Wind Retrieval Research Articles

Evaluation of Nearshore QuikSCAT 4.1 and ERA-5 Wind Stress and Wind Stress Curl Fields over Eastern Boundary Currents

Fields of coastal wind stress and wind stress curl in the 10–100 km next to the land control the processes of upwelling and downwelling of nutrients and water properties that are vital to highly productive coastal marine ecosystems. Here we ask the question: Do the present surface wind stress products from a satellite-borne scatterometer (QuikSCAT) and an atmospheric reanalysis model (ERA-5) systematically overestimate the magnitude of wind speed and stress in the 10–50 km next to the coast? We compare QuikSCAT wind speed retrievals to the relatively unused wind speed retrievals from satellite altimeters, which are able to approach closer to the coast than scatterometers without land reflections, due to their smaller radar footprints. Altimeter data on tracks approaching and crossing the coast indicate that the increases in coastal QuikSCAT wind speed values and ERA-5 coastal wind stress values are unrealistic. For analyses of wind speed and stress requiring high accuracy, especially those involving wind stress curl, we suggest considering individual Level 2B scatterometer wind retrievals as suspect at distances of 10 km and less from the coast, along with use of the Poor Coastal Processing flag. We found that similar increases in wind stress values next to the coast in gridded ERA-5 fields are not due to errors in the model physics or wind speeds. They are created during the interpolation of wind stress from the original model grid to a regular rectangular grid. We recommend that researchers who are analyzing wind stress and wind stress curl should calculate wind stress themselves from the gridded ERA-5 vector wind speed fields, rather than using the interpolated model wind stress or curl fields.

Wind speed retrieval from Chinese Gaofen-3 synthetic aperture radar using an analytical approach in the nearshore waters of China’s seas

ABSTRACT Empirical wind-retrieval algorithms for synthetic aperture radar (SAR), such as the geophysical model function (GMF) CMOD5N in the C-Band, are currently well developed. The retrieval accuracy of GMFs, however, is lower in the presence of marine phenomena such as oceanic fronts and mesoscale eddies, making SAR wind retrieval challenging under these conditions. In this study, we proposed a scheme for wind retrieval from Chinese Gaofen-3 (GF-3) SAR images using an analytical approach to obtain the wind speed in the nearshore waters of China’s seas. Approximately 300 images were acquired from 2018 to 2021 in the copolarization channels [vertical–vertical (VV) and horizontal–horizontal (HH)]. The images were collocated with the measurements from the Advanced Scatterometer (ASCAT) and the Chinese Haiyang-2B (HY-2B) scatterometer. Our analytical approach was based on the Bragg resonant and non-Bragg (NB) scattering model for the calculation of the normalized radar backscatter cross-section (NRCS), which accounted for the influence of wave-current interactions and breaking waves in nearshore waters. We used the sea surface currents obtained from the HYbrid Coordinate Ocean Model (HYCOM) to simulate the Bragg resonant roughness. We derived the contribution of the NB breaking waves from the SAR-measured NRCS values in both the VV and HH channels. By comparing the retrieval results obtained using the analytical approach with the scatterometer products, we concluded that the root mean square error (RMSE) of the wind speed was 2.01 m s−1 with a 0.91 correlation coefficient (COR), which was less than the 2.93 m s−1 RMSE and 0.72 COR for the GMF CMOD5N. The variation in the bias of the HYCOM currents was approximately 1 m s−1 for current speeds greater than 0.3 m s−1. These findings verified the stability of the analytical approach for SAR wind retrieval in complex sea states.

