In this paper, we will present results for tropical cyclone wind radii estimation (34-, 50-, 64-knot) using wind speed retrievals from ASCAT-B/C ultra-high-resolution (UHR) processing. ASCAT-UHR derived surface wind radii are compared to storms best tracks obtained from the International Best Track Archive for Climate Stewardship (IBTrACS) dataset. The results were developed using 275 ASCAT-B/C hurricane overpasses from 2022 and 2023, and spans wind speed range from 17-75 m/s. Results show that the root-mean-square-errors (RMSEs) of wind radii from ASCAT-UHR (25.05, 8.75, and 5.52 nautical miles for R34, R50, and R64 respectively) are comparable to the positional uncertainty of IBTrACS.

In the framework of the MAXSS project, a multi-mission (MM) wind product under tropical cyclone conditions has been generated for the period 2010-2020, i.e., a synergistic product that combines the European Center for Medium-range Weather Forecast fifth reanalysis (ERA5) output with several scatterometer and radiometer wind data adjusted to the wind scale of hurricane hunter in situ observations. The errors of the satellite and MM wind products have been estimated with triple collocation analysis, while the different spatial representation of the datasets (i.e., representativeness error r2) is accounted for and computed through spatial variance analysis. The error analysis shows that C-band scatterometers have the lowest standard deviation errors (0.9 m/s) compared to those of the Ku-band scatterometers (1.4-2.1 m/s), while radiometers have the largest errors (2.0-2.9 m/s). Finally, the analysis reveals that the MM wind product has a lower error (1.6 m/s) compared to ERA5 (2.6 m/s) under tropical cyclone conditions.

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 Imaging Wind and Rain Airborne Profiler (IWRAP) is a system of two airborne Doppler radars, operating at C- and Ku-band, that observe below the aircraft. Each radar transmits two conically-scanned pencil beams at approximately 30° and 50° Earth-incidence angle. The antennas make a complete rotation once every second and sample at approximately30m range gates. Since the 2021 hurricane season, the Ocean Surface Winds Team (OSWT) at NOAA/NESDIS/STAR have been retrieving and transmitting three-dimensional atmospheric wind vectors to the ground for near-real-time use. Small biases in the horizontal wind speed and direction were observed first in the downwind legs of the hurricane flight pattern. After more investigation, these errors were apparent throughout all storms as a function of aircraft drift angle. This paper describes a methodology the OSWT use for eliminating these drift-dependent errors.

The OceanSat-3 scatterometer (OSCAT-3) is a Ku-band radar instrument designed specifically to measure wind vectors over the ocean surface. Utilizing a conical scanning design with dual-polarization pencil beams at incidence angles of ~49° and ~58°, OSCAT-3 scans the Earth’s surface to measure the normalized radar cross section (sigma0) and brightness temperature, covering a swath width of 1800 km. In this paper, we introduce a supervised machine learning approach for rain rates estimation from OSCAT-3 measurements. Specifically, we tested ability of the Support Vector Machine (SVM) algorithm to estimate rain rate and flag suspect wind vector retrievals by utilizing regression and classification analysis respectively. For regression analysis, the machine learning model was trained using features derived from a combination of brightness temperatures, sea surface temperature (SST) and OSCAT-3 wind speed retrievals. The training rain rate targets were obtained from Global Precipitation Mission (GPM) Microwave Imager (GMI) measurements. In the case of rain flag classification analysis, training features were derived from brightness temperature and sigma0. The SVM rain rate model application for rain rate retrievals showed strong potential with the OSCAT-3 rain rates product exhibiting a bias of -0.2 mm/hr and a standard deviation of 1.0 mm/hr when compared to GMI rain rate measurements. However, the application of the SVM algorithm for data flagging purposes resulted in an over-flagging of the data under light rain condition. Consequently, for improved accuracy the final rain flag is determined based on OSCAT-3 rain rate estimation.

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 Compact Ocean Wind Vector Radiometer (COWVR) and WindSat are two fully polarimetric microwave radiometers that are designed to measure ocean surface vector winds (OSVW). In this paper, a comparison between the COWVR and WindSat common fully polarimetric channels will be presented. The main focus is to analyze the sensitivity of COWVR measured Stokes parameters (3rdand 4thStokes at 18.7 and 34 GHz) to multiple surface and atmospheric parameters and compare them to their WindSat counterparts (3rdand 4thStokes 18.7 and 37 GHz). Approximately 2000 randomly selected orbits of COWVR and WindSat were used in the analyses along with modeled geophysical parameters from the Global Data Assimilation System (GDAS) 0.25° resolution product. Preliminary results show that COWVR Stokes measurements exhibits strong wind vector signature comparable to that of WindSat.

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.

Prior to the 2022 Atlantic hurricane season, a new digital receiver and arbitrary waveform generator, the Tomorrow.io ARENA 522, was integrated with the Imaging Wind and Rain Airborne Profiler (IWRAP). IWRAP is a downward-pointing system of two Doppler radars that is routinely installed on the National Oceanic and Atmospheric Administration WP-3D Hurricane Hunter airplanes for hurricane and extratropical cyclone research. The primary research applications of IWRAP have been to observe ocean and atmospheric vector winds and their effects on the surrounding media. Sampling limitations due to a time-multiplexed switching system were overcome in this hurricane season using a frequency diversity technique, enabling full multi-beam sampling for improved atmospheric boundary layer observations.

