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The Tianmu-1 constellation is mainly based on BeiDou/Global Navigation Satellite System radio occultation (GNSS-RO) and BeiDou/GNSS reflectometry (GNSS-R) remote sensing technology, which can simultaneously achieve integrated stereo detection of GNSS-RO and GNSS-R. It is the first commercial GNSS remote sensing detection constellation in the world to achieve integrated business detection of “land surface, ocean, three-dimensional atmosphere, and ionized environment.” Furthermore, it is the first GNSS remote sensing detection constellation in China that is compatible with receiving the five major GNSS systems (i.e., global positioning system, BeiDou, Galileo, GLONASS, and Quasi-Zenith Satellite System). This study used Tianmu-1 GPS-R/BDS-R/GAL-R/GLO-R global wind speed data from July 2023 to December 2023, and used five datasets as evaluation data, including the ERA5 reanalysis, CYGNSS L2, SMOS, CCMP, and NDBC buoy wind speed products, to present the results of the mission's first evaluation of sea surface wind speed estimates. The results show that the Tianmu-1 retrieval wind speed has good consistency with the CYGNSS verification wind speed. When the wind speed range is 0–35 m/s, its minimum RMSE can reach 1.74 m/s. The RMSE of the Tianmu-1 retrieval wind speed and the other four verification wind speeds (ERA5, CCMP, SMOS_SCA, and SMOS_SCD) are 2.25, 2.33, 2.74, and 3.17 m/s, respectively. For the wind speed products of the four systems in the Tianmu-1 constellation, GAL showed the best accuracy results. In addition, a comparative verification was conducted with buoy wind speeds, the BDS system demonstrated the best accuracy results, with the RMSE of 1.97 m/s.

Increasing atmospheric carbon dioxide (CO2) from human activities underscores the need to understand air–sea CO2 flux in marine environments, particularly in marginal seas like the South China Sea (SCS). This research analyzes the spatial and temporal patterns of air–sea CO2 flux across four typical regions of the SCS: the northern SCS, western SCS, SCS basin, and northeastern SCS. Our results show that the SCS serves as a carbon source from spring to autumn and shifts to a carbon sink in winter. The northern SCS exhibits strong carbon sink behavior during winter, transitioning to a source in warmer months, while the western SCS and SCS basin consistently act as carbon sources year-round, with summer peaks. The northeastern SCS acts as a source in warmer months, becoming a weak sink in winter. Partial correlation analysis reveals that temperature and wind speed significantly influence air–sea CO2 flux, though regional differences exist. Notably, chlorophyll-a in the northern SCS is negatively correlated with air–sea CO2 flux, indicating that high primary productivity enhances CO2 absorption, whereas other regions show contrasting relationships. These findings provide valuable insights into the complex carbon cycle mechanisms in the SCS. [ABSTRACT FROM AUTHOR]

This study investigates the interannual variations in regional mean sea levels (MSLs) of the northeast Asian marginal seas (NEAMS) during August, focusing on the role of typhoon activity from 1993 to 2019. The NEAMS are connected to the Pacific through the East China Sea (ECS) and narrow, shallow straits in the east, where inflow from the southern boundary (ECS), unless balanced by eastern outflow, leads to significant convergence or divergence, as well as subsequent changes in regional MSLs. Satellite altimetry and tide-gauge data reveal that typhoon-induced Ekman transport plays a key role in MSL variability, with increased inflow raising MSLs during active typhoon seasons. In contrast, weak typhoon activity reduces inflow, resulting in lower MSLs. This study's findings have significant implications for coastal management, as the projected changes in tropical cyclone frequency and intensity due to climate change could exacerbate sea level rise and flooding risks. Coastal communities in the NEAMS region will need to prioritize enhanced flood defenses, early warning systems, and adaptive land use strategies to mitigate these risks. This is the first study to link typhoon frequency directly to NEAMS MSL variability, highlighting the critical role of wind-driven processes in regional sea level changes. [ABSTRACT FROM AUTHOR]

