Extreme heavy rainfall events are occurring with heightened frequency due to intensified global climate warming, posing growing risks to public safety and social development. It is of great significance for disaster prevention and reduction to study the short-term heavy rain. Based on the precipitation data from regional automatic stations in Hubei Province, short-term heavy rain probability forecast products, and mesoscale high-resolution numerical model data, this study adopts neighborhood optimal probability and multi-model integration methods for the short-term heavy rainfall location forecasting in Hubei Province with a lead time of up to 12 h. The results show that the neighborhood method obviously improves the prediction accuracy of the mesoscale numerical model for short-term heavy rain, with the area neighborhood method outperforming the single-point neighborhood method. The optimal area probability of CMA-MESO, CMA-SH9 and WH-RUC modes are all 5%, and the optimal neighborhood radius is 50, 60 and 60 km respectively. The multi-mode integration method shows significant improvement compared to the single-point neighborhood method with one model. The threat scores for all lead times indicate positive forecast skill, improving by 0.014 and 0.020 from April to September in 2023 and 2024, respectively. The improved multi-model integration method shows a substantial increase in probability of detection, especially in accuracy of various severe convection prediction in Hubei Province on August 7, 2023 and June 28, 2024.
Rainstorm patterns and extreme rainfall return periods are important meteorological parameters for cities response to rainstorm and flood disasters. Based on the 60-year minute-by-minute precipitation data from the national meteorological stations in Xining, the fuzzy recognition method and statistical analysis were used to classify and analyze the characteristics of rainfall processes in Xining. The Gumbel distribution, Pearson-III distribution, and generalized extreme value distribution were applied to fit the probability distributions of the annual maximum rainfall extremes for 11 different durations. The goodness of fit for each distribution was evaluated, and the return periods of rainfall extremes for each duration were estimated. The results show that, from 1954 to 2022, there were a total of 95 rainstorm events in Xining, with cumulative rainfall ranging from 25 to 40 mm. The number of rainstorm days showed significant interannual variation, with an overall increasing trend. The rainstorm pattern was predominantly unimodal, accounting for 85.3% of total rainstorm events, which mainly occurred from July to September. In the unimodal pattern, the peak rainfall was primarily concentrated in the early to mid-phase of the rainfall process. The high rainfall intensity occurred from 20:00 to 09:00 the following day, while the high-frequency rainfall periods were between 23:00 and 09:00 the following day, although these two periods did not fully coincide. The probability distributions of rainfall extreme values for different durations obeyed the Gumbel, Pearson-III, and generalized extreme value distributions well, and the Pearson type III distribution had the highest fitting accuracy. The maximum hourly rainfall for Type I events reached 43.7 mm, exceeding the 100-year return period; for Type II events, the maximum hourly rainfall was 38.1 mm, exceeding the 50-year return period; and for Type V events, the maximum hourly rainfall was close to 20.0 mm, approaching the 20-year return period. The maximum hourly rainfall for other patterns ranged from the 2-year to the 10-year return period. The identification of rainstorm patterns and extreme rainfall return periods in Xining can provide valuable scientific data for urban flood prevention and outdoor drainage system design.
It is of great significance to study the deviation characteristics in intelligent grid forecasting of precipitation during the flood season in Hedong area of Gansu for improving the accuracy level of regional precipitation forecasting and warning, and enhancing the ability of disaster prevention and reduction services. By using precipitation data in flood season from 1 766 automatic meteorological observation stations in Hedong area, 264 precipitation cases from 2018 to 2020 were selected. Based on the 3-hour interval intelligent grid precipitation forecasts issued by the Central Meteorological Observatory, the Contiguous Rain Area (CRA) of forecast field and the actual field are identified and matched, and classified according to hit, miss and false alarm, to further study the CRA deviation characteristics. The results show that, for the precipitation cases hit by the forecast, the falling area deviation of the intelligent grid forecast of the warm forcing precipitation is the largest, followed by precipitations of the oblique frontal generation and cold forcing categories. The intensity deviation of the cold forcing precipitation is the largest, followed by precipitations of the warm forcing and baroclinic frontogenetic categories. The maximum morphological deviation is found in baroclinic frontogenetic precipitation, followed by the cold forcing and warm forcing categories. The forecast area of the warm forcing and oblique frontal precipitations is biased towards the north and east, while the cold forcing precipitation is biased towards the south and east. The forecasted precipitation area of three types precipitation is larger for the beta scale and below, and smaller for the the alpha scale. Forecasters can establish localized model correction schemes based on the results of CRA spatial testing to improve the service capability of intelligent grid precipitation forecast.
Studying the characteristics of temperature differences on bridge decks and their prediction models can provide decision-making basis for traffic management departments to predict severe weather and reduce traffic accidents. Based on observation data from three traffic meteorological stations on the Tianxingzhou Bridge section in Wuhan over the past three years, including the minimum temperature, air temperature, wind speed, precipitation, etc., for every five minutes, the daily differences in the minimum temperature between the bridge deck and the road surface, the hourly variation characteristics of typical weather cases, and the temperature change patterns under different weather conditions are analyzed. The prediction models for the minimum temperature of the bridge deck are established by using multiple linear regression and BP (Back Propagation) neural network methods, and the models are driven and tested using intelligent grid minimum temperature prediction products. The results indicate that due to differences in engineering structure, pavement material, geographical environment, and environmental meteorological factors, the temperature of the bridge deck is usually lower than that of the pavement, and the temperature difference between the two is the largest under sunny conditions. The speed at which the temperature on the bridge deck drops below freezing point is faster, and the duration of low temperature maintenance is longer. Both multiple linear regression and BP neural network methods can achieve good prediction results. Among them, BP method is more suitable for scenarios that require high prediction accuracy, while multiple linear regression method is suitable for applications that require high prediction accuracy.
Heavy rainfalls and floods, waterlogging triggered by rainstorms are one of the most serious natural disasters in Ningxia. This paper constructs an integrated rainstorm hazard risk warning model in Ningxia to effectively predict the risk of heavy rainfall and issue risk warnings in advance, by using the Analytic Hierarchy Process (AHP) and Delphi method, and considering four factors including hazard, exposure, vulnerability, and disaster mitigation capacity, the model incorporates 14 evaluation indicators such as population, economy, elevation, and vegetation etc. in Ningxia. Combined with GIS technology, a rainstorm event simulation was conducted. The results show that the model comprehensively and objectively reflected integrated risk distribution during rainstorms. The analysis of the rainstorm on July 10, 2022 indicated that the regions with the highest hazard were Jinfeng District and Xixia District of Yinchuan, Litong District of Wuzhong, Qingtongxia County, eastern Yanchi County, and Yuanzhou District of Guyuan; the highest exposure was in Yinchuan; the highest vulnerability was in western Qingtongxia, Tongxin County, Haiyuan County, Xiji County, and Pengyang County; the weakest disaster mitigation capacity was in Haiyuan County, and the highest integrated risk areas were Jinfeng District of Yinchuan, Litong District of Wuzhong, Haiyuan and Xiji County. Integrating the model with smart grid forecasting, the integrated rainstorm hazard risk can be calculated, which provides scientific basis for precise prevention in practical operations.