In this paper, we present supervised machine learning employed with high-resolution ASCAT data to detect sea ice in the Alaska region for our ultra-high-resolution wind and sea ice ASCAT product. Two machine learning algorithms, Gaussian Naïve Bayes (GNB) and Support Vector Machine (SVM) were tested in this analysis. GNB utilizes a feature vector consisting of six ASCAT variables, while SVM employed the same inputs but with additional standardization prior to training. The training target consisted of collocated GDAS ice flag and ASMR-2 ice concentration data. The dataset was balanced and divided into training and testing sets for each model. The results showed that GNB achieved 94% accuracy, while SVM achieved 98% with reduced noise. Consequently, we chose SVM as the final algorithm for near real-time sea ice flag processing. SVM exhibited improved accuracy in identifying the sea ice edge, allowing us to enhance the usability of ultra-high-resolution ASCAT wind data in polar regions.

This paper describes the development and validation of a statistical algorithm to retrieve global all-weather sea surface wind speeds (GAWS) from microwave radiometers in operational environment. Measurements from the Advanced Microwave Scanning Radiometer-2 (AMSR2) are utilized to demonstrate the efficacy of the new all-weather wind speed data product. The GAWS algorithm exploits the linear combination of dual-polarized radiometer channels to significantly mitigate the effect of rain contamination while maintaining sensitivity to all wind speed regimes from global winds to tropical cyclone conditions. The GAWS algorithm was developed using ~1000 AMSR2 orbits from 2013 - 2021 covering all possible variations of brightness temperatures and wind speeds. The Global Data Assimilation System (GDAS) and the Hurricane Weather Research and Forecasting Model (HWRF) were used as the assumed surface truth for training and validation. Results from comprehensive quantitative and qualitative analyses show that GAWS retrievals are less susceptible to rain than standard microwave radiometer wind speeds and can reach hurricane force winds up to hurricane category 5 (> 70 m/s).

New radar remote sensing measurements of the turbulent hurricane boundary layer (HBL) are examined through analysis of airborne (Imaging Wind and Rain Airborne Profiler; IWRAP) and spaceborne (synthetic aperture radar; SAR) data from Hurricanes Dorian (2019) and Rita (2005). These two systems provide a wide range of storm intensities and intensity trends to examine the turbulent HBL. The central objective of the work is to document the characteristics of coherent turbulent structures (CTSs) found in the eyewall region of the HBL. Examination of the IWRAP data in Dorian shows that the peak, localized wind speeds are found inside the CTSs near the eye-eyewall interface. The peak winds are typically located at lower levels (0.15 - 0.50 km), but sometimes are found at higher levels (1.0 - 1.5 km) when the CTSs are stretched vertically. A SAR overpass of Dorian’s eyewall showed ocean surface backscatter perturbations at the eye-eyewall interface that have connections to the CTSs identified in IWRAP data. Wavelet analysis, including detailed significance testing, was performed on the IWRAP and SAR data to study the CTS wavelengths and power characteristics. Both datasets showed a multi-scale structure in the wavelet power spectrum with peaks at ~ 10 km (eyewall), ~ 4 - 5 km (merger of small-scale eddies) and ~ 2 km (native scale of the CTSs). The ~ 2 km native scale of the CTSs is robust across intensity trends (rapid intensification, weakening and steady-state), storm cases and region of the storm. This information is useful for turbulence parameterization schemes used in numerical models that require the specification of a turbulent length scale.

The NOAA near real-time operational ASCAT ocean surface wind vectors are produced at 12.5 and 25 km swath grid resolutions. To avoid land contamination due to the relatively large footprint size, the wind data in the inner most coastal regions (~15 - 25 km from the coast) are excluded from the final product. To obtain more retrievals in the coastal regions we utilize measurements containing up to 50% land contribution ratio and employ a combination of the enhanced resolution processing technique and a coastal normalized radar cross section correction method to achieve accurate wind retrievals to within ~1.0 - 2.5 km of the coast. The backscatter correction is implemented for each ASCAT coastal zone measurement. The mean and standard deviation of the land contribution for each measurement is determined, and then the land backscatter contribution is subtracted out from the actual measurement through an iteration method to estimate the ocean-only signal. An ocean calibration of the enhanced resolution backscatter was implemented before the wind retrieval step to improve the accuracy. Finally, a land contribution ratio ranking is implemented after the wind retrieval to further remove the remaining land contamination residuals. Additional quality control is also developed. The high-resolution coastal winds from ASCAT are validated against a variety of other independent wind measurements. The results are then presented and discussed.

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.

This paper describes the development of a new all-weather geophysical model function (AW-GMF) for the purpose of retrieving sea surface wind (SSW) under all weather conditions. The AW-GMF makes use of the Advanced Microwave Scanning Radiometer-2 (AMSR2) dual-polarized C-, X-, and K-band channels. We show how the linear combination of these channels significantly mitigate the effect of rain contamination while maintaining sensitivity to SSW regimes including tropical and extra-tropical cyclones. AW-GMF was trained using ~750 AMSR2 hurricane overpasses from 2013 - 2021. The Global Data Assimilation System (GDAS) 0.25° resolution product and the Hurricane Weather Research and Forecasting Model (HWRF) were used as the assumed surface truth. Preliminary results show that SSW retrievals using the AW-GMF are much less susceptible to rain and capable of retrieving hurricane force winds.

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 major gap in wind vector measurements exists in the lowest levels (below 500m) of the hurricane boundary layer (HBL). The 3-dimensional Doppler measurements from the Imaging Wind and Rain Airborne Profiler (IWRAP) on board the NOAA WP-3D aircraft are showing potential to close this gap. During the 2021 hurricane season IWRAP was configured to produce near real time measurements of the HBL winds. The IWRAP's raw data acquisition system allows for full HBL atmospheric profiling all the way down to the ocean surface using the spectral processing technique. For the first time ever, high resolution HBL wind and reflectivity profiles below 500m were retrieved from IWRAP measurements in Hurricane Ida on August 29th, 2021. The spectral processing technique utilized and subsequent wind and reflectivity profiles are presented and discussed.