Assimilating Near-Surface Wind Retrievals from High-Frequency Radars

Abstract High-frequency radars (HFR) are traditionally used in coastal environments to observe ocean current and wave characteristics. With an HFR forward model, HFR adjoint model, and the Simulating Waves Nearshore model, HFR Doppler spectra observations were used to estimate near-surface winds in the Southern California Bight in October 2017. The HFR 10-m wind retrievals were assimilated into the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) with the COAMPS four-dimensional variational (4DVar) assimilation system to integrate the HFR wind retrievals into the initial conditions. Impact of the HFR-derived winds on the forecast are evaluated in terms of adjoint-derived forecast sensitivity observation impact (FSOI), and by an observing system experiment that compared forecasts from simulations that assimilated the HFR wind retrievals to a control simulation that excluded HFR winds. The addition of the HFR-estimated wind observations reduced the error in the forecasted dry energy norm in the lowest model level and also contributed to small improvements in the 10-m wind field over a 25-day experiment. The potential benefit of this new method to estimate near-surface ocean winds near the coast for data assimilation and improved numerical weather prediction is an exciting advancement in remote sensing of coastal winds and expands the benefit of existing HFR networks beyond their intended use. More importantly, wind fields retrieved from HFR have the potential to fill an observation gap near the shoreline where ship and buoy observations are sparse and scatterometer observations are unavailable due to land contamination.

A Multi-Platform In Situ Kinematic and Microphysical Analysis of a Hybrid Parallel–Trailing Stratiform Mesoscale Convective System

Abstract Parallel stratiform–type mesoscale convective systems (MCS) have been found to comprise less than 20% of the central U.S. MCSs, but parallel stratiform MCSs have been reviewed by few studies and only a handful have been sampled using airborne platforms. This study conducts a detailed review of the 11 May 2011 MCS that was observed during the Midlatitude Continental Convective Clouds Experiment. In situ data from the University of North Dakota Cessna Citation II Weather Research Aircraft and the Oklahoma Mesonet are used in conjunction with radar reflectivity data and a multiple-Doppler wind retrieval to present an in-depth analysis of the kinematic and microphysical processes within this hybrid parallel–trailing stratiform MCS. Results suggest the MCS started with parallel stratiform characteristics and then developed trailing stratiform features over the course of a couple of hours such as leading line convection and the presence of rear inflow, which is a feature not observed in previous studies of parallel stratiform MCSs. Based on surface observations and multi-Doppler wind field retrievals, the cold pool influenced the 11 May 2011 MCS and may have helped it to transition toward the trailing stratiform mode. Within the stratiform region, in situ measurements show hydrometeor aggregation and sublimation occurring above the melting layer via a faster rate of decline in total hydrometer concentration compared to the total water content, the presence of a bimodal droplet size spectra, and increasing dispersion and decreasing slope, with respect to increasing temperature in both the unimodal and bimodal gamma distributions. Significance Statement The purpose of this study is to investigate the wind pattern of a nontypical type of thunderstorm complex. The wind pattern is found to be a combination of features from both a common type of thunderstorm complex and another less common type. The behavior of snowflakes as they fall through this thunderstorm was also investigated. The snowflakes in this storm grew bigger by linking up with other snowflakes as they fell down, and then melted before they reached the ground. It is hoped that results from this study are useful to future work to improve weather prediction.

Influence of Residual Amplitude and Phase Error for GF-3 Quad-Polarization SAR on Wind Vector Retrieval

High-resolution wind vector is important to investigate local winds’ variability over the global ocean. Quad-polarization Synthetic Aperture Radar (SAR) can provide wind vector independently without any external wind direction inputs. Although quad-polarization SAR wind retrieval algorithms have been widely studied, improvements are still required. The amplitude and phase imbalance of polarization channel cannot be neglected for improving the wind vector retrieval precision. In this study, rainforest was performed to remove the amplitude and phase imbalance of polarization channel of GF-3 SAR. To explore the applicability of this method for sea surface measurement, the influence of residual amplitude and phase error for GF-3 quad-polarization SAR on wind vector retrieval was assessed. Variation of amplitude and phase imbalance of sea surface for transmit and receive channel were assessed against collocated wind speed and incidence angle. Considering the polarization difference of VV channel relative to HH channel, the residual amplitude and phase error was found to be closely related to wind speed and polarization isolation. Correction of residual amplitude and phase error were employed to improve the retrieval precision of wind vector. It is revealed that the wind speed retrieval precision of VV polarization improved with correction of residual amplitude error. In addition, the influence of residual amplitude and phase error on wind direction retrieval can be neglected. Thus, it is concluded that correction of amplitude and phase error has the potential to improve wind vector retrievals from GF-3 quad-polarization SAR.