To improve the fine-grained forecasting and early warning level of high-impact weather in cities, this paper utilizes 7 categories of data from the HMB-KPS millimeter-wave cloud radar at Rongcheng meteorological station of Xiong’an, including radar reflectivity factor, radial velocity, etc and surface meteorological observations, as well as weather analysis, radar echo image morphology analysis and other methods to analyze the major high-impact weathers in Xiong’an from January 2020 to December 2023， such as torrential rains，strong winds，fog，haze，and sand-dust weathers.The results are as follows: 1) The millimeter-wave cloud radar clearly and stereoscopically detects the structural distribution of cloud systems in high-impact weather including precipitation. In spring, summer and autumn, when clouds with echoes intensity equal to or more than 21 dBZ (15 dBZ in winter) and vertical extension height equal to or more than 5 km (2 km in winter) appear, and the lowest cloud base reaches 150 m, it can be considered as the characteristic index of start time of surface precipitation, and the average time advance is about 20 minutes. Conversely，when echo intensity weakens and cloud base height rises, precipitation tends to end. Among all levels of precipitation events, torrential rains have the maximum vertical extensions, liquid water content, etc. 2) When the extension height of the echo top reaches more than 8 km and then the height of the cloud top falls rapidly to less than 2 km, the ground is prone to strong winds of more than 8 magnitudes. The echo characteristic of precipitation accompanied by strong winds is that obvious U or V notch is formed in the strong echo area, and the maximum radial velocity in the notch area is 12.5 m·s-1. 3) Cloud radar inversion droplet spectrum data products can better reflect the particle distribution characteristics of precipitation with different levels. The Vertically Integrated Liquid Water (VIL) has an early warning significance for precipitation and wind, and the advance is 0-30 min or more. 4) For non-precipitation high-impact weather (fog, haze, sand-dust), the millimeter-wave radar echoes are overall weaker, with indistinct transitions between weather phenomena. However, during weather transitions, the radar can rapidly and clearly detect the precipitation clouds, improving fine-grained forecasting and early warning capabilities of precipitation.
In order to further understand the prediction ability of current numerical prediction models, this paper selects 47 heavy rainfall processes that occurred in the Sichuan Basin from 2018 to 2020 and classifies them, then based on merged precipitation products and ground observation data, the prediction ability of European Centre for Medium-Range Weather Forecasts (ECMWF), China Meteorological Administration Mesoscale Model (CMA_MESO) and Southwest Center WRF ADAS Real-time Modeling System (SWC_WARMS) models in the range, intensity, extreme value, time and displacement deviation of heavy rainfall processes is validated and assessed by using threat score, space-time sliding and other methods. The results show that the 08:00 (UTC+08:00) prediction of each model is better than the 20:00 (UTC+08:00) prediction, the ECMWF is better in moderate rain and heavy rain prediction, the SWC_WARMS has a higher score in the rainstorm prediction. The prediction range of moderate rain by various models is generally larger than the actual, and gradually turns to underestimate with the increase of magnitude, in which SWC_WARMS is closer to the actual. For rainfall intensity, the average precipitation and extreme value of ECMWF and CMA_MESO are generally smaller than the actual, and the prediction of SWC_WARMS is closer to the actual. The time deviation of predictions of three models is not obvious, only a few initial forecast times have a time deviation of -6 to 3 h, the displacement deviation of ECMWF products is the smallest, the prediction of ECMWF and SWC_WARMS are mainly northerly in latitudinal direction, while in meridional direction, the prediction of ECMWF is mainly to the west, and the predictions of CMA_MESO and SWC_WARMS are mainly to the east.
In order to measure the ability of numerical models to forecast heavy precipitation processes, the four prediction products of the European Centre for Medium-Range Weather Forecasts (ECMWF), China Meteorological Administration Mesoscale Model (CMA-MESO 3KM), Southwest Center WRF ADAS Real-time Modeling System (SWC), China Meteorological Administration for Global Forecast System (CMA-GFS) are selected, and 93 heavy precipitation processes (≥25 mm·d-1) in Sichuan Province from 2018 to 2020 are tested from the aspects of precipitation location, precipitation area and precipitation intensity using the target object test method, and on this basis, the prediction ability of models for 36 h forecast aging is discussed. The results are as follows: (1) With the approaching of forecasting time, the average forecast level of each model is higher, and the position of rain belt is better grasped. (2) The prediction ability of each model for frontal precipitation process is better, while for warm-region precipitation process it is poor. (3) The heavy warm-region precipitation process can be corrected based on the large-scale model forecasts combined with the local mesoscale model products. For frontal precipitation process, on the basis of forecast of ECMWF model, the precipitation area and magnitude of heavy rain and above are adjusted according to the location of rain belt in CMA-MESO 3KM model.
In order to improve the accuracy of quantitative precipitation forecasting in Shandong Province, the deep feedforward neural network (DFNN) and the optimal threat score (TS) weight ensemble method for precipitation grading were used to study the multi-model ensemble precipitation forecasting. Four groups of DFNN (ES, EM, SM, ESM) deep learning models were obtained by using the 24-hour cumulative precipitation forecast of Global Numerical Prediction System of the European Centre for Medium-Range Weather Forecasts, the Shanghai Numerical Prediction Model System of the China Meteorological Administration and the Mesoscale Numerical Weather Prediction System of the China Meteorological Administration from April to September 2019 for supervised training, and the Mul-OTS (Multi-mode Optimal Threat Score) integrated model was established by using the optimal TS weight integration method of multi-model precipitation classification. The down-scale grid prediction was made by using the accumulated precipitation of each model for 24 h from April to September 2020, and the comparison test and case analysis of five integrated schemes were carried out. The results show that the average relative error was reduced by the five integrated schemes with different starting time and lead times. The ESM scheme was the best, and the Mul-OTS scheme was the worst. All the four groups of DFNN schemes improved the accuracy of sunny and rainy prediction, the ESM scheme was the best, and the Mul-OTS scheme was lower than the model forecast. The four groups of DFNN schemes all improved the TS and ETS scores of each precipitation grade, and the improvement amplitude of weak precipitation was greater than that of strong precipitation. The Mul-OTS scheme was a negative technique for the correction of small precipitation levels, and the correction effect was better for the correction of large precipitation levels, but it was still inferior to the ESM scheme. A case study found that the ESM scheme for precipitation intensity and fall area forecast was superior to other integrated schemes. Therefore, the optimal ESM scheme was adopted to establish a quantitative precipitation grid forecasting system, which provides important support for intelligent grid forecasting.
The error test of gust forecast has a certain guiding significance for the refined forecast correction in practice, and provides a reference for how to eliminate the influence of the daily variation of error in the refined forecast. The 10 m gust and 10 m average wind forecast data of the European Centre for Medium-Range Weather Forecast (ECMWF) are selected from the fine grid for 3-72 h day by day from 2017 to 2019, and based on the real maximum wind data 3 hours by 3 hours of 9 national meteorological observation stations in Dalian area, the error test of forecast is analyzed. The results are as follows: According to the forecast error test based on the forecast and the actual situation, the mean error（ME）of the ECMWF fine grid forecast is 0.96 m·s-1, which indicates that the forecast is larger on the whole. However, the statistical conclusions of the forecast errors of the two classifications are inconsistent for each wind level, and the test according to the forecast wind level is more consistent with the actual forecast work based on the model forecast. According to the statistical test of the forecast, the forecast errors of each wind direction, each wind level and each station are obviously different. The larger the wind level is, the greater the degree of forecast bias is, and wind direction also shows the trend of error increasing with wind level increase. The average error of gust forecast has obvious daily variation, with the largest error around 08:00 and the smallest error around 20:00, which is mainly caused by the daily variation of the average error of 10 m average wind prediction. The correlation coefficients between all forecast cases and the observations for each predictive aging are above 0.7, and when it comes to each wind level and wind direction, the correlation of each wind direction is good, but the correlation of each wind level is significantly reduced, and the reliability of the wind forecast of the magnitude 8 and above is decreased greatly.
