center (BCC) Climate Prediction System version 2 (CPSv2), developed based on Climate System Model BCC-CSM1.1m. The SCF reanalysis data from National Snow and Ice Data Center (NSIDC) and other common variables reanalysis datasets are also used against the model forecasts. The prediction skills of Eurasian SCF in January and April are investigated, which separately represent the snow cover situation of winter and spring. The possible causes of model prediction errors are also discussed partly using the simulation data of two BCC climate models, BCC-CSM1.1m and BCC-CSM2-MR, respectively participating the phase 5 of Coupled Model Intercomparison Project (CMIP5) and phase 6 (CMIP6). Empirical orthogonal function (EOF), spatial and temporal correlation analysis, statistical test and other common methods are also adopted. The results show that, BCC-CSM1.1m is capable of forecasting the SCF in Eurasia two months ahead. However, the prediction skill varies both in space and time. In comparison with January, the model shows a better prediction skill both in climatology and interannual variability of Eurasian SCF in April. The prediction skill is highest in western Europe in January and in western Siberia in April. Lower-than-observed SCF are found in most areas of Eurasia except Tibetan Plateau in the predictions for LM0 (0 lead month). This coherent negative biases hardly varies with longer lead time in January, while the biases in key area of April reverse to positive and gradually increase. Analysis indicates that the SCF biases in January and April are positively related with those of precipitation and negatively related with those of surface temperature in the model. Moreover, since the corelated region between the precipitation biases and SCF biases reduces to some small areas in contrast with the surface temperature, the biases of SCF in the model exhibit closer relationship with surface temperature biases. In addition, comparing simulations from two BCC models, it's also found that the systematic biases originated from model resolution, parameterization scheme, etc. are also fundamental factors, which can explain the obvious underestimation of SCF in high latitude where observed SCF is nearly 100%. The model ability to predict Eurasian snow cover fraction (SCF) is evaluated by using the hindcast data during 1984-2019 from the Beijing Climate Center (BCC) Climate Prediction System version 2 (CPSv2), developed based on Climate System Model BCC-CSM1.1m. The SCF reanalysis data from National Snow and Ice Data Center (NSIDC) and other common variables reanalysis datasets are also used against the model forecasts. The prediction skills of Eurasian SCF in January and April are investigated, which separately represent the snow cover situation of winter and spring. The possible causes of model prediction errors are also discussed partly using the simulation data of two BCC climate models, BCC-CSM1.1m and BCC-CSM2-MR, respectively participating the phase 5 of Coupled Model Intercomparison Project (CMIP5) and phase 6 (CMIP6). Empirical orthogonal function (EOF), spatial and temporal correlation analysis, statistical test and other common methods are also adopted. The results show that, BCC-CSM1.1m is capable of forecasting the SCF in Eurasia two months ahead. However, the prediction skill varies both in space and time. In comparison with January, the model shows a better prediction skill both in climatology and interannual variability of Eurasian SCF in April. The prediction skill is highest in western Europe in January and in western Siberia in April. Lower-than-observed SCF are found in most areas of Eurasia except Tibetan Plateau in the predictions for LM0 (0 lead month). This coherent negative biases hardly varies with longer lead time in January, while the biases in key area of April reverse to positive and gradually increase. Analysis indicates that the SCF biases in January and April are positively related with those of precipitation and negatively related with those of surface temperature in the model. Moreover, since the corelated region between the precipitation biases and SCF biases reduces to some small areas in contrast with the surface temperature, the biases of SCF in the model exhibit closer relationship with surface temperature biases. In addition, comparing simulations from two BCC models, it's also found that the systematic biases originated from model resolution, parameterization scheme, etc. are also fundamental factors, which can explain the obvious underestimation of SCF in high latitude where observed SCF is nearly 100%.

