# A Study on Assimilation of CYGNSS Wind Speed Data for Tropical Convection during 2018 January MJO
## 1 Introduction  
Owing to better global analyses and advances in numerical models and physics parameterizations, tropical cyclone (TC) forecasts have improved steadily in the past two decades (See, e.g., [1, 2]). Despite the substantial amount of effort, however, accurate predictions of the structure and intensity of TCs and tropical convections remain challenging. The structure and intensity of TCs are not only influenced by the large-scale environment; the air-sea exchange also plays a vital role. The variations in structure and intensity also depend largely on the meso- and micro- scale in-storm dynamic, physics, and radiative processes, as well as the interaction of environmental flow and storm-scale processes [3, 4]. An accurate description of the atmospheric state and the environment is essential for simulations of tropical cyclones and tropical convections. Since tropical cyclones and maritime tropical convection spend most of their lifetime over oceans where conventional data and aircraft measurements are limited in space and time, the need for a moreaccurate representation of storms and their environment motivates the application of satellite-retrieved products. Satellite wind products, especially the surface wind from scatterometers, can provide information about the location and intensity of tropical cyclones and tropical convections. However, these observation systems [e.g., Quick Scatterometer--QuikSCAT, Advanced Scatterometer--ASCAT, and OceanSat-2 Scatterometer--OSCAT] suffer from severe limitations under extreme conditions (e.g., heavy precipitation, strong winds \(>\) 35 m s\({}^{-1}\), etc. ), and long revisit times (\(\sim\)12+ h) also limit their usefulness in detecting rapid changes in intensity and location [5,6]. To obtain ocean surface wind data with high-spatial and temporal resolutions, and in adverse surface weather conditions of thick clouds and precipitation, the Cyclone Global Navigation Satellite System (CYGNSS) mission was launched in December 2016 [7]. The CYGNSS constellation is composed of eight bistatic microsatellites in a slightly elliptical orbit plane, at a 35\({}^{\circ}\) inclination angle, and between roughly 510 and 540 km altitude. As low Earth orbiting (LEO) satellites, CYGNSS provides a relatively frequent revisit time, with a median value of 2.8 h and a mean of 7.2 h [7,8]. Each CYGNSS microsatellite is equipped with a Delay Doppler Mapping Instrument (DDMI) that consists of a multichannel Global Navigation Satellite System reflectometry (GNSS-R) receiver, a low-gain zenith antenna to receive the direct global positioning system (GPS) signals, and two high-gain downward pointing antennas to receive the scattered GPS signals from the ocean surface [9,10]. CYGNSS measures the pattern and intensity of the scattered and reflected GPS signals at specular reflection points within the \(\pm\)35\({}^{\circ}\) latitude coverage zone. CYGNSS uses the GPS \"L1\" channel, which suffers no appreciable attenuation, even under heavy rainfall conditions. This allows CYGNSS to retrieve ocean surface winds for a variety of tropical phenomena, e.g., the tropical cyclone core region and deep convection associated with the Madden-Julian Oscillation (MJO; [11,12]), with a level of accuracy that was previously impossible in practice. Each CYGNSS observatory is capable of sampling 4 different specular reflection tracks at a frequency of 1 Hz, and ocean surface wind speed is retrieved along the specular tracks at a resolution of approximately 25 km. It is expected that the CYGNSS will be able to accurately monitor the rapidly changing wind of precipitating regions over the tropics (e.g., [13]), and to improve forecasting operations (e.g., [14]). Numerical models commonly struggle in predicting the accurate intensity of TCs. It is especially challenging to capture the rapid intensity change of TCs during the intensification or weakening stages [15].