Synthetic Aperture Radar Wind Research Articles

A New Approach for Ocean Surface Wind Speed Retrieval Using Sentinel-1 Dual-Polarized Imagery

A synthetic aperture radar (SAR) has the capability to observe ocean surface winds with a high spatial resolution, even under extreme conditions. The purpose of this work was to develop a new method for wind speed retrieval with the combination of SAR dual-polarized signals. In this study, we collected 28 tropical cyclone imageries observed using the Sentinel-1 dual-polarization mode. These imageries were collocated with radiometer wind speed measurements and reanalysis of wind vector products. In the new method, the wind speed was set as the output. VV-polarized (vertical transmitting–vertical receiving polarized) normalized radar cross section (NRCS), incident angle, VH-polarized (vertical transmitting–horizontal receiving polarized) NRCS, and wind direction were set as the inputs. Based on different output combinations, wind retrieval models were developed with multiple linear regression (MLR). According to the validation and comparison, the proposed models performed better than the traditional piecewise VH-polarization geophysical model functions (GMFs). The impact of thermal noise on the retrieval of low wind speeds (<10 m/s) could be partially reduced. The input of wind direction is unnecessary if the combination of VV- and VH-polarized imageries has been utilized. These results suggest that the use of MLR and the dual-polarization combination can improve SAR wind retrieval accuracy. Compared with SMAP measurements, our SAR retrievals can provide fine structures of TC wind fields.

Dual-Level Contextual Attention Generative Adversarial Network for Reconstructing SAR Wind Speeds in Tropical Cyclones

Synthetic Aperture Radar (SAR) imagery plays an important role in observing tropical cyclones (TCs). However, the C-band attenuation caused by rain bands and the problem of signal saturation at high wind speeds make it impossible to retrieve the fine structure of TCs effectively. In this paper, a dual-level contextual attention generative adversarial network (DeCA-GAN) is tailored for reconstructing SAR wind speeds in TCs. The DeCA-GAN follows an encoder–neck–decoder architecture, which works well for high wind speeds and the reconstruction of a large range of low-quality data. A dual-level encoder comprising a convolutional neural network and a self-attention mechanism is designed to extract the local and global features of the TC structure. After feature fusion, the neck explores the contextual features to form a reconstructed outline and up-samples the features in the decoder to obtain the reconstructed results. The proposed deep learning model has been trained and validated using the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric model product and can be directly used to improve the data quality of SAR wind speeds. Wind speeds are reconstructed well in regions of low-quality SAR data. The root mean square error of the model output and ECMWF in these regions is halved in comparison with the existing SAR wind speed product for the test set. The results indicate that deep learning methods are effective for reconstructing SAR wind speeds.

High-resolution calibrated and validated Synthetic Aperture Radar Ocean surface wind data around Australia

The dataset consists of ocean surface wind speed and direction at 10 m height and 1 km spatial resolution around the wider Australian coastal areas, spanning 4 years (2017 to 2021) of measurements from Sentinel-1 A and B imaging Synthetic Aperture Radar (SAR) platforms. The winds have been derived using a consistent SAR wind retrieval algorithm, processing the full Sentinel-1 archive in this region. The data are appropriately quality controlled, flagged, and archived as NetCDF files representing SAR wind field maps aligned with satellite along-track direction. The data have been calibrated against Metop-A/B Scatterometer buoy-calibrated, wind measurements and examined for potential changes in calibration over the duration of the data. The calibrated data are further validated by comparisons against independent Altimeter (Cryosat-2, Jason-2, Jason-3, and SARAL) wind speeds. Several methods for data access are also listed. The database is potentially useful for offshore industries (oil and gas, fisheries, shipping, offshore wind energy), public recreational activities (fishing, sailing, surfing), and protection and management of coasts and natural habitats.

