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Ensemble-Based Assimilation of Satellite All-Sky Microwave Radiances Improves Intensity and Rainfall Predictions for Hurricane Harvey (2017).
Zhang, Yunji; Sieron, Scott B; Lu, Yinghui; Chen, Xingchao; Nystrom, Robert G; Minamide, Masashi; Chan, Man-Yau; Hartman, Christopher M; Yao, Zhu; Ruppert, James H; Okazaki, Atsushi; Greybush, Steven J; Clothiaux, Eugene E; Zhang, Fuqing.
Affiliation
  • Zhang Y; Department of Meteorology and Atmospheric Science Center for Advanced Data Assimilation and Predictability Techniques The Pennsylvania State University University Park PA USA.
  • Sieron SB; I.M. Systems Group Inc. (IMSG) College Park MD USA.
  • Lu Y; Nanjing University Nanjing China.
  • Chen X; Department of Meteorology and Atmospheric Science Center for Advanced Data Assimilation and Predictability Techniques The Pennsylvania State University University Park PA USA.
  • Nystrom RG; National Center for Atmospheric Research (NCAR) University Park PA USA.
  • Minamide M; Department of Civil Engineering The University of Tokyo Tokyo Japan.
  • Chan MY; Department of Meteorology and Atmospheric Science Center for Advanced Data Assimilation and Predictability Techniques The Pennsylvania State University University Park PA USA.
  • Hartman CM; Department of Meteorology and Atmospheric Science Center for Advanced Data Assimilation and Predictability Techniques The Pennsylvania State University University Park PA USA.
  • Yao Z; Department of Meteorology and Atmospheric Science Center for Advanced Data Assimilation and Predictability Techniques The Pennsylvania State University University Park PA USA.
  • Ruppert JH; School of Meteorology The University of Oklahoma Norman OK USA.
  • Okazaki A; Department of Global Environment and Disaster Prevention Sciences Hirosaki University Hirosaki Japan.
  • Greybush SJ; Department of Meteorology and Atmospheric Science Center for Advanced Data Assimilation and Predictability Techniques The Pennsylvania State University University Park PA USA.
  • Clothiaux EE; Department of Meteorology and Atmospheric Science Center for Advanced Data Assimilation and Predictability Techniques The Pennsylvania State University University Park PA USA.
  • Zhang F; Department of Meteorology and Atmospheric Science Center for Advanced Data Assimilation and Predictability Techniques The Pennsylvania State University University Park PA USA.
Geophys Res Lett ; 48(24): e2021GL096410, 2021 Dec 28.
Article in En | MEDLINE | ID: mdl-35865360
ABSTRACT
Ensemble-based data assimilation of radar observations across inner-core regions of tropical cyclones (TCs) in tandem with satellite all-sky infrared (IR) radiances across the TC domain improves TC track and intensity forecasts. This study further investigates potential enhancements in TC track, intensity, and rainfall forecasts via assimilation of all-sky microwave (MW) radiances using Hurricane Harvey (2017) as an example. Assimilating Global Precipitation Measurement constellation all-sky MW radiances in addition to GOES-16 all-sky IR radiances reduces the forecast errors in the TC track, rapid intensification (RI), and peak intensity compared to assimilating all-sky IR radiances alone, including a 24-hr increase in forecast lead-time for RI. Assimilating all-sky MW radiances also improves Harvey's hydrometeor fields, which leads to improved forecasts of rainfall after Harvey's landfall. This study indicates that avenues exist for producing more accurate forecasts for TCs using available yet underutilized data, leading to better warnings of and preparedness for TC-associated hazards in the future.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies / Risk_factors_studies Language: En Journal: Geophys Res Lett Year: 2021 Document type: Article

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies / Risk_factors_studies Language: En Journal: Geophys Res Lett Year: 2021 Document type: Article