This study investigates the interannual variations in regional mean sea levels (MSLs) of the northeast Asian marginal seas (NEAMS) during August, focusing on the role of typhoon activity from 1993 to 2019. The NEAMS are connected to the Pacific through the East China Sea (ECS) and narrow, shallow straits in the east, where inflow from the southern boundary (ECS), unless balanced by eastern outflow, leads to significant convergence or divergence, as well as subsequent changes in regional MSLs. Satellite altimetry and tide-gauge data reveal that typhoon-induced Ekman transport plays a key role in MSL variability, with increased inflow raising MSLs during active typhoon seasons. In contrast, weak typhoon activity reduces inflow, resulting in lower MSLs. This study’s findings have significant implications for coastal management, as the projected changes in tropical cyclone frequency and intensity due to climate change could exacerbate sea level rise and flooding risks. Coastal communities in the NEAMS region will need to prioritize enhanced flood defenses, early warning systems, and adaptive land use strategies to mitigate these risks. This is the first study to link typhoon frequency directly to NEAMS MSL variability, highlighting the critical role of wind-driven processes in regional sea level changes.

Data-driven predictions of marine environmental variables are typically focused on single variables. However, in real marine environments, there are correlations among different oceanic variables. Additionally, sea–air interactions play a significant role in influencing the evolution of the marine environment. Both internal dynamics and external drivers contribute to these changes. In this study, a data-driven model is proposed using sea surface height anomaly (SSHA), sea surface temperature (SST), and sea surface wind (SSW) in the Bohai Sea. This model combines multivariate empirical orthogonal functions (MEOFs) with long and short-term memory (LSTM). MEOF analysis is used on the multivariate dataset of SSHA and SST, considering the correlation among sea surface variables. SSW is introduced as a predictor to enhance the predictability of the multivariate sea surface model. In the case of the Bohai Sea, the comparative tests of the model without wind field effect, the fully coupled model, and the proposed prediction model were carried out. MEOF analysis is employed in comparative experiments for oceanic variables, atmospheric variables, and combined atmospheric and oceanic variables. The results demonstrate that using wind field as a predictor can improve the forecast accuracy of SSHA and SST in the Bohai Sea. The root mean square errors (RMSE) for SSHA and SST in a 7-day forecast are 0.016 m and 0.3200 °C, respectively. [ABSTRACT FROM AUTHOR]

Gaofen-3-02 (GF3-02) is the first C-band synthetic aperture radar (SAR) satellite with terrain observation with progressive scans of SAR (TOPSAR) imaging mode in China, which plays an essential role in marine environment monitoring. Given the weak scattering characteristics of the ocean, the system thermal noise superimposed on SAR images has significant interference, especially in cross-polarization channels. Noise-Equivalent Sigma-Zero (NESZ) is a measure of the sensitivity of the radar to areas of low backscatter. The NESZ is defined to be the scattering cross-section coefficient of an area which contributes a mean level in the image equal to the signal-independent additive noise level. For TOPSAR, NESZ exhibits the shape of the SAR scanning gain curve in the azimuth and the shape of the antenna pattern in the range. Therefore, the accurate measurement of NESZ plays a vital role in the application of spaceborne SAR sea surface cross-polarization data. This paper proposes a theoretical calculation method for the NESZ curve in GF3-02 TOPSAR mode based on SAR noise inner calibration data and the imaging algorithm. A method for correcting the error existing in the theoretical curve of NESZ is also proposed according to the relationship between sea surface backscattering and wind speed and the same characteristics of target scattering in the overlapping area of adjacent sub-swaths. According to assessment with wide-swath TOPSAR cross-polarization data, the GF3-02 TOPSAR mode has a very low thermal noise level, which is better than −33 dB at the edge of each beam, and controlled below −38 dB at the center of the beam. The two-dimensional reference curves of the NESZ of each beam are provided to the GF3-02 TOPSAR users. After discussing the relationship between normalized radar cross section (NRCS) and wind speed, we provide a formula for NRCS related to wind speed and radar incidence angle. Compared with the NRCS derived from this formula and the NESZ-subtracted NRCS of SAR images, the bias is −0.004 8 dB, the Root Mean Square Error is 1.671 dB and the correlation coefficient is 0.939. [ABSTRACT FROM AUTHOR]