Effect of precipitation on synthetic aperture radar hurricane wind field retrieval

C-band synthetic aperture radar (SAR) with all-weather observation capability is gradually being used for the high-resolution observation of hurricane. The extreme wind speed in hurricanes is retrieved by combining the co- and cross-polarized SAR signals. However, the heavy precipitation associated with hurricanes will seriously affect the retrieval of SAR sea surface wind speed. In this study, the effects of precipitation-related parameters and phenomena on wind speed retrieval under strong hurricane conditions were examined by using the data from co-located global precipitation measurement Dual-frequency Precipitation Radar, SAR on Sentinel-1 and Radarsat-2, and stepped frequency microwave radiometer. The results show that precipitation will increase the retrieval error of wind speed, and the effect of convective precipitation is more obvious than that of stratiform precipitation. When the wind speed is less than 30 m/s and the rain rate is less than 20 mm/h, the distribution of the melting layer has no obvious effect on SAR wind speed retrieval. Rain cells are mainly the product of convective precipitation and can also be produced when the storm top height is very low. An increase in the vertical-horizontal (VH) polarized normalized radar cross section (NRCS) in rain cells is usually greater than that of the vertical-vertical (VV) polarized NRCS because the sensitivity of the VH signal in a hurricane area is usually greater than that of the VV signal. When the rain rate is low, the difference between the retrieved wind speed inside and outside the rain cells is within the error range of hurricane wind speed retrieval. When the rain rate is further increased, the difference will exceed 10 m/s.

A Joint Method for Wave and Wind Field Parameter Inversion Combining SAR with Wave Spectrometer Data

Synthetic aperture radar (SAR) and wave spectrometer are common methods for observing the ocean wind field and waves on the sea surface, but both have limitations. Due to the influence of velocity bunching modulation, SAR wave observation is limited by the azimuth cut-off phenomenon. Meanwhile, SAR relies on an external wind direction source, so it is difficult for SAR to observe wind fields independently. There is no azimuthal cut-off phenomenon when the spectrometer observes the sea surface, but its azimuth resolution is much lower than SAR. Combining the above characteristics, the joint inversion of SAR and wave spectrometer for sea-state parameters becomes possible. In this paper, a joint method for wind field and wave parameter inversion combining SAR with spectrometer data is proposed. In this method for wave parameter inversion, the wave spectrum of the spectrometer was used as a first-guess spectrum of SAR wave spectrum inversion, the fit wave spectrum obtained by joint inversion and the modified wave spectrum of the spectrometer were fused to form a new spectrum, and the wave parameters were calculated. For wind field parameter inversion, wind direction was obtained using a wave spectrometer, and was used as the input of the SAR wind field inversion. Wind speed was obtained using the CMOD5.N method. Collocated data from Sentinel-1 SAR, the wave spectrometer, the National Data Buoy Center (NDBC) buoy, and the European Centre for Medium-Range Weather Forecasting (ECMWF) are used to verify the proposed method. Sea parameters retrieved from the spectrometer and SAR are compared to the buoy and ECMWF data. The results show that the root mean square errors of significant wave height, mean wave period, wind direction and wind speed are 0.37 m, 1.02 s, 22.7° and 1.06 m/s with ECMWF data, and 0.35 m, 0.78 s, 18.22° and 0.92 m/s with buoy data, respectively. By comparing the inversion results with the L2 products of SAR and SWIM, it can be concluded that the inversion accuracy of the joint method is higher in the middle and low sea conditions. Therefore, the joint inversion method for wind field and wave parameters proposed in this paper has good results, which verifies the accuracy of the joint inversion method.

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.

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.

A Joint Inversion Method of Wave and Wind Field Parameters Based on SAR SLC Data

Synthetic aperture radar (SAR) is an important method for observing the ocean wind field and waves on the sea surface. For most SAR wind field and wave parameter inversion methods, third-party data are required to provide initial information, such as reference wind direction and first guess spectrum. This makes it impossible for SAR to observe ocean wind fields and waves independently. To solve this problem, this letter proposes a joint inversion method based on Sentinel-1 SAR SLC data. In this method, the wind direction is inverted by the LG method, and the wave propagation direction extracted by the SAR image cross-spectrum is used to remove the 180° ambiguity of the wind direction inversion result. The inverted wind direction is used as the reference wind direction in the CMOD5.N method to invert wind speed. Then, the first guess spectrum constructed using the inverted wind speed and inverted wind direction is input into the MPI method to invert the wave spectrum and wave parameters. The wind field and wave parameter inversion results are compared with the European Center for Medium-Range Weather Forecast (ECMWF) ERA5 reanalysis data. The wind field and wave parameter inversion results are in good agreement with the ERA5 data under medium or high sea conditions.