Retrieval improvements for the ALADIN Airborne Demonstrator in support of the Aeolus wind product validation

Abstract. The realization of the European Space Agency's Aeolus mission was supported by the long-standing development and field deployment of the Atmospheric LAser Doppler INstrument (ALADIN) Airborne Demonstrator (A2D) which, since the launch of the Aeolus satellite in 2018, has been serving as a key instrument for the validation of ALADIN, the first-ever Doppler wind lidar (DWL) in space. However, the validation capabilities of the A2D are compromised by deficiencies of the dual-channel receiver which, like its spaceborne counterpart, consists of a Rayleigh and a complementary Mie spectrometer for sensing the wind speed from both molecular and particulate backscatter signals, respectively. Whereas the accuracy and precision of the Rayleigh channel is limited by the spectrometer's high alignment sensitivity, especially in the near field of the instrument, large systematic Mie wind errors are caused by aberrations of the interferometer in combination with the temporal overlap of adjacent range gates during signal readout. The two error sources are mitigated by modifications of the A2D wind retrieval algorithm. A novel quality control scheme was implemented, which ensures that only backscatter return signals within a small angular range are further processed. Moreover, Mie wind results with large bias of opposing sign in adjacent range bins are vertically averaged. The resulting improvement of the A2D performance was evaluated in the context of two Aeolus airborne validation campaigns that were conducted between May and September 2019. Comparison of the A2D wind data against a high-accuracy, coherent DWL that was deployed in parallel on board the same aircraft shows that the retrieval refinements considerably decrease the random errors of the A2D line-of-sight (LOS) Rayleigh and Mie winds from about 2.0 to about 1.5 m s−1, demonstrating the capability of such a direct detection DWL. Furthermore, the measurement range of the Rayleigh channel could be largely extended by up to 2 km in the instrument's near field close to the aircraft. The Rayleigh and Mie systematic errors are below 0.5 m s−1 (LOS), hence allowing for an accurate assessment of the Aeolus wind errors during the September campaign. The latter revealed different biases of the Level 2B (L2B) Rayleigh-clear and Mie-cloudy horizontal LOS (HLOS) winds for ascending and descending orbits, as well as random errors of about 3 m s−1 (HLOS) for the Mie and close to 6 m s−1 (HLOS) for the Rayleigh winds, respectively. In addition to the Aeolus error evaluation, the present study discusses the applicability of the developed A2D algorithm modifications to the Aeolus processor, thereby offering prospects for improving the Aeolus wind data quality.

The use of Sentinel-1 OCN products for preliminary deep offshore wind energy potential estimation: A case study on Ionian sea

The exploitation of wind energy potential has been strongly limited in the Mediterranean area due to its critical bathymetry. However, the spread of new deep offshore technologies such as floating turbines could drastically change the actual scenario, allowing wind energy to be exploited even in areas with critical bathymetric conditions. In such framework, remote sensing technologies could significantly help the development of offshore wind farms and may represent a valid resource for better planning wind energy solutions. In this article, a satellite based methodology for assessing the wind energy potential of offshore areas is presented and applied on a study case in the Ionian sea to identify the most effective areas for energy harvesting. The study is based on the use of the Sentinel-1 OCeaN (OCN) products, which are Level-2 sea-based data products provided by Copernicus (the principle European Union’s earth observation programme), containing wind retrieval information. Despite their proven accuracy and their ease of use, OCN products have been rarely used in recent scientific production for energetic purposes. An initial investigation on wind speed characteristics has been carried out using as reference data-set the one provided by the Global Wind Atlas (GWA). A sub-area with maximum coastal wind speed gradient has been identified in the Region of Interest (ROI). Afterwards, 54 Sentinel-1 images have been processed. Data have been aggregated on a monthly domain (February 2020 - January 2021) to produce 12 wind speed maps of the ROI sub-area. A productivity analysis has been carried out in three specific sites. Results show: (i) the effectiveness of an OCN products based methodology for wind energy solutions planning, (ii) the reliability of the monthly based data aggregation (proven by a spatial dispersion analysis) and (iii) the specific wind energy production potentialities of the chosen ROI, where a single wind turbine installation could ensure an annual power production up to 19.06 GWh.

