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Article

Optimizing the Assimilation of the GOES-16/-17 Atmospheric Motion Vectors in the Hurricane Weather Forecasting (HWRF) Model

1
Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin-Madison, Madison, WI 53706, USA
2
NOAA NESDIS Center for Satellite Applications and Research, College Park, MD 207040, USA
3
I.M. Systems Group (IMSG), Rockville, MD 20852, USA
4
NOAA/NWS/NCEP/Environmental Modeling Center, College Park, MD 20740, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Vladimir N. Kudryavtsev
Remote Sens. 2022, 14(13), 3068; https://doi.org/10.3390/rs14133068
Received: 3 May 2022 / Revised: 21 June 2022 / Accepted: 23 June 2022 / Published: 26 June 2022
Hourly and 15 min GOES-16 and -17 atmospheric motion vectors (AMVs) are evaluated using the 2020 version of the operational HWRF to assess their impact on tropical cyclone forecasting. The evaluation includes infrared (IR), visible (VIS), shortwave (SWIR), clear air, and cloud top water vapor (CAWV and CTWV) AMVs derived from the ABI imagery. Several changes are made to optimize the assimilation of these winds. The observational error profile is inflated to avoid overweighting of the AMVs. The range of allowable AMV wind speeds entering the assimilation system is increased to include larger wind speeds observed in tropical cyclones. Two data quality checks, commonly used for rejecting AMVs, namely QI and PCT1, have been removed. These changes resulted in a 20–40% increase in the number of AMVs assimilated. One additional change, specific to infrared AMVs, is narrowing the atmospheric layer where IR AMVs are rejected from 400–800 hPa to 400–600 hPa. The AMVs’ impact on forecast skill is assessed using storms from the North Atlantic and the Eastern Pacific, respectively. Overall, GOES-16 and -17 AMVs are beneficial for improving tropical cyclone forecasting. Positive analysis and forecast impact are obtained for track error, intensity error, minimum central pressure error, and storm size. View Full-Text
Keywords: data assimilation; atmospheric motion vectors; HWRF; GOES-16 and 17; tropical cyclone forecasting data assimilation; atmospheric motion vectors; HWRF; GOES-16 and 17; tropical cyclone forecasting
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MDPI and ACS Style

Lim, A.H.N.; Nebuda, S.E.; Jung, J.A.; Daniels, J.M.; Bailey, A.; Bresky, W.; Bi, L.; Mehra, A. Optimizing the Assimilation of the GOES-16/-17 Atmospheric Motion Vectors in the Hurricane Weather Forecasting (HWRF) Model. Remote Sens. 2022, 14, 3068. https://doi.org/10.3390/rs14133068

AMA Style

Lim AHN, Nebuda SE, Jung JA, Daniels JM, Bailey A, Bresky W, Bi L, Mehra A. Optimizing the Assimilation of the GOES-16/-17 Atmospheric Motion Vectors in the Hurricane Weather Forecasting (HWRF) Model. Remote Sensing. 2022; 14(13):3068. https://doi.org/10.3390/rs14133068

Chicago/Turabian Style

Lim, Agnes H. N., Sharon E. Nebuda, James A. Jung, Jaime M. Daniels, Andrew Bailey, Wayne Bresky, Li Bi, and Avichal Mehra. 2022. "Optimizing the Assimilation of the GOES-16/-17 Atmospheric Motion Vectors in the Hurricane Weather Forecasting (HWRF) Model" Remote Sensing 14, no. 13: 3068. https://doi.org/10.3390/rs14133068

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