DOI: 10.1002/2016JD025355
论文题名: Assimilating the global satellite mapping of precipitation data with the Nonhydrostatic Icosahedral Atmospheric Model (NICAM)
作者: Kotsuki S. ; Miyoshi T. ; Terasaki K. ; Lien G.-Y. ; Kalnay E.
刊名: Journal of Geophysical Research: Atmospheres
ISSN: 2169897X
出版年: 2017
卷: 122, 期: 2 起始页码: 631
结束页码: 650
语种: 英语
英文关键词: data assimilation
; ensemble Kalman filter
; Gaussian transformation
; GSMaP
; NICAM
; numerical weather prediction
Scopus关键词: atmospheric modeling
; data assimilation
; ensemble forecasting
; Gaussian method
; Kalman filter
; numerical model
; precipitation assessment
; satellite data
; weather forecasting
; Japan
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. ©2016. The Authors. 资助项目: 15K18128
; NNX1AE44G
Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/62725
Appears in Collections: 影响、适应和脆弱性 气候减缓与适应
There are no files associated with this item.
作者单位: RIKEN Advanced Institute for Computational Science, Kobe, Japan; Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD, United States; Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan
Recommended Citation:
Kotsuki S.,Miyoshi T.,Terasaki K.,et al. Assimilating the global satellite mapping of precipitation data with the Nonhydrostatic Icosahedral Atmospheric Model (NICAM)[J]. Journal of Geophysical Research: Atmospheres,2017-01-01,122(2)