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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Tsunami Wavefield Reconstruction and Forecasting Using the Ensemble Kalman Filter
Offshore sensor networks like DONET and S‐NET, providing real‐time estimates of wave height through measurements of pressure changes along the seafloor, are revolutionizing local tsunami early warning. Data assimilation techniques, in particular, optimal interpolation (OI), provide real‐time wavefield reconstructions and forecasts. Here we explore an alternative assimilation method, the ensemble Kalman filter (EnKF), and compare it to OI. The methods are tested on a scenario tsunami in the Cascadia subduction zone, obtained from a 2‐D coupled dynamic earthquake and tsunami simulation. Data assimilation uses a 1‐D linear long‐wave model. We find that EnKF achieves more accurate and stable forecasts than OI, both at the coast and across the entire domain, especially for large station spacing. Although EnKF is more computationally expensive than OI, with development in high‐performance computing, it is a promising candidate for real‐time local tsunami early warning.
more » « less- NSF-PAR ID:
- 10457242
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Geophysical Research Letters
- Volume:
- 46
- Issue:
- 2
- ISSN:
- 0094-8276
- Page Range / eLocation ID:
- p. 853-860
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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