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Haiyang-2 scatterometers (HY-2A/B/C/D) have limitations in high wind speed retrieval due to the complexity of the remote sensing mechanism and the influence of rainfall on the radar cross section under the conditions of tropical cyclones. In this study, we focus on the evaluation of Chinese scatterometer operational wind products from HY-2B/C/D over the period from July 2019 to December 2021. HY-2B/C/D scatterometer wind products are collocated with SMAP (Soil Moisture Active Passive) L-band radiometer remotely sensed measurements. The results show that the underestimation of high wind speed occurs in the HY-2B/C/D wind speed products. The machine learning algorithms are explored to improve this underestimation issue, including the back propagation neural network (BP-NN), K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), random forest (RF), and Bayesian ridge (BR) regression algorithms. Comparisons show that the BP-NN algorithm shows the best performance with a small RMSE (root-mean-square error) of 3.40 m/s, and high correlation coefficient of 0.88, demonstrating an improvement of 20.4% in RMSE (root-mean-square error) compared with the HY-2B/C/D wind speed products. In addition, the revised high winds are in good agreement with the ground truth measurements from the SFMR (Stepped Frequency Microwave Radiometer), which are useful for tropical cyclone disaster prevention and mitigation and are of vital importance in the numerical simulation of storm surges. [ABSTRACT FROM AUTHOR]

The accuracy and consistency of a quintuple collocation analysis of ocean surface vector winds from buoys, scatterometers, and NWP forecasts is established. A new solution method is introduced for the general multiple collocation problem formulated in terms of covariance equations. By a logarithmic transformation, the covariance equations reduce to ordinary linear equations that can be handled using standard methods. The method can be applied to each determined or overdetermined subset of the covariance equations. Representativeness errors are estimated from differences in spatial variances. The results are in good agreement with those from quadruple collocation analyses reported elsewhere. The geometric mean of all solutions from determined subsets of the covariance equations equals the least-squares solution of all equations. The accuracy of the solutions is estimated from synthetic data sets with random Gaussian errors that are constructed from the buoy data using the values of the calibration coefficients and error variances from the quintuple collocation analysis. For the calibration coefficients, the spread in the models is smaller than the accuracy, but for the observation error variances, the spread and the accuracy are about equal only for representativeness errors evaluated at a scale of 200 km for u and 100 km for v. Some average error covariances differ significantly from zero, indicating weak inconsistencies in the underlying error model. Possible causes for this are discussed. With a data set of 2454 collocations, the accuracy in the observation error standard deviation is 0.02 to 0.03 m/s at the one-sigma level for all observing systems. [ABSTRACT FROM AUTHOR]

Triple collocation analysis is an established technique for calculating the relative linear intercalibration coefficients and observation error variances for physical quantities measured simultaneously in space and time by three different observation systems. A simple parameterized error model is used. It relies on a few assumptions, one of which is that the observation errors are independent of the magnitude of the observed quantities. This is referred to as error orthogonality. Using an ocean surface vector winds data set of 44,948 collocations, this study shows that the violation of error orthogonality does affect the calibration coefficients but has only a small second-order effect on the observation error variances of the calibrated data. [ABSTRACT FROM AUTHOR]

In 2021, a novel NOAA-Saildrone project deployed five uncrewed surface vehicle Saildrones (SDs) to monitor regions of the Atlantic Ocean and Caribbean Sea frequented by tropical cyclones. One of the SDs, SD-1045, crossed Hurricane Sam (Category 4) on September 30, providing the first-ever surface-ocean videos of conditions in the core of a major hurricane and reporting near-surface winds as high as 40 m/s. Here, we present a comprehensive analysis and interpretation of the Saildrone ocean surface wind measurements in Hurricane Sam, using the following datasets for direct and indirect comparisons: an NDBC buoy in the path of the storm, radiometer tropical cyclone (TC) winds from SMAP and AMSR2, wind retrievals from the ASCAT scatterometers and SAR (RadarSat2), and HWRF model winds. The SD winds show excellent consistency with the satellite observations and a remarkable ability to detect the strength of the winds at the SD location. We use the HWRF model and satellite data to perform cross-comparisons of the SD with the buoy, which sampled different relative locations within the storm. Finally, we review the collective consistency among these measurements by describing the uncertainty of each wind dataset and discussing potential sources of systematic errors, such as the impact of extreme conditions on the SD measurements and uncertainties in the methodology. [ABSTRACT FROM AUTHOR]