The ocean surface current influences the roughness of the sea surface, subsequently affecting the scatterometer's measurement of wind speed. In this study, the effect of surface currents on ASCAT-retrieved winds is investigated based on in-situ observations of both surface winds and currents from 40 open ocean moored buoys in the tropical and mid-latitude oceans. A total of 28,803 data triplets, consisting of buoy-observed wind vectors, current vectors, and ASCAT Level 2 wind vectors, were collected from the dataset spanning over 10 years. It is found that the bias between scatterometer-retrieved wind speed and buoy-observed wind speed is negatively correlated with the ocean surface current speed. The wind speed bias is approximately 0.96 times the magnitude of the downwind surface current. The root-mean-square error between the ASCAT wind speeds and buoy observations is reduced by about 15% if rectification with ocean surface currents is involved. Therefore, it is essential to incorporate surface current information into wind speed calibration, particularly in regions with strong surface currents. [ABSTRACT FROM AUTHOR]

Sea surface winds and waves are very important phenomena that exist in the air–sea boundary layer. With the advent of climate change, cascade effects are bringing more attention to these phenomena as warmer sea surface temperatures bring about stronger winds, thereby altering global wave conditions. Synthetic aperture radar (SAR) is a powerful sensor for high-resolution surface wind and wave observations and has accumulated large quantities of data. Furthermore, deep learning methods have been increasingly utilized in geoscience, especially the inversion of ocean information from SAR imagery. Here, we propose a method to invert various parameters of ocean surface winds and waves using Sentinel-1 SAR IW mode data. To ensure this method is more robust and scalable, we augmented the input data with dual-polarized SAR imagery, an incident angle, and a more constrained homogeneity test. This method adopts a deeper structure in order to retrieve more wind and wave parameters, and the use of residual networks can accelerate training convergence and improve regression accuracy. Using 1600 training samples filtered by a novel homogeneity test and with significant wave heights between 0 and 10 m, results from error parameters including the root mean square error (RMSE), scatter index (SI), and correlation coefficient (COR) show the great performance of this proposed method. The RMSE is 0.45 m, 0.76 s, and 1.90 m/s for the significant wave height, mean wave period, and wind speed, respectively. Furthermore, the temporal variation and spatial distribution of the estimates are consistent with China–France Oceanography Satellite (CFOSAT) observations, buoy measurements, WaveWatch3 regional model data, and ERA5 reanalysis data. [ABSTRACT FROM AUTHOR]

A neural network (NN) algorithm for the sea surface wind speed retrieval from the MTVZA-GYa Russian satellite microwave radiometer measurements is presented. The algorithm is based on the physical modeling of the brightness temperature of microwave radiation in the ocean–atmosphere system using new theoretical geophysical model functions of the dependence of ocean radiation on wind speed. The algorithm is validated by comparing the wind fields retrieved from the MTVZA-GYa data with those obtained from the AMSR2 radiometer (Japan) for different areas of the World Ocean with a difference in measurement time not exceeding five minutes. The validation has shown that the NNs with a number of neurons from 3 to 8 provide the smallest root-mean-square difference between the AMSR2 and MTVZA-GYa retrieved wind speeds, namely 1.6 m/s. When mapping the wind speed in tropical cyclones, the best fit to the wind fields from the AMSR2 data is obtained using the NN with . [ABSTRACT FROM AUTHOR]

There are generally high chlorophyll-a concentrations (Chl-a) where upwelling is prevalent. High Chl-a is often observed in upwelling areas of the northeastern coast of Hainan Island during the summer. Using the satellite remote sensing data, including sea surface temperature, sea surface wind and Chl-a data from 2009 to 2022, we analyze the spatial-temporal and inter-annual variation of Chl-a on the northeastern coast of Hainan Island. Then, the possible influence of environmental factors on Chl-a are further examined by using satellite data, as well as Ekman transport and Ekman pumping velocity derived from the wind products. Finally, the key factors affecting the changes of Chl-a are discussed by correlation analysis. The results show the significant interannual variation of Chl-a in the region, with the maximum of summer Chl-a during the prevalent period of upwelling. The correlation analyses reveal that there is a higher correlation coefficient between the summer Chl-a and the upwelling index (i.e., upwelling regional temperature anomaly), suggesting the role played by upwelling in the summer high Chl-a is more important than the other environmental factors. It is speculated that the summer Chl-a increase is not only influenced by wind-induced upwelling but also by the upwelling caused by tidal mixing, large-scale circulation, topographic changes, and typhoon events. [ABSTRACT FROM AUTHOR]

