摘要
利用高质量的2472个气象台站逐日降水资料和薄盘样条法,对1961 2010年中国大陆降水资料进行了空间内插,得到了中国地面降水0.5°×0.5°格点日值数据集。对该数据集的评估结果表明:夏季误差明显大于其他季节,冬、春、夏、秋季分别有96.7%,91.8%,63.2%和94.0%的台站绝对误差在±1 mm·d-1之间;冬、春、夏、秋季分别有53%,68%,72%和70%的台站相对误差在±10%之间。基于台风"碧利斯"等3个强降水事件的分析和插值试验表明,格点化降水可以较好地描述一定范围内的面雨量。对比CP和APHRO两套格点资料表明,APHRO格点资料在刻画长江以南的年降水量气候态空间特征方面与观测值较一致;CP格点资料对青藏高原、天山山脉和塔里木盆地等大地形附近的降水空间特征描述较准确。另外,CP和APHRO格点资料都可以再现江淮梅雨日变化特征。当出现大雨和中雨日时,两套网格化降水资料同等程度地弱化了降水强度,而出现小雨日时,CP格点资料更接近实测降水。
Precipitation is an important meteorological factor,ecological and w ater balance process in ecosystem.By using the quality controlled observational daily precipitation data series over China M ainland observed by 2472 gauges,through the ANUSPLIN softw are developed by the Australian National University based on the thin plate smooth spline method,the datasets of daily grid-based precipitation are established over China in recent 50 years from 1961 to 2010. The research results show the mean bias error of large part gauge is betw een- 10 mm ·mon- 1and 10 mm ·mon- 1. There are 96. 7%,91. 8%,63. 2% and 94. 0% gauge that the bias error is betw een- 1 mm ·d- 1and 1 mm ·d- 1. This developed datasets are helpful to explore the spatial and temporal distributions of the precipitation. Based on results of typhoon Bilis and 7 interpolation experiments,RBEafalls off to the bottom w hen M equal to 9 and mounts up to the peak w hen M equal to 1. The APHRO grid value can represent the pattern of South China precipitation w hich reflect in gauge data. The CP grid data is more exact in describing the precipitation characteristic of Qinghai-Xizang Plateau,the Tianshan M ountains and Tarim Basin. M oreover,both CP data and APHRO data are able to depict the daily variation of Jiang- Huai M eiyu mainly. The tw o kinds of grid data reduce the rain intensity in the same w ay w hen heavy rain or moderate rain comes. Over the light rain,CP data has more veracity.
出处
《高原气象》
CSCD
北大核心
2015年第1期50-58,共9页
Plateau Meteorology
基金
公益性行业(气象)科研专项项目(GYHY201306044
GYHY201206013)
中国气象局气候变化专项项目(CCSF201518)
关键词
降水
空间内插
格点数据集
评估
Precipitation
Interpolate
Grid datasets
Assessment