Abstract:
keep_len="250">The Sichuan-Tibet Region is a key area for the development of western China, where severe convective weather such as thunderstorm gales occur frequently. However, due to the complex terrains, synoptic systems, and the lack of meteorological observations, it is especially challenging to make accurate prediction. To better understand the mechanism of severe convective weather over the plateau, a rare severe convective event in the Sichuan-Tibet Region on 8 Sep 2016 is analyzed with weather reports, hourly and minutely surface observations, sounding data and Doppler weather radar data from China Meteorological Administration and ERA-Interim 0.5°×0.5° reanalysis data from European Centre for Medium-Range Weather Forecasts (ECMWF). The result shows that hourly rainfall of over 10 mm and hails of over 18 mm are observed at several weather stations, indicating a hybrid moist convective event. The meso-scale convective system (MCS) occurs near a shear line at low level with weak cold advection at 500 hPa. Large environmental convective available potential energy (CAPE), vertical wind shear, and the thick moist atmospheric layer are conductive to the formation of supercell. The initial convection is generated along a surface convergence line, with multiple γ meso-scale cells embedded in stratiform cloud in the north and cluster cells in the south. They move to the southeast, enter the favorable environment and merge with each other, enabling the cell on the south side to quickly develop into a supercell. When the supercell grows matured, the characteristic of front inflow gap, hook echoes and mesoscale cyclone at low levels are clear. The strong echo region tilts forward with height. There is significant overshooting top with the echo top height up to 15 km above ground in the upper troposphere, and obvious echo overhang capping bounded weak-echo region (BWER) in the middle layer. Mid-altitude radial convergence, weakening of updrafts and rapid drop of the reflectivity core indicate the occurrence of downbursts inside the storm. The cooling effect due to the entrainment of midlevel dry air is favorable to the growing of big hails and raindrops, and the formation of downdrafts. Moreover, the drag effect related to the rapid drop of heavy raindrops and hails, and the narrow tube effect of the canyon terrain, contribute to the formation of thunderstorm gales near the ground. The Sichuan-Tibet Region is a key area for the development of western China, where severe convective weather such as thunderstorm gales occur frequently. However, due to the complex terrains, synoptic systems, and the lack of meteorological observations, it is especially challenging to make accurate prediction. To better understand the mechanism of severe convective weather over the plateau, a rare severe convective event in the Sichuan-Tibet Region on 8 Sep 2016 is analyzed with weather reports, hourly and minutely surface observations, sounding data and Doppler weather radar data from China Meteorological Administration and ERA-Interim 0.5°×0.5° reanalysis data from European Centre for Medium-Range Weather Forecasts (ECMWF). The result shows that hourly rainfall of over 10 mm and hails of over 18 mm are observed at several weather stations, indicating a hybrid moist convective event. The meso-scale convective system (MCS) occurs near a shear line at low level with weak cold advection at 500 hPa. Large environmental convective available potential energy (CAPE), vertical wind shear, and the thick moist atmospheric layer are conductive to the formation of supercell. The initial convection is generated along a surface convergence line, with multiple γ meso-scale cells embedded in stratiform cloud in the north and cluster cells in the south. They move to the southeast, enter the favorable environment and merge with each other, enabling the cell on the south side to quickly develop into a supercell. When the supercell grows matured, the characteristic of front inflow gap, hook echoes and mesoscale cyclone at low levels are clear. The strong echo region tilts forward with height. There is significant overshooting top with the echo top height up to 15 km above ground in the upper troposphere, and obvious echo overhang capping bounded weak-echo region (BWER) in the middle layer. Mid-altitude radial convergence, weakening of updrafts and rapid drop of the reflectivity core indicate the occurrence of downbursts inside the storm. The cooling effect due to the entrainment of midlevel dry air is favorable to the growing of big hails and raindrops, and the formation of downdrafts. Moreover, the drag effect related to the rapid drop of heavy raindrops and hails, and the narrow tube effect of the canyon terrain, contribute to the formation of thunderstorm gales near the ground.