One of the challenges in ocean surface current retrieval from synthetic aperture radar (SAR) data is the estimation and removal of the wave-induced Doppler centroid (DC). This article demonstrates empirically the relationship between the dc derived from spaceborne X-band InSAR data and the ocean surface wind and waves. In this study, we analyzed over 300 TanDEM-X image pairs. It is found that the general characteristics of the estimated dc follow the theoretically expected variation with incidence angle, wind speed, and wind direction. An empirical geophysical model function (GMF) is fit to the estimated dc and compared to existing models and previous experiments. Our GMF is in good agreement (within 0.2 m/s) with other models and data sets. It is found that the wind-induced Doppler velocity contributes to the total Doppler velocity with about 15% of the radial wind speed. This is much larger than the sum of the contributions from the Bragg waves (~0.2 m/s) and the wind-induced drift current (~3% of wind speed). This indicates a significant (dominant) contribution of the long wind waves to the SAR dc. Moreover, analysis of dual-polarized data shows that the backscatter polarization ratio ($PR=\sigma ^{0}_{VV}/\sigma ^{0}_{HH}$) and the dc polarization difference ($PD={|dc_{VV}|-|dc_{HH}|}$) are systematically larger than 1 and smaller than 0 Hz, respectively, and both increase in magnitude with incidence angle. The estimated PR and PD are compared to other theoretical and empirical models. The Bragg scattering theory-based (pure Bragg and composite surface) models overestimate both PR and PD, suggesting that other scattering mechanisms, e.g., wave breaking, are involved. In general, it is found that empirical models are more consistent with both backscatter and Doppler data than theory-based models. This motivates a further improvement of SAR dc GMFs. [ABSTRACT FROM AUTHOR]

Abstract The study intends to examine the impact of assimilation of ocean surface winds on the simulation of the tropical cyclones Ockhi, Mekunu, and Luban formed over the north Indian Oceans. We have conducted two assimilation experiments, cyclic control (CyclicCntl) and Hybrid, using the Weather Research and Forecast model. The CyclicCntl experiment and Hybrid experiment are the analysis generated without and with adding ocean surface winds, respectively. The ocean surface winds from Ocean Scatterometer, Advanced Scatterometer, and Windsat are assimilated using the ensemble three‐dimensional variational assimilation technique. The analysis generated by Hybrid and CyclicCntl is compared with the ocean surface winds observations from buoys, and it is found that the assimilation of ocean surface winds reduces the root mean square error between observation and analysis for zonal and meridional winds by 2–3 m s−1 (42%) compared to CyclicCntl simulations. The comparison with the International Best Track Archive for Climate Stewardship locations of cyclones derived from the analyses indicates that track error is maximum in genesis and landfall stage for both the Hybrid and CyclicCntl experiments, and the average track error is less than 80 km for the three cyclones. In a Hybrid experiment, during the intensifications from deep depression to the very severe cyclonic storm, the wind speed enhancement is dominant over the southeast and northwest sectors of the eyewall regions and subsequently in the rainband region. Thus, the assimilation of ocean surface winds from the three sensors improves the prediction of intensity and the evolution of tropical cyclones in general.

The study intends to examine the impact of assimilation of ocean surface winds on the simulation of the tropical cyclones Ockhi, Mekunu, and Luban formed over the north Indian Oceans. We have conducted two assimilation experiments, cyclic control (CyclicCntl) and Hybrid, using the Weather Research and Forecast model. The CyclicCntl experiment and Hybrid experiment are the analysis generated without and with adding ocean surface winds, respectively. The ocean surface winds from Ocean Scatterometer, Advanced Scatterometer, and Windsat are assimilated using the ensemble three‐dimensional variational assimilation technique. The analysis generated by Hybrid and CyclicCntl is compared with the ocean surface winds observations from buoys, and it is found that the assimilation of ocean surface winds reduces the root mean square error between observation and analysis for zonal and meridional winds by 2–3 m s−1 (42%) compared to CyclicCntl simulations. The comparison with the International Best Track Archive for Climate Stewardship locations of cyclones derived from the analyses indicates that track error is maximum in genesis and landfall stage for both the Hybrid and CyclicCntl experiments, and the average track error is less than 80 km for the three cyclones. In a Hybrid experiment, during the intensifications from deep depression to the very severe cyclonic storm, the wind speed enhancement is dominant over the southeast and northwest sectors of the eyewall regions and subsequently in the rainband region. Thus, the assimilation of ocean surface winds from the three sensors improves the prediction of intensity and the evolution of tropical cyclones in general. Plain Language Summary: The study presents the impact of assimilation of ocean surface winds on the analysis of tropical cyclones (TCs) formed over the north Indian Ocean viz. Ockhi, Mekunu, and Luban. Ocean surface winds transfer heat, moisture, and momentum between ocean and atmosphere, and it is one of the essential parameters for TC evolution and movement. The ocean surface winds data are acquired from the three sensors viz. Advanced Scatterometer, Ocean Scatterometer, and Windsat. We have carried out two experiments with the Weather Research and Forecast (WRF) model. In the first experiment, cyclic control (CyclicCntl), generates analysis at each 6‐h interval without any data assimilation, while the Hybrid experiment generates a 6‐hourly analysis with data assimilation. The ocean surface winds retrieved from the above‐mentioned sensors are assimilated in the WRF model. The track and intensity generated using both the analyses are compared with International Best Track Archive for Climate Stewardship observations. The average track error is less than 80 km for all three cyclones. The intensity simulation is a challenging task in numerical prediction. The intensity simulation is better in the Hybrid experiment compared to CyclicCntl. During the intensifications from deep depression to the very severe cyclonic storm, the wind speed enhancement is dominant over the southeast and northwest sectors of the eyewall regions. Key Points: The assimilation of ocean surface winds reduces the root mean square error in zonal and meridional winds between analysis and observation by 2–3 m s−1 (42%)Assimilation of ocean surface winds improves the intensity simulation of the tropical cycloneDuring the intensification of the cyclone, the wind speed enhancement is dominant over the southeast and northwest sectors of the eyewall regions [ABSTRACT FROM AUTHOR]

