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    Research on multi-model integrated precipitation forecast based on feed forward
    neural network
    ZHU Wengang, SHENG Chunyan, FAN Sudan, RONG Yanmin, QU Meihui
    Journal of Arid Meteorology    2024, 42 (1): 117-128.   DOI: 10. 11755/j. issn. 1006-7639(2024)-01-0117
    Abstract41)      PDF(pc) (7246KB)(86)    PDF(mobile) (7246KB)(0)    Save
     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.

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    Verification and analysis of gust forecast of ECMWF fine grid model in Dalian area
    WANG Lei, YANG Jingtai, BIAN Ruobin, SUI Yuxiu, SUN Yuecheng, ZHOU Lili, WEI Yuanyuan
    Journal of Arid Meteorology    2024, 42 (1): 129-136.   DOI: 10. 11755/j. issn. 1006-7639(2024)-01-0129
    Abstract48)      PDF(pc) (2320KB)(81)    PDF(mobile) (2320KB)(17)    Save
    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.

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    Research on road icing warning model based on Logistic regression and
    neural network in Gansu Province
    BAO Lili, CHENG Peng, WANG Xiaoyong, HE Jinmei, YAN Xinyang, YIN Chun, LI Xiaoqin, ZHAO Wenjing
    Journal of Arid Meteorology    2024, 42 (1): 137-145.   DOI: 10. 11755/j. issn. 1006-7639(2024)-01-0137
    Abstract48)      PDF(pc) (7091KB)(81)    PDF(mobile) (7091KB)(5)    Save
    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.

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    Verification and correction of 2 m temperature merging product of CLDAS in
    Lanzhou and Wuwei, Gansu Province
    GUO Runxia, LIU Xinwei, WANG Yicheng, LIU Na, ZHOU Zihan
    Journal of Arid Meteorology    2024, 42 (1): 146-155.   DOI: 10. 11755/j. issn. 1006-7639(2024)-01-0146
    Abstract60)      PDF(pc) (13418KB)(107)    PDF(mobile) (13418KB)(185)    Save
    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.

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    The application of three interpolation methods to temperature in southwestern China
    GAI Changsong, CAO Lijuan, YANG Yuanyan
    Journal of Arid Meteorology    2023, 41 (5): 792-801.   DOI: 10.11755/j.issn.1006-7639(2023)-05-0792
    Abstract110)   HTML2)    PDF(pc) (8955KB)(397)       Save

    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 ℃.

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    Discussion on correction method of intelligent grid temperature forecast products in the eastern Hexi Corridor
    LI Tianjiang, YANG Xiaoling, ZHANG Zhanwen, LI Yanying, NIE Xin
    Journal of Arid Meteorology    2023, 41 (5): 802-810.   DOI: 10.11755/j.issn.1006-7639(2023)-05-0802
    Abstract102)   HTML2)    PDF(pc) (18314KB)(348)       Save

    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.

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    Applicability assessment of CLDAS temperature and precipitation products in Inner Mongolia
    DONG Zhulei, ZHAO Yanli, FENG Xiaojing, LIU Shimeng
    Journal of Arid Meteorology    2023, 41 (5): 811-819.   DOI: 10.11755/j.issn.1006-7639(2023)-05-0811
    Abstract157)   HTML6)    PDF(pc) (11743KB)(430)       Save

    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.

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    Fine objective forecast method of precipitation phases in the winter half-year in Baoji City
    LU Ye, MENG Miaozhi, QIAO Danyang, REN Huan, HE Yao, HAN Jie
    Journal of Arid Meteorology    2023, 41 (5): 820-827.   DOI: 10.11755/j.issn.1006-7639(2023)-05-0820
    Abstract159)   HTML1511129089)    PDF(pc) (3273KB)(415)       Save

    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).

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    Accuracy analysis of fog and haze identification based on CLDAS land surface fusion data in Tianjin
    GUO Yang, SHI Chunxiang, XU Bin, SI Peng, XU Mei, WANG Min, SUN Meiling
    Journal of Arid Meteorology    2023, 41 (4): 657-665.   DOI: 10.11755/j.issn.1006-7639(2023)-04-0657
    Abstract121)   HTML3)    PDF(pc) (8188KB)(574)       Save

    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.

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    Deviation correction of precipitation forecast by ECMWF model based on quantile mapping method in Sichuan Province
    CAO Pingping, XIAO Dixiang, LONG Keji, WANG Jiajin, YANG Kangquan
    Journal of Arid Meteorology    2023, 41 (4): 666-675.   DOI: 10.11755/j.issn.1006-7639(2023)-04-0666
    Abstract228)   HTML7)    PDF(pc) (10050KB)(563)       Save

    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.

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    Comparison of cloud products of ECMWF-HR and FY-2G satellite in the central and eastern parts of Northwest China
    WEI Dong, SHA Hong’e, QIN Haojun, LYU Qiaoyi, LIU Liwei, FU Zhao
    Journal of Arid Meteorology    2023, 41 (3): 483-490.   DOI: 10.11755/j.issn.1006-7639(2023)-03-0483
    Abstract148)   HTML7)    PDF(pc) (4768KB)(568)       Save

    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.

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    Characteristics of meteorological elements and objective forecast verification at the key venues of “the 14th National Games”
    PAN Liujie, LIANG Mian, QI Chunjuan, LI Peirong, ZHU Qingliang
    Journal of Arid Meteorology    2023, 41 (3): 491-502.   DOI: 10.11755/j.issn.1006-7639(2023)-03-0491
    Abstract90)   HTML5)    PDF(pc) (14978KB)(423)       Save

    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.

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    Evaluation of precipitation forecast of CMA-MESO model in summer of 2021
    CAI Yi, XU Zhifang, GONG Xi, ZHONG Ruomei, HUANG Guansheng, LONG Haichuan
    Journal of Arid Meteorology    2023, 41 (3): 503-515.   DOI: 10.11755/j.issn.1006-7639(2023)-03-0503
    Abstract175)   HTML4)    PDF(pc) (35253KB)(478)       Save

    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.

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    Verification and assessment of persistent rainfall forecasts of GRAPES-REPS in pre-summer of 2017 in southern China
    WANG Yehong, ZHAO Yuchun
    Journal of Arid Meteorology    2023, 41 (2): 328-340.   DOI: 10.11755/j.issn.1006-7639(2023)-02-0328
    Abstract121)   HTML1)    PDF(pc) (23384KB)(535)       Save

    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.

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    Construction of artificial precipitation demand level index of the reservoir based on drought and water level characteristics
    JIANG Shujie, CHENG Ying, FANG Nan, ZHOU Yuquan, SHAN Zhonghua, ZHANG Lei
    Journal of Arid Meteorology    2023, 41 (2): 341-349.   DOI: 10.11755/j.issn.1006-7639(2023)-02-0341
    Abstract219)   HTML1879179263)    PDF(pc) (5136KB)(658)       Save

    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.

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