In the Adriatic Sea, the sea surface wind forecasts are often underestimated, with detrimental effects on the accuracy of sea level and storm surge predictions. Among the various causes, this mainly depends on the meteorological forcing of the wind. In this paper, we try to improve an existing numerical method, called "wind bias mitigation", which relies on scatterometer wind observations to determine a multiplicative factor Δ w , whose application to the model wind reduces its inaccuracy with respect to the scatterometer wind. Following four different mathematical approaches, we formulate and discuss seven new expressions of the multiplicative factor. The eight different expressions of the bias mitigation factor, the original one and the seven formulated in this study, are assessed with the aid of four datasets of real sea surface wind events in a variety of sea level conditions in the northern Adriatic Sea, several of which gave rise to high water events in the Venice Lagoon. The statistical analysis shows that some of the seven new formulations of the wind bias mitigation factor are able to lower the model-scatterometer bias with respect to the original formulation. For some other of the seven new formulations, the absolute bias, with respect to scatterometer, of the mitigated model wind field, results lower than that supplied by the unmodified model wind field in 81% of the considered storm surge events in the area of interest, against the 73% of the original formulation of the wind bias mitigation. This represents an 11% improvement in the bias mitigation process, with respect to the original formulation. The best performing of the seven new wind bias mitigation factors, that based on the linear least square regression of the squared wind speed ( L L S R E ), has been implemented in the operational sea level forecast chain of the Tide Forecast and Early Warning Centre of the Venice Municipality (CPSM), to provide support to the operation of the MO.SE. barriers in Venice. [ABSTRACT FROM AUTHOR]

Understanding the sea surface wind structure during tropical cyclones (TCs) is the key for study of ocean response and parameterization of air‐sea surface in numerical simulation. However, field observations are scarce. In 2019, three wave gliders were deployed in the South China Sea and the adjacent Western Pacific region, which acquired sea surface wind structure of eight TCs. Analysis of the field data suggests that the wave glider‐observed surface winds are consistent with most analysis/reanalysis data (i.e., ERA5, Cross‐Calibrated Multi‐Platform, and National Centers for Environmental Prediction‐Global Data Assimilation System) and Soil Moisture Active Passive. Both wave glider observations and analysis/reanalysis data indicate that TC wind fields induce an obvious increase in speed toward the sea surface together with a sharp change in direction, showing an asymmetric wind structure which is sensitive to TC translation speed and intensity. Larger mean values of wind speed and inflow angle are located on the right side along TC tracks. The inflow angle shows a highly dynamic dependence on the radial distance from the TC center, the TC intensity, as well as the TC‐relative azimuth. Comparisons between field observations and theoretical models indicate that the most widely used, ideal TC wind profile models can largely represent the observed sea surface wind structure, but generally underestimate the wind speed due to lack of consideration of background wind. Moreover, simple ideal models (e.g., the modified Rankine vortex model) may outperform complex models when accurate information of TCs is limited. Wave glider observations have potential for better understanding of air‐sea exchanges and for improvements of the corresponding parameterization schemes. Plain Language Summary: Tropical cyclones (TCs) are destructive synoptic‐scale phenomena. The TC‐induce sea surface wind field is important for storm surge modeling and coastal flooding forecasting. In this study, we analyze sea surface wind responses to the passages of eight TCs, based on observational data from three wave gliders deployed in the South China Sea and the adjacent Western Pacific Ocean, as well as to evaluate the performance of the widely used analytical surface wind models. The wave glider observations are found in agreement with most analysis/reanalysis data (i.e., ERA5, Cross‐Calibrated Multi‐Platform, and National Centers for Environmental Prediction‐Global Data Assimilation System) and Soil Moisture Active Passive, which enhance confidence in the use of sea surface wind data. Field observations and analysis/reanalysis data both show a strong variability of TC inflow angle, which is associated with the radial distance from the TC center, the TC intensity, as well as the TC‐relative azimuth. Comparisons between field observation and theoretical models further indicate that the idealized TC wind profile models can largely reproduce the observed sea surface wind, but the simpler ideal model (i.e., modified Rankine vortex model) likely to have a better performance than complex models when the accurate information of TCs is limited to the maximum wind speed and the maximum wind speed radius. Key Points: High‐frequency observations of sea surface winds from wave gliders are in accordance with ERA5, Cross‐Calibrated Multi‐Platform, and National Centers for Environmental Prediction‐Global Data Assimilation System, as well as Soil Moisture Active PassiveTropical cyclone (TC) Inflow angle depends on radial distance from TC center, TC intensity, and TC‐relative azimuthSimple ideal wind models (e.g., the modified Rankine vortex model) outperforms complex models when TC information is limited [ABSTRACT FROM AUTHOR]