In order to better carry out the road icing prediction and early warning services, the hourly observation data of traffic meteorological stations in the high incidence area of road icing in Gansu Province (the east of Wuwei, Gansu) were used to analyze the spatial and temporal distribution characteristics of road icing, explore the correlation between road icing and meteorological factors, and construct the road icing warning model by using Logistic regression method and neural network algorithm. The results showed that road icing in Gansu Province occurred mainly in winter (from December to February of the following year), and the frequency of road icing was higher from 00:00 to 10:00 and from 22:00 to 23:00. Logistic regression model and neural network model had high prediction accuracy for non-icing events, with 91.9% and 96.2%, respectively. For the occurrence of icing events, the prediction accuracy of Logistic regression model was low, at 31.6%, while that of neural network model could reach 44.6%, indicating that the two models had certain indicative significance for road icing warning, and the prediction effect of neural network model was better than that of Logistic regression model.
In order to make a further understand of the difference and representativeness between gridded merging real-time product and observed data, the paper evaluated and corrected the CLDAS 2 m temperature merging product based on the observational data of automatic stations in Lanzhou and Wuwei region. The results are as follows: (1) The hourly temperature and daily minimum temperature products are lower than observations, and these errors decrease with the altitude going up below 2 500 m. The mean error of the daily maximum temperature product is negative around the altitude of 1 500 m, and changes to positive values above 1 500 m, then the positive mean error increases with the increase of altitude. The errors of daily maximum and minimum temperature are larger than those of hourly temperature, but their mean errors are all within 2 ℃. (2) The near gridding validation further shows that the diurnal change of CLDAS hourly temperature is generally similar to observations in the daytime, while it is relatively 0.2 ℃ lower than observations at night. The daily average temperature of CLDAS merging product is generally lower about 1 ℃, and the negative deviation in Lanzhou urban area is relatively small. Spatial distribution of high temperature days above 30 ℃ of merging products is basically consistent with observations, but there are more actual high temperature days in Lanzhou urban area. (3) Both the linear regression and the decaying averaging method have a certain correction effect on CLDAS temperature merging products, and the latter has a better correction effect. The correction effect becomes better with the altitude increasing. To sum up, the CDLAS temperature merging products can better reflect the characteristics of actual temperature change in Lanzhou and Wuwei region, but its ability to reflect the daily maximum and minimum temperature is not as good as the hourly temperature, and the error is relatively large in complex terrain.
It is difficult to forecast heavy precipitation under complex terrain in mountainous areas， which formation mechanism is complicated， and often brings serious geological disasters. Based on conventional observation data， European Centre for Medium-Range Weather Forecasts ERA5 reanalysis data， FY-4A satellite cloud imagery， Doppler radar data and forecast products from various models， the factors contributing and model forecasting performance of local short-time heavy precipitation process in the Hanjiang Basin of southern Shaanxi from the night on 3 to the early morning on 4 June 2022 were examined and analyzed. The results are as follows： （1） This process is a short-time heavy precipitation triggered by the front in the Hanjiang Basin of southern Shaanxi. Due to shallow convection instability and weak vertical wind shear， the heavy precipitation exhibited localized characteristics with significant intensity. The accumulated precipitation in 12 hours exceeds 50 mm in many stations， with a maximum of 104.8 mm. （2） The two ends of the front are blocked by the topography and move slowly and are difficult to cross the high mountains. Consequently， convection is continuously triggered within the basin， generating heavy precipitation， and the secondary circulation formed in the surface layer of the basin can enhance convective activity. （3） A cold pool formed in the front of front continuously triggers the backward propagation of new convective cells within the basin to form a train effect. Meanwhile， the intense radar reflectivity factor， exceeding 50 dBZ， is located below the 0 ℃ isotherm level， which has high precipitation efficiency and prolonged duration， thus bringing a short-time heavy precipitation with a maximum of 62.6 mm·h-1. （4） Global models displayed limited capability in forecasting this process， while mesoscale regional models can reflect the characteristics of frontal convection and precipitation， especially CMA-TRAM and CMA-GD models can reflect the triggering and development trend of local strong convection well. However， the intensity and organization of the convective system induced by the frontal cold pool of the front still have substantial forecast deviations.
The global climate model BCC-CSM2-MR (Beijing Climate Center-Climate System Model version 2-Medium Resolution) independently developed by the National (Beijing) Climate Center, which has participated in the Climate Model Inter-comparison Project Phase 6. Based on the BCC-CSM1.1m version, the BCC-CSM2-MR model is optimized in aspects of atmospheric radiation transport scheme, deep convection processes and gravity wave drag. Therefore, the improvement of the model’s ability to simulate precipitation and temperature in East Asia needs further assessment. Utilizing gridded observational datasets and station observations in China, the paper thoroughly compares the performances of BCC-CSM2-MR and BCC-CSM1.1m in simulating seasonal mean precipitation (temperature) and daily precipitation (temperature) extremes in East Asia. The results are as follows: (1) Compared with the BCC-CSM1.1m, the BCC-CSM2-MR improves the model performance in simulating seasonal mean precipitation in most sub-regions of East Asia, especially for summer precipitation in the Tibetan Plateau. In particular, the model can better reproduce the annual cycle of precipitation in southeastern China, the Korean Peninsula and Japan. (2) The ability of the BCC-CSM2-MR to simulate the seasonal mean temperature in East Asia has not been improved significantly, and the simulated biases of monthly temperature change in most sub-regions of East Asia are greater than those of the BCC-CSM1.1m. (3) In terms of daily extreme precipitation (temperature), the simulation ability of the BCC-CSM2-MR is obviously better than that of the BCC-CSM1.1m, which significantly improves the simulation ability of daily extreme precipitation (temperature) in southeast China. Overall, the improvement of the BCC-CSM2-MR in deep convection process parameter scheme is beneficial to the simulation of precipitation in East Asia.
Based on the rainfall station observations and the products of Multi-source Merged Precipitation Analysis System of China Meteorological Administration (CMPAS), eight kinds of satellite-based precipitation products (FY-4A, CMOPRH-RT, IMERG-Early, IMERG-Late, GSMaP-Now, GSMaP-Gauge, PERSIANN-Now, PERSIANN-CCS) are comprehensively evaluated during the record-breaking extremely heavy precipitation process in East Gansu on July 15, 2022 by using quantitative analysis, classification and structural similarity methods. The results show that eight kinds of satellite-based precipitation products basically reflect the spatial distribution characteristics of precipitation with more in the central and eastern regions and less in the northwest. Except for the GSMaP-Now product, the other seven satellite-based precipitation products all underestimate the precipitation at the center of the rainstorm. The eight kinds of satellite-based precipitation products have a good ability to describe the peak value of heavy precipitation, and both peak stages of the heavy precipitation process are reflected, but all of them seriously underestimate the magnitude of heavy rainfall and above. For precipitation of different magnitudes, the GSMaP-Gauge is the best for estimating precipitation of magnitude below torrential rain, while the CMOPRH-RT is the best for heavy rain and above, and all products cannot correctly hit the precipitation of torrential heavy rainfall. In terms of the structural similarity index, the CMOPRH-RT product can best represent the structural distribution of the precipitation process from three aspects of total precipitation, precipitation magnitude, and precipitation morphological distribution. In summary, for this precipitation event, the CMOPRH-RT precipitation product had the best performance in all aspects.