The NASA CYGNSS satellite constellation measures ocean surface winds using the existing network of the Global Navigation Satellite System (GNSS) and was designed for measurements in tropical cyclones (TCs). Here, we focus on using a consistent methodology to validate multiple CYGNSS wind data records currently available to the public, some focusing on low to moderate wind speeds, others for high winds, a storm-centric product for TC analyses, and a wind dataset from NOAA that applies a track-wise bias correction. Our goal is to document their differences and provide guidance to users. The assessment of CYGNSS winds (2017–2020) is performed here at global scales and for all wind regimes, with particular focus on TCs, using measurements from radiometers that are specifically developed for high winds: SMAP, WindSat, and AMSR2 TC-winds. The CYGNSS high-wind products display significant biases in TCs and very large uncertainties. Similar biases and large uncertainties were found with the storm-centric wind product. On the other hand, the NOAA winds show promising skill in TCs, approaching a level suitable for tropical meteorology studies. At the global level, the NOAA winds are overall unbiased at wind regimes from 0–30 m/s and were selected for a test assimilation into a global wind analysis, CCMP, also presented here.

Ocean surface winds, currents, and waves play a crucial role in exchanges of momentum, energy, heat, freshwater, gases, and other tracers between the ocean, atmosphere, and ice. Despite surface waves being strongly coupled to the upper ocean circulation and the overlying atmosphere, efforts to improve ocean, atmospheric, and wave observations and models have evolved somewhat independently. From an observational point of view, community efforts to bridge this gap have led to proposals for satellite Doppler oceanography mission concepts, which could provide unprecedented measurements of absolute surface velocity and directional wave spectrum at global scales. This paper reviews the present state of observations of surface winds, currents, and waves, and it outlines observational gaps that limit our current understanding of coupled processes that happen at the air-sea-ice interface. A significant challenge for the coming decade of wind, current, and wave observations will come in combining and interpreting measurements from (a) wave-buoys and high-frequency radars in coastal regions, (b) surface drifters and wave-enabled drifters in the open-ocean, marginal ice zones, and wave-current interaction “hot-spots,” and (c) simultaneous measurements of absolute surface currents, ocean surface wind vector, and directional wave spectrum from Doppler satellite sensors.

Scatterometers provide very stable ocean vector wind data records. This is because they measure the ratio of backscattered to incident microwave signal over the ocean surface as opposed to an absolute quantity (e.g., emitted microwave signal). They provide an optimal source of observations for building a long ocean vector wind Climate Data Record (CDR). With this objective in mind, observations from different satellite platforms need to be assessed for high absolute accuracy versus a common ground truth and for fine cross-calibration during overlapping periods. Here we describe the methodology for developing a CDR of ocean surface winds from the C-band ASCAT scatterometers onboard MetOp-A, -B, and -C. This methodology is based on the following principles: a common Geophysical Model Function (GMF) and wind algorithm developed at Remote Sensing Systems (RSS) and the use of in situ and satellite winds to cross-calibrate the three scatterometers within the accuracy required for CDRs, about 0.1 m/s at the global monthly scale. Using multiple scatterometers and radiometers for comparison allows for the opportunity to isolate sensors that are drifting or experiencing step-changes as small as 0.05 m/s. We detected and corrected a couple of such changes in the ASCAT-A wind record. The ASCAT winds are now very stable over time and well cross-calibrated with each other. The full C-band wind CDR now covers 2007-present and can be easily extended in the next decade with the launch of the MetOp Second Generation scatterometers.