The Chinese marine dynamic environment satellite HY-2B was launched in October 2018 and carries a Ku-band scatterometer. This paper focuses on the accuracies of HY-2B scatterometer wind data during the period from November 2018 to May 2021. The HY-2B wind data are validated against global moored buoys operated by the U.S. National Data Buoy Center and Tropical Atmosphere Ocean, numerical model data by the National Centers for Environmental Prediction, and the Advanced Scatterometer data issued by the Remote Sensing System. The results showed that the wind speeds and directions observed by the HY-2B scatterometer agree well with these buoy wind measurements. The root-mean-squared errors (RMSEs) of the HY-2B wind speed and direction are 0.74 m/s and 11.74°, respectively. For low wind speeds (less than 5 m/s), the standard deviation of the HY-2B-derived wind direction is higher than 20°, which implies that the HY-2B wind direction for low wind speeds is less accurate than that for moderate to high wind speed ranges. The RMSE of the HY-2B wind speed is slightly larger in high latitude oceans (60°–90°S and 60°–90°N) than in low latitude regions. Furthermore, the dependence of the residuals on the cross-track location of wind vector cells and the stability of the HY-2B scatterometer wind products are discussed. The wind stability assessment results indicate that a clear yearly oscillation is observed for the HY-2B wind speed bias which is due to seasonal weather variations. In general, the accuracy of HY-2B winds meets the operational precision requirement and is consistent with other wind data. [ABSTRACT FROM AUTHOR]

The launch of the Haiyang-2 (HY-2) satellite constellation fosters the quick acquisition of global sea surface vector winds from the perspective of remote sensing. This study intends to develop a six-hourly mesoscale analysis of sea surface winds based on the microwave scatterometers onboard the HY-2 satellite series, with the objective of meeting the considerable demand for accurate and gap-free ocean wind forcing products. First, the accuracy of HY-2 scatterometers (HSCATs) in measuring wind is evaluated. In particular, the standard deviation (SD) errors of HSCATs data are assessed using the collocated buoy measurements with different temporal windows in order to account for the temporal representativeness errors in the blending analysis. Afterwards, a two-dimensional variational (2D-Var) method is implemented to blend the HSCATs measured winds and the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis winds over the global ocean surface. This approach is different from existing methods in that it takes both wind error and background error correlation into account. The results show that the blended wind product is of a promising quality compared with independent wind references. Interestingly, the blended winds derived from 2D-Var in combination with an empirical background error correlation show smaller SD errors than those using a Gaussian error correlation function. Overall, the blended wind product should be valuable for forcing global ocean models or describing air-sea interaction processes on a scale close to the scatterometer measurements. [ABSTRACT FROM AUTHOR]

Wave development in Mutsu Bay is simulated by using high-resolution wind fields derived from synthetic aperture radars to investigate the steady state response of waves under the strong winds. This study conducts two case studies for the westerly and easterly wind cases. In Mutsu Bay, westerly or easterly winds dominate and the strong winds often occur with an increase in speed in downwind direction. Waves develop along the direction of the strong winds. Compared with the results derived from the wind fields with constant wind speeds, the wave development derived from the finely resolved wind fields exhibits more rapid growth of wave with distance and more localized distribution of high waves. Thus, even in the semi-enclosed bay, wind speed and significant wave height can exceed representative limiting conditions for offshore construction and operation due to the locally strong winds. [ABSTRACT FROM AUTHOR]

