Analysis and Assimilation of CYGNSS Wind Data for Improved 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 high-resolution Weather Research and Forecast (WRF) model numerical simulation for tropical convection events over the Indian Ocean. Experiments were specifically performed to demonstrate the assimilation of CYGNSS Level 3 data, using the hybrid Ensemble three-dimensional variational (3DVAR) technique of WRF Data Assimilation (WRFDA) system. Case studies (e.g., tropical convective storm over Central Indian Ocean on 1 May 2017) have been conducted to investigate the assimilation of CYGNSS wind data. 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 over mainly oceanic region. Details of the methodology of data assimilation, result and verification of data impact on model forecast will be presented at the conference.

Importantly, the presentation will also discuss the quality of the Level 3 CYGNSS wind speed data itself, how it can be treated prior to WRFDA, and its influence in the data assimilation process and on subsequent WRF model forecast performance. We will also report on our research progress of turbulent flux estimation related to convection-sustaining heat and moisture budgets, and the potential of CYGNSS wind observations to improve such estimates.