国家气候中心

This study aims to estimate the variation characteristics of rainfall erosivity in 10 river basins in China.The daily precipitation data was then selected from 1961 to 2023.Three characteristic values of erosive rainfall were combined,such as the total erosive rainfall,erosive rainy days,and mean erosive rainfall intensity.The trend and cause of annual rainfall erosivity were analyzed by the linear regression and Spearman's rank partial correlation analysis.The extreme variations were obtained from the trend of annual maximum daily rainfall erosivity and difference of event rainfall erosivity under a 10-year return period during the two periods 1961-2023 and 1961-1990.The recurrence period was statistically fitted for the goodness of fit using the Generalized extreme value distribution(GEV)function and Kolmogorov-Smirnov method.Then,the dangerous situations of rainfall erosivity were comprehensively determined to consider the overall trends and extreme variations in China.At last,the annual rainfall erosivity was predicted using the Hurst index by rescaled range analysis P/S.Some recommendations were given on soil erosion prevention and control in different key erosion regions.The results show that:1)The ever-increasing trend was found in the annual rainfall erosivity in the Southeast-and Northwest-river basin from 1961 to 2023.But the Southwest-river basin showed a significant decreasing trend.There was no outstanding increasing trend in most other river basins;2)The positive rate of change was observed in the annual erosive rainfall,rainy days,and mean erosive rainfall intensity in most river basins.Particularly,the significantly increasing trend was found in the mean erosive rainfall intensity in the Yellow,Yangtze,southeast,Pearl,and northwest river basins.The different causes contributed to the trends of annual rainfall erosivity in the 10 river basins.The annual mean intensity of erosive rainfall and/or rainfall was dominated by the trend of annual rainfall erosivity in most river basins.3)A positive rate of change was found in the maximum daily rainfall erosivity in most parts of China.The number of stations with the extreme maximum historical value was increasing over time,with the peak in the 2010s over that in the 1990s.The ratio of stations with the increasing event rainfall erosivity under a 10-year return period between the two periods 1961-2023 and 1961-1990 to the total was 64.3％;4)Either the total amount or the extreme of rainfall erosivity was damaged to the soil and water conservation.The rainfall erosivity showed a proportion of more than 80％of total stations in most parts of the country over the past 60 years.Among them,both the total and extreme rainfall erosivity were the most prominent and widespread in all river basins;5)The annual rainfall erosivity in most stations in most 10 river basins was predicted to continue the ever-increasing trends during 1961-2023,except for Southwest river basin with ever decreasing trend.The current and future climate conditions are not optimistic for soil erosion control in China.It is very necessary for long-term plans and effective measures,according to the total amount and extreme changes of rainfall erosivity in specific regions.The findings can also provide scientific references for the prevention and control of soil erosion.

An unprecedented persistent heavy precipitation occurred in Henan province during 17-22 July 2021,causing huge economic losses.Currently,extreme precipitation forecasting is still a hotspot and a difficult issue in sub-seasonal climate prediction research.Regional climate models provide a new way to further improve sub-seasonal precipitation forecasting in China with finer spatial resolution and better parameterization of physical processes compared to that of the global models.This study uses the regional Climate-Weather Research and Forecasting model(CWRF)nested with the China Meteorological Administration Climate Prediction System version 3(CMA_CPSv3)to improve prediction capabilities for this persistent heavy precipitation event.It is shown that the spatial distribution,magnitude,and forecast accuracy of precipitation predicted by CWRF are improved compared to that predicted by CMA_CPSv3.Although both models underestimate the amount of precipitation,the CWRF forecasts larger accumulated precipitation and spatial distribution of precipitation is more consistent with observation.CWRF forecasts initialized on 26 June and 29 June are better than that of CMA_CPSv3 on the same initial dates.The CWRF significantly improves the forecast of low-level wind fields and low-level jets in East Asia compared with the CMA_CPSv3.The CWRF is particularly effective in improving the simulation of directions of low-level jets and water vapor fluxes,allowing water vapor to converge on the windward slopes of mountain ranges and providing favorable water vapor conditions for precipitation.The CWRF better forecasts the water vapor flux convergence and ascending motions over Zhengzhou,and all these improvements lead to higher precipitation forecasting skill of CWRF.