The NOAA near real-time operational ASCAT ocean surface wind vectors are produced at 12.5 and 25 km swath grid resolutions. However, due to land contamination most coastal zones are left without any wind retrievals. In this paper, we show how combination of the enhanced resolution normalized radar backscattered cross section (sigma0) algorithm with an aggressive land contribution ratio rejection technique and a land contamination correction algorithm allows for accurate wind to be retrieved within 1-2.5 km of the coast. To achieve acceptable accuracy for operational applications, an additional calibration of the enhanced resolution sigma0 was also found necessary. This algorithm was tested within the Alaskan coastal waters. Variability in the sea ice along the Alaskan coasts also required implementation of a high-resolution ice detection algorithm, which yielded a combined wind and ice product from the ASCAT measurements. The results are presented and discussed.

Accurate high and extreme sea surface wind observations are essential for the meteorological, ocean, and climate applications. To properly assess and calibrate the current and future satellite-derived extreme winds, including those from the C-band scatterometers, building a consolidated high and extreme wind reference data set is crucial. In this work, a new approach is presented to assess the consistency between moored buoys and stepped-frequency microwave radiometer (SFMR)-derived winds. To overcome the absence of abundant direct collocations between these two data sets, the reprocessed Advanced Scatterometer (ASCAT)-A winds at the 12.5-km resolution, from 2009 to 2017, have been used to perform an indirect SFMR/buoy winds’ intercomparison. The ASCAT/SFMR analysis reveals an ASCAT wind underestimation for winds of above 15 m/s. SFMR measurements are calibrated using GPS drop-wind-sondes (dropsondes) data and averaged along-track to represent ASCAT spatially. On the other hand, ASCAT and buoy winds are in good agreement up to 25 m/s. The buoy high-wind quality has been confirmed using a triple collocation approach. Comparing these results, both SFMR and buoy winds appear to be highly correlated with ASCAT at the high-wind regime; however, they show a very different wind speed scaling. An SFMR-based recalibration of ASCAT winds is proposed, the so-called ASCAT dropsonde-scale winds, for use by the extreme wind operational community. However, further work is required to reconcile dropsonde (thus, SFMR) and buoy wind measurements under extreme wind conditions.

The airborne Stepped Frequency Microwave Radiometer (SFMR) provides measurements of 10-m ocean-surface wind speed in high and extreme wind conditions. These winds are calibrated using the surface-adjusted wind estimates from the so-called dropsondes. The surface-adjusted winds are obtained from layer-averaged winds scaled to 10-m altitude to eliminate the local surface variability not associated with the storm strength. The SFMR measurements and, consequently, the surface-adjusted dropsonde winds represent a possible reference for satellite instrument and model calibration/validation at high and extreme wind conditions. To this end, representativeness errors that those measurements may introduce need to be taken into account to ensure that the storm variability is correctly resolved in satellite retrievals and modelling. In this work, we compare the SFMR winds with the dropsonde surface-adjusted winds derived from the so-called WL150 algorithm, which uses the lowest 150-meter layer between 10 m to 350 m. We use nine years of data from 2009 to 2017. We focus on the effects of the layer altitude and thickness. Our analysis shows that the layer altitude has a significant impact on dropsonde/SFMR wind comparisons. Moreover, the averaged winds obtained from layers thinner than the nominal 150 m and closer to the surface are more representative of the SFMR surface wind speed than the WL150 speeds. We also find that the surface-adjusted winds are more representative of 10-km horizontally averaged SFMR winds. We conclude that for calibration/validation purposes, the WL150 algorithm can introduce noise and the use of actual 10-m dropsonde measurements should be further investigated.

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.

Microwave radiometers are designed to capture the Earth's electromagnetic radiation in the form of brightness temperatures. At low and medium frequencies, the relatively large footprint of microwave radiometers results in mixing land and water brightness temperatures in coastal areas and lakes. This mixing of signals, also known as land contamination, limits the usability of radiometer measurements and makes them unsuitable for geophysical retrievals up to ~ 100 km away from the coastline. In this paper, we present preliminary results of applying a land contamination correction on AMSR2 measurements to extract coastal information. The correction technique relies on calculating the land fraction within AMSR2 footprints using a high-resolution land mask and a representative antenna pattern.

The Advanced Microwave Scanning Radiometer-2 (AMSR2) on board the Global Change Observation Mission-Water (GCOM-W) launched in May 2012 by the Japan Aerospace Exploration Agency (JAXA) is acquiring electromagnetic radiation from the Earth for the purpose of monitoring its environmental and climate system. Among a suite of oceanic environmental data records (EDR), the Ocean Surface Winds Team of the Satellite Applications and Research (STAR) group at the National Oceanic and Atmospheric Administration (NOAA) has developed an all-weather wind speed (AWS) by exploiting AMSR2 brightness temperature (Tb) measurements. The new product provides wind speeds in normal and extreme weather conditions with minimal flagging and excellent accuracy. Validation results of this novel AMSR2 AWS product, represented in this paper, show a mean bias of 0 m/s and an rms error < 2 m/s when compared to numerical weather models under all weather conditions.

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.

The Stepped Frequency Microwave Radiometer (SFMR) is a key instrument in tropical cyclones and high-latitude winter storms research. Through the observed brightness temperature (Tb) over a range of C-band frequencies, the SFMR derives wind-speed and rain rate. However, the instrument requires 5 to 10 seconds of averaging to cycle through all the frequencies, so regions of strong wind gradients and/or narrow rain features may be overlooked. The University of Massachusetts Amherst Microwave Remote Sensing Laboratory (MIRSL) developed a specialized version of the SFMR, the UMass Simultaneous Frequency Microwave Radiometer (USFMR) that operates six frequency channels simultaneously, eliminating the averaging time. In collaboration with NOAA/NESDIS/STAR we plan to use this instrument in studies of high latitude winter storms. We describe the instrument hardware, recent comparisons with operational SFMR measurements during hurricane flight in 2019, and current and planned investigation of retrieval inconsistencies in non-tropical cyclone environments. In collaboration with NOAA/NESDIS/STAR we plan to use this instrument in studies of high latitude winter storms. We describe the instrument hardware, recent comparisons with operational SFMR measurements during hurricane flight in 2019, and current and planned investigation of retrieval inconsistencies in non-tropical cyclone environments.

During the 2020 hurricane season, scientists at the National Oceanic and Atmospheric Administration (NOAA)/National Environmental Satellite, Data, and Information Service (NESDIS)/Center for Satellite Applications and Research (STAR) Ocean Surface Winds Team (OSWT) in collaboration with Remote Sensing Solutions (RSS) operated the RSS Ka-band Interferometric Altimeter (KaIA) from the NOAA WP-3D aircraft, making the first comprehensive set of airborne Ka-band radar altimeter measurements of a tropical cyclone. KaIA is a nadir-looking Ka-band interferometric radar altimeter with a real-time tracker/retracker and is capable of centimetric radar altimetry. This paper shows significant wave height (SWH) retrievals from the 2020 hurricane season and compares them to wave models, and satellite and buoy observations.

How strong does the wind blow in a hurricane? This proves a question that is difficult to answer, but has far-reaching consequences for satellite meteorology, weather forecasting and hurricane advisories. In the EUMETSAT CHEFS project, KNMI, ICM and IFREMER worked with international colleagues to address this question to prepare for the EPS-SG SCA scatterometer, which introduces C-band cross-polarization measurements to improve the detection of hurricane-force winds. To calibrate the diverse available satellite, airplane and model winds, in-situ wind speed references are needed. Unfortunately, these prove rather inconsistent in the wind speed range of 15 to 25 m/s, casting doubt on the higher winds too. Should we trust dropsondes at high and extreme winds or perhaps put more confidence inthe moored buoy references? This dilemma will be presented to initiate a discussion with the international community gathered at IGARSS ‘21.

Operational weather analysis, forecasting, and warning utilize a wide variety of data products and tools, including satellite imagery and derived products. Satellite observations provide information where in-situ measurements are lacking or not readily available. Passive microwave satellite observations are routinely exploited by forecasters at the National Oceanic and Atmospheric Administration (NOAA), National Weather Service (NWS) in the United States (U.S.) to support their weather analysis and forecasts. In this paper, we present examples of hurricane Dorian observations from Advanced Scanning Radiometer -2 (AMSR-2) on the Global Change Observation Mission (GCOM), which is part of the Japanese Aerospace Exploration Agency (JAXA). We compare NOAA AMSR-2 ocean EDR products with storm finding documented within National Hurricane Center Dorian discussions from Aug 24th through September 9th, 2019. NOAA AMSR-2 products are part of NWS forecasting product suite and are regularly used in daily operations. While Microwave Imagery product has been used the most for hurricane forecasting we examine usefulness of other ocean products by following Hurricane Dorian from its formation on August 24th to its demise on September 6th.

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.

Beginning in 2015, scientists at the National Oceanic and Atmospheric Administration (NOAA)/NESDIS/STAR and UMass Amherst collaborated with the European Space Agency (ESA) to collect ocean surface normalized radar cross-section (NRCS) measurements at co- and cross-polarizations in extreme wind conditions from the NOAA Hurricane Hunter aircraft using a prototype antenna for the next-generation European spaceborne scatterometer. Since then, more research has been done to understand the effects of ocean-surface wind vectors on NRCS from both satellite and aircraft. Here we show the data from several seasons of flight experiments to understand the airborne measurements of NRCS in context.

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.

In Winter 2017, the University of Massachusetts Amherst's Imaging Wind and Rain Airborne Profiler (IWRAP) was flown on a National Oceanic and Atmospheric Administration (NOAA) WP-3D Hurricane Hunter aircraft under the direction of scientists from Center for Satellite Applications and Research (STAR) at NOAA/National Environmental Satellite, Data, and Information Service (NESDIS) over the North Atlantic ocean out of Shannon, Ireland. IWRAP is a dual-frequency, conically scanning, profiling Doppler radar initially developed by Microwave Remote Sensing Laboratory (MIRSL) at the University of Massachusetts Amherst that is routinely installed on the NOAA WP-3D research aircraft. The flight on February 6, 2017, targeted a region of high winds (greater than 30 m/s) that was also observed by the Sentinel-1B satellite's synthetic aperture radar. Sentinel-1B was configured to observe in extended wide swath mode in both VV- and VH-polarizations, whereas the IWRAP C-band radar was configured to measure all of VV-, VH-, and HH-polarizations. IWRAP and Sentinel-1B VV and VH normalized radar cross section (NRCS) at the same Earthincidence angle along the flight path match reasonably well during the entire flight, but some additional trends between aircraft and satellite can be observed. IWRAP VV-polarized NRCS generally match the CMOD5.h geophysical model function (GMF), suggesting errors in the Sentinel-1B processing chain.

Operational weather analysis, forecasting, and warning utilize a wide variety of data products and tools, including satellite imagery and derived products. Satellite observations provide information where in-situ measurements are lacking or not readily available. Passive microwave satellite observations are routinely exploited by forecasters at the National Oceanic and Atmospheric Administration (NOAA), National Weather Service (NWS) in the United States (U.S.) to support their weather analysis and forecasts. In this paper, we present examples of ocean measurements and derived products from Advanced Scanning Radiometer -2 (AMSR-2) on the Global Change Observation Mission (GCOM), which is part of the Japanese Aerospace Exploration Agency (JAXA) that supported critical forecasts.

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 Advanced Microwave Scanning Radiometer-2 (AMSR2) on board the Global Change Observation Mission-Water (GCOM-W) launched in May 2012 by the Japanese Exploration Agency (JAXA) is acquiring earth electromagnetic radiation for the purpose of monitoring Earth's environmental and climate system. The ocean vector winds team part of the National Oceanic and atmospheric administration (NOAA), National Environmental Satellite, Data, and Information Service (NESDIS), Center for Satellite Applications and Research (STAR), has exploited AMSR2 observations of brightness temperature (Tb) to develop an environmental data record (EDR), which includes several oceanic parameters. The purpose of this paper is to show the validation results for these geophysical parameters when compared to other active and passive microwave sensors and numerical weather models.

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.

In this paper, airborne measurements of the ocean surface normalized radar cross-section (NRCS) taken at co- and cross-polarizations in high-wind conditions are reported. The measurements were taken in Hurricane Matthew during which it was a Category 4 hurricane on the Saffir-Simpson Hurricane Wind Scale. Saturation of the co-polarized NRCS and lack of saturation in the cross-polarized NRCS is observed, consistent with previous results. The results have implications for planned and future scatterometers (e.g., MetOp-SG) that aim to increase the maximum observable wind speeds by using cross-polarized measurements.

Passive microwave radiometry is a special application of microwave communications technology for the purpose of collecting Earth's electromagnetic radiation. With the use of radiometers onboard earth orbiting satellites, scientists are able to monitor the Earth's environment and climate system on both short- and long-term temporal scales with near global coverage.

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 Stepped Frequency Microwave Radiometer (SFMR) is an instrument flown on research and reconnaissance aircraft through tropical and extratropical cyclones providing rain rate and surface wind speed estimates. Errors have been observed with the retrievals from SFMR, especially in extratropical cyclones over cold water, when compared with other sensors. In this paper, some of the SFMR wind speed errors that manifest over cold water are characterized using comparisons with in situ measurements by dropwindsondes.

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.

We report on airborne measurements of the cross-polarized (VH) ocean surface normalized radar crosssection (NRCS) at incidence angles between 15° and 40° obtained at C-band in high-wind (> 30 m s−1) conditions. The present observations were taken in Hurricane Patricia on 23 October 2015 and extend the wind speed range of the existing cross-polarization ocean surface NRCS literature [1]–[4]. The NRCS at the smaller incidence angles decrease as wind speed increases, as expected. At the larger incidence angles, saturation of the NRCS is not observed up to at least 70 m s−1. The results have implications for planned and future scatterometers (e.g., MetOp-SG) that aim to increase the maximum observable wind speeds.

Airborne co-polarization and cross-polarization observations of ocean surface normalized radar cross section (NRCS) were conducted over the North Atlantic during January and February 2015. Observations were made using the University of Massachusetts' Imaging Wind and Rain Airborne Profiler (IWRAP) radar system and a prototype antenna for the next-generation European scatterometer aboard MetOp-SG. Both were installed on a National Oceanic and Atmospheric Administration (NOAA) WP-3D research aircraft to characterize the wind response of the ocean-surface cross-polarization NRCS. During the flights, numerous constant-roll-angle circle maneuvers were performed at several different angles to collect NRCS measurements over a range of incidence angles. Surface winds at speeds between 8 and 34 ms-1were observed at incidence angles from 20° to 60° at all polarization combinations. The majority of measurements fell between 8 and 20 ms-1. Wind-direction dependence similar to copolarized NRCS was observed in the cross-polarized (VH) NRCS. The amplitude of the VH NRCS with respect to direction is less than that of copolarized NRCS at all wind speeds. Incidence angle dependence was also observed in the VH NRCS at all wind speeds. As a function of wind speed, the mean VH NRCS (A0) has a similar shape to the VV NRCS. The VH NRCS appears to not saturate at most incidence angles, unlike the VV and HH NRCS. VH and HH geophysical model functions (GMFs) were developed as functions of wind speed, incidence angle, and wind-relative azimuth for the wind speeds and incidence angles observed.

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.

We report on airborne measurements of the sea-surface normalized radar cross-section (NRCS) at incidence angles of approximately 22° obtained at both C-band and Ku-band in high-wind (> 25ms−1) conditions. Measurements obtained over numerous research flights through tropical cyclones and high-latitude winter storms between 2011 and 2014 are composited to yield geophysical model functions in rain-free conditions. The present observations extend the results of [1], who reported high-wind NRCS for incidence angles from 30° to 50°, to a smaller incidence angle. Saturation of the mean NRCS is observed at both frequencies. In some cases the NRCS is observed to decrease with increasing wind speed beyond the saturation. The results have implications for planned and future scatterometers aiming to increase the observed swath width by extending the range of incidence angles.

Airborne dual-polarization observations of sea surface normalized radar cross section (NRCS) were conducted over the North Atlantic during January-February 2011. Observations were made using the University of Massachusetts' Imaging Wind and Rain Airborne Profiler radar system installed on the National Oceanic and Atmospheric Administration's WP-3D research aircraft during several winter storm events to determine the high-wind response of the sea surface NRCS for both horizontal and vertical polarizations. During the flights, the aircraft performed several constant-roll circle maneuvers to allow collection of NRCS over a range of incidence angles. We find consistency with prior reports in the polarization ratio observed at moderate incidence angles at the winds encountered. For larger incidence angles, we observe a measurable decrease in polarization ratio with increasing wind speed.

The Advanced Scatterometer (ASCAT) on the MetOp-A satellite is a radar instrument designed specifically to retrieve the ocean surface wind speed and direction. The ASCAT wind vector products are produced and utilized operationally in support of the National Oceanic and Atmospheric Administration (NOAA)'s weather forecasting and warning mission. The standard ASCAT winds at NOAA are produced using the ASCAT wind data processor developed at the Royal Netherlands Meteorological Institute (KNMI) utilizing the CMOD5.n geophysical model function (GMF). Recent validation of the ASCAT wind retrievals revealed a low bias at high wind speeds when compared to both the QuikSCAT winds and the National Centers for Environmental Prediction numerical weather prediction (NWP) model winds. The goal of this paper is to investigate the ASCAT high-wind-speed performance and to modify, as appropriate, the high-wind-speed portion of CMOD5.n GMF. This effort would potentially improve the utility of ASCAT wind retrievals in supporting wind warning and analysis and thus better mitigate the loss of QuikSCAT data products. Traditionally, the GMF is developed empirically by collocating scatterometer measurements and other truth data such as buoy and NWP model winds. However, NWP models are known to underestimate the intensity of higher wind speeds, and data sources such as ship-based or buoy-based observations provide an inadequate quantity of measurements for empirical GMF development. In this paper, a method utilizing aircraft-based scatterometer measurements in the high-wind-speed regimes is used in conjunction with satellite scatterometer measurements to refine the satellite GMF. As a result of this paper, a high wind C-band satellite GMF, CMOD5.h, was developed and implemented in NOAA's ASCAT processor. The validation comparison of the high wind and standard ASCAT wind products revealed 0.6-m/s reduction in the wind speed bias for winds greater than 15 m/s with respect to QuikSCAT, WindSat, and Step Frequency Microwave Radiometer high wind measurements.

The Cyclone Global Navigation Satellite System (CYGNSS) is a spaceborne mission concept focused on tropical cyclone (TC) inner core process studies. CYGNSS attempts to resolve the principle deficiencies with current TC intensity forecasts, which lies in inadequate observations and modeling of the inner core. CYGNSS consists of 8 GPS bistatic radar receivers deployed on separate nanosatellites. The primary science driver is rapid sampling of ocean surface winds in the inner core of tropical cyclones.

The National Research Council Decadal Survey identified the need for a future mission to provide accurate real-time observations of ocean wind vectors from calm to tropical cyclone wind conditions with or without the presence of rain. Tasked by the National Oceanic and Atmospheric Administration (NOAA), the Jet Propulsion Laboratory (JPL) developed a future scatterometer design that would leverage its success on the heritage of QuikSCAT but would provide more accurate measurements under all weather conditions through the use of Ku- and C-band coincident measurements of the ocean surface. To design a cost effective instrument for all weather operations from space the existing risks need to be mitigated. The work described in this paper attempts to validate results reported at hurricane strength winds in and investigate the effects of splash caused by precipitation on the scatterometer wind estimation.

The Japan Aerospace Exploration Agency's (JAXA's), Global Change Observation Mission (GCOM) will provide environmental satellite remote-sensing data for at least 13 years starting in 2011. The utilization of these data will yield great benefits for the research and operational user communities. NOAA is working collaboratively with JAXA and NASA to fully utilize the GCOM data in support its diverse operational mission which includes weather and ocean forecasting and warning, climate monitoring and prediction, fishery and marine mammal management, ocean spill response and mitigation.

In an effort to establish an operational ocean surface vector wind satellite capability, NOAA has been exploring the possibility of flying a U.S. scatterometer on board the Japan Aerospace Exploration Agency's (JAXA's) Global Change Observation Mission (GCOM) satellite series. The Dual Frequency Scatterometer (DFS) has been designed by NASA's Jet Propulsion Laboratory (JPL) and proposed as a baseline scatterometer onboard the GCOM-W2 satellite. This study documents the impact that the DFS instrument will have on different National Weather Service (NWS) weather forecasting and warning products and services. With a 50% improvement in the accuracy of wind estimates in high wind regimes, a 20% improvement in resolution and its ability to see through rain, DFS will address NWS's operational OSVW requirements significantly better than a QuikSCAT-like instrument. It is expected that DFS data will have a medium to high impact for all marine weather and tropical cyclone analysis and warning applications, real time diagnostics and climatological wind applications for which wind data are necessary.

The current ASCAT winds retrieval is based on the CMOD5.n geophysical model function (GMF) with the ASCAT wind data processor developed at the Royal Netherlands Meteorological Institute (KNMI). Recent validation of ASCAT wind retrieval reveals that high wind retrievals were underestimated as being compared to the operational QuikSCAT scatterometer. The goal in this paper is to improve ASCAT wind retrievals at high winds. In this paper we map the radar backscatter (σ0) as a function of extreme wind conditions as measured by an airborne scatterometer and adjusted the isotropic term in CMOD5.n to follow the aircraft GMF trend. The geophysical model QuikSCAT wind inputs are improved for σ0that calculated from QuikSCAT wind inputs are improved for σ0approximately > -15 dB and in very good agreement with the ASCAT σ0measurement. The wind retrieval validations show wind speed rms error is improved at approximately wind speed > 12 m/s and example mean wind composite from two winter seasons shows significant in detection of storm-force winds.

In this paper we used QuikSCAT measurements over extratropical storms that reached hurricane force (HF) wind strength in the North Pacific over a period of 7 cold seasons from 2001-2008 to study the average wind speed distribution within these intense cyclones. During this period a total of 225 cyclones with HF winds were identified and tracked in the North Pacific. December proved to be most active month with 56 separate storms reaching HF strength over the 7 year period. The peak activity was found to be over the western portion of the ocean basin. The Pacific cyclones appear to have preferred tracks and have an average heading of ~50° from north. The average storm motion was found to be ~24 knots. Most hurricane force events last between 6-24 hours. 50% of the 12 hour events occurred during December and 75% of the 30 hour events occurred during November and December.

Validation of the ASCAT wind vectors show that the ASCAT wind speed errors are within 2 m/s RMS error for wind speeds up to 15 m/s, however they exhibit and increasing low bias beyond 15 m/s. An examination of the ASCAT ¿0revealed some additional sensitivity at the higher wind speeds that was not adequately represented by the current geophysical model function (GMF). A revised GMF is empirically derived using a near-real-time QuikSCAT as a surface truth. A new DC term in the GMF is derived and replaced in the operational CMOD5.5 GMF. Validation of the revised GMF shows that the wind speed retrievals are closer to QuikSCAT for wind speeds > 15 m/s than the operational retrievals, while wind direction retrievals remain the same for all wind speeds as expected.

The Microwave Remote Sensing Laboratory (MIRSL) at the University of Massachusetts developed and operates the Imaging Wind and Rain Airborne Profiler (IWRAP). With support from NASA, NOAA, and ONR, UMASS has collaborated with NOAA-NESDIS and NOAA-AOC to operate the instrument aboard the NOAA WP-3D "Hurricane Hunter" aircraft since the 2002 hurricane season and for four winter experiments sampling high-latitude storms. The goals of these experiments have been to characterize storm boundary layers and to develop improved retrievals for space-based scatterometers, specifically, NASA SeaWinds Scatterometer on QuikSCAT and Advanced Scatterometer (ASCAT) which was developed by ESA on behalf of EUMETSAT. IWRAP is a conically-scanning, dual-frequency (C-and Ku-band) coherent radar with dual-polarization capability. The incidence angles are nominally: 30, 35, 40, and 50 degrees. At times, the instrument has been configured to address specific scientific and engineering problems. Examples include: C-band measurements of high incidence angle backscatter for ASCAT validation, and C-band horizontal polarization measurements to develop a high wind speed geophysical model function (GMF). Independent measurements of surface wind speed and column integrated rain rate are made coincidently with the UMass Simultaneous Frequency Radiometer (USFMR) and AOC Stepped Frequency Microwave Radiometer (SFMR). Since 2005, IWRAP has operated in a raw data mode, recording individual radar pulses to enable the separation of near surface backscatter contributions (i.e. surface scattering versus volume scattering). The intensity of tropical cyclones ranged from tropical storm to category five hurricane, and high-latitude storms with up to hurricane force winds.

The ASCAT, launched on board MetOp-A satellite on October 19th2006, is a C-band scatterometer operating at 5.255 GHz using fan-beam antennae to measure near surface vector wind over the world's ocean. NOAA produces near-real-time ASCAT wind product at 50 and 25 km resolutions. These wind data are validated against global wind field model and satellite observation from QuikSCAT. The results show ASCAT wind speed retrievals perform well for low to moderate wind speed under most weather conditions, but are underestimated for wind speeds ¿ 15 m/s. The standard deviation wind direction errors are well below 20 degrees for wind speed ¿ 5 m/s.

In this paper, we develop a novel geophysical model function (GMF) for the WindSat radiometer relating the vertically (V-pol) and horizontally (H-pol) polarized brightness temperature (TB) to the ocean surface wind field. The brightness temperature data from the 10, 18 and 37 GHz channels were used in this analysis. The brightness temperature combination of the form (AV-H) is found to be mostly independent of the atmospheric variations, where A is a constant number for each frequency. The GMF was developed empirically using the collocated wind vectors from the QuikSCAT scatterometer retrieval as truth and the WindSat’s (AV-H) TB’s. We examined the characteristic of the GMF and explored the opportunity to improve our current WindSat wind direction retrieval that utilizes only 3rdand 4thStokes measurements, by integrating this GMF. The strength of the wind directional signals is encouraging for moderate wind speed at 8 m/s and higher.

Surface wind vector measurements over the oceans are vital for scientists and forecasters to understand the Earth's global weather and climate. In the last two decades, operational measurements of global ocean wind speeds were obtained from passive microwave radiometers (Special Sensor Microwave/ Imagers); and over this period, full ocean surface wind vector data were obtained from several National Aeronautics and Space Administration and European Space Agency scatterometry missions. However, since SeaSat-A in 1978, there have not been other combined active and passive wind measurements on the same satellite until the launch of Japan Aerospace Exploration Agency's Advanced Earth Observing Satellite-II in 2002. This mission provided a unique data set of coincident measurements between the SeaWinds scatterometer and the Advanced Microwave Scanning Radiometer (AMSR). The AMSR instrument measured linearly polarized brightness temperatures (TB) over the ocean. Although these measurements contained wind direction information, the overlying atmospheric influence obscured this signal and made wind direction retrievals not feasible. However, for radiometer channels between 10 and 37 GHz, a certain linear combination of vertical and horizontal brightness temperatures causes the atmospheric dependence to cancel and surface parameters such as wind speed and direction and sea surface temperature to dominate the resulting signal. In this paper, an empirical relationship between AMSR TB's (specifically A.TBV- TBH) and surface wind vectors (inferred from SeaWinds' retrievals) is established for three microwave frequencies: 10, 18, and 37 GHz. This newly developed wind vector model function for microwave radiometers can serve as a basis for wind vector retrievals either separately or in combination with active scatterometer measurements.

