Oversampling Reflectivity Observations From a Geostationary Precipitation Radar Satellite: Impact on Typhoon Forecasts Within a Perfect Model OSSE Framework

Abstract

For the past two decades, precipitation radars (PR) onboard low-orbiting satellites such as Tropical Rainfall Measuring Mission (TRMM) have provided invaluable insight into global precipitation variability and led to advancements in numerical weather prediction through data assimilation. Building upon this success, planning has begun on the next generation of satellite-based PR instruments, with the consideration for a future geostationary-based PR (GPR), bringing the advantage of higher observation frequency over previous and current PR satellites. Following the successful demonstration by a recent study to test the feasibility of a GPR to obtain three-dimensional precipitation data, this study takes the first step to investigate the potential usefulness of GPR observations for numerical weather prediction by performing a perfect model observing system simulation experiment (OSSE) for a West Pacific tropical cyclone (TC). Data assimilation experiments are performed assimilating reflectivity observations obtained for a range of beam sampling spans, following a previous finding that oversampling improves observation quality. Results showed observations obtained with finer sampling spans of 5 km and 10 km were able to better capture key tropical cyclone features in analyses, including the eye, heavy rainfall associated with the eyewall, and outer convective rainbands. Results also showed that through increased moistening and upward velocity within the inner storm environment, assimilation of observations drove an intensification of the secondary circulation and deepening of the storm, leading to an improvement in TC intensity error. Intensity forecasts were found improved for assimilation of observations obtained with increasingly finer beam sampling span, suggesting an important benefit of oversampling. Plain Language SummaryIn a recent study, the feasibility of a future precipitation radar based onboard a geostationary satellite (GPR) that could obtain three-dimensional precipitation measurements was successfully tested. In this study, we take the first step to investigate whether reflectivity observations can be used to improve analyses and forecasts of global weather systems. We perform data assimilation experiments that use simulated GPR observations for a West Pacific tropical cyclone, with observations obtained with varying radar beam sampling spans to generate observation oversampling, following a previous finding that this improves observation quality. Results found that key convective features of the tropical cyclone (TC), including the eye, eyewall structure, and outer rainbands, were all better captured in simulations assimilating observations obtained with finer beam sampling spans, with 5 km sampling providing best results. Observations were also found to have a positive impact on TC intensity in both model analyses and forecasts, with forecast errors for minimum sea level pressure improved at all lead times up to 18 h. TC intensity forecasts were also improved with increasingly finer beam span, sugge...

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Regression-Based Ensemble Perturbations for the Zero-Gradient Issue Posed in Lightning-Flash Data Assimilation with an Ensemble Kalman Filter

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