First Observations of G‐Band Radar Doppler Spectra

AbstractThe first Doppler spectra ever acquired by an atmospheric radar at 200 GHz (G‐band) are presented. The observations were taken during a light precipitation event in May (rain rates <2 mm hr−1) at Chilbolton Observatory, UK, with coincident Ka‐band and W‐band Doppler radar measurements. The collected rain spectra agree with Mie theory predictions: at G‐band they show significant reductions in the spectral power return—as compared to theoretical Rayleigh scattering—corresponding to high Doppler velocities (i.e., large raindrops) with the presence of multiple peaks and “Mie notches” in correspondence to the maxima and minima of the raindrop backscattering cross sections. The first two G‐band Mie troughs correspond to smaller velocities/sizes than the first W‐band Mie notch. These features offered by G‐band radars pave the way toward applying, in rain, Mie notch vertical wind retrievals and multifrequency drop size distribution microphysical retrievals to smaller rain rates and smaller characteristic sizes than ever before.

Statistical validation of Aeolus L2A particle backscatter coefficient retrievals over ACTRIS/EARLINET stations on the Iberian Peninsula

Abstract. The Global Observing System (GOS) has encountered some limitations due to a lack of worldwide real-time wind measurements. In this context, the European Space Agency (ESA) has developed the Aeolus satellite mission, based on the ALADIN (Atmospheric Laser Doppler Instrument) Doppler wind lidar; this mission aims to obtain near-real-time wind retrievals at the global scale. As spin-off products, the instrument retrieves aerosol optical properties such as particle backscatter and extinction coefficients. In this work, a validation of Aeolus reprocessed (baseline 10) co-polar backscatter coefficients (βAeoluspart) is presented through an intercomparison with analogous ground-based measurements taken at the ACTRIS (Aerosols, Clouds and Trace gases Research InfraStructure Network)/EARLINET (European Aerosol Research Lidar Network) stations of Granada (Spain), Évora (Portugal) and Barcelona (Spain) over the period from July 2019 until October 2020. Case studies are first presented, followed by a statistical analysis. The stations are located in a hot spot between Africa and the rest of Europe, which guarantees a variety of aerosol types, from mineral dust layers to continental/anthropogenic aerosol, and allows us to test Aeolus performance under different scenarios. The so called Aeolus-like profiles (βAeoluslike,355part) are obtained from total particle backscatter coefficient and linear particle depolarization ratio (δlinearpart) profiles at 355 and 532 nm measured from the surface, through a thorough bibliographic review of dual-polarization measurements for relevant aerosol types. Finally, the study proposes a relation for the spectral conversion of δlinearpart, which is implemented in the Aeolus-like profile calculation. The statistical results show the ability of the satellite to detect and characterize significant aerosol layers under cloud-free conditions, along with the surface effect on the lowermost measurements, which causes the satellite to largely overestimate co-polar backscatter coefficients. Finally, the Aeolus standard correct algorithm middle bin (SCAmb) shows a better agreement with ground-based measurements than the standard correct algorithm (SCA), which tends to retrieve negative and meaningless coefficients in the clear troposphere. The implementation of Aeolus quality flags entails a vast reduction in the number of measurements available for comparison, which affects the statistical significance of the results.

GNSS-R Wind Speed Retrieval of Sea Surface Based on Particle Swarm Optimization Algorithm

Spaceborne global navigation satellite system reflectometry (GNSS-R) techniques have been developed for sea surface wind speed retrieval in recent years. In order to utilize both the leading edge slope (LES) and normalized bistatic radar cross-section (NBRCS), the minimum variance estimator (MVE) is used in the cyclone GNSS (CYGNSS) algorithm for wind retrieval. However, due to the high correlation of two observables, the root mean square error (RMSE) of the MVE estimated winds is not improved significantly. In this article, a new method by combining retrievals from delay-Doppler map (DDM) observables based on particle swarm optimization (PSO) is proposed. LES and NBRCS observables from CYGNSS V2.1 products are used, and then wind retrievals from them are combined by PSO. In order to validate the performance, European Center for Medium-Range Weather Forecasts (ECMWF) and cross-calibrated multi-platform (CCMP) ocean surface wind vector analysis product 10-m ocean surface wind products are used as ground truth. The results show that, when using ECMWF winds, the RMSE of MVE retrievals is 2.21 m/s, while that of PSO is 1.95 m/s: an improvement of 12%; when using CCMP winds, the RMSE of MVE retrievals is 2.15 m/s, while that of PSO is 1.92 m/s: an improvement of 11%. Therefore, we conclude that the PSO algorithm is an improvement on the state-of-the-art MVE GNSS-R-based wind speed retrieval techniques. However, the PSO-based wind retrievals show the dependence on the GPS block type and the CYGNSS satellite identifier that the MVE-based techniques suffer from.