The interpolation of meteorological observation data is an important technique to improve data integrity and recover authenticity of missing values. The applicability analysis of three interpolation methods namely standardized series, spatial regression and random forest to daily mean temperature series in five major climatic divisions and monthly mean temperature series at two centennial stations in Qianwei and Beibei is carried out in order to improve the accuracy of temperature interpolation in southwestern China, and four test indicators including mean absolute error, root mean square error and the proportion of samples (P0.8 and P0.5) with the bias between the interpolation value and the observation within ±0.8 ℃ and ±0.5 ℃ are used to evaluate. The results show that three interpolation methods are better in interpolating daily mean temperature in five climatic zones and monthly mean temperature at two centennial stations in southwestern China, among them the spatial regression method has the highest accuracy and the best applicability, and its interpolation accuracy is higher than those of other two methods in five climatic zones. The P0.8 test indicator of daily mean temperature interpolated by the spatial regression method reaches about 0.90 in Sichuan Basin with a relatively flat topography, and it reaches more than 0.60 in mountainous area in southwestern Sichuan and northern Yunnan with a most rugged topography, which indicates that the terrain has obvious influence on the accuracy of temperature interpolation. The optimal numbers of reference stations can effectively reduce interpolation errors, and the interpolation errors of more than 95% samples at centennial stations can be controlled within ±0.5 ℃.
In order to improve correction ability and forecasting level of intelligent grid. Based on the slice data of Gansu Province of objective guidance product from Central Meteorological Observatory of China and daily grid temperature data from Chinese Land Data Assimilation System Version 2.0 (CLDAS-V2.0) of CMA, the maximum and minimum temperature of 0.05°×0.05° grid points in the eastern Hexi Corridor (101.0°E-104.5°E, 36.0°N-40.0°N) were corrected, tested and evaluated by using Kalman filtering method and sliding training correction method. The results are as follows: (1) For seasonal comparison, the mean absolute errors of maximum and minimum temperature of Kalman filter and sliding training correction products were both smaller than objective guidance product at all seasons, and all values were less than 2.00 ℃. The forecast accuracy of maximum and minimum temperature of Kalman filter and sliding training correction products were greater than 70% at all seasons. which the maximum temperature was 6%-13% higher and the minimum temperature was 8%-24% higher. (2) For spatial comparison, the mean absolute errors of the maximum and minimum temperature of Kalman filter and sliding training correction products were 1.00-2.00 ℃, but greater than 2.00 ℃ in a few areas. The forecast accuracy of maximum (minimum) temperature of Kalman filter and sliding training correction products were greater than 70% (60%-70%) in most areas, and greater than 80%(70%) in a few areas. (3) As a whole, the correction skills of maximum and minimum temperature of Kalman filter and sliding training correction products were basically positive, and were greater than 0.300 in a few seasons and a few areas. It showed that the two correction methods have good prediction and correction ability, which can provide certain technical support for the future temperature forecasting operations.
The CMA land data assimilation system (CLDAS) provides high spatio-temporal resolution datasets, which offers valuable data support for the fine meteorological services, while the applicability assessment of data is an important basis for its application. Based on CN05.1 gridded observation data from the National Meteorological Information Center and observation data at 119 national meteorological stations in Inner Mongolia, the applicability of 2 m mean temperature and precipitation products of CLDAS in Inner Mongolia was examined and evaluated, and was compared with ERA5 from the European Centre for Medium-range Weather Forecasts (ECMWF) and the CRU TS (Climatic Research Unit gridded Time Series) reanalysis data from the UK. The results indicate that three datasets can effectively reflect the spatial distribution characteristics of annual precipitation and annual mean temperature in Inner Mongolia, but they underestimate annual precipitation and overestimate annual mean temperature in most areas, and CLDAS datasets can also describe the influence of terrain change on temperature and precipitation. The spatial distributions of precipitation variability from CLDAS and CRU TS data are better than that from ERA5 data in Inner Mongolia. The linear trends of CRU TS and ERA5 temperature data are similar to CN05.1 observation data, but the warming rates are higher than observations, while the CLDAS temperature product shows the cooling trend in local areas of Inner Mongolia. Whether monthly or seasonal scale, the correlation coefficients between CLDAS precipitation, mean temperature and observation values at 119 stations in Inner Mongolia are higher than those of CRU TS and ERA5 data, and their average absolute errors are smaller than those of CRU TS and ERA5 data. Compared with the station observation data, the largest errors of CLDAS temperature and precipitation products appear in the Hetao region of Inner Mongolia.
In order to explore the forecasting method of precipitation phases in the winter half-year under the condition of complex topography, and further improve the spatio-temporal resolution and forecast accuracy of precipitation phases, the observation data at 11 national weather stations of Baoji City in the winter half-year (from November to next March) during 2010-2019 were used to analyze the spatio-temporal distribution characteristics of precipitation phases including rain, sleet and snow. Combined with the fifth generation atmospheric reanalysis data from European Centre for Medium-Range Weather Forecasts in the same period, the identification factor and its threshold of precipitation phases was selected and confirmed. On this basis of that, a fine objective forecast method of precipitation phases was established, and the prediction effect was tested. The results show that the rainfalls were more during the early and late winter in Baoji City, the proportions of precipitation days with three phases were similar in rain-snow transformation period, while the snowfalls were more in midwinter period. The spatial distribution of precipitation phases was closely relevant to topography, with more rainfalls in low altitude Chuanyuan region on both sides of Weihe River and more snowfalls in southern and northern high altitude mountainous areas. The surface temperature (T2), 850 hPa and 700 hPa temperature (T850, T700) and geopotential thickness from 1 000 hPa to 850 hPa and 850 hPa to 700 hPa (H850-1000, H700-850) were selected to identify precipitation phases in the winter half-year in Baoji City. The T2 (H850-1000) thresholds of rain were 2.9 ℃ (1 307 gpm), 2.1 ℃ (1 308 gpm) and 1.8 ℃ (1 310 gpm) in the early and late winter, rain-snow transformation and midwinter periods at Weibin station in Chuanyuan region, respectively, while the thresholds of snow were 0.7 ℃ (1 302 gpm), 0.3 ℃ (1 303 gpm) and 0.7 ℃ (1 308 gpm), respectively. However, the phase identifications at Taibai station in mountainous area were different from Weibin station, the T2 (H700-850) thresholds of rain were generally greater than 2.6 ℃ (1 551 gpm) during the rainfall and less than -0.3 ℃ (1 540 gpm) during the snowfall at Taibai station, and their thresholds of sleet were generally between rain and snow. In addition, they must be associated with T850 and T700 to determine the phase transformation of rain and snow. The fine objective forecast method of precipitation phases in each period of the winter half-year in different topography areas of Baoji City was established based on the combined criterion of temperature and geopotential thickness, which could predict accurately hourly precipitation phases in the winter half-year from November 2020 to January 2022, with threat score (TS) up to 100% at Weibin station and more than 80% at Taibai station, which was better than a single physical quantity ( temperature or geopotential thickness).