This paper examines WindSat wind retrievals from two perspectives. The first is a statistical analysis, comparing both WindSat and QuikSCAT to model output. The second is an analysis geared toward weather forecasters based on individual case studies.

Since the January 6, 2003 launch of the Naval Research Laboratory satellite Coriolis, the WindSat instrument onboard has provided over a year of unprecedented polarimetric microwave measurements of the globe. The WindSat radiometer has five operating frequencies at 6.8, 10.7, 18.7, 23.8 and 37 GHz, with the 10.7, 18.7, and 37 GHz channels providing fully polarimetric signals. The primary mission of Coriolis is to exploit the unique information provided by WindSat's polarimetric capabilities to retrieve the complete ocean surface wind vector (speed and direction), though the retrieval of numerous other environmental parameters is being actively pursued as well. As part of a pre-NPOESS risk reduction effort, the NOAA/NESDIS/Office of Research and Applications has been collaborating with the Naval Research Laboratory's Remote Sensing Division in the calibration/validation of WindSat in preparation for the release of WindSat data products to the scientific and operational communities. An extensive overview is presented of the WindSat calibration/validation effort being put forth at NOAA/NESDIS and the associated comparison databases constructed for that purpose. These databases include data of WindSat measurements collocated with measurements from oceanographic buoys, ships, other satellites, and global data assimilation models. The strengths and limitations of these various datasets will be discussed in detail. This includes a synopsis of the colocation strategies used in matchup database construction for comparing WindSat measurements with other satellite based measurements, focusing particularly on similar orbit SSM/I data and its use in brightness temperature calibration. In addition, the use of NCEP's Global Data Assimilation System (GDAS) as a powerful source of plentiful comparison data is explored, particularly with regard to WindSat model function development

During the 2002 and 2003 ONR CBLAST Hurricane Program Field (HPF) and the NOAA/NESDIS Hurricane Ocean Winds and Rain Experiment, the University of Massachusetts (UMass) installed IWRAP, a conically scanning dual-band (C- and Ku-band), dual-polarized pencil-beam airborne Doppler radar that profiles the volume backscatter and Doppler velocity from rain and the backscatter from the ocean surface simultaneously at four incidence angles covering incidence angles from 25 to 55 degrees. From the measurements acquired during missions flown through hurricanes Gustav, Isadore, and Lili (2002) and Fabian and Isabel (2003), high wind regime Geophysical Model Functions have been derived at both frequencies and polarizations. Concrete saturation effects in the NRCS are presented, and sensitivity in the wind direction at high wind speed is discussed through the analysis of the second harmonic of the NRCS.

The wind vector affects a broad range of naval missions, including strategic ship movement and positioning, aircraft carrier operations, aircraft deployment, effective weapons use, underway replenishment, and littoral operations. Furthermore, accurate wind vector data aids in short-term weather forecasting, the issuing of timely weather warnings, and the gathering of general climatological data. WindSat is a satellite-based multifrequency polarimetric microwave radiometer developed by the Naval Research Laboratory for the US Navy and the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Integrated Program Office (IPO). It is designed to demonstrate the capability of polarimetric microwave radiometry to measure the ocean surface wind vector from space. The sensor provides risk reduction for the development of the Conical Microwave Imager Sounder (CMIS), which is planned to provide wind vector data operationally starting in 2010.

The accuracy of the high resolution QuikSCAT wind product was quantified using spatially and temporally collocated 10 m equivalent neutral stability winds, calculated from selected NOAA NDBC buoy measurements. Only buoys that had sample correlation higher than 1.5 were used in this validation, a total of 5704 records. The validation followed the Freilich nonlinear statistical analysis approach. This analysis of the collocated buoy-scatterometer data set yielded the following statistics for wind speed: deterministic offset -0.25 m/s; linear gain 1.02; standard deviation of component errors 1.9 m/s; and RMS error 2.2 m/s. Wind direction errors were more pronounced for lighter winds, typically for winds up to 5 m/s. The RMS directional error for buoy-QuikSCAT pairs for which /spl Delta//spl theta/<90/spl deg/ is 20.6/spl deg/.

Performance of the NOAA/NESDIS near realtime QuikSCAT wind retrieval algorithm was assessed in the case of tropical cyclone IRIS, that occurred in Northern Atlantic, in October 2001. For this storm, the estimated wind field failed to produce a circular structure characteristic of tropical cyclones. We established that the Aviation Forecasting Field that was used to initialize ambiguity removal process was the primary cause of error, since it completely missed this feature. We proposed an initialization process that uses the information available from backscatter data to estimate possible regions of tropical cyclones. The proposed procedure allows estimation of wind fields that are less influenced by an external field. However, in the case of tropical storm IRIS the wind retrieval algorithm failed to generate solutions that would allow ambiguity removal to close circulation even when clustered errors in the initial field were broken by the new initialization procedure.