Impact of Polarization Basis on Wind and Wave Parameters Estimation Using the Azimuth Cutoff From GF-3 SAR Imagery

The azimuth cutoff wavelength of SAR is an important parameter for retrieval of sea surface wind and wave. Earlier studies have fully demonstrated the substantial dependence of azimuth cutoff wavelength on polarization, but the present studies only focus on H-V linear polarization bases (HH, HV/VH, and VV) without considering the effects of other polarization bases (e.g., linear rotated, circular, and elliptical polarization). Benefiting from the quad-polarization advantage of GaoFen-3 SAR wave mode data and the support of polarization basis transformation theory, this study used 4,648 SAR data to study the correlation between cutoff wavelength and wind and wave parameters (e.g., significant wave height, and wind speed) under different polarization bases, and analyzed the variation of correlation coefficient caused by polarization basis change. Finally, the results were applied to evaluating the performance of wind and wave parameters retrieval. The results of the study show that the azimuth cutoff is strongly dependent on the polarization state of electromagnetic wave. The azimuth cutoff wavelength under the elliptical polarization bases has higher correlation with wind and wave than that under H-V linear, circular, and linear rotated polarization bases. Using the azimuth cutoff wavelength of the elliptical polarization bases can significantly improve the retrieval accuracy of wind and wave parameters. This study shall enhance the capabilities of polarized SAR systems to precisely derive more ocean surface properties. The result implies that polarization basis is an important factor that must be considered in future ocean SAR studies.

The NOAA Track-Wise Wind Retrieval Algorithm and Product Assessment for CyGNSS

A novel approach in addressing cyclone global navigation satellite system (CyGNSS) intersatellite and GPS-related calibration issues is proposed, based on a track-wise <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sigma ^{o}$ </tex-math></inline-formula> 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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sigma ^{o}$ </tex-math></inline-formula> bias correction maintains CyGNSS signal sensitivity. Both intersatellite and GPS-related calibration issues are removed after correction. Long-term <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sigma ^{o}$ </tex-math></inline-formula> downward trend, observed throughout the CyGNSS mission, is greatly reduced. Using the corrected <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sigma ^{o}$ </tex-math></inline-formula> 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.

Fostering the Need of L-Band Radiometer for Extreme Oceanic Wind Research

Ocean surface winds from space-borne radiometers and scatterometers are crucial inputs for numerical models for the operational weather and oceanic sea state forecasts. The rainfall, associated mainly with deep convective clouds, influences the wind retrievals from these sensors during extreme winds conditions (higher than 30 m <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{s}^{-1}$ </tex-math></inline-formula> ), as the signal strongly gets affected by the intervening atmosphere, mainly the precipitation. This study emphasizes the importance of the winds from L-band radiometric measurements from Soil Moisture Active Passive (SMAP) satellite compared to the operational Advanced Scatterometer (ASCAT) (C-band) and SCATSAT-1 (Ku-band) scatterometers, National Centers for Environmental Prediction (NCEP) final analysis, and European Centre for Medium-Range Weather Forecasts reanalysis 5 (ERA5) reanalysis wind products. Investigation at global scale suggests that except SMAP, no other selected data are able to capture wind speed more than 56 m <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{s}^{-1}$ </tex-math></inline-formula> , and large underestimations are found in presently available scatterometers and reanalysis. These high wind speed errors are more prominent when verified with Joint Typhoon Warning Center (JTWC) best track data for global storms. Moreover, the high winds near storms from scatterometers are generally flagged as rainy pixels and not used for operational applications. These limitations of capturing high winds in scatterometers have been addressed here using histogram corrections approach using SMAP retrieved winds. Moreover, the regional model simulations for a case study suggest that the modified scatterometer winds have larger impact on tropical storm prediction mainly on track and intensity.