Abstract:
keep_len="250">Squall line often leads to heavy rain, gale and hail, which is a difficult key problem in nowcasting. In order to explore the feasibility of deep learning for squall line identification, the training, validation and test set sample sets are established based on the radar data of Zhengzhou and Zhumadian in Henan Province during 2008-2020. The convolutional neural network (CNN) algorithm is used to construct a squall line identification model. The critical success index (CSI), equitable threat score (ETS), hit rate (POD) and false positive rate (FAR) are used to quantitatively evaluate the identification effect of the model. The influence of different sample composition and network structure on squall line identification effect are compared. The results show that the composition ratio of sample is imbalanced, because squall line accounts for very small proportion in all kinds of weather processes. This imbalance will degrade the classification performance of the identification model to squall line samples. The imbalance of sample composition can be improved by changing sampling mode and optimizing network structure, both can improve the identification efficiency, especially the latter. However, the combination of the two methods does not bring further improvement. The over fitting problem in network training can be alleviated by increasing the sparsity and randomness of the network structure. The validation set shows that CSI is 0.87, ETS is 0.82, POD is 0.96, and FAR is 0.10. Based on the test set, the echo can be correctly identified by network as non-squall line in the weak stage of convection development, and as squall line in the strong stage of squall line development. The echo intensity and spatial distribution of squall line cases differ greatly, and the samples in the test set have the image features which are not included in the training set, and therefore the identification effect reduces. The test set show that CSI is 0.66, ETS is 0.58, POD is 0.86, and FAR is 0.24. The research reveals that CNN can extract and learn the image features of squall line echo, and it has a certain ability to identify squall line. Squall line often leads to heavy rain, gale and hail, which is a difficult key problem in nowcasting. In order to explore the feasibility of deep learning for squall line identification, the training, validation and test set sample sets are established based on the radar data of Zhengzhou and Zhumadian in Henan Province during 2008-2020. The convolutional neural network (CNN) algorithm is used to construct a squall line identification model. The critical success index (CSI), equitable threat score (ETS), hit rate (POD) and false positive rate (FAR) are used to quantitatively evaluate the identification effect of the model. The influence of different sample composition and network structure on squall line identification effect are compared. The results show that the composition ratio of sample is imbalanced, because squall line accounts for very small proportion in all kinds of weather processes. This imbalance will degrade the classification performance of the identification model to squall line samples. The imbalance of sample composition can be improved by changing sampling mode and optimizing network structure, both can improve the identification efficiency, especially the latter. However, the combination of the two methods does not bring further improvement. The over fitting problem in network training can be alleviated by increasing the sparsity and randomness of the network structure. The validation set shows that CSI is 0.87, ETS is 0.82, POD is 0.96, and FAR is 0.10. Based on the test set, the echo can be correctly identified by network as non-squall line in the weak stage of convection development, and as squall line in the strong stage of squall line development. The echo intensity and spatial distribution of squall line cases differ greatly, and the samples in the test set have the image features which are not included in the training set, and therefore the identification effect reduces. The test set show that CSI is 0.66, ETS is 0.58, POD is 0.86, and FAR is 0.24. The research reveals that CNN can extract and learn the image features of squall line echo, and it has a certain ability to identify squall line.