This study simulates V-band sea surface reflectance and normalized radar cross-section (NRCS) for sea surface air pressure barometry using a differential absorption radar operating at three spectrally even spaced close frequency bands (65.5, 67.75 and 70.0 GHz) with ± 15° cross-track scanning angle. The reflectance ratios of two neighboring frequency pairs and the ratio of the two ratios or three-channel approach are the focus of this study. Impacts of major sea surface geophysical variables such as sea surface temperature, wind, salinity, whitecap, and incidence angle on these reflection properties are analyzed. The reflection simulation is essentially based on geometric optics of rough sea surface. Simulation shows that NRCS values are sufficiently strong within the scanning angle and sea surface salinity would only introduce minimal variations in the surface reflection. The impact of sea surface reflection variations with sea surface temperature, wind, and whitecaps on sea surface barometry are mitigated when the ratios of frequency-paired radar signals are used. Furthermore, the ratios of a three-channel approach are very close to unity and calibration or compensation for the reflectance ratios may not be needed for sea level pressure retrievals. These results improve our understanding of sea surface reflection variations and would help the system design and development.

Data-driven predictions of marine environmental variables are typically focused on single variables. However, in real marine environments, there are correlations among different oceanic variables. Additionally, sea–air interactions play a significant role in influencing the evolution of the marine environment. Both internal dynamics and external drivers contribute to these changes. In this study, a data-driven model is proposed using sea surface height anomaly (SSHA), sea surface temperature (SST), and sea surface wind (SSW) in the Bohai Sea. This model combines multivariate empirical orthogonal functions (MEOFs) with long and short-term memory (LSTM). MEOF analysis is used on the multivariate dataset of SSHA and SST, considering the correlation among sea surface variables. SSW is introduced as a predictor to enhance the predictability of the multivariate sea surface model. In the case of the Bohai Sea, the comparative tests of the model without wind field effect, the fully coupled model, and the proposed prediction model were carried out. MEOF analysis is employed in comparative experiments for oceanic variables, atmospheric variables, and combined atmospheric and oceanic variables. The results demonstrate that using wind field as a predictor can improve the forecast accuracy of SSHA and SST in the Bohai Sea. The root mean square errors (RMSE) for SSHA and SST in a 7-day forecast are 0.016 m and 0.3200 °C, respectively.

Numerical representation and climate projections of sea surface winds over Southeast Asia (SEA) are assessed here using an ensemble of the Coupled Model Intercomparison Project Phase 5 (CMIP5) downscaled simulations performed over the 20th and 21st centuries under Representative Concentration Pathways (RCPs) 4.5 and 8.5 scenarios within the Coordinated Regional Climate Downscaling EXperiment (CORDEX)‐SEA project. The ensemble is based on two regional climate models (RCMs: RegCM4 and RCA4), and CMIP5 simulations are performed with five global climate models (GCMs: CNRM_CM5, HadGEM2, GFDL, MPI‐ESM‐MR, EC‐Earth). Comparison with QuikSCAT satellite data shows that dynamical downscaling improves sea surface wind speed representation, mainly by reducing its underestimation. The level of improvement depends on the RCM choice, GCM performance, and wind strength. Our results reveal significant differences in modelled projections of sea surface wind, depending on the model, RCP, region, and season. GCMs simulate weak and contrasted changes, stronger for RCP8.5, with no clear common trend. RCA4 simulates weak changes, with high similarities between pairs, but contrasted results between RCPs. RegCM4 simulate stronger changes, with a weakening of average and intense winds for all seasons, stronger in June–August, and in RCP8.5 than in RCP4.5. RCA4 and RegCM4 simulate different changes, with no clear common trend except a weakening of seasonal and intense winds and an increase of seasonal wind interannual variability for June–August in RCP4.5, stronger for RegCM4. This corresponds to a weakening of the boreal summer monsoon and a slight increase of its interannual variability and presumably to a decrease of the tropical cyclone frequency. Differences in seasonal sea surface wind changes between models are related to differences in sea level pressure gradient changes. For a given RCM, those differences are partly related to the differences between parent GCMs. Finally, results suggest that uncertainties related to the RCM choice are larger than those related to the GCM choice. [ABSTRACT FROM AUTHOR]

