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

doi:10.1029/2021gl096410

i

Ensemble‐Based Assimilation of Satellite All‐Sky Microwave Radiances Improves Intensity and Rainfall Predictions for Hurricane Harvey (2017)

Supporting Files

2021
By Zhang, Yunji ; Sieron, Scott B. ; Lu, Yinghui ; ...

Details

Journal Title:

Geophysical Research Letters
Personal Author:

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
NOAA Program & Office:

NWS (National Weather Service) ; OAR (Oceanic and Atmospheric Research) ; WPO (Weather Program Office)
Description:

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.
Keywords:

Cyclones Data Assimilation Forecasting Data Assimilation Collaborative Science, Technology, and Applied Research (CSTAR) program
Source:

Geophysical Research Letters, 48(24)
DOI:

https://doi.org/10.1029/2021gl096410
ISSN:

0094-8276 ; 1944-8007
Format:

PDF
Publisher:

American Geophysical Union (AGU)
Document Type:

Journal Article
Funding:

Grant no. NA18OAR4590369 ; Grant no. NA18NWS4680054
License:

https://creativecommons.org/licenses/by-nc-nd/4.0/
Rights Information:

CC BY-NC-ND
Compliance:

CHORUS
Main Document Checksum:

urn:sha256:58e7951edf9257177974d8b823463b9b8ad787ff63c4b23b8583f11fef59e844
Download URL:

https://repository.library.noaa.gov/view/noaa/49885/noaa_49885_DS1.pdf
File Type:

[PDF - 5.15 MB ]

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