Fog and haze are disaster weathers which endanger human health and affect social and economic development. Accurate and detailed monitoring data can play an important role in the prevention and control of fog and haze. The accuracy of China Meteorological Administration Land Data Assimilation System (CLDAS) visibility and relative humidity fusion products in identifying fog, light fog and haze is analyzed by using the observation data of national stations in Tianjin and its surrounding areas from December 1, 2017 to November 30, 2020, Himawari-8 L1 full-disk data and L3 aerosol optical depth product. The results show that compared with the station observation data, the average detection rates of CLDAS products in identifying light fog, fog and haze are 90.4%, 84.2% and 78.8%, respectively. The detection rates of light fog in different months are 81.1%-96.4%. In the months with more fog and haze, the detection rates are about 80.0%. The cases analysis shows that the fog, light fog and haze identified by CLDAS products are basically consistent with the results of Himawari-8 satellite and observations. The failure of CLDAS products to correctly identify fog, light fog and haze mainly shows that fog is misjudged as light fog (3.8%-21.4% at different stations) and haze is missed (8.6%-25.0% at different stations). When the horizontal visibility of the station is between 0 and 0.75 km, the error of CLDAS visibility mainly causes fog to be mistakenly identified as light fog. When the horizontal visibility of the station is between 0.75 and 7.5 km，the error of CLDAS visibility mainly leads to haze being missed. When the station visibility is between 7.5 and 15 km, the error of CLDAS visibility mainly leads to light fog and haze being reported empty. When the relative humidity of the station is greater than 40% and less than or equal to 60%, the error of CLDAS relative humidity mainly leads to haze being misjudged as light fog. In general, the accuracy of CLDAS products in identifying fog, light fog and haze in Tianjin area is good, which can provide reference for fine monitoring of fog, light fog and haze, and improve the status quo of scarce visibility observation stations and insufficient space coverage in fog and haze monitoring.
In order to implement the localized application of ECMWF (European Centre for Medium-Range Weather Forecasting) model well and improve the accuracy of precipitation forecast in Sichuan Province, the systematic deviation characteristics of forecast of precipitation with various magnitudes from ECMWF model were analyzed from July to September during 2020-2021. The result shows that the rain days forecasted by ECMWF model are more than the observations in Sichuan Province from July to September during 2020-2021, especially in Panxi region and western Sichuan Plateau. The heavy rain days forecasted by the model are more than the observations in southwestern Basin and Panxi region, while they are less than the observations in southern Basin. Then, the correction experiment about 24-hour cumulative precipitation forecast was carried out based on quantile mapping method, and it was applied to heavy rainfall forecast. After the correction using quantile mapping method, the TS (Threat Score) of forecast of rainstorm and above is improved by 7%-15%, and the TS of forecast of precipitation with various magnitudes is 2%-4% higher than the multi-model integrated objective forecast products. The POD (Probability of Detection) of forecast of heavy rain, rainstorm and above is improved by 10%-20%. The corrected location of rain belt in particular rainstorm areas is closer to the actual.
The ECMWF-HR cloud forecast products are verified by using the total cloud cover inversion products of FY-2G satellite from October 2019 to September 2020 and the diurnal variation characteristics of ECMWF-HR total cloud cover products in the central and eastern parts of Northwest China are diagnosed in selected key areas to provide references for the application of cloud forecasting. The results show that the total cloud forecasted by the ECMWF-HR is relatively stable and has obvious diurnal characteristics in the study area. Forecast deviation is small in the daytime and at night it increases by 10%-20%. Meanwhile, there are obviously seasonal characteristics of cloud forecast product, and it has positive deviation in summer half year and the spatial distribution of the deviation is even. It shows regional distribution characteristics in winter half year with negative deviations in the western Qilian Mountains and positive deviation in Gansu and the south part of Shanxi, and the deviation is lower in winter half year than in summer half year in other areas. In general, the cloud forecast product of ECMWF-HR is relatively reliable in the study area, but in two regions, there are significant anomalies. Total cloud forecast needs to be increased by about 10%-30% in the western Qilian Mountains and decreased by about 20%-30% in Gansu and the south part of Shanxi on the base of ECMWF-HR product. The model correction results are relatively close to the satellite inversion results, with an average absolute deviation of 4.5% and similar diurnal variation characteristics.
The fixed-point refined analysis and forecast evaluation of meteorological elements are of great significance to the meteorological guarantee of major sports events. Based on the precipitation analysis product of three-source fusion data from the National Meteorological Information Center and the ERA5 reanalysis data from European Centre for Medium-Range Weather Forecasts (ECMWF), the characteristics of meteorological elements were studied at the key venues (Xi’an Olympic Sports Centre, Yan’an University Gymnasium and Hanjiang open water area in Ankang) of the 14th National Games, and the prediction performance of precipitation, temperature and wind products of ECMWF, China Meteorological Administration Mesoscale Model (CMA-MESO) and grid-guided precipitation forecast products (SCMOC) was inspected at the three key venues. The main conclusions are as follows: (1) The probability of precipitation at the three key venues was all high in the historical period of the 14th National Games. On the opening and closing days at Xi’an venue, the probability of precipitation was 46% and 44% and the average daily precipitation was 24.6 mm and 9.8 mm, respectively, and the peak of precipitation and precipitation probability mostly appeared from afternoon to evening. (2) The temperature was relatively low at night and increased rapidly in the daytime, and the daily average temperature mostly fluctuated between 12 ℃ and 18 ℃ at the three venues during the 14th National Games, which is generally appropriate to race. The easterly or southerly winds prevailed at the three venues, and the wind speed at Xi’an and Ankang venues was low, which is suitable to sport events, while the frequency of wind force above grade 4 at Yan’an venue was higher, which may have an adverse effect to sport events. (3) In general, the rain probability prediction accuracy of SCMOC at the three venues was the highest in the historical same period of the 14th National Games, but the frequency of precipitation forecast was significantly lower than the observation, which had the risk of missing forecast. In addition, SCMOC had obvious advantages for the rain probability prediction to precipitation processes with circulation situation of blocking pattern and two-trough and one-ridge pattern, while ECMWF had better performance to precipitation processes with low vortex bottom pattern, and TS value was stable. The accuracy of temperature prediction of ECMWF was better than that of SCMOC and CMA-MESO, while the wind speed forecast of SCMOC had absolute advantages. (4) During the 14th National Games, the performance differences among three forecast products were basically consistent with the historical period, but the overall forecast scores were higher than the historical period.
Based on the 3-hour precipitation forecast data and the observation data at surface meteorological stations in summer (from June to August) of 2021 in China, the precipitation forecast performance of CMA-MESO (China Meteorological Administration Mesoscale Model) with 3 km resolution was diagnosed and analyzed from multiple perspectives, which provides reference for forecasters and basis for model system improvements. The results show that the CMA-MESO 3 km model can better predict the spatial and temporal distribution characteristics of average 3 h cumulative precipitation and effective precipitation frequency in different regions. The prediction ability of regional precipitation is stronger than that of single station, and the prediction effect of continuous precipitation is better than that of local short-term heavy precipitation. According to the statistical results with different forecast leading times, the 3 h precipitation prediction is the largest in 8 period predictions, and it is much larger than the observation. Meanwhile, the 6 h, 9 h and 12 h precipitation predictions are closer to the observation. The analysis results of short-term strong precipitation cases show that the CMA-MESO 3 km model forecast for short-term heavy rainfall is more accurate, and the 3 h and 6 h predictions and their temporal variation characteristics are very close to observation. In addition, the regional average precipitation of the eight-period predictions are very close to the observation.