Based on the Ocean Projection and Extension neural Network (OPEN) method, a novel approach is proposed to retrieve sea surface wind speed for C-band synthetic aperture radar (SAR). In order to prove the methodology with a robust dataset, five-year normalized radar cross section (NRCS) measurements from the advanced scatterometer (ASCAT), a well-known side-looking radar sensor, are used to train the model. In situ wind data from direct buoy observations, instead of reanalysis wind data or model results, are used as the ground truth in the OPEN model. The model is applied to retrieve sea surface winds from two independent data sets, ASCAT and Sentinel-1 SAR data, and has been well-validated using buoy measurements from the National Oceanic and Atmospheric Administration (NOAA) and China Meteorological Administration (CMA), and the ASCAT coastal wind product. The comparison between the OPEN model and four C-band model (CMOD) versions (CMOD4, CMOD-IFR2, CMOD5.N, and CMOD7) further indicates the good performance of the proposed model for C-band SAR sensors. It is anticipated that the use of high-resolution SAR data together with the new wind speed retrieval method can provide continuous and accurate ocean wind products in the future. [ABSTRACT FROM AUTHOR]

On 23 November 2021, the Gaofen-3-02 (GF3-02) satellite was successfully launched in the Jiuquan Satellite Launch Center of China. The primary payload is C-band Synthetic Aperture Radar (SAR), with a maximum resolution of 1 m, and includes 12 imaging modes such as Spotlight, Strip, and TOPSAR, which will play an essential role in marine environment monitoring. As an important marine environmental parameter, the wind speed accuracy retrieved by GF3-02 SAR directly reflects its performance and effectiveness as an operational product. Therefore, based on the wind data of buoys of the National Data Buoy Center (NDBC), ECMWF reanalysis V5 (ERA5), and HY-2B Scatterometer (SCA), a preliminary accuracy assessment of the wind speed retrieved by GF3-02 SAR is carried out in this paper. The wind speed retrieval accuracy of GF3-02 SAR in the co-polarization (HH+VV) data under different Geophysical Model Functions (GMFs) is discussed by using 478 level-1A Single Look Complex (SLC) ocean products acquired in Quad-Polarization Strip I (QPSI) and produced by the National Satellite Ocean Application Service (NSOAS) from January to March 2022. The results show that the optimal root mean square errors (RMSE) are 1.40 m/s, 1.18 m/s, and 1.24 m/s for the VV polarization and 1.39 m/s, 1.19 m/s, and 1.52 m/s for the HH polarization compared to the NDBC wind speed, the ERA5 wind speed, and the HY-2B SCA wind speed, respectively. The preliminary results show that GF3-02 SAR has good wind speed retrieval ability and can meet the needs of operational products. [ABSTRACT FROM AUTHOR]

Spaceborne Synthetic Aperture Radar (SAR) can observe the ocean surface with high spatial resolution and wide swath under all-weather conditions, day and night. Thus, it is a crucial microwave sensor for obtaining information on sea surface wind and wave fields. This paper reviews various geophysical model functions for wind and wave retrieval and SAR applications in studies of marine atmospheric boundary layer phenomena, offshore wind energy resource development, typhoon monitoring/forecast. The use of traditional SAR and new types of interferometric and polarized SAR data in ocean research are discussed. With the advance of radar satellite technology, the constellation of SAR satellites has become a new trend in the global ocean observations. Many SAR research algorithms have become mature enough to be implemented operationally to provide sea surface wind and wave fields to the scientific communities for ocean dynamic environment monitoring.

