1237 Assimilation of CYGNSS Wind Speed for Tropical Convection during 2018 MJO Onset

Wednesday, 15 January 2020
Hall B (Boston Convention and Exhibition Center)
X. Li, Univ. of Alabama in Huntsville, Huntsville, AL; and T. J. Lang and J. R. Mecikalski

The Cyclone Global Navigation Satellite System (CYGNSS) is a constellation of eight small satellites that has been launched in December 2016. The main objective of CYGNSS is to retrieve near-surface wind speeds over the ocean using the direct and reflected signals from Global Positioning System (GPS) satellites. CYGNSS also features rapid revisits over tropics with an average revisit time of 4 hours.

In this study, we conducted high-resolution Weather Research and Forecast (WRF) model simulations to investigate forecast of tropical convection during the onset of the MJO event over Central Indian Ocean in January 2018. Experiments were performed to assimilate the CYGNSS v2.1 Level 2 wind speed retrievals, using the hybrid Ensemble four-dimensional variational (En4DVAR) technique of WRF Data Assimilation (WRFDA) system. Different CYGNSS wind speed products will be assimilated to investigate the strategy for CYGNSS data to be used in data assimilation. We will also examine data assimilation techniques (timing, frequency, resolution, and observation errors) for CYGNSS data. Experiments will be conducted with a combined satellite datasets (IMERG precipitation, ASCAT ocean surface wind, and CYGNSS surface wind speed) to explore the optimal way for different information to be incorporated to improve the initial condition that could lead to a more accurate precipitation forecast. Comparisons between “control” WRF model simulations and simulations using WRFDA will be presented to discuss the impact of the CYGNSS and the combined datasets. 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.

- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner