# Latest Advances in the Global Navigation Satellite System--Reflectometry (GNSS-R) Field
## 3 Ocean Applications
### Wind Speed
CYGNSS wind speed data have supported a variety of atmospheric science analyses. CYGNSS data are useful in studies of tropical precipitation processes because the L-band GNSS signals penetrate precipitation [71]. For example, ref. [72] compared winds from CYGNSS and ASCAT around maritime thunderstorms with and without lightning to try and determine if near-surface winds are stronger in thunderstorms. Surprisingly, [PERSON] that the conclusion depended on the sensor used in the analysis, and so this result will be reexamined with future CYGNSS data versions. Additionally, GNSS-R wind speed data have supported other derived datasets. For example, CYGNSS winds can be used as input into the calculations of ocean surface heat fluxes [73; 74]. The CYGNSS ocean heat flux data are available, alongside all other CYGNSS datasets. [23; 75] used the CYGNSS-based flux product to investigate the climatological mean of the surface fluxes across low-latitude (\(\pm 40\) degrees) extratropical cyclones. Ref. [76] took the analysis presented in [73] further and examined the distribution of fluxes within low-latitude extratropical cyclones during different development phases. For a more tropics-centered investigation, ref. [77] were able to use the CYGNSS flux dataset to investigate the coupling between surface heat flux and enhanced convection and precipitation in the Madden-Julian oscillation (MJO). The CYGNSS-based ocean surface flux product will enable researchers to perform other studies of air-sea interaction in the future. GNSS-R has been used for tropical cyclone applications since the initial aircraft-based studies were performed [78; 79]. These initial airborne experiments showed that the technique is useful for observing the ocean surface, regardless of precipitation, for estimates of near-surface wind speed. Given the technique's strengths in observing through the use of precipitation, CYGNSS was developed with the motivation to better understand TCs with a spaceborne GNSS-R platform. Prior to launch, methods were developed to estimate TC intensity, size, and structure from the CYGNSS level-2 wind speed data over TCs [79; 80]. While the CYGNSS mission was motivated to observe TCs, the creation of robust estimates of wind speed has been challenging. First, calibration of the level-1 data is key for accurate estimation of wind speed, especially for high winds [61]. Calibration of CYGNSS data was initially challenging because of unexpected and unobservable fluctuations in the properties of the GPS transmitter signal power. Now, with a dynamic calibration technique, these challenges have been overcome. Additionally, GNSS-R observations have a non-unique mapping between observations and wind speed. The scattered signal also depends on the state of the sea surface. Significant wave height has been incorporated into the latest CYGNSS wind speed estimation algorithms, and work is ongoing to figure out a robust solution that incorporates sea state into estimates of wind speed [81; 82]. Ref. [35] shows the current performance of wind speed data products developed for TC analysis [83]. GNSS-R is a relatively new source of near-surface wind speed data, and these data are useful for supporting weather analysis. CYGNSS observations have been developed for the analysis of TCs and related phenomena. For TC-analysis, many researchers rely on idealized models of TC wind fields, together with GNSS-R observations, in order to characterize various aspects of the storm [80; 84]. For example, ref. [85] developed a method to estimate the center location of the TC circulation from the collection of level-2 wind speed observations over a storm. Ref. [86] detailed a new storm-centric TC wind data product that grids wind speed data into a storm-relative grid for TC analysis. Ref. [87] focused on the estimation of the maximum wind speed of a storm, which is valuable for determining one aspect of the destructive potential of TCs.