Meteorological gridded data is typically stored in file formats within distributed file repositories, such as network-attached Storage (NAS). During operations, business systems often need to download files locally, parse them, and subsequently perform analyses and calculations. This traditional approach presents several challenges, including difficulties in data retrieval, prolonged response times, and inability to meet demands for real-time computation and interactive applications. To address these issues, National Meteorological Information Center has developed PostGrid, an integrated database for meteorological gridded data and computing, based on Tianqing Spatial Analysis Library and is specifically designed for distributed environments. The PostGrid database consists of two primary layers: Data layer and operator layer. Data layer is responsible for storing various types of gridded meteorological data. When data is imported into the database, it is stored in a standardized and uniform manner. Each dataset comprises two components: A header file and entity data, both of which are stored in binary format. The header file contains basic descriptive information about the meteorological gridded data, while the entity data store specific layers or fields obtained by partitioning the original gridded dataset. By organizing data according to various dimensions, such as weather elements, forecast start times, spatial layers, levels, and samples-the data layer facilitates the efficient retrieval and analysis of meteorological gridded data. This structured approach significantly enhances the database's capacity to read and process data, rendering it far more efficient than traditional methods. Operator layer in PostGrid is implemented using SQL functions within the database. These operators facilitate a range of operations on gridded data, including matrix calculations, spatial analysis, statistical aggregation, dimensionality reduction, and data filtering. Furthermore, operators are designed to facilitate distributed parallel computing, enabling faster and more efficient processing of large datasets. By leveraging capabilities of parallel computing, PostGrid can perform complex calculations that would typically require minutes, reducing the processing time to just milliseconds. This capability significantly enhances the performance and flexibility of meteorological data services. Performance tests and real-world applications have demonstrated that PostGrid significantly enhances the efficiency of meteorological data services, reducing the time required for traditional aggregation calculations from minutes to just milliseconds. The database's capability to integrate both data and computation within a unified platform marks a significant advancement in the management of large-scale meteorological data. It enables faster data retrieval, real-time computation, and supports more advanced interactive applications, making it an invaluable tool for meteorological services and with the potential for widespread application across various sectors within meteorology.

The Yangtze River basin,particularly its middle and lower reaches,is significantly influenced by the monsoon climate and the pervasive effects of global warming,resulting in frequent occurrences of meteorological calamities such as high temperatures,heatwaves,droughts,and floods.The compilation of heatwave and drought index datasets holds substantial scientific value for monitoring extreme weather events and assessing meteorological disaster risks amidst climate change.This dataset is based on daily temperature and precipitation observations from 1961 to 2020 in the middle and lower reaches of the Yangtze River.After data screening,quality inspection,and removal of outliers,the heat wave index and the number of consecutive days without effective precipitation were adopted as the discrimination criteria.Three indicators,namely the number of days,frequency,and the maximum duration of heat waves and droughts,were calculated at a daily time scale for 66 stations.The results for each indicator were further sorted and statistically summarized by time period and province,producing the dataset of heat wave and drought indices for the middle and lower reaches of the Yangtze River.Test results of spatio-temporal consistency and comparisons with existing literature data indicate that this dataset is highly reliable and can provide data support for analyzing the spatio-temporal variation characteristics of heat waves and droughts in different temporal and spatial ranges in the middle and lower reaches of the Yangtze River.The consistency of the temporal scale facilitates in-depth exploration of the spatio-temporal correlation between heat waves and drought events,and can also serve as a fundamental data resource for further research on the responses of heat waves and drought events to climate change and their influences on agricultural and livestock production and livelihoods.