Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2018JD029643 |
Assimilating Every-10-minute Himawari-8 Infrared Radiances to Improve Convective Predictability | |
Sawada, Yohei1,2; Okamoto, Kozo1,2; Kunii, Masaru1,3; Miyoshi, Takemasa2,4,5,6,7 | |
2019-03-16 | |
发表期刊 | JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES |
ISSN | 2169-897X |
EISSN | 2169-8996 |
出版年 | 2019 |
卷号 | 124期号:5页码:2546-2561 |
文章类型 | Article |
语种 | 英语 |
国家 | Japan; USA |
英文摘要 | Improving the predictability of sudden local severe weather is a grand challenge for numerical weather prediction. Recently, the capability of geostationary satellites to observe infrared radiances has been significantly improved, and it is expected that the "Big Data" from the new generation geostationary satellites could contribute to improving convective predictability. We examined the potential impacts of assimilating frequent infrared observations from a new generation geostationary satellite, Himawari-8, on convective predictability. We implemented the real-data experiment in which Himawari-8 all-sky moisture-sensitive infrared radiances of band 8 (6.2 mu m) and band 10 (7.3 mu m) were assimilated into the high-resolution (2 km) limited area model, Japan Meteorological Agency's Non-Hydrostatic Model, every 10 min by the Local Ensemble Transform Kalman Filter. The frequent infrared observations from Himawari-8 improve the analysis and forecast of isolated convective cells and sudden local severe rainfall induced by weak large-scale forcing. The results imply that satellite data assimilation can contribute to better forecasting severe weather events in smaller spatiotemporal scales than the previous studies. |
英文关键词 | geostationary satellite Himawari-8 convective-scale data assimilation |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000462139800010 |
WOS关键词 | ENSEMBLE KALMAN FILTER ; BIG DATA ASSIMILATION ; RADAR OBSERVATIONS ; GOES-R ; PARAMETERIZATION ; SIMULATION ; PREDICTION ; SUPERCELL ; PROGRESS ; IMPACTS |
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/181674 |
专题 | 气候变化 |
作者单位 | 1.Meteorol Res Inst, Tsukuba, Ibaraki, Japan; 2.RIKEN, Ctr Computat Sci, Kobe, Hyogo, Japan; 3.Japan Meteorol Agcy, Numer Predict Div, Tokyo, Japan; 4.RIKEN, Interdisciplinary Theoret & Math Sci Program, Kobe, Hyogo, Japan; 5.RIKEN, Predict Sci Lab, Cluster Pioneering Res, Kobe, Hyogo, Japan; 6.Univ Maryland, Dept Atmospher & Ocean Sci, College Pk, MD 20742 USA; 7.Japan Agcy Marine Earth Sci & Technol JAMSTEC, Yokohama, Kanagawa, Japan |
推荐引用方式 GB/T 7714 | Sawada, Yohei,Okamoto, Kozo,Kunii, Masaru,et al. Assimilating Every-10-minute Himawari-8 Infrared Radiances to Improve Convective Predictability[J]. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES,2019,124(5):2546-2561. |
APA | Sawada, Yohei,Okamoto, Kozo,Kunii, Masaru,&Miyoshi, Takemasa.(2019).Assimilating Every-10-minute Himawari-8 Infrared Radiances to Improve Convective Predictability.JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES,124(5),2546-2561. |
MLA | Sawada, Yohei,et al."Assimilating Every-10-minute Himawari-8 Infrared Radiances to Improve Convective Predictability".JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES 124.5(2019):2546-2561. |
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