Characteristics of suspended sediment in Sentinel-1 synthetic aperture radar observations

ABSTRACT The main purpose of this study is to quantitatively examine the characteristics of suspended sediment on synthetic aperture radar (SAR) observations of sea roughness. In this work, seven Sentinel-1 (S-1) SAR images in 2019 acquired in the Interferometric Wide-swath (IW) mode at vertical-vertical (VV) polarization channel were collected, where Yangtze River freshwater dominants and suspended sediment concentration (SSC) are higher in continental shelf of East China Sea (ECS). These images were collocated with waves simulated from WAVEWATCH-III (WW3) and SSC inverted from the Haiyang-1C (HY-1C) Coastal Zone Imager (CZI) image. The comparison of WW3-simulated SWH yielded a 0.45-m root-mean-square error (RMSE) with a 0.78 correlation (COR) when comparing the simulations with the measurements from Haiyang-2B (HY-2B). The SAR-measured normalized radar cross-section (NRCS) is linearly related with the HY-1 C SSC at SSC > 1200 mg l–1, which is caused by the change of dielectric constant of sea water and viscosity. Moreover, the NRCS is simulated by the three-scale radar backscattering model. It is generally found that HY-1 C SSC enhances the NRCS in difference between simulations and observations up to 8 dB at SSC of 2000 mg l–1, which could distort the accuracy of SAR wind retrieval.

Estimating Tropical Cyclone Wind Structure and Intensity From Spaceborne Radiometer and Synthetic Aperture Radar

We present a relatively simple method to estimate tropical cyclone (TC) surface wind structure (34-, 50-, and 64-kt wind radii) and intensity [maximum wind speed (MWS)] from wind fields acquired from the L-band SMAP radiometer and C-band Sentinel-1A/B and RADARSAT-2 synthetic aperture radar (SAR) between 2015 and 2020. The radiometer and SAR-derived wind radii and MWS are systematically compared with the best-track estimates. The root-mean-square errors (RMSEs) of R34, R50, and R64 are 31.2, 21.8, and 17.0 nmi (1 nmi = 1.852 km) for radiometer, and 21.7, 16.5, and 16.3 nmi for SAR, respectively. These error values are smaller than the averaged best-track uncertainty estimates for the three wind radii. Compared with the best-track reports, the bias and RMSE for the MWS estimates are -0.2 m/s and 5.8 m/s for radiometer, and 4.4 m/s and 9.1 m/s for SAR, respectively. These results are for the wind speeds in the range of 17-80 m/s. For the two typical TCs (Lionrock and Noru) in the Northwest Pacific Ocean, our results show that a combination of the radiometer and SAR wind data acquired within a very short time interval has the potential to simultaneously obtain reasonable measurements of the wind radii and intensity parameters. Moreover, for a TC with a long lifecycle, such as Typhoon Noru, we demonstrate that the high-resolution and multitemporal synergistic observations from SAR and radiometer are valuable for studying fine-scale features of the wind field and characteristics of wind asymmetry associated with intensity change, as well as the evolution of TC surface wind structure and intensity.

Optimal Interpolation Model for Synthetic Aperture Radar Wind Retrieval

The variational model inversion (VAR) method for synthetic aperture radar (SAR) wind retrieval based on Bayesian theory can overcome the limitation of the traditional wind streak algorithm by introducing background wind and considering all sources of error, but its optimal solution is unstable and the time latency is long. In this paper, we propose a new wind retrieval method by applying optimal interpolation (OI) theory to construct a formula that considers the SAR information, background information coming from the numerical prediction model, and their associated wellcharacterized errors. The retrieved wind vector can be acquired by the analytic solution of the OI formula. Experimental results of the simulation data and Sentinel-1 SAR data show that the OI wind retrieval method can effectively reduce the background wind error and is more sensitive to wind speed than wind direction. Compared with other methods, the accuracy of the OI method is similar to that of the VAR method, but significantly higher than that of the direct wind retrieval (DIRECT) method. The time latency of the OI method is the shortest, and the calculation efficiency is much higher than that of the VAR method. The OI method can be effectively applied to SAR wind retrieval and has unique advantages.Sea surface wind is a crucial parameter for studying the physical quantity of the sea surface and plays an important role in many fields such as weather forecasts [1, 2], wind energy resource management [3,4], wave numerical simulation [5,6], and oil spill monitoring [7,8]. Because of the limited spatial and temporal coverage, high-precision sea surface wind acquired from buoys, ships and offshore platforms are not meeting the growing demand [9].In recent decades, with the development of satellite remote sensing, the technology of acquiring sea surface wind using satellite sensor detection data has gradually matured and improved. Among various satellite sensors, microwave radiometers and scatterometers play an important role in providing global sea surface wind. However, microwave radiometers and scatterometers can acquire only low spatial resolution (12.5-50 km) sea surface wind. This relatively low spatial resolution is