The self-developed global/regional assimilation and prediction system-regional ensemble prediction system (GRAPES-REPS) was put into operation in 2014 in China. In order to deeply understand the precipitation ensemble forecast ability of this system and conveniently apply the precipitation probability forecast, in this paper, the 24 h accumulated precipitation with different magnitudes forecasted by GRAPES-REPS at different lead time within 72 hours is evaluated by using statistical analysis and case analysis taking three continuous precipitation processes in southern China from mid-May to late June 2017 for example. The results are as follows: (1) The ensemble mean forecast of GRAPES-REPS has obvious advantage for light rain and moderate rain. The advantage decreases gradually with the increase of precipitation magnitude and no advantage appears for rainstorm. The ensemble mean forecast is close to the observation for light rain, while it has a tendency of null (missing) forecast for moderate rain (rainstorm) or heavy rain at a longer lead time. (2) The optimal members include control forecast and two perturbation forecasts that use a combination of MRF boundary layer scheme and KF-eta cumulus convection scheme, which is different to the other members. (3) The spread of precipitation ensemble forecasts is insufficient overall, especially at 0-24 h forecast lead time with the U-shaped Talagrand distribution and the higher (lower) forecast probability for small- (large-) magnitude precipitation. The spread increases obviously with the increase of forecast lead time, and the Talagrand distribution is gradually close to the expected-probability distribution. (4) The ensemble forecasts do have a reference value for precipitation with different magnitudes at every forecast lead time, with the probability forecast of heavy rain and rainstorm being better than that of light rain and moderate rain. (5) The ensemble gives a better forecast for precipitation pattern with different magnitudes as a whole, especially it has an ability of probability forecast for the warm-sector rainstorms in the central and southern Guangdong Province, which is missing in the forecasts of National Meteorological Observatory.
In order to provide a quantitative method to describe artificial precipitation demand, taking Zhiyan reservoir in Lanxi as the research object, based on precipitation, runoff and water level data, the percentile threshold of monthly water level index (WLI) with different grades of the reservoir was derived from real sample probability distribution of water level, the drought index (DI) was constructed by using the entropy weight method combined with the standardized precipitation index (SPI) and the standardized streamflow index (SSI), then WLI was integrated with DI to generate the demand level index (DLI) to describe artificial precipitation demand of reservoir objectively. The temporal characteristics of WLI, DI and DLI were studied, the applicability of DLI was analyzed based on the reservoir history records, the main conclusions are as follows: (1) The constructed percentile threshold of monthly WLI with different grades was able to reflect the water shortage of the reservoir precisely in different periods of a year. (2) There was no significant change in meteorological drought from 1990 to 2019, meanwhile hydrological drought showed an increasing trend, and the increasing trend was most obvious in spring. (3) The total occurrence frequency of meteorological (hydrological) drought in summer and autumn was 33.9% (35.0%), it is higher than that （30.0% (28.3%)） in winter and spring. The occurrence frequency of severe and extreme drought in spring was the highest, and meteorological and hydrological drought accounted for 11.2% and 10.0% respectively in spring. Hydrological drought did not lag behind and had a more serious effect than meteorological drought. (4) The inter-annual distribution of DLI was similar to that of WLI, and the seasonal distribution of DLI was similar to that of DI. Artificial precipitation demand appeared more frequently and last longer in the years after 2004 than before 2004. Demand occurred most frequently in summer, accounting for 40.0%, however the demand of high and very high level occurred most frequently in spring, accounting for 14.4%. (5) The integrated DLI grades could well reflect the actual demand of the reservoir, and when DLI grade was greater than or equal to 4 for several months, the reservoir might be short of water and emergency measures required to be taken.
Based on multi-source observation data and data fusion and assimilation technology, National Meteorological Information Center has developed and released real-time analysis products that has passed the industry admittance review. In order to ensure the objectivity and authenticity of the evaluation results of the real-time analysis products, the representativeness of the data source—ground station data participating in the inspection and evaluation of the products was studied. 10 precipitation impact indicators such as longitude and latitude, slope, slope direction and data availability of ground meteorological stations from May to August 2020 were selected. On the basis of indicator screening, the weight of each impact indicator to form a comprehensive impact indicator of each station was determined by using correlation analysis, principal component analysis, and grading inspection of the comprehensive impact indicator were conducted. The results show that five of the 10 impact indicators are retained after screening, and their weights from large to small are data availability, equipment stability, slope variability, surface roughness and altitude. The comprehensive impact value of stations in Sichuan Basin is mostly above 0.9, and the stations with low comprehensive impact values are mainly distributed in Ganzi, Aba and Liangshan prefectures of Sichuan Province, which is closely related to the complex terrain and poor representativeness of station in these areas. Through the grading evaluation of the comprehensive impact index, it is reasonable to take the data of stations with the comprehensive impact index value above 0.8 as the "true value" data source for the evaluation of precipitation real-time analysis products.
In order to learn more about the performance of convective-scale ensemble forecast system for precipitation prediction in the Sichuan-Chongqing region, the control forecast (CNTL), the ensemble mean (MEAN) and the probability-matched ensemble mean (PM) of convective-scale ensemble prediction system are comprehensively analyzed based on daily precipitation data collected at 7 213 stations in the Sichuan-Chongqing region in warm season (from May to September) from 2020 to 2021, and differences between rainfall forecasts starting at 08:00 and 20:00 are compared. Results are as follows: (1) The forecast performance of PM and MEAN is better than that of CNTL. MEAN is skillful at forecasting moderate rain and heavy rain, and PM has obvious advantages for large rainfall. (2) Positive forecast deviations of light rainfall frequency are obvious in the whole research region, while for moderate rain and above, positive deviations are concentrated in high-altitude mountains such as the Daba Mountain, the Huaying Mountain and the Wuling Mountain, and negative deviations are mainly located in the Sichuan Basin and hilly areas. Positive (negative) deviations of light rain and moderate rain (heavy rain and rainstorm) predicted by MEAN are more obvious than those predicted by CNTL and PM. (3) The critical success index (CSI) and probability of detection (POD) scores with lead time of 36 h for the forecasts starting at 08:00 are higher than those with lead time of 48 h for the forecasts starting at 20:00, but the overestimation of rainfall frequency starting at 08:00 is more obvious in high-altitude mountains. (4) Compared with CNTL, PM and MEAN are better for the rainfall area of the heavy rain process from September 4 to 7, 2021 in the Sichuan Basin, which is related to the fact that ensemble forecast can better capture the position and morphology of the weather system.
Based on the daily 2 m maximum and minimum temperature data from 1990 to 2019 in Sichuan Province, the temperature transitional weather processes have been analyzed statistically. Then a correction model of temperature change during transitional processes of temperature has been performed by using of NCEP/NCAR (National Center for Environmental Prediction/National Center for Atmospheric Research) daily reanalysis data and the LightGBM (Light Gradient Boosting Machine) algorithm.The results show that the area with the most temperature transitional processes is the slope transition zone between the plateau and the basin, while the least is in the basin. The number of temperature transitional processes in each region has an obviously seasonal differences with the most in spring and the least in winter, and the temperature transitional processes in spring is significantly more than those in the other three seasons. For the training set from 1990 to 2019，the LightGBM model has good performances with an overall accuracy of 78.64% and a mean absolute error of 1.35 ℃. For the independent testing set in 2020，the LightGBM model has an overall accuracy of 53.60% and a mean absolute error of 2.19 ℃, which are better than those of ECMWF (European Centre for Medium-Range Weather Forecasting), SCMOC and SPCO models.
