High-quality and high-resolution precipitation data are the basis for mesoscale numerical weather forecasting,model verification,and hydrological monitoring,which play an important role in meteorological and hydrologi...High-quality and high-resolution precipitation data are the basis for mesoscale numerical weather forecasting,model verification,and hydrological monitoring,which play an important role in meteorological and hydrological disaster prevention and mitigation.In this study,high-density rain gauge data are used to evaluate the fusion accuracy of the China Meteorological Administration Multisource Precipitation Analysis System(CMPAS),and four CMPAS products with different spatial and temporal resolution and different data sources are compared,to derive the applicability of CMPAS.Results show that all the CMPAS products show high accuracy in the Sichuan Basin,followed by Panxi Area and the western Sichuan Plateau.The errors of the four products all rise with the increase in precipitation.CMPAS overestimates precipitation in summer and autumn and underestimates it in spring and winter.Overall,the applicability of these fused data in the Sichuan Basin is quite good.Due to the lack of observations and the influence of the terrain and meteorological conditions,the evaluation of CMPAS in the plateau area needs further analysis.展开更多
The estimation of precipitation quantiles has always been an area of great importance to meteorologists, hydrologists, planners and managers of hydrotechnical infrastructures. In many cases, it is necessary to estimat...The estimation of precipitation quantiles has always been an area of great importance to meteorologists, hydrologists, planners and managers of hydrotechnical infrastructures. In many cases, it is necessary to estimate the values relating to extreme events for the sites where there is little or no measurement, as well as their return periods. A statistical approach is the most used in such cases. It aims to find the probability distribution that best fits the maximum daily rainfall values. In our study, 231 rainfall stations were used to regionalize and find the best distribution for modeling the maximum daily rainfall in Northern Algeria. The L-moments method was used to perform a regionalization based on discordance criteria and homogeneity test. It gave rise to twelve homogeneous regions in terms of LCoefficient of variation(L-CV), L-Skewness(L-CS) and L-Kurtosis(L-CK). This same technique allowed us to select the regional probability distribution for each group using the Z statistic. The generalized extreme values distribution(GEV) was selected to model the maximum daily rainfall of 10 groups located in the north of the steppe region and the generalized logistic distribution(GLO) for groups representing the steppes of Central and Western Algeria. The study of uncertainty by the bias and RMSE showed that the regional approach is acceptable. We have also developed maximum daily rainfall maps for 2, 5, 10, 20, 50 and 100 years return periods. We relied on a network of 255 rainfall stations. The spatial variability of quantiles was evaluated by semi-variograms. All rainfall frequency models have a spatial dependence with an exponential model adjusted to the experimental semi-variograms. The parameters of the fitted semi-variogram for different return periods are similar, throughout, while the nugget is more important for high return periods. Maximum daily rainfall increases from South to North and from West to East, and is more significant in the coastal areas of eastern Algeria where it exceeds 170 mm for a return period of 100 years. However, it does not exceed 50 mm in the highlands of the west.展开更多
基金supported by the Sichuan Meteorological Bureau,the Sichuan Meteorological Observation and Data Centerthe Heavy Rain and Drought-Flood Disasters in Plateau and Basin Key Laboratory of Sichuan Province[grant number SCQXKJQN202121]+1 种基金the Key Technology Development Project of Weather Forecasting[grant number YBGJXM(2020)1A-08]the Innovative Development Project of the China Meteorological Administration[grant number CXFZ2021Z007]。
文摘High-quality and high-resolution precipitation data are the basis for mesoscale numerical weather forecasting,model verification,and hydrological monitoring,which play an important role in meteorological and hydrological disaster prevention and mitigation.In this study,high-density rain gauge data are used to evaluate the fusion accuracy of the China Meteorological Administration Multisource Precipitation Analysis System(CMPAS),and four CMPAS products with different spatial and temporal resolution and different data sources are compared,to derive the applicability of CMPAS.Results show that all the CMPAS products show high accuracy in the Sichuan Basin,followed by Panxi Area and the western Sichuan Plateau.The errors of the four products all rise with the increase in precipitation.CMPAS overestimates precipitation in summer and autumn and underestimates it in spring and winter.Overall,the applicability of these fused data in the Sichuan Basin is quite good.Due to the lack of observations and the influence of the terrain and meteorological conditions,the evaluation of CMPAS in the plateau area needs further analysis.
文摘The estimation of precipitation quantiles has always been an area of great importance to meteorologists, hydrologists, planners and managers of hydrotechnical infrastructures. In many cases, it is necessary to estimate the values relating to extreme events for the sites where there is little or no measurement, as well as their return periods. A statistical approach is the most used in such cases. It aims to find the probability distribution that best fits the maximum daily rainfall values. In our study, 231 rainfall stations were used to regionalize and find the best distribution for modeling the maximum daily rainfall in Northern Algeria. The L-moments method was used to perform a regionalization based on discordance criteria and homogeneity test. It gave rise to twelve homogeneous regions in terms of LCoefficient of variation(L-CV), L-Skewness(L-CS) and L-Kurtosis(L-CK). This same technique allowed us to select the regional probability distribution for each group using the Z statistic. The generalized extreme values distribution(GEV) was selected to model the maximum daily rainfall of 10 groups located in the north of the steppe region and the generalized logistic distribution(GLO) for groups representing the steppes of Central and Western Algeria. The study of uncertainty by the bias and RMSE showed that the regional approach is acceptable. We have also developed maximum daily rainfall maps for 2, 5, 10, 20, 50 and 100 years return periods. We relied on a network of 255 rainfall stations. The spatial variability of quantiles was evaluated by semi-variograms. All rainfall frequency models have a spatial dependence with an exponential model adjusted to the experimental semi-variograms. The parameters of the fitted semi-variogram for different return periods are similar, throughout, while the nugget is more important for high return periods. Maximum daily rainfall increases from South to North and from West to East, and is more significant in the coastal areas of eastern Algeria where it exceeds 170 mm for a return period of 100 years. However, it does not exceed 50 mm in the highlands of the west.
