基于格点数据的西北干旱区极端降水事件分析

根据国家气象信息中心发布的0.5°×0.5°中国地面逐日降水格点数据集,选取最大1 日降水量(RX1day)、最大5 日降水量(RX5day)、R95 极端降水量(R95)、湿天降水量(PRCPTOT)、持续干旱日数(CDD)和日降水强度(SDII)等指数,利用Sen 斜率法、相关分析法等方法分析了1961-2011 年中国西北干旱区极端降水特征,并探讨了各极端降水指数与北极涛动的关系,此外还与早期基于气象台站观测数据的相关研究结果进行了对比。结果表明:在时间尺度上,除CDD显著下降(P<0.01)外,其他指数均呈微弱上升趋势。在空间上,RX1day、RX5day、R95、PRCPTOT和SDII 在研究区内主要呈现西部明显增加、东部微弱减小的趋势,但CDD则整体呈现减小趋势。基于NCAR资料的夏季北极涛动指数与西北干旱区的CDD有很好的相关性(P<0.05),表明夏季北极涛动与该区的干旱事件发生之间存在一定联系。与早期站点数据的研究相比,格点数据覆盖度更好,能更为细致地描述空间变化特征,特别是在天山、阿尔泰山等山区的细节刻画上表现出一定的优势。

Extreme Precipitation Events in Arid Areas in Northwest China Based on Gridded Data

According to the gridded Chinese ground precipitation dataset with a resolution of 0.5°× 0.5° released by National Meteorological Information Center, extreme precipitation indices including maximum 1- day precipitation (RX1day), maximum 5-day precipitation (RX5day), very wet day precipitation (R95), wet day precipitation (PRCPTOT), consecutive dry days (CDD) and simple daily intensity index (SDII) in arid areas in Northwest China during 1961-2011 is analyzed using Sen's slope, correlation analysis and other methods. The connection between each extreme precipitation index and the Arctic oscillation is discussed, and the gridded indices in this study are compared with those in previous research using observation data. The result indicates that most indices show a slightly increasing trend, except CDD with statistically significant decrease (P<0.01). Spatially, RX1day, RX5day, R95, PRCPTOT and SDII have significantly increased in the western part and slightly decreased in the east part, and CDD generally presents a decreasing trend. The higher correlation (P<0.05) exists between NCAR-based summer Arctic oscillations index and CDD, which indicates a relation between Arctic oscillation in summer and drought events in the study area. Compared with the previous research using observation data, the gridded data have greater spatial coverage, which is good at describing detailed spatial variation, especially in the mountainous regions like the Tianshan Mountains and Altai Mountains.