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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.
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Improving Short-Term QPF Using Geostationary Satellite All-Sky Infrared Radiances: Real-Time Ensemble Data Assimilation and Forecast during the PRECIP 2020 and 2021 Experiments
Abstract The Prediction of Rainfall Extremes Campaign In the Pacific (PRECIP) aims to improve our understanding of extreme rainfall processes in the East Asian summer monsoon. A convection-permitting ensemble-based data assimilation and forecast system (the PSU WRF-EnKF system) was run in real time in the summers of 2020–21 in advance of the 2022 field campaign, assimilating all-sky infrared (IR) radiances from the geostationary Himawari-8 and GOES-16 satellites, and providing 48-h ensemble forecasts every day for weather briefings and discussions. This is the first time that all-sky IR data assimilation has been performed in a real-time forecast system at a convection-permitting resolution for several seasons. Compared with retrospective forecasts that exclude all-sky IR radiances, rainfall predictions are statistically significantly improved out to at least 4–6 h for the real-time forecasts, which is comparable to the time scale of improvements gained from assimilating observations from the dense ground-based Doppler weather radars. The assimilation of all-sky IR radiances also reduced the forecast errors of large-scale environments and helped to maintain a more reasonable ensemble spread compared with the counterpart experiments that did not assimilate all-sky IR radiances. The results indicate strong potential for improving routine short-term quantitative precipitation forecasts using these high-spatiotemporal-resolution satellite observations in the future. Significance Statement During the summers of 2020/21, the PSU WRF-EnKF data assimilation and forecast system was run in real time in advance of the 2022 Prediction of Rainfall Extremes Campaign In the Pacific (PRECIP), assimilating all-sky (clear-sky and cloudy) infrared radiances from geostationary satellites into a numerical weather prediction model and providing ensemble forecasts. This study presents the first-of-its-kind systematic evaluation of the impacts of assimilating all-sky infrared radiances on short-term qualitative precipitation forecasts using multiyear, multiregion, real-time ensemble forecasts. Results suggest that rainfall forecasts are improved out to at least 4–6 h with the assimilation of all-sky infrared radiances, comparable to the influence of assimilating radar observations, with benefits in forecasting large-scale environments and representing atmospheric uncertainties as well.
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- PAR ID:
- 10436794
- Date Published:
- Journal Name:
- Weather and Forecasting
- Volume:
- 38
- Issue:
- 4
- ISSN:
- 0882-8156
- Page Range / eLocation ID:
- 591 to 609
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1175/WAF-D-22-0156.1