Vertical Vortex Development in Hurricane Michael (2018) during Rapid Intensification

Abstract The landfall of Hurricane Michael (2018) at category-5 intensity occurred after rapid intensification (RI) spanning much of the storm’s lifetime. Four Hurricane Hunter aircraft missions observed the RI period with tail Doppler radar (TDR). Data from each of the 14 aircraft passes through the storm were quality controlled via a combination of interactive and machine-learning techniques. TDR data from each pass were synthesized using the Spline Analysis at Mesoscale Utilizing Radar and Aircraft Instrumentation (SAMURAI) variational wind retrieval technique to yield three-dimensional kinematic fields of the storm to examine inner-core processes during RI. Vorticity and angular momentum increased and concentrated in the eyewall region. A vorticity budget analysis indicates that the tendencies became more axisymmetric over time. In this study, we focus in particular on how the eyewall vorticity tower builds vertically into the upper levels. Horizontal vorticity associated with the vertical gradient of tangential wind was tilted into the vertical by the eyewall updraft to yield a positive vertical vorticity tendency inward atop the existing vorticity tower, which is further developed locally upward and outward along the sloped eyewall through advection and stretching. Observed maintenance of thermal wind balance from a thermodynamic retrieval shows evidence of a strengthening warm core, which aided in lowering surface pressure and further contributed to the efficient intensification in the latter stages of this RI event.

The Fusion of Physical, Textural, and Morphological Information in SAR Imagery for Hurricane Wind Speed Retrieval Based on Deep Learning

This study developed a deep-learning-based model to retrieve sea surface hurricane winds from synthetic aperture radar (SAR) imagery. We introduce the essential idea, residual learning, of the Residual Net into the artificial neural network and design a deep cross-layer concatenation network. The model inputs include SAR measured physical parameters in backscattering energy, the texture feature represented by the grey level co-occurrence matrix, and the morphological hurricane feature. We collected 45 satellite SAR images from Sentinel-1 over hurricane conditions. These images were divided into 39 and 6 for model development and independent testing. A total of 16,127 wind samples acquired from 39 SAR images and simultaneously measured by the Stepped Frequency Microwave Radiometer were collected as model tuning datasets, among which 80% and 20% were used for training and validation. Our validation results show that the deep-learning-based model achieved a correlation coefficient (CORR) and root-mean-square error (RMSE) of 0.98 and 1.72 m/s for wind speeds up to 75 m/s. We further applied the model to six independent SAR images. The model significantly outperformed two existing geophysical algorithms and one backpropagation neural network algorithm with the RMSE is 2.61 m/s and a CORR of 0.95. Moreover, statistical analysis in different wind speed regimes indicates that our model shows a stable performance improvement than comparable algorithms. The RMSE decreases 10%~ 70%, especially the reduction of RMSE is more than 45% at high wind speed (> 42 m/s). Furthermore, adding an independent rainfall estimate to the deep-learning model further enhanced the wind retrieval algorithm.

Towards the Sea Wind Measurement with the Airborne Scatterometer Having the Rotating-Beam Antenna Mounted over Fuselage

Extension of the existing airborne radars’ applicability is a perspective approach to the remote sensing of the environment. Here we investigate the capability of the rotating-beam radar installed over the fuselage for the sea surface wind measurement based on the comparison of the backscatter with the respective geophysical model function (GMF). We also consider the robustness of the proposed approach to the partial shading of the underlying water surface by the aircraft nose, tail, and wings. The wind retrieval algorithms have been developed and evaluated using Monte-Carlo simulations. We find our results promising both for the development of new remote sensing systems as well as the functional enhancement of existing airborne radars.