US East Coast synthetic aperture radar wind atlas for offshore wind energy

Abstract. We present the first synthetic aperture radar (SAR) offshore wind atlas of the US East Coast from Georgia to the Canadian border. Images from RADARSAT-1, Envisat, and Sentinel-1A/B are processed to wind maps using the geophysical model function (GMF) CMOD5.N. Extensive comparisons with 6008 collocated buoy observations of the wind speed reveal that biases of the individual systems range from −0.8 to 0.6 m s−1. Unbiased wind retrievals are crucial for producing an accurate wind atlas, and intercalibration of the SAR observations is therefore applied. Wind retrievals from the intercalibrated SAR observations show biases in the range of to −0.2 to 0.0 m s−1, while at the same time improving the root-mean-squared error from 1.67 to 1.46 m s−1. The intercalibrated SAR observations are, for the first time, aggregated to create a wind atlas at the height 10 m a.s.l. (above sea level). The SAR wind atlas is used as a reference to study wind resources derived from the Wind Integration National Dataset Toolkit (WTK), which is based on 7 years of modelling output from the Weather Research and Forecasting (WRF) model. Comparisons focus on the spatial variation in wind resources and show that model outputs lead to lower coastal wind speed gradients than those derived from SAR. Areas designated for offshore wind development by the Bureau of Ocean Energy Management are investigated in more detail; the wind resources in terms of the mean wind speed show spatial variations within each designated area between 0.3 and 0.5 m s−1 for SAR and less than 0.2 m s−1 for the WTK. Our findings indicate that wind speed gradients and variations might be underestimated in mesoscale model outputs along the US East Coast.

Southern Caspian Sea wind speed retrieval from C-band Sentinel-1A SAR images

ABSTRACT Synthetic aperture radar (SAR) imagery has shown the good capability for ocean wind speed retrieval. However, SAR wind speed retrieval above enclosed seas has been investigated rarely. In this study, the performance of geophysical model functions (GMFs) for the southern Caspian Sea wind speed retrieval from VV-polarized C-band Sentinel-1A SAR images is investigated. The used GMFs are recently developed empirical CMOD7 and C_SARMOD2, and two theoretical GMFs, which are derived in a simplified form based on the small-slope approximation (SSA) method with Hwang-Elfouhaily (H-E) and Hwang-McDaniel (H-M) composite sea spectra. Required wind directions for retrievals are obtained from wind streaks on SAR images by the local gradient (LG) method, a weather prediction model and in situ measurements. Estimations of all GMFs show good statistical agreements with predictions and in situ measurements. At 25-km resolution, all retrievals have mean biases better than ±0.7 m s−1, and root-mean-square deviations (RMSDs) and standard deviations below 2 m s−1 relative to predicted wind speeds. At 1-km resolution, using in situ measured directions, root-mean-square errors (RMSEs) of retrievals relative to in situ measured wind speeds are below 1.9 m s−1, and mean biases are better than ±0.7 m s−1. Of all the GMFs, CMOD7 has the best performance with the smallest RMSEs, and C_SARMOD2 has a good performance, except for very low winds. The theoretical GMF with H-M spectrum has a better performance than the model with H-E spectrum, and both of them can be used to retrieve Caspian Sea wind speeds from Sentinel-1A data, except for the crosswind condition with radar incident angle of about 45°.

On accuracy of SAR wind speed retrieval in coastal area

In coastal region, the overestimation of wind speed are observed in SAR wind speed retrievals and the contamination from land and ships are considered as one of the main reasons. A novel method for calculating the value of normalized radar cross section (NRCS) cell has been developed to improve high-resolution synthetic aperture radar (SAR) wind retrieval in the case of contamination. The ratio of median and mean (Rmmpp) has been proposed to assess the effect of the distribution of NRCS on wind retrievals quantitatively. In this research, Sentinel-1 interferometric wide-swath (IW) dual polarization SAR data were collocated with in situ wind measurements from National Data Buoy Center (NDBC) for comparison. CMOD5, CMOD7 and the C-band Cross-Polarization Ocean (C-2PO) were used for wind retrievals. The statistic validation results show that the proposed method is more robust than the conventional method for wind speed retrievals. In addition, the Rmmpp could be used as an indicator for the reliability of SAR wind and the accuracy of wind speed increase with the Rmmpp increasing from 0.2 to 1.0 in the coastal area.