Abstract:
keep_len="250">Based on the surface and sounding datasets, ERA5 reanalysis data from European Centre for Medium-Range Weather Forecasts (ECMWF) and the satellite images, the characteristics of convection-triggering drylines in the drainage area of Huanghe and Huaihe Rivers of China from April to September during 2010-2019 are analyzed. The result shows that the drylines mainly appear at Dezhou of Shandong, and surroundings in the north of Henan. Most of them are distributed in the quasi northwest-southeast and northeast-southwest direction, with the length of 100-200 km and the width of 50-100 km, and they generally occur at 1400 BT or 1700 BT during the daytime. The drylines mostly occur under the 500 hPa cold vortex located in Northeast China and North China, with convergence lines (or shear lines) on 700 hPa and 850 hPa weather chart, and within the low pressure behind the coastal high on the surface chart. The statistics of the surface elements shows that the temperature on the dry side is 1.9 ℃ higher than that on the wet side, while the dew point temperature on the wet side is 6.8 ℃ higher than that on dry side. The gradient of temperature, dew point temperature and specific humidity on both sides of drylines are -2.7 ℃·(100 km)^-1, 10.1 ℃·(100 km)^-1 and 5.9 g·kg^-1·(100 km)^-1, respectively. According to the statistics of sounding environment parameters, precipitable water in the wet side is higher than that on the dry side. The specific humidity on the wet side is higher than that on the dry side at 925 hPa, 850 hPa and 700 hPa. The mean convective available potential energy on the wet side is much larger than that on the dry side. The temperature differences are very small on both sides of the drylines at 850 hPa and 500 hPa, 700 hPa and 500 hPa. The significant difference on both sides of the dry side in convective available potential energy is mainly caused by the difference in water condition of the lower layers in the drainage area of Huanghe and Huaihe Rivers of China. The hydrostatic instability (conditional instability) on both sides of the drylines is similar. Also, the vertical wind shear of 0-6 km is a little bit stronger on wet side than that on the dry side. Based on the surface and sounding datasets, ERA5 reanalysis data from European Centre for Medium-Range Weather Forecasts (ECMWF) and the satellite images, the characteristics of convection-triggering drylines in the drainage area of Huanghe and Huaihe Rivers of China from April to September during 2010-2019 are analyzed. The result shows that the drylines mainly appear at Dezhou of Shandong, and surroundings in the north of Henan. Most of them are distributed in the quasi northwest-southeast and northeast-southwest direction, with the length of 100-200 km and the width of 50-100 km, and they generally occur at 1400 BT or 1700 BT during the daytime. The drylines mostly occur under the 500 hPa cold vortex located in Northeast China and North China, with convergence lines (or shear lines) on 700 hPa and 850 hPa weather chart, and within the low pressure behind the coastal high on the surface chart. The statistics of the surface elements shows that the temperature on the dry side is 1.9 ℃ higher than that on the wet side, while the dew point temperature on the wet side is 6.8 ℃ higher than that on dry side. The gradient of temperature, dew point temperature and specific humidity on both sides of drylines are -2.7 ℃·(100 km)^-1, 10.1 ℃·(100 km)^-1 and 5.9 g·kg^-1·(100 km)^-1, respectively. According to the statistics of sounding environment parameters, precipitable water in the wet side is higher than that on the dry side. The specific humidity on the wet side is higher than that on the dry side at 925 hPa, 850 hPa and 700 hPa. The mean convective available potential energy on the wet side is much larger than that on the dry side. The temperature differences are very small on both sides of the drylines at 850 hPa and 500 hPa, 700 hPa and 500 hPa. The significant difference on both sides of the dry side in convective available potential energy is mainly caused by the difference in water condition of the lower layers in the drainage area of Huanghe and Huaihe Rivers of China. The hydrostatic instability (conditional instability) on both sides of the drylines is similar. Also, the vertical wind shear of 0-6 km is a little bit stronger on wet side than that on the dry side.

Abstract:
keep_len="250">Crop growth model describes the development and growth process of crop. Development process is the physiological age of crops, which is related to the morphological changes of crops, and is a landmark stage of crop growth to achieve qualitative change. Development process is the time indicator of crop growth model. Reasonable description of the crop development process is the premise of high accuracy of crop growth model. At present, although different crop development models have been developed and widely used in crop growth models, their simulation ability can hardly meet the needs of crop growth simulation. These models only focus on the impact of meteorological conditions in a certain period (day), but do not particularly consider the period of crop development, which may be an important factor for the low accuracy of model simulation.It is assumed that the development rate of a crop on a certain day is not only related to the meteorological conditions of that day, but also related to its development stage. The development stage of crop is represented by the date, so that the temperature-day (T_Ad) and development unit-day (C_HUd) models are constructed. In addition, according to the principle of heat unit corrected by temperature model (T_HUa), the development unit corrected by temperature model (C_HUa) is constructed. Based on the principle of response-adaptation of temperature model (R_AM), the response-adaptation of development unit model (C_HUr) is established.The adaptability of the development model for sugarcane is analyzed by using the field data of 30 agrometeorological stations in China from 1980 to 2019, and the advantages and disadvantages between the traditional development model and the reconstructed model are compared. The results show that C_HUd and T_Ad model have better adaptability to simulate the development process of sugarcane, especially in the later stage of the development process when the temperature is decreasing. Compared with the original model (C_HU), the adaptive ability of C_HUa model for sugarcane decreases from seedling emergence to stem elongation but increases from stem elongation to maturity. The theoretical description of C_HUr model is not tenable. Sorted by simulation ability, the order of development models is as follows: C_HUd, T_Ad, R_AM, C_HU, C_HUa and the heat unit (T_HU) model, and their simulation ability values (S_CV) calculated by root mean square difference are 4.3, 3.9, 3.7, 3.3, 3.0 and 2.8, respectively.The reconstruction of the development model will further improve the CAMM and promote the development of crop growth simulation theory. Crop growth model describes the development and growth process of crop. Development process is the physiological age of crops, which is related to the morphological changes of crops, and is a landmark stage of crop growth to achieve qualitative change. Development process is the time indicator of crop growth model. Reasonable description of the crop development process is the premise of high accuracy of crop growth model. At present, although different crop development models have been developed and widely used in crop growth models, their simulation ability can hardly meet the needs of crop growth simulation. These models only focus on the impact of meteorological conditions in a certain period (day), but do not particularly consider the period of crop development, which may be an important factor for the low accuracy of model simulation.It is assumed that the development rate of a crop on a certain day is not only related to the meteorological conditions of that day, but also related to its development stage. The development stage of crop is represented by the date, so that the temperature-day (T_Ad) and development unit-day (C_HUd) models are constructed. In addition, according to the principle of heat unit corrected by temperature model (T_HUa), the development unit corrected by temperature model (C_HUa) is constructed. Based on the principle of response-adaptation of temperature model (R_AM), the response-adaptation of development unit model (C_HUr) is established.The adaptability of the development model for sugarcane is analyzed by using the field data of 30 agrometeorological stations in China from 1980 to 2019, and the advantages and disadvantages between the traditional development model and the reconstructed model are compared. The results show that C_HUd and T_Ad model have better adaptability to simulate the development process of sugarcane, especially in the later stage of the development process when the temperature is decreasing. Compared with the original model (C_HU), the adaptive ability of C_HUa model for sugarcane decreases from seedling emergence to stem elongation but increases from stem elongation to maturity. The theoretical description of C_HUr model is not tenable. Sorted by simulation ability, the order of development models is as follows: C_HUd, T_Ad, R_AM, C_HU, C_HUa and the heat unit (T_HU) model, and their simulation ability values (S_CV) calculated by root mean square difference are 4.3, 3.9, 3.7, 3.3, 3.0 and 2.8, respectively.The reconstruction of the development model will further improve the CAMM and promote the development of crop growth simulation theory.

Abstract:
keep_len="250">Low temperature freezing injury is the main meteorological disaster affecting the yield and quality of kiwifruit in China. To explore the damage mechanism of low temperature stress on kiwifruit and establish the indices of kiwifruit overwintering freezing injury, the impacts on fruit parent branches of kiwifruit are investigated by simulating natural freezing injury process with MSX-2F artificial simulated frost box system. Growth recovery method, tissue browning method, cell freezing point temperature method and cell membrane damage rate method are used to describe the characteristics of freezing injury quantitatively. By establishing the logistic analysis model of the relationship between freezing injury index and low temperature, the characteristics of freezing injury of 6 varieties are studied systematically. The results show that the response of different varieties to low temperature are significantly different. The supercooling point of Ruiyu and Hayward are lower, which are -3.4 ℃ and -3.2 ℃, respectively. The supercooling point of Xuxiang, Jinfu and Cuixiang are basically similar, which are -2.0 ℃, -1.7 ℃ and -1.7 ℃, respectively. The supercooling point of Hongyang is the highest, which is -1.4 ℃. The half-lethal temperature of buds of Hayward (-16.5 ℃) is the lowest. The half-lethal temperature of Ruiyu (-14.8 ℃), Xuxiang (-14.9 ℃) and Jinfu (-14.2 ℃) is intermediate. And the half-lethal temperature of Cuixiang (-13.4 ℃) and Hongyang (-13.8 ℃) are the highest. The differences in the degree and site of injury caused by different intensities of low temperature are significant. The freezing injury caused by -16 ℃ to -10 ℃ mainly affects the activity of the main bud of the resulting parent shoot. When the temperature is below -18 ℃, the low temperature damages the activity of main and secondary buds. And when the temperature is below -20 ℃, a large number of parent shoots are killed by low temperature injury. Among varieties, the frost resistance of Hayward is the strongest, Ruiyu, Jinfu and Xuxiang are the middle, and Cuixiang and Hongyang are the weakest. Taking the freezing injury index of the resulting parent branch bud as the main parameter, the 5-grade low temperature freezing injury index of the resulting parent branch is constructed by different varieties. Its freezing temperature ranges of level 1-5 are -11.0 ℃ to -10.5 ℃, -14.5 ℃ to -10.5 ℃, -16.5 ℃ to -12.0 ℃, -20.0 ℃ to -13.5 ℃, -20.0 ℃ to -15.0 ℃, respectively. Low temperature freezing injury is the main meteorological disaster affecting the yield and quality of kiwifruit in China. To explore the damage mechanism of low temperature stress on kiwifruit and establish the indices of kiwifruit overwintering freezing injury, the impacts on fruit parent branches of kiwifruit are investigated by simulating natural freezing injury process with MSX-2F artificial simulated frost box system. Growth recovery method, tissue browning method, cell freezing point temperature method and cell membrane damage rate method are used to describe the characteristics of freezing injury quantitatively. By establishing the logistic analysis model of the relationship between freezing injury index and low temperature, the characteristics of freezing injury of 6 varieties are studied systematically. The results show that the response of different varieties to low temperature are significantly different. The supercooling point of Ruiyu and Hayward are lower, which are -3.4 ℃ and -3.2 ℃, respectively. The supercooling point of Xuxiang, Jinfu and Cuixiang are basically similar, which are -2.0 ℃, -1.7 ℃ and -1.7 ℃, respectively. The supercooling point of Hongyang is the highest, which is -1.4 ℃. The half-lethal temperature of buds of Hayward (-16.5 ℃) is the lowest. The half-lethal temperature of Ruiyu (-14.8 ℃), Xuxiang (-14.9 ℃) and Jinfu (-14.2 ℃) is intermediate. And the half-lethal temperature of Cuixiang (-13.4 ℃) and Hongyang (-13.8 ℃) are the highest. The differences in the degree and site of injury caused by different intensities of low temperature are significant. The freezing injury caused by -16 ℃ to -10 ℃ mainly affects the activity of the main bud of the resulting parent shoot. When the temperature is below -18 ℃, the low temperature damages the activity of main and secondary buds. And when the temperature is below -20 ℃, a large number of parent shoots are killed by low temperature injury. Among varieties, the frost resistance of Hayward is the strongest, Ruiyu, Jinfu and Xuxiang are the middle, and Cuixiang and Hongyang are the weakest. Taking the freezing injury index of the resulting parent branch bud as the main parameter, the 5-grade low temperature freezing injury index of the resulting parent branch is constructed by different varieties. Its freezing temperature ranges of level 1-5 are -11.0 ℃ to -10.5 ℃, -14.5 ℃ to -10.5 ℃, -16.5 ℃ to -12.0 ℃, -20.0 ℃ to -13.5 ℃, -20.0 ℃ to -15.0 ℃, respectively.

