Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1002/2016JD025355 |
Assimilating the global satellite mapping of precipitation data with the Nonhydrostatic Icosahedral Atmospheric Model (NICAM) | |
Kotsuki, Shunji1; Miyoshi, Takemasa1,2,3; Terasaki, Koji1; Lien, Guo-Yuan1; Kalnay, Eugenia2 | |
2017-01-27 | |
发表期刊 | JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES
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ISSN | 2169-897X |
EISSN | 2169-8996 |
出版年 | 2017 |
卷号 | 122期号:2 |
文章类型 | Article |
语种 | 英语 |
国家 | Japan; USA |
英文摘要 | This study aims to propose two new approaches to improve precipitation forecasts from numerical weather prediction (NWP) models through effective data assimilation of satellite-derived precipitation. The assimilation of precipitation data is known to be very difficult mainly because of highly non-Gaussian statistics of precipitation variables. Following Lien et al., this study addresses the non-Gaussianity issue by applying the Gaussian transformation (GT) based on the empirical cumulative distribution function (CDF) of precipitation. We propose a method that constructs the CDF with only recent 1 month samples, without using a long period of samples needed previously. We also propose a method to use the inverse GT, with which we can obtain realistic precipitation fields from biased NWP model outputs. We assimilate the Japan Aerospace eXploration Agency's Global Satellite Mapping of Precipitation (GSMaP) data into the Nonhydrostatic Icosahedral Atmospheric Model (NICAM) at 112 km horizontal resolution. Assimilating the GSMaP data results in improved weather forecasts compared to the control experiment assimilating only rawinsonde data. We find that horizontal observation thinning is necessary, probably due to the horizontal observation-error correlations in the GSMaP data. We also obtained precipitation fields similar to GSMaP from the NICAM precipitation forecasts by using the inverse GT, leading to an improved precipitation forecast. |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000396116900004 |
WOS关键词 | TRANSFORM KALMAN FILTER ; DIRECT 4D-VAR ASSIMILATION ; PASSIVE MICROWAVE ; RAINFALL ASSIMILATION ; ERROR STATISTICS ; MESOSCALE MODEL ; ENSEMBLE ; SYSTEM ; GSMAP ; VALIDATION |
WOS类目 | Meteorology & Atmospheric Sciences |
WOS研究方向 | Meteorology & Atmospheric Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/32426 |
专题 | 气候变化 |
作者单位 | 1.RIKEN Adv Inst Computat Sci, Kobe, Hyogo, Japan; 2.Univ Maryland, Dept Atmospher & Ocean Sci, College Pk, MD 20742 USA; 3.Japan Agcy Marine Earth Sci & Technol, Yokohama, Kanagawa, Japan |
推荐引用方式 GB/T 7714 | Kotsuki, Shunji,Miyoshi, Takemasa,Terasaki, Koji,et al. Assimilating the global satellite mapping of precipitation data with the Nonhydrostatic Icosahedral Atmospheric Model (NICAM)[J]. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES,2017,122(2). |
APA | Kotsuki, Shunji,Miyoshi, Takemasa,Terasaki, Koji,Lien, Guo-Yuan,&Kalnay, Eugenia.(2017).Assimilating the global satellite mapping of precipitation data with the Nonhydrostatic Icosahedral Atmospheric Model (NICAM).JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES,122(2). |
MLA | Kotsuki, Shunji,et al."Assimilating the global satellite mapping of precipitation data with the Nonhydrostatic Icosahedral Atmospheric Model (NICAM)".JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES 122.2(2017). |
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