The Cross‐Calibrated Multiplatform (CCMP) ocean surface wind data set was originally developed by Atlas and coworkers to blend cross‐calibrated satellite winds, in situ data, and wind analyses from numerical weather prediction. CCMP uses a variational analysis method to smoothly blend these data sources into a gap‐free gridded wind estimate every 6 hr. CCMP version 2.0 is currently produced by Remote Sensing Systems using consistently cross‐calibrated satellite winds, in situ data from moored buoys, and background winds from the ERA‐Interim reanalysis. The reanalysis fields are only available after a delay of several months, making it impossible to produce CCMP 2.0 in near real time. Measurements from in situ sources such as moored buoys are also often delayed. To overcome these obstacles and produce a near‐real‐time (NRT) version of CCMP (CCMP‐NRT), two changes are made to the input data sets: The background winds are now the operational 0.25‐degree NCEP analysis winds, and no in situ data are used. This allows CCMP‐NRT to be routinely processed with a latency of less than 48 hr. An intercomparison of the CCMP‐NRT results with CCMP 2.0, and independent measurements from moored buoys shows that CCMP‐NRT provides a modest improvement over the background wind from NCEP in regions where satellite data are available. Analysis shows that the inclusion of in situ measurement in CCMP improves the agreement with these measurements, artificially reducing estimates of the error. Plain Language Summary: Satellites that orbit the Earth estimate winds over the ocean by evaluating the roughness the ocean surface. These satellites make their measurements at different time of the day, and there are spatial gaps between the satellite measurements. These characteristics make the data hard to use. The Cross‐Calibrated Multiplatform (CCMP) vector wind analysis uses a mathematical technique that combines satellite measurements, in situ measurements, and a background wind field into complete maps of ocean winds every 6 hr. This paper describes a new version of CCMP that can be constructed only a few days after the satellite measurement are made, opening up near‐real‐time applications for these data. Key Points: A new version of the CCMP ocean wind data set is introduced, which can be produced in near real timeThe influence of buoy measurements and the choice of background model on the CCMP analysis is investigated and quantifiedA comparison with buoy measurements shows that CCMP is an improvement over the winds in the numerical weather prediction fields used as the background [ABSTRACT FROM AUTHOR]

Outflow boundaries induced by cold-pools are a key characteristic of convective systems related to microphysical and kinematic processes during the mature stage of their lifecycle. Over the ocean, such kinematic processes are associated with low-level wind modulations that are captured by scatterometers. This study investigates the ability of the Advanced Scatterometer (ASCAT) wind retrievals to detect the outflow boundary associated with an oceanic mesoscale convective system (MCS). Leveraging a new technique to identify cold pools that is based on features that enclose elevated magnitude of the gradient of the wind, termed as ‘Gradient Feature’ (GF), wind retrievals at 50-, 25- and 7-km spatial resolution were utilized to explore how the characteristics of the outflow boundary vary with resolution. Ground-based radar retrievals were also implemented to assess and correct, when possible, the performance of the ASCAT retrievals. The magnitude of the gradient of the wind for the coarser resolution was an order of magnitude smaller (10−4 s−1) than the finer ones (10−3 s−1). An increase in the magnitude of the gradient wind field associated with the outflow boundary was captured by all resolutions and a respective feature was identified by the GF method. The location of the features relative to the distance from the front edge of the MCS decreased with resolution, indicating the importance of the high resolution ASCAT product to capture their extent, as well as additional smaller scale features. The effect of the background wind field on the selection of the final wind field during the ambiguity removal process for the high-resolution product is also discussed.

The NASA Cyclone Global Navigation Satellite System (CYGNSS) was launched in December 2016, providing an unprecedented opportunity to obtain ocean surface wind speeds including wind estimates over the hurricane inner‐core region. This study demonstrates the influence of assimilating an early version of CYGNSS observations of ocean surface wind speeds on numerical simulations of two notable landfalling hurricanes, Harvey and Irma (2017). A research version of the National Centers for Environmental Prediction operational Hurricane Weather Research and Forecasting model and the Gridpoint Statistical Interpolation‐based hybrid ensemble three‐dimensional variational data assimilation system are used. It is found that the assimilation of CYGNSS data results in improved track, intensity, and structure forecasts for both hurricane cases, especially for the weak phase of a hurricane, implying potential benefits of using such data for future research and operational applications. Plain Language Summary: The NASA Cyclone Global Navigation Satellite System (CYGNSS) was launched in December 2016. It provides an unprecedented opportunity to obtain ocean surface wind speeds over a hurricane inner‐core region. In this study, we combined the early version of CYGNSS data with all other observations that are currently available for operational forecasts to form initial conditions (inputs data) for a numerical weather prediction model. A research version of the National Oceanic and Atmospheric Administration operational hurricane forecast model named the Hurricane Weather Research and Forecast (HWRF) model is used. Results show that adding CYGNSS data into HWRF model results in improved track, intensity, and structure forecasts for two notable landfalling hurricanes, Harvey and Irma (2017), demonstrating the potential benefits of using CYGNSS data for future research and operational applications. Key Points: The NASA Cyclone Global Navigation Satellite System (CYGNSS) provides an unprecedented opportunity to obtain ocean surface wind data over a hurricane inner‐core regionThis study found that the assimilation of CYGNSS data results in improved track, intensity, and structure forecasts for two notable landfalling hurricanes, Harvey and Irma (2017) [ABSTRACT FROM AUTHOR]