In recent years, the artificial intelligence has made a breakthrough in image identification. In order to find out the practical value of artificial intelligence models in radar echo nowcasting in Wuhan City, the radar echo and precipitation observation data in Wuhan from 2015 to 2020 are used to train four deep learning models (PredRNN++, MIM, CrevNet and PhyDNet), then these trained models and radar echo observation data in flood season of 2021 are used to do nowcasting of radar echo. And on this basis, the precipitation processes are selected by using precipitation intensity and area indexes in Wuhan, and the performance of four deep learning models and optical flow method in radar echo nowcasting are tested and evaluated in Wuhan in flood season of 2021 in terms of mean square error (MSE), structural similarity index measurement (SSIM), probability of detection (POD), false alarm rate (FAR) and critical success index (CSI). The results are as follows: (1) On the whole, MSE of MIM model is the smallest, while its POD is the highest, and SSIM of MIM and PredRNN++ models are the highest. FAR of four deep learning models is lower than that of optical flow method, and it is the lowest for PhyDNet model. Except for CrevNet model, CSI of other three deep learning models is higher than that of optical flow method, and it is the highest for MIM model. (2) CSI of optical flow method is the highest during 0-12 minutes of forecast, while that of MIM model is the highest from 18 to 120 minutes, which shows the advantage of deep learning model for long prediction time. (3) With the increase of echo intensity, POD and CSI of four deep learning models and optical flow method decrease rapidly, while the variation characteristics of FAR of optical flow method and deep learning models are different. (4) For the regional precipitation processes, the prediction ability of deep learning models firstly reduces and then enhances significantly with the increase of precipitation intensity, while the optical flow method is insensitive to the change of precipitation intensity, so the increments of CSI of deep learning models are the highest under the strong precipitation processes compared with optical flow method. For the local convective precipitation processes with general intensity, the prediction ability of all models and optical flow method significantly reduces. (5) The analysis results of a rainstorm case show that deep learning models not only have prediction ability to the change of echo intensity to a certain extent, but also have better prediction ability to echo movement than optical flow method, so they have a good operational prospect.
Weighted mean temperature (Tm) is a key parameter in the retrieval of atmospheric precipitable water (PW) from ground-based Global Positioning System (GPS). In order to improve the accuracy and reliability of the retrieval of PW in Hainan Island, temporal variation characteristics of Tm calculated based on Haikou radiosonde data during 2008-2010 and the relation with meteorological factors at Haikou station are analyzed. On this basis, based on radiosonde and surface observation data during 2008-2012, single-factor and multi-factor Tm regression equations and Tm regression models with day of year factor are established at Haikou, and the models are validated by using radiosonde and surface observation data during 2013-2014. Based on the local Tm regression models, the ground-based GPS PW retrieval of Haikou is performed from May to October 2012, and the retrieval accuracy is verified. The results show that: by comparison of the true Tm, the RMSE of single-factor and two-factor local Tm models are 2.000 and 1.978 K, superior to Bevis and constant model. The local model of Tm has good consistency with Tm calculated by radiosonde data. The GPS PW from single-factor Tm model exhibits much stronger correlations with radiosonde PW than GPS PW based on Bevis model, and the RMSE of GPS PW by single-factor Tm model is lower than that based on Bevis model. Compared with the multi-factor linear Tm model, GPS PW based on the Tm model with day of year factor has significantly improved accuracy. The local models could meet the accuracy requirements of the PW from ground-based GPS data of Haikou.
In order to better exploit the detection advantages of wind profile radar in upper layer, the detection data of ST wind profile radar during 2014-2017 from Huainan Climate and Environment Observatory (HCEO), Institute of Atmospheric Physics, Chinese Academy of Sciences, were used to evaluate the detection performance of ST wind profile radar under different detection mode combinations, and the influence of meteorological environment on detection performance was explored. On this basis, the applicability of detection mode combinations was discriminated in practical operations. The results show that ST wind profile radar can achieve different detection purposes by combining high or low modes with switching altitude. However, there are differences in detection performance under different combinations of mode. On the one hand, the detection performance of radar gradually decreases before the mode conversion and rapidly increases after the mode conversion, while it gradually decreases with height under the high mode. On the other hand, the detection performance of radar doesn’t change significantly in process of the conversion, while it gradually decreases after the high mode to a certain height. In addition, the reducing degree of detection performance gradually increases as the transition altitude approach in autumn and winter. The precipitation reduces the detection performance in lower and middle layers of troposphere. So, we select suitable mode combination based on the detection performance of radar to atmospheric boundary layer, troposphere and stratosphere.
Based on rainfall enhancement operation records in Zhejiang Province and Doppler radar data, sounding data and hourly rainfall observations from automatic stations as well as MICAPS weather chart from June to November of 2018-2020, the indexes of Doppler radar echo for rainfall enhancement in summer and autumn in Zhejiang Province were researched with the inverse method after evaluation of rainfall enhancement effect in order to guide cloud seeding operation more scientific and reasonable. The results show that the shear line, upper trough and typhoon are the most favorable weather systems for artificial rainfall enhancement in Zhejiang Province, accounting for 28.6%, 21.4% and 21.4%, respectively. According to radar echo and precipitation characteristics, cloud types can be divided into stratiform cloud, cumuliform cloud, mixed cloud giving priority to stratiform cloud and mixed cloud giving priority to cumuliform cloud. Mixed cloud is the most common type for rainfall enhancement, accounting for up to 82.5%. The number of samples with obvious rainfall enhancement effect is small, only accounting for 13.4%. Radar echo intensity, radar echo top, vertical integrated liquid water and thickness of negative temperature layer are valid criteria for operating conditions. The indexes of Doppler radar echo for rainfall enhancement are different in different seasons and for different cloud types. So the indexes of Doppler radar echo and the discriminant equation of operating conditions should be established separately. Unreasonable operation was the main reason why we failed to get positive effect of rainfall enhancement, which accounted for 49.2% of all the samples. Many other reasons leading to failure of rainfall enhancement included but not limited to inappropriate time, position and object. The indexes of Doppler radar echo for rainfall enhancement established in this article are scientific and easy-to-use. These studies have evident significance to command cloud seeding operation.
In order to study the main geographical influencing factors of summer precipitation and the best interpolation method of precipitation in the complex Sichuan Basin, especially the mountainous area around the basin, Sichuan was divided into four regions by using cluster analysis based on 10 years (2010-2019) summer precipitation data of 157 automatic meteorological stations in Sichuan Province. The correlation analysis and the multiple regression analysis methods were used to screen out the geographical influencing factors of precipitation in each region. In addition to using the cooperative Kriging interpolation method, the traditional interpolation method is used to compare. The interpolation results are tested by cross-validation method. The results are as follows: (1) The geographic influencing factors that can be used to characterize the summer precipitation in Sichuan were mainly longitude, latitude, altitude, slope and normalized difference vegetation index. (2) Due to the diversity and complexity of the topography in Sichuan, the effect of precipitation interpolation after the division was better than that before the division.(3) When the number of precipitation influencing factors in the selected area was moderate, the coKriging interpolation method was better, and when the number of precipitation characterization factors in the selected area was single or too many, the radial Basis function interpolation method or empirical Bayesian Kriging interpolation method were more effective.
