# Joint Use of Spaceborne Microwave Sensor Data and CYGNSS Data to Observe Tropical Cyclones  
[PERSON]  
1 School of Electronic Information, Wuhan University, Wuhan 430072, China; [EMAIL_ADDRESS] (S.W. ); [EMAIL_ADDRESS] (B.N.) [PERSON]  
2 School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430  
sensors can measure the global ocean surface wind speed in all weather and time conditions. However, their performance is severely limited by the long revisit period, and this makes it difficult to obtain adequate samples during the rapidly evolving stages of a TC's life cycle. In addition, the coverage of a microwave radiometer or scatterometer is not continuous because of the gap between its swaths. As a result, the complete structure of a TC cannot be obtained in some cases. For example, the inner core of Hurricane Florence was located outside the coverage of the Soil Moisture Active and Passive (SMAP) satellite at 08:30 Coordinated Universal Time (UTC) on 10 September 2018, as described in Section 3, and hence it is impossible to determine the location of Hurricane Florence by only using the SMAP satellite data. Therefore, the joint use of multiple satellite data to provide more information about TCs is very important to improve the accuracy of TC prediction [6]. The global navigation satellite system-reflectometry (GNSS-R) technique is a new application of satellite navigation systems that was first proposed by [PERSON] in 1993 and it has attracted a lot of attention within the remote sensing community [7]. GNSS-R is a passive radar system that uses GNSS satellites as transmitters of opportunity, e.g., the Global Position System (GPS), GLONASS, Galileo, and Beidou. In recent years, the GNSS-R technique has been widely studied both at the theoretical and practical level. Various theoretical models have been proposed for GNSS-R [8,9]. In the meantime, numerous experiments have been conducted to evaluate the potential of GNSS-R in applications such as ocean altimetry [10], as well as the detection of sea ice, oil slicks, ocean surface wind fields, snow depth, soil moisture, and vegetation status [11]. In order to demonstrate the potential of spaceborne GNSS-R in monitoring the global environment, several satellites equipped with the GNSS-R payload were launched during the last two decades [12]. One of them is the National Aeronautics and Space Administration (NASA) Cyclone Global Navigation Satellite System (CYGNSS) that was launched on 15 December 2016. The CYGNSS constellation consists of eight low-earth orbiting microsatellites, and its scientific goal is to understand the relationship between ocean surface properties, moist atmospheric thermodynamics, radiation, and convective dynamics in the inner core of TCs [13]. Its primary objectives are to measure ocean surface wind speed in the range of 3-70 m/s with a spatial coverage between 35\({}^{\circ}\) N and 35\({}^{\circ}\) S latitude, a spatial resolution of 25 \(\times\) 25 km, and a mean revisit time that is better than 12 h [14]. As a result, more information about TCs can be obtained, which enhances our understanding of their physical processes and helps to forecast their genesis and intensification. After the launch of CYGNSS, many studies have reported on its data processing, as well as its potential in the observation of TCs. A calibration algorithm used by the CYGNSS mission to produce its Level 1 data products is described in [15]. A wind speed retrieval algorithm to obtain the CYGNSS Level 2 data products and its retrieval error was introduced in [16,17], respectively. In addition, several methods have been proposed to estimate the center location, intensity and integrated kinetic energy of TCs [18, 19, 20], and the performance of the GNSS constellation was studied at both a theoretical and practical level when it was employed to observe the ocean surface winds within the TC inner core [21, 22, 23, 24, 25, 26, 27, 28].