A model-based procedure for ocean wind speed estimation using global navigation satellite system reflectometry is presented. The method is based on the least-squares matching between the measured scattered power function volume and the volume of the Zavorotny–Voronovich wind-dependent scattered power model. Geometric terms depending on the orbit path as well as the antenna pattern and known propagation losses are considered in the model. Error sources are investigated, and their impact on wind speed estimates is evaluated and minimized. The performance of the proposed algorithm is assessed by simulating delay–Doppler maps in a realistic ocean scattering scenario with Cyclone Global Navigation Satellite System (CYGNSS) observatories, whereas the validation of the algorithm is carried out by comparisons of retrievals from real delay–Doppler maps collected by the space CYGNSS observatories and ground truth data processed within the collaborative NASA CYGNSS Science Team. [ABSTRACT FROM AUTHOR]

This paper investigates 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 is characterized. The correlation with sea surface temperature (SST), wind direction, and rain is also investigated. The SGR-ReSI measurements exhibit clear sensitivity to wind speeds up to 20 m/s. There is also apparent sensitivity to 35 m/s wind speeds although the collocation dataset becomes sparser. A dependence on the swell is also observed for winds <6 m/s. Additionally, a small correlation with SST is observed in which the slope of the SGR-ReSI measurements is positive for winds <5 m/s, and reverses for winds >5 m/s. A weak wind direction signal was also observed, and an investigation of rain impacts did not conclusively confirm any influence on the data. These results are shown through an analysis of global statistics as well as an analysis of several case studies. This publicly released SGR-ReSI dataset provided a first opportunity to comprehensively investigate the sensitivity of GNSS-R measurements to various ocean surface parameters. The upcoming NASA cyclone global navigation satellite system will utilize a similar receiver to the SGI-ReSI; therefore, this data provides a valuable prelaunch insight. [ABSTRACT FROM PUBLISHER]

The Indian Space Research Organisation launched the Oceansat-2 scatterometer (OSCAT) for atmospheric and oceanographic applications. In this paper, a case study has been performed to assess the impact of OSCAT-retrieved wind vectors on the simulation of tropical cyclone Phet over the Arabian Sea. Three-dimensional variational data assimilation of the Weather Research and Forecasting model is used for this purpose. In addition to OSCAT winds, wind speed and precipitable water derived from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) are also used for assimilation to evaluate the impact of scatterometer and radiometer data on tropical cyclone prediction. Results show that an \sim60-km track error is observed in control and TMI experiments when compared with Joint Typhoon Warning Center observed cyclone center at 1800 UTC 01 June 2010. An approximately 40-km track error is determined in the initial center position of OSCAT experiments. The mean track error forecast is less in OSCAT experiments (\sim80 km) in comparison with TMI experiments (\sim 110 km). Only OSCAT data experiments are able to predict the track of the cyclone toward the Oman coast. Assimilation of scatterometer wind direction improves the track forecast; but it degrades the forecast of the intensity, maximum magnitude, and evolution of the cyclone. None of these experiments are able to capture the observed minimum sea level pressure (964 hPa at 1200 UTC 02 June 2010) accurately. TMI experiments are slightly better than OSCAT experiments in capturing the intensity of cyclone Phet, whereas wind direction from OSCAT improves the track forecast of the cyclone. [ABSTRACT FROM AUTHOR]