For precipitation forecast products with different methods and time, a large number of evaluation results often exist together. At present, we’re still lacking effective measures on how to analyze comprehensively and systematically these results. In this study, the agglomerative hierarchical cluster analysis is introduced to classify and analyze the different evaluation results of different forecast products, based on a grid precipitation forecast dataset of each member of the national forecast technology and method competition of CMA from June to September 2019, the central station guide forecast (SCMOC) of the National Meteorological Center, the seamless analysis and forecasting leading-edge system forecast of Chinese Academy of Meteorological Sciences and objective forecast products of 31 provinces (municipalities and autonomous regions)， the global modelforecast of ECMWF (European Centre for Medium-Range Weather Forecasts) and NCEP (National Centers for Environmental Prediction). The results show that the agglomerative hierarchical clustering results can clearly distinguish their similarities and differences between different forecast products. The different evaluation indicators lead to different clustering results, but the forecast products with high similarity are still divided into a same subclass. The identification effect of four different inter-class similarity measurement methods on categories characteristics was different, and the Ward method was followed by Complete, Average and Single method from clear to fuzzy. In addition, the precipitation prediction ability for different administrative regions and forecast products was different, the accuracy of rain probability forecast in North China and East China was better than that in other regions, and most objective forecasts to rain probability and precipitation relative error were better than model forecast of ECMWF, while they to heavy precipitation were worse than ECMWF model, there are still greater difficulties in interpretation to heavy precipitation forecast.
With continuous growth of forecast service demand and increasingly refined forecast content, the forecast of short-term heavy precipitation above 20 mm·h-1 can not meet the forecast service demand fully. It is very necessary to carry out research on forecast methods about short-term heavy precipitation with different rainfall intensity. The 51 355 samples of short-term heavy rainfall from national and regional meteorological stations in nine provinces and one city in southern China from June to August during 2016-2019 were divided into four rainfall grades according to their rainfall intensity (R), namely, I: 20≤R<30 mm·h-1, II: 30≤R<50 mm·h-1, III: 50≤R<80 mm·h-1, and IV: R≥80 mm·h-1. The samples of all rainfall grades were spatiotemporal matched with the initial field of CMA-MESO (China Meteorological Administration mesoscale model) in the same period, and the percentile statistics were applied to 22 physical quantities extracted from these samples. The XGBoost (extreme gradient boosting) machine learning method was used to rank importance of those 22 physical quantities to determine their weight coefficients. Based on the continuous probability prediction method, the ascending and descending half ridge functions were selected as the membership function, the probability prediction models of short-term heavy precipitation with different rainfall grades were established. The real-time operational prediction was carried out in flood season of 2020 using these prediction medels, and the hourly probability prediction products of short-term heavy precipitation with different rainfall grades for 0-36 h prediction time during 15 heavy rainstorm precesses in Hubei Province from June to August 2020 were tested. The results show that for the grade I probability prediction products, the TS score (0.145) using 60% as threshold works best, with a corresponding hit rate of 55.7%; for the grade II probability prediction products, the TS score (0.083) using 65% as threshold works best, with a corresponding hit rate of 39.1%; for the grade III probability prediction products, the TS score (0.03) using 70% as threshold works best, with a corresponding hit rate of 21.7%; for the grade IV probability prediction products, the TS score (0.005) using 80% as threshold works best, with a corresponding hit rate of 5.8%.The results also suggest that probability prediction products help to correct the CMA-MESO model in predicting short-term heavy precipitation with different rainfall grades at the same time. The hourly prediction test of three heavy precipitation processes shows the hit rate of 40%-80%, the false rate of 50%-90%, and 36 h prediction time for the grade I probability forecast products, which are generally better than CMA-MESO precipitation forecast at the same time. A model was established to forecast short-term heavy precipitation with different grades in this study, and it outperforms existing numerical models and can be a good reference for meteorologists to forecast short-term heavy precipitation and correct precipitation forecast biases in CMA-MESO.
In the flood season (from June to August) of 2020, Gansu Province experienced intensive precipitation with long duration and wide ranges. The performances of three global models (ECMWF, GRAPES_GFS and NCEP_GFS) and four regional models (GRAPES_3 km, GRAPES_LZ10 km, GRAPES_LZ3 km and regional model SMS-WARMS in East China) for 24-hour accumulated precipitation forecast were evaluated in this paper. The main results are as follows: (1) The ECMWF model surpassed the other two global models in forecast performance, while among regional models, the GRAPES_3 km and the SMS-WARMS were better, and the latter was more stable. (2) The regional models had lower accuracy of rain probability forecast and TS, ETS, POD than those of global models for light and moderate rain, but for rainstorms they outperformed global models; the POD and Bias of regional models for heavy rain and rainstorms were significantly higher than those of global models. (3) According to the differences of 500 hPa circulation pattern, the precipitation in Gansu could be divided into two types including subtropical high marginal type and low trough type. Four subtropical high marginal precipitation processes and three low trough precipitation processes in flood season of 2020 were tested and evaluated. For global models and regional models, they all had better capability in predicting precipitation with different magnitudes for the former type than the latter one. The ECMWF model and regional models were better than the NCEP_GFS model and the GRAPES_GFS model in predicting heavy rain and rainstorm. Among global models, the ECMWF model had the best forecast effect for the two precipitation types, and the East China regional model had the best forecast effect for the two precipitation types among regional models. (4) All the seven models had good forecasting capability for the spatial orientation of moderate and heavy rain for both rainfall types, while the forecast effect of rainfall location for subtropical high marginal type was better than that of low-trough type, but the predicted precipitation intensity was stronger than observations, especially for the center of precipitation.
A forest fire occurred in Qinyuan County, Shanxi Province on June 5, 2020. Based on the analysis of weather situation, radar echo, lightning location and other multi-source meteorological data, and European Centre for Medium-Range Weather Forecasts （ECMWF） fifth-generation global atmospheric reanalysis （ERA5）,the evolution characteristics of meteorological elements including temperature and precipitation were analyzed in the early stage and during the forest fire, and combined with the field investigation, the cause of the forest fire were given. The results show that the forest fire was caused by a positive lightning, the lightning point was at the edge of convective cloud and the lightning occurred at 15：39 BST on June 5, with the current intensity of 42.2 kA.There was no precipitation in the areas around fire site for 2 consecutive days in the early stage, the 2 m temperature in the areas around fire site increased significantly on the day of the forest fire, with the air temperature of 30-33 ℃, and the precipitation was less than 0.1 mm and it was breezy.
Based on the SRTM (shuttle radar topography mission)data, the ground clutter and other clutters around Tianshui radar station were filtered, then the Z-I function with localized parameters was established on the basis of six precipitation processes with three types in Southeast Gansu after filtering the ground clutter and other clutters of radar data, and at last the reflectivity factor of Xifeng new generation weather radar in Qingyang was compared with the one in Tianshui within the coincidence range. The results show that SRTM data can well simulate the distribution of ground clutter; radar reflectivity is ahead of precipitation; the Z-I function with localized parameters in Tianshui, which had a smaller A and bigger b, is significantly different to common ones; Tianshui new generation weather radar may have a systematic problem of low echo intensity.
The prediction based on dynamic downscaling prediction technology of the climate extension of weather research and forecasting (CWRF) model to summer precipitation has a certain deviation, so it is difficult to achieve accurate prediction. This paper analyzed the correlated meteorological elements with summer precipitation based on the climatic characteristics of summer precipitation in the main land of China. And on this basis, the reforecasts of summer precipitation by CWRF model in China during 1996-2019 were corrected by using the combined method of dendritic network (DD) and artificial neural network (ANN). Finally, the correction effect was tested by mean square error (MSE), anomaly correlation coefficient (ACC) and temporal correlation coefficient (TCC), etc. The results show that the correction effect to summer precipitation based on the artificial dendritic neural network (ADNN) algorithm model was better than the historical reforecasts of CWRF model in China. The ACC and TCC both increased by about 0.10, MSE dropped by about 26%, and the overall trend anomaly test scores improved by 6.55, which indicated that the ADNN machine learning method could achieve correction to summer precipitation forecasts of CWRF model to a certain extent, thus it could improve the accuracy of precipitation forecasts of CWRF model.