Support vector machine tropical wind speed retrieval in the presence of rain for Ku-band wind scatterometry

Abstract. Wind retrieval parameters, i.e. quality indicators and the two-dimensional variational ambiguity removal (2DVAR) analysis speeds, are explored with the aim to improve wind speed retrieval during rain for tropical regions. We apply the well-researched support vector machine (SVM) method in machine learning (ML) to solve this complex problem in a data-oriented regression. To guarantee the effectiveness of SVM, the inputs are extensively analysed to evaluate their appropriateness for this problem, before the results are produced. The comparisons between distributions and differences between data of rain-contaminated winds, corrected winds and good quality C-band winds illustrate that the rain-distorted wind distributions become more nominal with SVM, hence much reducing the rain-induced biases and error variance. Further confirmation is obtained from a case with synchronous Himawari-8 observation indicating rain (clouds) in the scene. Furthermore, the estimation of simultaneous rain rate is attempted with some success to retrieve both wind and rain. Although additional observations or higher resolution may be required to better assess the accuracy of the wind and rain retrievals, the ML results demonstrate benefits of such methodology in geophysical retrieval and nowcasting applications.

High-Resolution Polar Low Winds Obtained from Unsupervised SAR Wind Retrieval

High-resolution sea surface observations by spaceborne synthetic aperture radar (SAR) instruments are sorely neglected resources for meteorological applications in polar regions. Such radar observations provide information about wind speed and direction based on wind-induced roughness of the sea surface. The increasing coverage of SAR observations in polar regions calls for the development of SAR-specific applications that make use of the full information content of this valuable resource. Here we provide examples of the potential of SAR observations to provide details of the complex, mesoscale wind structure during polar low events, and examine the performance of two current wind retrieval methods. Furthermore, we suggest a new approach towards accurate wind vector retrieval of complex wind fields from SAR observations that does not require a priori wind direction input that the most common retrieval methods are dependent on. This approach has the potential to be particularly beneficial for numerical forecasting of weather systems with strong wind gradients, such as polar lows.

Hurricane Florence (2018): Long duration single‐ and dual‐Doppler observations and wind retrievals during landfall

AbstractA ground‐based C‐band Shared Mobile Atmospheric Research and Teaching (SMART) polarimetric radar and the National Weather Service WSR‐88D radar near Wilmington, North Carolina simultaneously observed Hurricane Florence (2018) as it made landfall as a category 1 hurricane. The slow translation of Florence allowed for more than 30 hr of data collection before, during and after the tropical cyclone came ashore. Nearly 26 hr of three‐dimensional wind retrievals every 6–10 min were constructed from the radar observations, providing an unprecedented view of the evolution of rainbands, the inner core and the eyewall of Hurricane Florence. This article describes the radar data, the procedures used for automated quality control, data processing and the wind retrievals that have been constructed. The location of the data and wind retrieval archive is given. These data can be used to study the dynamics and rainfall of Hurricane Florence, to quantify the impact of winds on the natural and built environment, to validate numerical simulations of the tropical cyclone, and as an educational resource for courses in radar and tropical meteorology.

CFOSAT Rotating Fan‐Beam Scatterometer Backscatter Measurement Processing

AbstractThe China‐France Oceanography Satellite (CFOSAT) was successfully launched on October 29, 2018, which carries two innovative payloads, that is, the surface waves investigation and monitoring (SWIM) instrument and the rotating fan‐beam scatterometer (RFSCAT). CFOSAT scatterometer (CSCAT) is dedicated to the monitoring of sea surface wind vectors and climate studies, but also for the applications over land and polar regions. CSCAT employs a “rotating fan‐beam” design rather than “pencil‐beam” approach and “fixed fan‐beam” approach adopted by the scatterometers in orbit before. In this paper, the data processing approach of CSCAT is described. The geolocation reference mapping model is employed in CSCAT slice geolocation. In noise processing, CSCAT adopts the method of weighted least squares polynomial surface fitting to conduct noise correction factor estimation adaptively. The attitude cube algorithm used for X integral factor computation is developed to obtain precise backscatter coefficients based on the radar equation. Because of the interdependency of the CSCAT data processing and the hardware configuration and onboard signal processing of the instrument, the instrument module configuration and signal processing design are also analyzed and presented comprehensively. Especially, the influence and compensation of Doppler frequency are introduced. The backscatter coefficient accuracy is estimated on the Amazon forest, and the results show that the backscatter coefficient accuracy of CSCAT on 25 × 25 km grids is less than 0.5 dB except that the near end and far end of the beam are slightly higher. The statistical results show the wind retrieval results meet the CFOSAT mission requirements.