A combined analysis of ocean surface wind vector measurements by the European Advanced Scatterometer (ASCAT) and the National Aeronautics and Space Administration QuikSCAT (QS) scatterometer using buoy measurements, numerical weather prediction model analyses, and spectral decomposition reveals significant statistical differences between the two data sets. While QS wind speeds agree better with buoy wind speeds than ASCAT above 15 m s-1, ASCAT wind directions agree better with buoy directions overall than QS. In contrast, it is shown that sea-level pressure (SLP) fields derived from ASCAT and QS measurements compare better with each other than the winds in a statistical sense, even though ASCAT bulk pressure gradients (BPGs) are slightly weaker than buoy pressure gradients and have slightly lower spectral energy than QS. Weaker BPGs in ASCAT are consistent with the low bias in ASCAT wind speeds. Thus, it is proposed that scatterometer-derived SLP fields can be used as a filter to improve the wind directions. This improves the QS wind directions but has less effect on the more accurate ASCAT wind directions. The unfiltered ASCAT wind vector statistics compare well with the statistics of the direction-filtered QS winds. It is suggested that this methodology might provide a basis for minimizing the discrepancies between various satellite wind measurement data sets in view of producing a long-term record of satellite-derived SLP fields and ocean surface wind vectors. [ABSTRACT FROM AUTHOR]

In this paper, we perform a comparison of wind speed measurements from the ENVISAT Advanced Synthetic Aperture Radar (ASAR), the MetOp-A Advanced Scatterometer (ASCAT), the U.S. National Data Buoy Center's moored buoys, and the U.S. Navy Operational Global Atmospheric Prediction System (NOGAPS) model. These comparisons were made in near U.S. coast regions over a 17-month period from March 2009 to July 2010. The ASAR wind speed retrieval agreed well with the scatterometer and model estimates, with mean differences ranging from -0.69 to 0.85 m/s and standard deviations between 1.16 and 1.77 m/s, depending upon the ASAR beam mode type. The results indicate that ASAR-derived ocean surface wind speeds are as accurate as the ASCAT and NOGAPS wind products. Comparisons between ASCAT winds and synthetic aperture radar (SAR) winds averaged at different spatial resolutions show very little change. This demonstrates that it is suitable that the scatterometer wind retrieval geophysical model function, i.e., CMOD5, is used for SAR wind retrieval. The impact of C-band VV polarization SAR calibration error on wind retrieval is also discussed. [ABSTRACT FROM PUBLISHER]

We describe a method for retrieving winds from colocated Ku- and C-band ocean wind scatterometers. The method utilizes an artificial neural network technique to optimize the weighting of the information from the two frequencies and to use the extra degrees of freedom to account for rain contamination in the measurements. A high-fidelity scatterometer simulation is used to evaluate the efficacy of the technique for retrieving hurricane force winds in the presence of heavy precipitation. Realistic hurricane wind and precipitation fields were simulated for three Atlantic hurricanes, Katrina and Rita in 2005 and Helene in 2006, using the Weather Research and Forecasting model. These fields were then input into a radar simulation previously used to evaluate the Extreme Ocean Vector Wind Mission dual-frequency scatterometer mission concept. The simulation produced highresolution dual-frequency normalized radar cross-section (NRCS) measurements. The simulated NRCS measurements were binned into 5 x 5 km wind cells. Wind speeds in each cell were estimated using an artificial neural network technique. The method was shown to retrieve accurate winds up to 50 m/s even in intense rain. [ABSTRACT FROM AUTHOR]

MODIS (Moderate Resolution Imaging Spectroradiometer) level-3 aerosol data, NCEP (National Centers for Environmental Prediction) reanalysis winds and QuikSCAT ocean surface winds were made use of to examine the role of atmospheric circulation in governing aerosol variations over the Bay of Bengal (BoB) during the first phase of the ICARB (Integrated Campaign for Aerosols, gases and Radiation Budget) campaign (March 18-April 12, 2006). An inter-comparison between MODIS level-3 aerosol optical depth (AOD) data and ship-borne MICROTOPS measurements showed good agreement with correlation 0.92 (p < 0.0001) and a mean MODIS underestimation by 0.01. During the study period, the AOD over BoB showed high values in the northern/north western regions, which reduced towards the central and southern BoB. The wind patterns in lower atmospheric layers (> 850 hPa) indicated that direct transport of aerosols from central India was inhibited by the presence of a high pressure and a divergence over BoB in the lower altitudes. On the other hand, in the upper atmospheric levels, winds from central and northern India stretched south eastwards and converged over BoB with a negative vorticity indicative of a downdraft. These wind patterns pointed to the possibility of aerosol transport from central India to BoB by upper level winds. This mechanism was further confirmed by the significant correlations that AOD variations over BoB showed with aerosol flux convergence and flux vorticity at upper atmospheric levels (600-500 hPa). AOD in central and southern BoB away from continental influences displayed an exponential dependence on the QuikSCAT measured ocean surface wind speed. This study shows that particles transported from central and northern India by upper atmospheric circulations as well as the marine aerosols generated by ocean surface winds contributed to the AOD over the BoB during the first phase of ICARB. [ABSTRACT FROM AUTHOR]

