The potential impact of swell direction to WindSAT foam fraction and to WindSAT retrieved winds is explored. It has been found that foam fraction increases as the swell direction becomes opposite to the wind direction. This increase becomes more prominent as the WindSAT frequency channel increases given the same polarization (e.g. up to +.9% for the 37 GHz H-pol). WindSAT foam fraction derived from the V-polarization shows the least increase compared to H-polarization (+~.2%). The wind speed bias between WindSAT and model wind increases slightly (up to 0.5 m/s) as the swell direction becomes opposite to the wind direction. These results indicate that a swell traveling in the opposite direction of wind driven sea foam can disturb the surface roughness, enough to noticeably increase foam coverage. Additionally, the increase of WindSAT winds in such conditions indicates a residual dependence to swell, which should be appropriately addressed in the retrieval process.

The soil moisture active passive (SMAP) instrument has been used to infer sea surface wind speed from its brightness temperature measurements. To do so, the SMAP wind speed retrieval process requires the removal of sea surface temperature (sst) and sea surface salinity (sss) impact on brightness temperature. Estimating the so-called excess surface emissivity ($\Delta {e}$), that is the sst normalized difference between the measured brightness temperature of the sea surface and the corresponding brightness temperature of a flat surface, is one way of accomplishing such a task. In this article, we investigate whether SMAP$\Delta {e}$contains residual dependencies to sst and sss. To do so, v5.0 SMAP brightness temperature measurements, derived by the Jet Propulsion Laboratory, are used. For any fixed numerical weather prediction model wind speed above 15 m/s, down to a 20% decrease in SMAP$\Delta {e}$is observed as the sst increases from 274 to 304 K. For any fixed wind speed between 8 and 15 m/s, the sst residual dependence is weaker with SMAP$\Delta {e}$exhibiting a 1%–2% decrease as the sst increases. Below 8 m/s, this pattern becomes prevalent again, when the significant wave height (Hs) is greater than 3.5 m. SMAP$\Delta {e}$decreases as much as 50% with increasing sss, most notably below 8 and above 15 m/s when Hs is considered. This analysis has also shown that below 8 m/s and for swell dominant seas, a decrease in either sss or sst results in a decrease in SMAP$\Delta {e}$sensitivity to wind-induced sea surface roughness.

The Soil Moisture Active Passive instrument (SMAP) sea surface wind speed potential dependence to sea surface salinity (sss) and sea surface temperature (sst) is explored. SMAP JPL v5.0 and SMAP REMSS v0.10 are used for this analysis. The SMAP wind speed error (i.e. SMAP-NCEP) is bin averaged per sss and per sst ranges, respectively, and plotted against NCEP wind. This analysis shows that both SMAP wind speed products exhibit clear dependence to both sss and sst: for NCEP winds less than 15 m/s, sst bin averaged error curves for both products show spreads below 1 m/s; for NCEP winds greater than 15 m/s, the maximum spread between the error curves can be greater than 2 m/s. Spreads between sss bin averaged curves can be as high as 5 m/s. This maximum spread is reduced below 1 m/s if data with salinity less than 32 psu is excluded.

We consider the impacts of wind direction and model function choice on retrieved wind speed statistics using SMAP Version 5 L2 product retrieval algorithms. Attempts to retrieve ocean wind direction using SMAP measurements lead to arti-facts in wind speed statistics below 12 m/s which are absent if the wind direction is taken from ancillary data. Model functions derived from Aquarius matchups achieve biases within 0.5 psu and standard deviations within 2 m/s at speeds greater than 5 m/s, with the better performance from NCEP-based matchups. The SMAP-derived model function obtains bet-ter agreement with the global wind speed distribution of the NCEP ancillary product, but with greater scatter and poorer performance near coastal regions. Further effort is needed to improve SMAP wind retrievals for use in operational applications.

The GNSS reflectometry response to sea surface salinity (SSS) and sea surface temperature (SST) is explored using CyGNSS normalized bistatic radar cross section measurements (NBRCS). SSS data from the NASA's Soil Moisture Active-Passive instrument is used for this analysis, including data from NOAA Optimum SST measurements. This study shows that CyGNSS NBRCS is in fact dependent on the SSS under all wind speed conditions, where CyGNSS NBRCS monotonically increases as the salinity increases. CyGNSS NBRCS dependence on the SST is shown to be weaker, compared to its dependence on the SSS. Impact to NOAA current 25 km wind speed product is also assessed where the wind speed error (i.e. CyGNSS-ECMWF) increases with decreasing salinity, whereas the error varies with increasing SST. These findings warrant the inclusion of both SSS and SST effects into future GNSS based wind retrieval algorithms.

A novel approach in addressing cyclone global navigation satellite system (CyGNSS) intersatellite and GPS-related calibration issues is proposed, based on a track-wise$\sigma ^{o}$bias correction method. This method makes use of both ancillary data from numerical weather prediction models and a semiempirical geophysical model function. Care is taken, so the track-wise$\sigma ^{o}$bias correction maintains CyGNSS signal sensitivity. Both intersatellite and GPS-related calibration issues are removed after correction. Long-term$\sigma ^{o}$downward trend, observed throughout the CyGNSS mission, is greatly reduced. Using the corrected$\sigma ^{o}$measurements, a wind retrieval method is also presented and its product thoroughly assessed for a three-year period against European Centre for Medium-Range Weather Forecasts (ECMWFs), Advanced Scatterometer (ASCAT) A/B, Advanced Microwave Scanning Radiometer (AMSR)-2, GMI, WindSat, hurricane weather research and forecasting (HWRF) model, and the stepped frequency microwave radiometer (SFMR) winds. The overall wind speed bias and standard deviation of the error (stde) against ECMWF are 0.16 and 1.19 m/s, while these are −0.11 and 1.12 m/s against ASCAT A/B, respectively. The same metrics against AMSR-2/GMI/WindSat (combined) are −0.19 and 1.11 m/s, respectively. The bias and stde against soil moisture active passive (SMAP) are −0.38 and 1.90 m/s, respectively. In the tropical cyclone environment, the bias and stde against HWRF are −0.54 and 2.90 m/s, and −4.71 and 5.88 m/s with SFMR. Finally, CyGNSS wind performance is gauged in the presence of rain. Below 10 m/s, the bias between CyGNSS and ECMWF increases as the rain rate increases. Between 10 and 15 m/s, biases are mostly absent. Above 15 m/s, results are inconclusive due to the low number of collocated rain samples. Overall, the presented CyGNSS wind speed product both exhibits consistency and reliability, showing promise of using GNSS-R derived winds for operational purposes.

The NOAA track-wise algorithm, used to infer sea surface wind speed from CyGNSS normalized bistatic radar cross section (NBRCS), is being used in conjunction with v3.0 NBRCS. A performance analysis is provided where NOAA CyGNSS winds derived from v2.1 NBRCS are compared with those derived from v3.0 NBRCS, using available data from August 2018 to November 2020. Additionally, v3.0 and v2.1 NBRCS related timeseries are also compared between each other. The overall standard deviation of the wind speed error, using v3.0 NBRCS, slightly increased from 1.23 m/s to 1.28 m/s. Using v3.0 NBRCS, the overall wind speed bias between NOAA CyGNSS winds and interpolated ECMWF winds, showed a 0.05 m/s decrease overall compared to using v2.1 NBRCS.

In November 2019, the Ocean Surface Winds Team at NOAA-NESDIS-STAR released to the public their first version of sea surface winds for the Cyclone Global Navigation Satellite System (v1.0). The reported winds are provided on a 25km grid along each track. A boxcar averaging step is included in order to remove unwanted noise in the signal. Calibration issues are greatly reduced by the use of a track-wise bias correction applied to the normalized bi-static radar cross section. The wind speed is then retrieved on a track-by-track basis where both daily global and storm centered images are provided to the public at https://manati.star.nesdis.noaa.gov/datasets/CYGNSSData.php. For in-depth analyses, NetCDF files are also made available (access instruction provided on the aforementioned link). A comparison with collocated NOAA ASCAT A/B 25km wind product, between July and October 2018, resulted in a -0.13 m/s bias and a 1.23 m/s standard deviation.

In this paper we develop high wind portion of a Geophysical Model Function (GMF) for Scatsat-1 scatterometer measurements. Starting with NSCAT4 GMF the high wind portion has been modifying by utilizing measurements obtained by IWRAP instrument on board of NOAA P3 aircraft within tropical and extratropical cyclones. The Ao coefficient in NSCAT4 GMF was modeled so its slope is a linear function of a logarithm of wind speed and its first derivative of it equals to zero at saturation wind speed for particular incidence angle and measurements frequency as it was measured by IWRAP. The calibrated measured sigma0's from Scatsat-1 shows good agreement with the new GMF named NSCAT4.H at the high winds. The respective wind retrievals are higher and much better aligned with ASCAT high winds while the global winds statistic performance of ScatSat-1 winds remained unchanged. The new ScatSat-1 wind products produced by NSCAT4.H GMF has been implemented in NOAA's operational marine forecasting and warning applications and examples are presented here.

The Cyclone Global Navigation Satellite System (CYGNSS) mission provides ocean wind speed measurements using GNSS reflectometry observations from eight space-borne receivers. The various CYGNSS wind products including conventional CYGNSS L2 winds, NOAA L2 winds, and TES (Trailing Edge Slope) winds are currently available. These wind products were compared to the ECMWF model winds using 8 hours data on June 8, 2018. The initial results showed that all retrievals showed some correlations with the ECMWF model winds, but the bias and the standard deviation are quite different among the methods. Comparison of three products for larger data set will be evaluated and presented in detail.

The cyclone global navigation satellite system (CYGNSS), launched on December 15 2016, represents the first dedicated GNSS-R satellite mission specifically designed to retrieve ocean surface wind speeds in the Tropical Cyclone (TC) environment. The baseline wind retrieval algorithm for the CYGNSS mission makes use of two observables (the normalized bi-static radar cross section and the leading edge slope) to retrieve the average wind speed within a 25 km resolution cell. The premise of the algorithm is that these two observables are only a function of wind speed and incidence angle. Analysis of actual CYGNSS measurements during the course of the calibration and validation process, indicates that collected GNSS-R signals show a dependence on both winds and waves. This paper will present an alternative method in retrieving the wind speed from CYGNSS data, which will include the use of a geophysical model function dependent on both wind and wave data.

In this paper, we present the SCATSAT-1 wind data processor developed by NOAA. The sigma0 from L1B produced by ISRO was used as an input to our processor. Ocean surface wind vector products are produced at the grid resolutions of 12.5 km and 25 km. We experimented with different objective functions and the number of solutions. The ambiguity removal method utilized in our processor is the Two-Dimensional Variational Ambiguity Removal (2DVAR). The rain flag algorithm was developed based on the Bayes' theorem by calculating the rain probability given the rain sensitive parameter threshold.Finally, we validated our Scatsat-1 wind retrievals by both statistical analyses and visual inspection of the wind field for meteorological consistency to determine which objective function produced the best results. The performance of the Scatsat-1 wind retrievals compared to the Global Data Assimilation System (GDAS) winds shows reasonable wind speed and wind direction biases and standard deviation differences.

The cyclone global navigation satellite system (CYGNSS), launched on December 15, 2016, represents the first dedicated GNSS-R satellite mission specifically designed to retrieve ocean surface wind speeds in the Tropical Cyclone (TC) environment [1]. CYGNSS uses a constellation of eight microsatellite observatories that can receive both the direct and reflected signals from GNSS. The CYGNSS observatories are capable of collecting up to four simultaneous reflections each, thus providing high temporal-resolution of ocean surface observations. Thus far, most CYGNSS studies have utilized simulated data from the E2ES [2]-[9]. The current setup of the E2ES assumes the surface slope variances and correlation are completely locally wind-driven, and they are calculated solely based on the local wind speed and wind direction. Analysis of the actual CYGNSS measurements during the course of the calibration and validation process indicates that this assumption is not valid over a large portion of the measurements. While the primary objective of the CYGNSS mission is measuring ocean winds in tropical cyclones, examination of collected GNSS-R signal has shown that the measured signal is a function of both winds and waves. This paper discusses CYGNSS capability in measuring both winds and waves over the ocean, and evaluates its capability in tropical cyclones.

The cyclone global navigation satellite system (CYGNSS), launched on December 15, 2016, represents the first dedicated GNSS-R satellite mission specifically designed to retrieve ocean surface wind speeds in the Tropical Cyclone (TC) environment [3], [4]. CYGNSS will use a constellation of eight microsatellite observatories that can receive both the direct and reflected signals from GNSS. These observatories are capable of collecting four simultaneous reflections each, thus providing high temporal-resolution wind speed retrievals within TCs.

This presentation will include an overview of the recently launched NASA CYGNSS mission Level 1 calibration algorithms and their on-orbit validation [1], [2]. The validation of the Level 1 calibration will be performed in several steps, including a) a detailed noise floor analysis to assess the observed on-orbit noise power levels over the open ocean, b) multiple consistency checks using a forward model and co-located ocean wind and wave truth reference data and c) a term by term error analysis of all the non-ocean corrections applied to the final sigma0 estimates. An outline of the Level 1a (calibration from raw Level 0 instrument counts to units of watts for the received power) and the Level 1b (calibration from watts to bistatic scattering cross section) algorithms are each shown below. Three key components of the Level 1a calibration will be presented, namely, an analysis of the instrument (alone) and antenna noise characteristics over the ocean, a study of the range of received power levels from the surface, and comparisons with a forward model. The key components of the Level 1b calibration presented here will include validation of the main corrections applied to arrive at a surface sigma0 estimate, including receiver antenna gain, GPS transmitter and scattering area corrections.

The generalized curvature ocean surface scattering model [general curvature model (GCM)] is extended and revisited. Two key steps are addressed in this paper, namely, a necessary sea surface spectrum undressing procedure and the inclusion of a skewness phase-related component. Normalized radar cross-section (NRCS) simulations are generated at C-band for various wind conditions, polarizations, and incidence angles. Results are compared with CMOD5.n. Although the sea surface spectrum undressing procedure is a necessary step, the overall NRCS dynamic is notably affected only in low wind conditions (≤5 m/s). The inclusion of the skewness phase-related component makes the most impact to the NRCS dynamic where the upwind/downwind asymmetry is clearly detectable. A good agreement between the upwind/downwind asymmetry of the extended GCM and CMOD5.n is achieved for moderate winds (≈5-10 m/s) and moderate incidence angles (≈32°-40°). For low incidence angles (<;26°), the GCM tends to overestimate the upwind/downwind asymmetry compared with CMOD5.n.

We investigate the Global Navigation Satellite System Reflectometry (GNSS-R) measurements collected by the Space GNSS Receiver-Remote Sensing Instrument (SGR-ReSI) on board the TechDemoSat-1 (TDS-1) satellite. The sensitivity of the SGR-ReSI measurements to the ocean surface winds and waves are characterized. The effects of sea surface temperature, wind direction, and rain are also investigated. The SGR-ReSI measurements exhibited sensitivity through the entire range of wind speeds sampled in this dataset, up to 35 m/s. A significant dependence on the larger waves was observed for winds5 m/s. There appeared to be very little wind direction signal, and investigation of the rain impacts found no apparent sensitivity in the data. These results are shown through the analysis of global statistics and examination of a few case studies. This released SGR-ReSI dataset provided the first opportunity to comprehensively investigate the sensitivity of satellite-based GNSS-R measurements to various ocean surface parameters. The upcoming NASA's Cyclone Global Navigation Satellite System (CYGNSS) satellite constellation will utilize a similar receiver to SGI-ReSI and thus this data provides valuable pre-launch knowledge.

A hurricane maximum wind retrieval experiment is conducted using CYGNSS (Cyclone Global Navigation Satellite System) tracks simulated over a series of tropical cyclone scenes from the Hurricane Weather Research and Forecasting model (HWRF). This experiment makes use of a forward model relating simulated CYGNSS power-vs-delay waveforms to hurricane maximum winds. The forward model uses synthetic Willoughby modeled storms as `truth' data. 1148 HWRF storm scenes from the 2010-11 hurricane seasons, from both the Atlantic and Eastern pacific basins, are used as input wind field to the forward model. Retrieved maximum winds are compared to both NHC best-track and HWRF. Results show a definite potential in using the CYGNSS data in retrieving hurricane maximum winds, although the current retrieval performance is hindered due to the symmetrical nature of the modeled Willoughby storms.

A Doppler centroid Dcmodel based on the generalized curvature ocean surface scattering model (generalized curvature model or GCM) is presented. Two key features are included in this model: a skewness-related phase coefficient based on empirical skewness coefficients of sea-surface-slope probability density function (pdf) for wind speed less than 10 m/s and effects from wave breaking for wind speed greater than 10 m/s. Simulated Dcvalues are exclusively compared with the empirical geophysical Doppler model function named CDOP, for hh and vv polarizations, various wind conditions, and incidence angles. Good agreement is found overall between CDOP and simulated Dc values. The overall bias for simulated Dc-hhwith and without skewness are 2.63 versus -0.51 Hz (14.6 versus -2.8 cm/s), respectively; overall standard deviations are 2.76 versus 3.53 Hz (15.3 versus 19.6 cm/s). For simulated Dc-vv,overall bias values with and without skewness are -0.16 versus -2.52 Hz (-0.9 versus -14 cm/s); standard deviations are 3.56 versus 4.32 Hz (19.7 versus 24 cm/s). The overall bias for simulated Dc-hhwith and without the wave breaking component are -0.08 versus 0.12 Hz (-0.4 versus 0.7 cm/s), respectively; corresponding standard deviations are 3.32 versus 4.75 Hz (18.4 versus 26.3 cm/s). Bias values for simulated Dc-vvwith and without the wave breaking component are -1.83 versus -2.02 Hz (-10.2 versus -11.2 cm/s), with corresponding overall standard deviations of 3.43 versus 4.87 Hz (19 versus 27 cm/s). The largest deviation from CDOP, of about 18 Hz (0.99 m/s), is found in the upwind direction for a 26° incidence angle, 10-m/s wind speed, and hh polarization.

An alternative approach to sea surface wind retrieval using synthetic aperture radar (SAR) stripmap (SM) data is explored in this paper, using both the polarization residual Doppler frequency (PRDF) - the difference between the VV and HH Doppler centroids - and the normalized radar cross section (NRCS). The PRDF not only enables the possible elimination of unwanted biases present in both VV and HH Doppler estimates but also helps decrease the number of wind ambiguities down to two. In order to successfully infer the wind field from the PRDF, the use of a geophysical Doppler model function, such as the general curvature model (GCM)-Dop, is necessary. Using such a function, a simulated version of the PRDF at X-band is analyzed in terms of the sea surface wind field at various incidence angles. Simulations first show that the PRDF increases with increasing incidence angle regardless of wind field conditions. Actual PRDF measurements also exhibit strong correlation with radial components of the wind speed. An alternate SAR wind retrieval procedure, incorporating both the PRDF and the NRCS, is tested on a series of dual-polarized SM TerraSAR-X scenes carefully selected along the Norwegian coast. The geophysical model functions used for this analysis are the GCM-NRCS and GCM-Dop. A 1.13-m/s bias, with a correlation of 0.85 and a 1.86-m/s rmse, exists between the mean estimated wind speeds and in situ measurements, while a 15.43° bias, with a 0.93 correlation coefficient and a 34.1° rmse, is found between the mean estimated wind directions and in situ measurements.

The polarization residual Doppler frequency (difference between the VV and HH Doppler centroids) is investigated as a valid parameter in a sea surface SAR wind retrieval scheme. This parameter along with the normalized radar cross section are used as inputs to two purely theoretical geophysical model functions based on the Generalized Curvature Ocean Surface Scattering model. A cost function minimization is implemented as part of the proposed wind retrieval method. The methodology is tested using 21 co-polarized TerraSAR-X scenes where retrieved winds are compared against collocated in situ measurements. A 5.23 degree bias along with a 45.3 degree standard deviation and 0.88 correlation are obtained for the wind direction comparison, while a 1.49 m/s bias, 2.36 m/s standard deviation, and 0.72 correlation are obtained for the wind speed comparison.

The 25 km resolution standard wind products (L2B) are available operationally in near-real time from SeaWinds on QuikSCAT. This relatively low resolution can be enhanced to yield a 2.5 km ultra-high resolution (UHR) product that can be used to identify hurricane eye centers more accurately. A comparison is made between the analyst's choice of eye location based on UHR images and interpolated best-track position. In this analysis, the UHR images are divided into two categories based on the analyst's confidence level of finding the eye center location. In each category, statistical error quantities are computed. UHR images within the high-confidence category can provide, for a given year and basin, mean error distance as small as 15 km with a 9 km standard deviation. The use of these categories may facilitate the realization of QuikSCAT's effectiveness in helping to identify and track hurricane eye centers.