# Can We Improve Parametric Cyclonic Wind Fields Using Recent Satellite Remote Sensing Data?
## Data and Methods
### Remote Sensing Data  
The complete ASCAT and CYGNSS datasets (distributed by the KNMI and PODAAC data center, respectively) was collected for the 2017 hurricane season in the Atlantic and East Pacific.  
The CYGNSS mission [31] consists of a constellation of eight satellites in a circular orbit at -520 km altitude and 35\({}^{\circ}\) inclination. Each spacecraft receives direct and reflected Global Positioning System (GPS) L1 (1.575 Ghz) signals to infer surface wind speed and sea roughness, even for intense rainfalls typically observed during hurricanes. It allows for good spatial and temporal coverage, with mean and median revisit times over the tropics of 7.2 h and 2.8 h, respectively [32]. The 25 km-resolution data considered here (v2.1) have been validated using measurements by the Stepped Frequency Microwave Radiometer (SFMR) surface wind sensors on the National Oceanographic and Atmospheric Administration (NOAA)\"hurricane hunter\" P-3 aircraft. The measurements were made during reconnaissance flights through Atlantic hurricanes in the 2017 season. Twenty-five eyewall penetration passes by the aircraft were aligned and timed to coincide both spatially and temporally with overpasses by CYGNSS spacecraft. The RMS difference between CYGNSS and SFMR wind speeds, averaged over all 674 near-coincident measurements with wind speeds of 20-59 m/s is 22%. After removing the 4 m/s root-mean-square (RMS) error in SFMR measurements [40], the RMS error attributable to CYGNSS is found to be 17%. At lower wind speeds away from major storms, the error in CYGNSS winds is determined from comparisons with near-coincident ECMWF reanalysis wind products. In this case, the RMS error is found to be 1.4 m/s for wind speeds below 20 m/s. A detailed analysis of the CYGNSS performance assessment in both low and high wind speed regimes is provided in [41]. Regarding the contributing sources of error to CYGNSS winds in storms, roughly half of the 17% error is explained by overall biases between SFMR and CYGNSS winds. If that bias were removed by forcing the average CYGNSS wind to equal that of the SFMR winds, the remaining scatter between the two winds is reduced by roughly one half. The current (v2.1) CYGNSS high wind data product is not debiased in this way and so we can expect maximum biases of a few meters per second at high wind speeds.  
We consider here several Level 2-wind speed products:  
* The \"wind speed\" (_ws_) product is derived from the best fit to both the normalized bistatic radar cross-section (NBRCS) and leading edge slope (LES) of the integrated delay waveform given by the delay-Doppler maps (DDM [42]), using the geophysical model function (GMF) appropriate for fully developed seas;
* the \"yslf_les_wind_speed\" (_les_) wind product is derived from only the LES of the DDM, using a young seas/limited-fetch GMF; and
* the \"yslf_nbrcs_wind_speed\" (_nbrc_) product is derived from only the NBRCS, using the young seas/limited-fetch GMF.  
ASCAT [22; 43] consists of C-band scatterometers mounted on the satellites, MetOp-A and MetOp-B, which were launched in 2006 and 2012, respectively. The emitting antennas transmit pulses at 5.255 GHz and extend on either side of the instrument, which results in a double 500 km-wideswath of observations. These scatterometers provide very accurate estimates of wind speeds up to at least 17.5 m/s (34-kt). However, they lose sensitivity in extreme conditions and can be plagued by rain contamination. We use here the 25 km-resolution coastal product, which gives more wind data close to the coast [44].