Two preliminary, six-month long global WindSat vector wind datasets are validated using buoys and QuikSCAT measurements. Buoy comparisons yield speed and direction root mean square accuracies of 1.4 m/s and 25° for the ‘NESDIS0’ product and 13 m/s and 23° for the more recently produced ‘B 1’ product from the Naval Research Laboratory. WindSat along- and across-wind random component errors of 0.7–1.0 and 2.6–2.8 m/s (respectively) are larger than those calculated for QuikSCAT in the same period. Global WindSat-QuikSCAT comparisons generally confirmed the buoy analyses. While simple rain flags based directly on WindSat brightness temperature measurements alone are shown to overflag for rain systematically, the advanced ‘Environmental Data Record’ rain flag in the B1 product matches well with Special Sensor Microwave/Imager rain detection frequency and preserves the accuracy of the unflagged vector wind measurements. [ABSTRACT FROM AUTHOR]

WindSat is a space-based polarimetric microwave radiometer designed to demonstrate the capability to measure the ocean surface wind vector using a radiometer. We describe a nonlinear iterative algorithm for simultaneous retrieval of sea surface temperature, columnar water vapor, columnar cloud liquid water, and the ocean surface wind vector from WindSat measurements. The algorithm uses a physically based forward model function for the WindSat brightness temperatures. Empirical corrections to the physically based model are discussed. We present evaluations of initial retrieval performance using a six-month dataset of WindSat measurements and collocated data from other satellites and a numerical weather model. We focus primarily on the application to wind vector retrievals. [ABSTRACT FROM AUTHOR]

The global ocean surface wind vector is a key parameter for short-term weather forecasting, the issuing of timely weather warnings, and the gathering of general climatological data. In addition, it 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. WindSat is a satellite-based multifrequency polarimetric microwave radiometer developed by the Naval Research Laboratory for the U.S. Navy and the National Polar-orbiting Operational Environmental Satellite System Integrated Program Office. 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, which is planned to provide wind vector data operationally starting in 2010. WindSat is the primary payload on the Department of Defense Coriolis satellite, which was launched on January 6, 2003. It is in an 840-km circular sun-synchronous orbit The WindSat payload is performing well and is currently undergoing rigorous calibration and validation to verify mission success. [ABSTRACT FROM AUTHOR]

The NASA CYGNSS satellite constellation measures ocean surface winds using the existing network of the Global Navigation Satellite System (GNSS) and was designed for measurements in tropical cyclones (TCs). Here, we focus on using a consistent methodology to validate multiple CYGNSS wind data records currently available to the public, some focusing on low to moderate wind speeds, others for high winds, a storm-centric product for TC analyses, and a wind dataset from NOAA that applies a track-wise bias correction. Our goal is to document their differences and provide guidance to users. The assessment of CYGNSS winds (2017–2020) is performed here at global scales and for all wind regimes, with particular focus on TCs, using measurements from radiometers that are specifically developed for high winds: SMAP, WindSat, and AMSR2 TC-winds. The CYGNSS high-wind products display significant biases in TCs and very large uncertainties. Similar biases and large uncertainties were found with the storm-centric wind product. On the other hand, the NOAA winds show promising skill in TCs, approaching a level suitable for tropical meteorology studies. At the global level, the NOAA winds are overall unbiased at wind regimes from 0–30 m/s and were selected for a test assimilation into a global wind analysis, CCMP, also presented here. [ABSTRACT FROM AUTHOR]

Scatterometers provide very stable ocean vector wind data records. This is because they measure the ratio of backscattered to incident microwave signal over the ocean surface as opposed to an absolute quantity (e.g., emitted microwave signal). They provide an optimal source of observations for building a long ocean vector wind Climate Data Record (CDR). With this objective in mind, observations from different satellite platforms need to be assessed for high absolute accuracy versus a common ground truth and for fine cross-calibration during overlapping periods. Here we describe the methodology for developing a CDR of ocean surface winds from the C-band ASCAT scatterometers onboard MetOp-A, -B, and -C. This methodology is based on the following principles: a common Geophysical Model Function (GMF) and wind algorithm developed at Remote Sensing Systems (RSS) and the use of in situ and satellite winds to cross-calibrate the three scatterometers within the accuracy required for CDRs, about 0.1 m/s at the global monthly scale. Using multiple scatterometers and radiometers for comparison allows for the opportunity to isolate sensors that are drifting or experiencing step-changes as small as 0.05 m/s. We detected and corrected a couple of such changes in the ASCAT-A wind record. The ASCAT winds are now very stable over time and well cross-calibrated with each other. The full C-band wind CDR now covers 2007-present and can be easily extended in the next decade with the launch of the MetOp Second Generation scatterometers. [ABSTRACT FROM AUTHOR]

