Assimilation of CYGNSS Wind Data for Improving Tropical Convection Forecasts

The Cyclone Global Navigation Satellite System (CYGNSS) is a constellation of eight small satellites that receive both direct and reflected signals from Global Positioning System (GPS) satellites to retrieve near-surface wind speeds over the ocean. Since being launched in December 2016, CYGNSS provides rapid updates of global wind information for tropical cyclones as well as general tropical convection.

In this study, we conducted several cases studies with high-resolution Weather Research and Forecast (WRF) model to investigate forecast of tropical convection events. Experiments were specifically performed to demonstrate the assimilation of CYGNSS v2.1 Level 2 data, using the hybrid Ensemble three-dimensional variational (En3DVAR) technique of WRF Data Assimilation (WRFDA) system. Case studies were selected from tropical convective storms during the MJO event over Central Indian Ocean in January 2018. Comparisons between “control” WRF model simulations and simulations using WRFDA will be presented to discuss the preliminary results on the impact of the CYGNSS wind speed observations. We will examine different strategies (timing, frequency, resolution, and merging with other observations) for developing an improved assimilation of CYGNSS data that lead to the formation of a more accurate low-level wind field. Details of the methodology of data assimilation, results and verification of data impacts on model forecast will be presented at the conference. The presentation will also discuss the quality of the CYGNSS v2.1 Level 2 wind speed products, how it can be treated prior to use in the WRFDA system, and its influence within the data assimilation process, and on subsequent WRF model forecast performance.