Using sentinel-1A SAR wind retrievals for enhancing scatterometer and radiometer regional wind analyses

ABSTRACTScatterometer surface wind speed and direction observations in combination with radiometer wind speeds allow to generate surface wind analyses with high space and time resolutions over global as well as at regional scales. Regarding scatterometer sampling schemes and physics, the resulting surface wind analyses suffer from lack of accuracy in areas near coasts. The use of the synthetic aperture radar (SAR) onboard the Sentinel-1A satellite attempts to address the enhancement of surface wind analyses issues. In this study, SAR wind speeds and directions retrieved from backscatter coefficients acquired in interferometric wide (IW) swath mode are used. Their accuracy is determined through comprehensive comparisons with moored buoy wind measurements. SAR and buoy winds agree well at offshore and nearshore locations. The statistics characterizing the comparison of SAR and buoy wind speeds and directions are of the same order as those obtained from scatterometer (Advanced SCATterometer (ASCAT) and RapidScat) and buoy wind comparisons. The main discrepancy between SAR and buoy data are found for high wind speeds. SAR wind speeds exceeding 10 m s–1 tend to be underestimated. A similar conclusion is drawn from SAR and scatterometer wind speed comparisons. It is based on the underestimation of SAR backscatter coefficient (σ°) with respect to σ° estimated from scatterometer winds and the geophysical model function (GMF) named CMOD-IFR2 (Ifremer C band MODel). New SAR wind speeds are retrieved using CMOD-IFR2. The corrected SAR retrievals allow better determination of the spatial characteristics of surface wind speeds and of the related wind components in near-coast areas. They are used for enhancing the determination of the spatial structure function required for the estimation of wind fields gridded in space and time at the regional scale. The resulting wind fields are only determined from scatterometer wind observations in combination with radiometer retrievals. Their qualities are determined through comparisons with SAR wind speeds and directions, and through their application for determination of wind power off Brittany coasts.

Applications of satellite winds for the offshore wind farm site Anholt

Abstract. Rapid growth in the offshore wind energy sector means more offshore wind farms are placed closer to each other and in the lee of large land masses. Synthetic aperture radar (SAR) offers maps of the wind speed offshore with high resolution over large areas. These can be used to detect horizontal wind speed gradients close to shore and wind farm wake effects. SAR observations have become much more available with the free and open-access data from European satellite missions through Copernicus. Examples of applications and tools for using large archives of SAR wind maps to aid offshore site assessment are few. The Anholt wind farm operated by the utility company Ørsted is located in coastal waters and experiences strong spatial variations in the mean wind speed. Wind speeds derived from the Supervisory Control And Data Acquisition (SCADA) system are available at the turbine locations for comparison with winds retrieved from SAR. The correlation is good, both for free-stream and waked conditions. Spatial wind speed variations along the rows of wind turbines derived from SAR wind maps prior to the wind farm construction agree well with information gathered by the SCADA system and a numerical weather prediction model. Wind farm wakes are detected by comparisons between images before and after the wind farm construction. SAR wind maps clearly show wakes for long and constant fetches but the wake effect is less pronounced for short and varying fetches. Our results suggest that SAR wind maps can support offshore wind energy site assessment by introducing observations in the early phases of wind farm projects.

New European wind atlas offshore

The New European Wind Atlas (NEWA) is a joint research effort from eight European countries, co-funded under the ERANET Plus Program. The projects’s final aim is the creation and publication of an state-of-the-art electronic European wind atlas. An offshore wind atlas extending 100 km from the European coasts is foreseen within the project, based on mesoscale modelling and various observational datasets. Satellite wind retrievals from scatterometers and Synthetic Aperture Radar (SAR) instruments are used to validate offshore modelled wind fields and to identify the optimal model configuration. The aim is to present the initial outputs from the offshore wind atlas produced by the Weather and Research Forecasting (WRF) model, still in pre-operational phase, the METOP-A/B Advanced Scatterometer (ASCAT) and SAR derived winds. Different experiments were established to evaluate the model sensitivity for the various domains covered by the NEWA offshore atlas. ASCAT winds are used to assess the performance of the WRF-derived offshore atlas. In addition, ASCAT and SAR winds were used to create an offshore atlases, where various spatial wind characteristics, such as channelling and lee effects from complex coastal topographical features, are visualised.