This paper presents a method for joint retrieval of the ocean surface wind and current vectors using the backscatter and the Doppler frequency shift measured by spaceborne single-beam single-polarization synthetic aperture radar (SAR). The retrieval method is based on the Bayesian approach with the a priori information provided by atmospheric and oceanic models for surface wind and currents, respectively. The backscatter and Doppler frequency shift are estimated from the along-track interferometric SAR system TanDEM-X data. The retrieval results are compared against in-situ measurements along the Swedish west coast. It is found that the wind retrieval reduces the atmospheric model bias compared to in-situ measurements by about 1 m/s for wind speed, while the bias reduction in the wind direction is minor as the wind direction provided by the model was accurate in the studied cases. The ocean model bias compared to in-situ measurements is reduced by about 0.04 m/s and 12∘ for current speed and direction, respectively. It is shown that blending SAR data with model data is particularly useful in complex situations such as atmospheric and oceanic fronts. This is demonstrated through two case studies in the Skagerrak Sea along the Swedish west coast. It is shown that the retrieval successfully introduces small scale circulation features detected by SAR that are unresolved by the models and preserves the large scale circulation imposed by the models. • Synthetic aperture radar (SAR) can only provide wind speed and radial current. • Atmosphere and ocean models do not resolve small scale circulation in coastal areas. • Bayesian method combines model and SAR data for retrieval of wind and current vectors. • Retrieval is useful in atmospheric and oceanic coastal fronts. [ABSTRACT FROM AUTHOR]

Outflow boundaries induced by cold-pools are a key characteristic of convective systems related to microphysical and kinematic processes during the mature stage of their lifecycle. Over the ocean, such kinematic processes are associated with low-level wind modulations that are captured by scatterometers. This study investigates the ability of the Advanced Scatterometer (ASCAT) wind retrievals to detect the outflow boundary associated with an oceanic mesoscale convective system (MCS). Leveraging a new technique to identify cold pools that is based on features that enclose elevated magnitude of the gradient of the wind, termed as 'Gradient Feature' (GF), wind retrievals at 50-, 25- and 7-km spatial resolution were utilized to explore how the characteristics of the outflow boundary vary with resolution. Ground-based radar retrievals were also implemented to assess and correct, when possible, the performance of the ASCAT retrievals. The magnitude of the gradient of the wind for the coarser resolution was an order of magnitude smaller ( 10 − 4 s − 1 ) than the finer ones ( 10 − 3 s − 1 ). An increase in the magnitude of the gradient wind field associated with the outflow boundary was captured by all resolutions and a respective feature was identified by the GF method. The location of the features relative to the distance from the front edge of the MCS decreased with resolution, indicating the importance of the high resolution ASCAT product to capture their extent, as well as additional smaller scale features. The effect of the background wind field on the selection of the final wind field during the ambiguity removal process for the high-resolution product is also discussed. [ABSTRACT FROM AUTHOR]

A very high-resolution model for the Bay of Bengal is configured to simulate submesoscale flow features, which are otherwise absent in coarse resolution models. In this manuscript, we present the response of this model to simulate summer monsoon freshening in the northern Bay of Bengal when forced with two different winds, one based on weather forecast model output from National Centre for medium range weather forecast (NCMRWF) and the other based on satellite-derived SCATSAT ocean surface winds for 2017 as a case study. Our results reveal that the spatio-temporal evolution of lateral freshwater dispersal in the northern Bay of Bengal is sensitive to wind vorticity and eddy formation/evolution. A reasonably realistic decrease of 2–3 psu in the sea surface salinity at the mooring location of 18oN, 90oE towards the end of September 2017 is simulated in the model when forced with the SCATSAT winds, whereas significantly weaker freshening occurs at the mooring location when the model is forced with the NCMRWF winds. A filament of strong positive vorticity connecting two cyclonic eddies in the north central Bay in the NCMRWF wind forced simulations prevents the direct southward advection of freshwater, resulting in higher salinity at the mooring location. A similar mechanism that would prevent southward advection of fresh water is absent in the SCATSAT forced model run. Westward moving Rossby waves in the SCATSAT forced model run are found to play an important role in the evolution of freshening events at various locations in the northern Bay of Bengal. Lagrangian Coherent Structures (LCS) derived from the model currents suggest that the background conditions were favourable to advect freshwater from Ganges-Brahmaputra towards the mooring location in the northern Bay of Bengal in the SCATSAT forced simulations. [ABSTRACT FROM AUTHOR]