# A Study on Assimilation of CYGNSS Wind Speed Data for Tropical Convection during 2018 January MJO
## 1 Introduction
Another great challenge in forecasts of TCs and tropical convections is the wind speed distribution, especially over large variability regions [16,17]. Many studies have shown that the assimilation of wind observations is an effective method to improve the initial condition of numerical weather prediction (NWP) models, and hence, to improve intensity and wind predictions of TCs and tropical convection. A number of studies (e.g., [18, 19, 20, 21, 22, 23]) have shown that the assimilation of in situ wind data from dropwindsondes and flight level airborne surveillance provided significant improvements in TC track and intensity forecasts. The assimilation of satellite-derived motion vector winds has improved forecast skills of global and regional NWP models at operation centers, such as the National Centers for Environmental Prediction (NCEP), the European Centre for Medium-Range Weather Forecasts (ECMWF), the United Kingdom Met Office, and the Korea Meteorological Agency [24, 25, 26, 27]. Ocean surface winds, including retrievals from QuikSCAT, ASCAT, and OSCAT, are especially useful for improving TC position, intensity, and structure by providing additional measurements of enhancements of the air-sea exchange and the cyclonic circulation [28, 29, 30, 31, 32, 33, 34]. Prior to the use of real CYGNSS data, a number of previous studies evaluated the assimilation of the CYGNSS data derived from an end-to-end simulator into observing system simulation experiments (OSSEs). Their results suggested that the simulated CYGNSS data would positively impact the forecasts of TCs in both track and intensity [35, 36, 37, 38, 39]. The assimilation of real CYGNSS observation was recently investigated by [PERSON] et al. [14]; the authors indicated the potential of CYGNSS data in improving the track, intensity, and structure of TCs. Unfortunately, due to data quality issues, [PERSON] et al. [14] were only able to assimilate CYGNSS v2.0 fully developed seas (FDS) data, which greatly underestimates the wind field within deep convection regions related to TCs. A recent study ([PERSON] et al. [40]) indicated that, with a number of improvements to the data calibration, v2.1 is of much better quality than the previous version; this is especially true for the young seas/limited fetch (TSLF) product. [PERSON] et al. [41] calculated the root-mean-square (RMS) difference between CYGNSS v2.0 YSLF data and Stepped Frequency Microwave Radiometers (SFMRs) data onboard the NOAA P-3 aircraft in and near hurricanes. For wind retrievals above 20 m s\({}^{-1}\), the RMS was 5.01 m s\({}^{-1}\) when the uncertainty of the SFMR measurements was removed. When CYGNSS v2.1 YSLF data were evaluated, [PERSON] et al. [40] indicated that this RMS value had been reduced to 3.2 m s\({}^{-1}\). In the current study, we examined the assimilation of both FDS and YSLF v2.1 wind speed products. Through the present study, our objectives are: (1) to evaluate the impact and reveal the challenge of utilizing the CYGNSS ocean surface wind speed data in a numerical model, and (2) to explore how to better utilize the CYGNSS data and improve short-term forecast skill.