Improvement of Satellite-based Precipitation Estimates over China Based on Probability Density Function Matching Method

Yu Jingjing; Shen Yan; Pan Yang; Zhao Ping; Zhou Zijiang

Vol.24, NO.5, 2013

Article Contents

Article Navigation > Journal of Applied Meteorological Science > 2013 > 24(5): 544-553

Yu Jingjing, Shen Yan, Pan Yang, et al. Improvement of satellite-based precipitation estimates over China based on probability density function matching method. J Appl Meteor Sci, 2013, 24(5): 544-553.

Citation:

Yu Jingjing, Shen Yan, Pan Yang, et al. Improvement of satellite-based precipitation estimates over China based on probability density function matching method. J Appl Meteor Sci, 2013, 24(5): 544-553.

Citation:

PDF( 2425 KB)

Improvement of Satellite-based Precipitation Estimates over China Based on Probability Density Function Matching Method

1.
National Meteorological Information Center, Beijing 100081
2.
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081

Received Date: 2012-10-15
Rev Recd Date: 2013-04-03
Publish Date: 2013-10-31

Abstract

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.
- satellite-based precipitation estimates;
- gauge-based precipitation;
- systematic bias;
- probability density function matching method

FullText(HTML)

References(30)

Figures and Tables

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

DownLoad: Full-Size Img PowerPoint

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)

DownLoad: Full-Size Img PowerPoint

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

DownLoad: Full-Size Img PowerPoint

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

DownLoad: Full-Size Img PowerPoint

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

DownLoad: Full-Size Img PowerPoint

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

DownLoad: Full-Size Img PowerPoint

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

DownLoad: Full-Size Img PowerPoint

图 8 2009年夏季中国区域订正前、订正后逐时CMORPH卫星降水产品平均偏差对比

Fig. 8 The contrast of time series of mean bias between original CMORPH and adjusted CMORPH hourly data in summer 2009 over China

DownLoad: Full-Size Img PowerPoint

References

[1]	Morrissey M L, Maliekal J A, Greene J S, et al.The uncertainty of simple spatial averages using rain gauge networks.Water Resour Res, 1995, 31(8):2011-2017. doi: 10.1029/95WR01232
[2]	Villarini G, Krajewski W F.Empirically-based modeling of spatial sampling uncertainties associated with rainfall measurements by rain gauges.Adv Water Resour, 2008, 31(7):1015-1023. doi: 10.1016/j.advwatres.2008.04.007
[3]	Xie P P, Arkin P A.Global precipitation:A 17-year monthly analyses based on gauge observations, satellite estimates, and numerical model outputs.Bull Amer Meteor Soc, 1997, 78(11):2539-2558. doi: 10.1175/1520-0477(1997)078<2539:GPAYMA>2.0.CO;2
[4]	Adler R F, Huffman G J, Chang A, et al.The Version-2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis (1979-Present).J Hydrometeor, 2003, 4(6):1147-1167. doi: 10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2
[5]	Lu N M, You R, Zhang W J.A fusing technique with satellite precipitation estimate and rain-gauge data.Acta Meteor Sinica, 2004, 18(2):141-146.
[6]	Huffman G J, Adler R F, Bolvin D T, et al.The TRMM Multisatellite Precipitation Analysis (TMPA):Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales.J Hydrometeor, 2007, 8(1):38-55. doi: 10.1175/JHM560.1
[7]	Yang S, Smith E A.Convective-stratiform precipatation variability at seasonal scale from 8-year of TRMM observations:Implications for multiple modes of diurnal variability.J Climate, 2008, 21:4087-4114. doi: 10.1175/2008JCLI2096.1
[8]	师春香, 谢正辉.基于静止气象卫星观测的降水时间降尺度研究.地理科学进展, 2008, 27(4):15-22. doi: 10.11820/dlkxjz.2008.04.003
[9]	徐海明, 何金海, 谢尚平.卫星资料揭示的中尺度地形对南海夏季气候的影响.大气科学, 2007, 31(5):1021-1031. http://cpfd.cnki.com.cn/Article/CPFDTOTAL-ZGQX200610003041.htm
[10]	刘还珠.台风暴雨天气预报的现状和展望.气象, 1998, 24(7):5-10. doi: 10.7519/j.issn.1000-0526.1998.07.002
[11]	赵姝慧, 周毓荃.利用多种卫星研究台风"艾云尼"宏微观结构特征.高原气象, 2010, 29(5):1254-1260. http://www.cnki.com.cn/Article/CJFDTOTAL-GYQX201005019.htm
[12]	师春香, 刘玉洁.国外部分卫星产品质量评价和质量控制方法.应用气象学报, 2004, 15(增刊):142-151. http://www.cnki.com.cn/Article/CJFDTOTAL-YYQX2004S1020.htm
[13]	Shen Y, Xiong A Y, Wang Y, et al.Performance of high-resolution satellite precipitation products over China.J Geophys Res, 2010, 115, D02114, doi: 10.1029/2009JD012097.
[14]	潘旸, 宇婧婧, 廖捷, 等.地面和卫星降水产品对台风莫拉克降水监测能力的对比分析.气象, 2011, 37(5):564-570. doi: 10.7519/j.issn.1000-0526.2011.05.007
[15]	Eugenia K. 大气模式、资料同化和可预报性. 蒲朝霞, 杨福泉, 译. 北京: 气象出版社, 2005: 120-127.
[16]	任芝花, 王改利, 邹凤玲, 等.中国降水测量误差的研究.气象学报, 2003, 61(5):621-627. doi: 10.11676/qxxb2003.062
[17]	任芝花, 熊安元, 邹凤玲.中国地面月气候资料质量控制方法的研究.应用气象学报, 2007, 18(4):516-523. doi: 10.11898/1001-7313.20070412
[18]	李庆祥, 江志红, 黄群, 等.长江三角洲地区降水资料的均一性检验与订正试验.应用气象学报, 2008, 19(2):219-226. doi: 10.11898/1001-7313.20080238
[19]	Wang W Q, Xie P P.A multiplatform-merged (MPM) SST analysis.J Climate, 2007, 20(9):1662-1679. doi: 10.1175/JCLI4097.1
[20]	江志红, 丁裕国, 宋桂英.黄淮流域夏半年旱涝概率时空分布的研究.自然灾害学报, 1998, 7(1):94-104. http://www.cnki.com.cn/Article/CJFDTOTAL-ZRZH801.015.htm
[21]	Xie P P, Xiong A Y.A conceptual model for constructing high-resolution gauge-satellite merged precipitation analyses.J Geophys Res, 2011, 116, D21106, doi: 10.1029/2011JD016118.
[22]	Turk F J, Ebert E E, Oh H J, et al.Validation of an Operational Global Precipitation Analysis at Short Time Scales.12th Conf on Satellite Meteorology and Oceanography, Amer Mereor Soc, 2003.
[23]	Huffman G J, Adler R F, Stocker E F, et al.Analysis of TRMM 3-hourly Multi-satellite Precipitation Estimates Computed in Both Real Time and Post-real Time.12th Conf on Satellite Meteorology and Oceanography, Amer Mereor Soc, 2003.
[24]	Joyce R J, Janowiak J E, Arkin P A, et al.CMORPH:A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution.J Hydrometeor, 2004, 5(3):487-503. doi: 10.1175/1525-7541(2004)005<0487:CAMTPG>2.0.CO;2
[25]	李春晖, 梁建茵.基于shepard和OI方法对雨量计逐时资料的分析.应用气象学报, 2010, 21(4):416-422. doi: 10.11898/1001-7313.20100404
[26]	Xie P P, Chen M Y, Yang S, et al.A gauge-based analysis of daily precipitation over East Asia.J Hydrometeor, 2007, 8(3):607-626. doi: 10.1175/JHM583.1
[27]	沈艳, 冯明农, 张洪政, 等.我国逐日降水量格点化方法.应用气象学报, 2010, 21(3):279-286. doi: 10.11898/1001-7313.20100303
[28]	任芝花, 赵平, 张强, 等.适用于全国自动站小时降水资料的质量控制方法.气象, 2010, 36(7):123-132. doi: 10.7519/j.issn.1000-0526.2010.07.019
[29]	沈艳, 潘旸, 徐宾, 等.最优插值在对降水量空间分析中的参数优化.成都信息工程学院学报, 2012, 27(2):219-224. http://www.cnki.com.cn/Article/CJFDTOTAL-CDQX201202017.htm
[30]	李海滨, 林忠辉, 刘苏峡.Kriging方法在区域土壤水分估值中的应用.地理研究, 2001, 20(4):446-452. http://www.cnki.com.cn/Article/CJFDTOTAL-DLYJ200104009.htm