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Improvement of Satellite-based Precipitation Estimates over China Based on Probability Density Function Matching Method
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摘要: 为考察概率密度匹配法 (PDF方法) 对中国区域卫星反演降水产品系统误差订正的适用性，基于逐日和逐时我国地面观测降水量资料，引入PDF方法，分别对逐日0.25°×0.25°水平分辨率和逐时0.1°×0.1°水平分辨率的CMORPH (Climate Prediction Center Morphing Technique) 卫星降水产品的系统误差进行订正。在分析CMORPH卫星降水产品误差特征的基础上，根据两种资料不同的时空分辨率和误差特点，调整概率密度匹配时选取样本的时间和空间范围，设计相应的订正方案。评估结果表明: PDF方法订正后, 两种分辨率卫星降水资料在中国区域系统误差均显著减小，达到了理想的订正效果。在我国站点稀疏的西部地区，订正后的CMORPH卫星降水产品仍保持卫星观测的降水空间分布，降水量也明显接近于地面观测降水量。可见，PDF方法是中国区域卫星反演降水产品系统误差订正的一种有效方法。Abstract: In the evaluation and adjustment of satellite-based precipitation estimates, the gauge-based precipitation data is usually taken as the objective criteria. Assuming gauge-based analysis at grid boxes with station reports as the true value, the satellite precipitation data are corrected after adjusting probability density function (PDF), which makes the PDF distribution of the corrected measurements the same as that of the station observation. One advantage of the PDF method is that it can remove the range dependent bias effectively, which is also the main cause for its recent popularity in correcting the error of satellite-based precipitation estimations.Aiming to investigate the applicability of the PDF method, and then to adjust the systematic bias of the high resolution satellite-based precipitation estimations over China, the daily satellite precipitation data with the resolution of 0.25° by 0.25° and hourly data of 0.1° by 0.1°from CMORPH (Climate Prediction Center Morphing Technique) are adjusted, based on grid precipitation data interpolating stations collected and quality controlled by NMIC (National Meteorological Information Center) of China Meteorological Administration. Although CMORPH data has good performance on the space structure of rainfall over China in summer time, it has obvious systematic errors. CMORPH data underestimate large precipitation values while overrate small ones.After analyzing the bias characteristic of CMORPH precipitation data, different matching schemes are designed by adjusting PDF matching samples of spatial and temporal scale separately. The generalized cross-validation (GCV) statistical tests are used in evaluating the quality of correcting data. The evaluation results suggest that the PDF distribution and precipitation values of corrected precipitation products are close to those of the gauge-based precipitation. Both adjusted daily and hourly satellite precipitation data over China get a less systematic bias than the original ones. Even in the area of sparse observations, such as the Western China, the improvement is also remarkable. The adjusted rainfall data over the Western China not only maintain the basic spatial construction of original satellite products, but also improves their quantity value through closing them to the gauge observations. So the research demonstrates that the PDF method is an effective way in correcting systematic bias of satellite-based precipitation estimates over China.
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图 1 2009年夏季中国区域逐日CMORPH卫星降水产品的平均偏差 (a) 平均偏差随时间演变，(b) 不同降水量范围的平均偏差
Fig. 1 The mean bias from CMORPH daily data in summer 2009 over China (a) time series of mean bias, (b) mean bias in different values of precipitation
图 2 2009年夏季中国区域平均日降水强度空间分布
(a) 逐日地面观测格点资料 (只显示有观测点的网格值)，(b) 逐日CMORPH卫星降水产品，(c) CMORPH卫星降水与地面观测格点场的差异 (只显示有观测点的网格值)
Fig. 2 The spatial distributions of daily mean precipitation intensity in summer 2009 over China
(a) CPA daily data (only values of grids with stations are shown), (b) CMORPH daily data, (c) differences between CPA and CMORPH daily data (only values of grids with stations are shown)
图 3 2009年夏季中国区域订正后逐日CMORPH卫星降水产品平均降水强度空间分布
Fig. 3 The spatial distributions of daily mean precipitation intensity from adjusted CMORPH daily data in summer 2009 over China
图 4 2009年夏季中国区域订正前、订正后逐日CMORPH卫星降水产品平均偏差及降水概率密度的对比
(a) 平均偏差随时间演变，(b) 降水概率密度分布
Fig. 4 The contrast of mean biases and probability density function between original CMORPH and adjusted CMORPH daily data in summer 2009 over China
(a) time series of mean bias, (b) distributions of probability density function
图 5 2009年夏季中国区域逐时CMORPH卫星降水产品的平均偏差随时间演变
Fig. 5 The time series of mean bias from CMORPH hourly data in summer 2009 over China
图 6 2009年6月30日23:00—7月1日00:00降水量空间分布
(a) 逐时地面观测格点资料，(b) 订正前逐时CMORPH卫星降水产品，(c) 订正后逐时CMORPH卫星降水产品
Fig. 6 The spatial distributions of precipitation from 2300 UTC 30 June to 0000 UTC 1 July in 2009 over China
(a) CPA hourly data, (b) original CMORPH hourly data, (c) adjusted CMORPH hourly data
图 7 2009年夏季中国区域平均逐时降水强度空间分布
(a) 逐时地面观测格点资料 (只显示有观测点的网格值)，(b) 订正前逐时CMORPH卫星降水产品，(c) 订正后逐时CMORPH卫星降水产品
Fig. 7 The spatial distributions of hourly mean precipitation intensity in summer 2009 over China
(a) CPA hourly data (only values of grids with stations are shown), (b) original CMORPH hourly data, (c) adjusted CMORPH hourly data
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