DOI: 10.1029/2018JD028723
Scopus记录号: 2-s2.0-85052864645
论文题名: Improving Short-Term Rainfall Forecasts by Assimilating Weather Radar Reflectivity Using Additive Ensemble Perturbations
作者: Yokota S. ; Seko H. ; Kunii M. ; Yamauchi H. ; Sato E.
刊名: Journal of Geophysical Research: Atmospheres
ISSN: 2169897X
出版年: 2018
卷: 123, 期: 17 起始页码: 9047
结束页码: 9062
语种: 英语
英文关键词: data assimilation
; ensemble Kalman filter
; error covariance inflation
; fractions skill score
; short-term rainfall forecast
; weather radar reflectivity
英文摘要: To improve short-term rainfall forecasts through direct assimilation of radar reflectivity, atmospheric variables associated with rainfall should be modified based on their correlation with reflectivity. However, it is difficult to estimate such correlations. The ensemble Kalman filter can estimate the correlation by means of ensemble forecasts, although the estimation is limited to when rainfall is forecast by at least one member at analysis points. To assimilate reflectivity effectively even at points at which no rainfall is forecast, we suggest adding ensemble reflectivity perturbations, which are correlated with atmospheric variables, before ensemble Kalman filter assimilation. In the present study, this correlation is calculated in the whole computational domain including the rainfall regions. We apply this procedure to assimilation experiments with 1-km horizontal grid interval for two tornadic supercells that occurred on 6 May 2012 and on 2 September 2013, and we succeed in improving short-term rainfall forecasts by modifying wind, temperature, and water vapor. ©2018. American Geophysical Union. All Rights Reserved. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/113171
Appears in Collections: 气候减缓与适应
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作者单位: Meteorological Research Institute, Japan Meteorological Agency, Tsukuba, Japan; Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan; Numerical Prediction Division, Forecast Department, Japan Meteorological Agency, Tokyo, Japan; Administration Division, Observation Department, Japan Meteorological Agency, Tokyo, Japan
Recommended Citation:
Yokota S.,Seko H.,Kunii M.,et al. Improving Short-Term Rainfall Forecasts by Assimilating Weather Radar Reflectivity Using Additive Ensemble Perturbations[J]. Journal of Geophysical Research: Atmospheres,2018-01-01,123(17)