Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2018JD028723 |
Improving Short-Term Rainfall Forecasts by Assimilating Weather Radar Reflectivity Using Additive Ensemble Perturbations | |
Yokota, S.1; Seko, H.1,2; Kunii, M.1,3; Yamauchi, H.1,4; Sato, E.1 | |
2018-09-16 | |
发表期刊 | JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES |
ISSN | 2169-897X |
EISSN | 2169-8996 |
出版年 | 2018 |
卷号 | 123期号:17页码:9047-9062 |
文章类型 | Article |
语种 | 英语 |
国家 | Japan |
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. Plain Language Summary Short-term numerical forecasts can be improved by the new assimilation procedure of weather radar reflectivity suggested in this study. With this procedure, the initial atmospheric states of simulations are corrected based on more reasonable correlation with radar reflectivity. |
英文关键词 | data assimilation weather radar reflectivity short-term rainfall forecast ensemble Kalman filter error covariance inflation fractions skill score |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000445617500009 |
WOS关键词 | YAMADA LEVEL-3 MODEL ; KALMAN FILTER ; CONVECTIVE PARAMETERIZATION ; DOPPLER RADAR ; CLOUD MODEL ; PREDICTION ; TEMPERATURE ; PRECIPITATION ; VERIFICATION ; FORMULATION |
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/33132 |
专题 | 气候变化 |
作者单位 | 1.Japan Meteorol Agcy, Meteorol Res Inst, Tsukuba, Ibaraki, Japan; 2.Japan Agcy Marine Earth Sci & Technol, Yokohama, Kanagawa, Japan; 3.Japan Meteorol Agcy, Forecast Dept, Numer Predict Div, Tokyo, Japan; 4.Japan Meteorol Agcy, Observat Dept, Adm Div, Tokyo, Japan |
推荐引用方式 GB/T 7714 | 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,2018,123(17):9047-9062. |
APA | Yokota, S.,Seko, H.,Kunii, M.,Yamauchi, H.,&Sato, E..(2018).Improving Short-Term Rainfall Forecasts by Assimilating Weather Radar Reflectivity Using Additive Ensemble Perturbations.JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES,123(17),9047-9062. |
MLA | Yokota, S.,et al."Improving Short-Term Rainfall Forecasts by Assimilating Weather Radar Reflectivity Using Additive Ensemble Perturbations".JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES 123.17(2018):9047-9062. |
条目包含的文件 | 条目无相关文件。 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论