Abstract:
keep_len="250">Peanut is an important kind of food, oil plants and cash crops which promotes the sustainable development in the modern agricultural economy. In recent years, the frequency and intensity of drought disaster and waterlogging disaster in the Huang-Huai-Hai Region has remarkably increased, which poses a huge impact on the production of spring peanut. Therefore, analyzing and making hazard assessment of drought disaster and waterlogging disaster during the growth period of spring peanut has a great significance for preventing drought disaster and waterlogging disaster, minimizing the damage, and taking disaster insurance in this region. Based on the daily meteorological data of 186 meteorological stations from 1960 to 2019 and combined with the growth data of spring peanut, the hazard assessment of drought disaster and waterlogging disaster is divided into 7 grades by using the standardized precipitation requirement index(I_SPR), and the spatial and temporary distribution characteristics of drought disaster and waterlogging disaster in Huang-Huai-Hai Region are analyzed. Based on the probability and intensity of drought disaster and waterlogging disaster, hazard index is constructed to evaluate the hazard during the growth period of spring peanut. High incidence of drought disaster is found in the northwestern and central part of the Yellow River Basin, the northeastern part of the Huaihe River Basin and the northern part of the Haihe River Basin. While the areas with high incidence of waterlogging disaster are mainly concentrated in most areas of the Yellow River Basin, the northern, southern part of the Huaihe River Basin and eastern part of the Haihe River Basin, and mainly with moderate waterlogging disaster. The areas with high hazard of drought disaster during the growth period of spring peanut are scattered, mainly concentrated in the northwest of the Yellow River Basin. While the areas with medium and high hazard of waterlogging disaster during the growth period of spring peanut are mainly distributed in the Yellow River Basin. In the northern and central planting areas of the Yellow River Basin, the phenomenon of drought disaster and waterlogging disaster abrupt alternation often occurs. Therefore, it is necessary to reduce the hazard of drought disaster and waterlogging disaster abrupt alternation, regulate crop exposure in planting areas, reduce the vulnerability of crop, improve the overall disaster prevention and mitigation capabilities, and promote management level in planting areas. The above research results can provide a reference for the drought disaster and waterlogging disaster prevention and loss reduction during the growth period of spring peanut and the construction of security production guarantee method system in Huang-Huai-Hai Region. Peanut is an important kind of food, oil plants and cash crops which promotes the sustainable development in the modern agricultural economy. In recent years, the frequency and intensity of drought disaster and waterlogging disaster in the Huang-Huai-Hai Region has remarkably increased, which poses a huge impact on the production of spring peanut. Therefore, analyzing and making hazard assessment of drought disaster and waterlogging disaster during the growth period of spring peanut has a great significance for preventing drought disaster and waterlogging disaster, minimizing the damage, and taking disaster insurance in this region. Based on the daily meteorological data of 186 meteorological stations from 1960 to 2019 and combined with the growth data of spring peanut, the hazard assessment of drought disaster and waterlogging disaster is divided into 7 grades by using the standardized precipitation requirement index(I_SPR), and the spatial and temporary distribution characteristics of drought disaster and waterlogging disaster in Huang-Huai-Hai Region are analyzed. Based on the probability and intensity of drought disaster and waterlogging disaster, hazard index is constructed to evaluate the hazard during the growth period of spring peanut. High incidence of drought disaster is found in the northwestern and central part of the Yellow River Basin, the northeastern part of the Huaihe River Basin and the northern part of the Haihe River Basin. While the areas with high incidence of waterlogging disaster are mainly concentrated in most areas of the Yellow River Basin, the northern, southern part of the Huaihe River Basin and eastern part of the Haihe River Basin, and mainly with moderate waterlogging disaster. The areas with high hazard of drought disaster during the growth period of spring peanut are scattered, mainly concentrated in the northwest of the Yellow River Basin. While the areas with medium and high hazard of waterlogging disaster during the growth period of spring peanut are mainly distributed in the Yellow River Basin. In the northern and central planting areas of the Yellow River Basin, the phenomenon of drought disaster and waterlogging disaster abrupt alternation often occurs. Therefore, it is necessary to reduce the hazard of drought disaster and waterlogging disaster abrupt alternation, regulate crop exposure in planting areas, reduce the vulnerability of crop, improve the overall disaster prevention and mitigation capabilities, and promote management level in planting areas. The above research results can provide a reference for the drought disaster and waterlogging disaster prevention and loss reduction during the growth period of spring peanut and the construction of security production guarantee method system in Huang-Huai-Hai Region.