Wind Direction Extraction from SAR in Coastal Areas

This paper aims to illustrate and test a method, based on the Two-Dimensional Continuous Wavelet Transform (2D-CWT), developed to extract the wind directions from the Synthetic Aperture Radar (SAR) images. The knowledge of the wind direction is essential to retrieve the wind speed by using the radar-backscatter versus wind speed algorithms. The method has been applied to 61 SAR images from different satellites (Envisat, COSMO-SkyMed, Radarsat-2 and Sentinel-1A,B), and the results have been compared with the analysis wind fields from the European Centre for Medium-range Weather Forecasts (ECMWF) model, with in situ reports and with scatterometer data when available. The 2D-CWT method provides satisfactory results, both in areas a few kilometres from the coast and offshore. It is reliable as it produces good direction estimates, no matter what the characteristics of the SAR are. Statistics reports a success in the SAR wind direction estimates in 95% of cases (in 83% of cases the SAR-ECMWF wind direction difference is < ± 20 ∘ , in 92 % < ± 30 ∘ ) with a mean directional bias B θ < 7 ∘ . The SAR derived wind directions cannot be said to be validated, as the data available at present cannot be really representative of the wind field in the coastal area. However, the figures given by SAR winds are highly valuable even not properly validated, providing an independent and unique view of the spatial variability of the wind over the sea, which is possible by using the 2D-CWT method to derive the wind directions.

Ocean Wind and Current Retrievals Based on Satellite SAR Measurements in Conjunction with Buoy and HF Radar Data

A total of 168 fully polarimetric synthetic-aperture radar (SAR) images are selected together with the buoy measurements of ocean surface wind fields and high-frequency radar measurements of ocean surface currents. Our objective is to investigate the effect of the ocean currents on the retrieved SAR ocean surface wind fields. The results show that, compared to SAR wind fields that are retrieved without taking into account the ocean currents, the accuracy of the winds obtained when ocean currents are taken into account is increased by 0.2–0.3 m/s; the accuracy of the wind direction is improved by 3 – 4 ° . Based on these results, a semi-empirical formula for the errors in the winds and the ocean currents is derived. Verification is achieved by analysis of 52 SAR images, buoy measurements of the corresponding ocean surface winds, and high-frequency radar measurements of ocean currents. Results of the comparisons between data obtained by the semi-empirical formula and data measured by the high-frequency radar show that the root-mean-square error in the ocean current speed is 12.32 cm/s and the error in the current direction is 6.32°.

Assimilation of Typhoon Wind Field Retrieved from Scatterometer and SAR Based on the Huber Norm Quality Control

Observations of sea surface wind field are critical for typhoon prediction. The scatterometer observation is one of the most important sources of sea surface winds, which provides both wind speed and wind direction information. However, the spatial resolution of scatterometer wind is low. Synthetic Aperture Radar (SAR) can provide a more detailed wind structure of the tropical cyclone. In addition, the cross-polarization observation of SAR can provide more detailed information of high speed wind (>25 m·s − 1 ) than the scatterometer. Nevertheless, due to the narrow swath of SAR, the number of retrieved sea surface wind data used in the data assimilation is limited, and another limitation of SAR wind observation is that it does not provide true wind direction information. In this paper, the joint assimilation of the Advanced Scatterometer (ASCAT) wind and Sentinel-1 SAR wind was investigated. Another limitation in the current operational typhoon prediction is the inefficient quality control (QC) method used in the data assimilation since a large number of high speed wind observations was rejected by the traditional Gaussian distribution QC. We introduce the Huber norm distribution quality control (QC) into the data assimilation successfully. A numerical simulation experiment of typhoon by Lionrock (2016) is conducted to test the proposed method. The experimental results showed that the new quality control scheme not only greatly increases the availability of wind data in the area of the typhoon center, but also improves the typhoon track prediction, as well as the intensity prediction. The joint assimilation of scatterometer and SAR winds does have a positive impact on the typhoon prediction.