Sea surface wind (10‐m high) is one of the most important variables for oceanic applications. The zonal and meridional surface wind components from seven global atmospheric reanalyses [National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research Reanalysis 1, NCEP/Department of Energy Reanalysis 2, NCEP/climate forecast system reanalysis (CFSR), European Centre for Medium‐Range Weather Forecasts (ECMWF) Interim Reanalysis (ERA‐Interim), Japanese 55‐year Reanalysis (JRA‐55), the Modern‐Era Retrospective Analysis for Research and Application V2, and the ERA5 Reanalysis] were compared with surface winds retrieved from QuikScat/SeaWinds and Ascat scatterometers, and with surface ground winds measured in eight coastal meteorological stations, at the Southwestern South Atlantic Ocean, between 35° and 55°S and 55° and 70°W, approximately. Bias, root mean squared error and the Pearson linear correlation coefficient for the zonal and meridional wind components were estimated in this study. The effects of the atmospheric stability, the surface currents and the discontinuity between land and sea roughness on the satellite data are discussed in this paper. It was concluded that Modern‐Era Retrospective Analysis for Research and Application, Version 2 reanalysis is the product that better fitted with satellite data, and ERA5 follows it in performance. CFSR, ERA‐Interim and JRA‐55 reanalyses also presented very good performances. Surface winds from NR1 and NR2 reanalysis showed the largest differences with satellite data. ERA5 reanalysis was the database that better compared with the coastal observations measured at the meteorological stations. Surface winds measured at meteorological stations are, in general, worse represented by the reanalyses than satellite data. This last could be due to the discontinuity between land and sea roughness, and because the topography is not completely represented by the atmospheric global models. [ABSTRACT FROM AUTHOR]

Estimating horizontal winds in and around typhoons is important for improved monitoring and prediction of typhoons and mitigating their damages. Here, we present a new algorithm for estimating typhoon winds using multiple satellite observations and its application to Typhoon Soulik (2018). Four kinds of satellite remote sensing data, along with their relationship to typhoon intensity, derived statistically from hundreds of historical typhoon cases, were merged into the final product of typhoon wind (MT wind): 1) geostationary-satellite-based infrared images (IR wind), 2) passive microwave sounder (MW wind), 3) feature-tracked atmospheric motion vectors, and 4) scatterometer-based sea surface winds (SSWs). The algorithm was applied to two cases (A and B) of Typhoon Soulik and validated against SSWs independently retrieved from active microwave synthetic aperture radar (SAR) and microwave radiometer (AMSR2) images, and vertical profiles of wind speed derived from reanalyzed data and dropsonde observations. For Case A (open ocean), the algorithm estimated the realistic maximum wind, radius of maximum wind, and radius of 15 m/s, which could not be estimated using the reanalysis data, demonstrating reasonable and practical estimates. However, for Case B (when the typhoon rapidly weakened just before making landfall in the Korean Peninsula), the algorithm significantly overestimated the parameters, primarily due to the overestimation of typhoon intensity. Our study highlights that realistic typhoon winds can be monitored continuously in real-time using multiple satellite observations, particularly when typhoon intensity is reasonably well predicted, providing timely analysis results and products of operational importance. [ABSTRACT FROM AUTHOR]

In the Adriatic Sea, the sea surface wind forecasts are often underestimated, with detrimental effects on the accuracy of sea level and storm surge predictions. Among the various causes, this mainly depends on the meteorological forcing of the wind. In this paper, we try to improve an existing numerical method, called “wind bias mitigation”, which relies on scatterometer wind observations to determine a multiplicative factor Δw, whose application to the model wind reduces its inaccuracy with respect to the scatterometer wind. Following four different mathematical approaches, we formulate and discuss seven new expressions of the multiplicative factor. The eight different expressions of the bias mitigation factor, the original one and the seven formulated in this study, are assessed with the aid of four datasets of real sea surface wind events in a variety of sea level conditions in the northern Adriatic Sea, several of which gave rise to high water events in the Venice Lagoon. The statistical analysis shows that some of the seven new formulations of the wind bias mitigation factor are able to lower the model-scatterometer bias with respect to the original formulation. For some other of the seven new formulations, the absolute bias, with respect to scatterometer, of the mitigated model wind field, results lower than that supplied by the unmodified model wind field in 81% of the considered storm surge events in the area of interest, against the 73% of the original formulation of the wind bias mitigation. This represents an 11% improvement in the bias mitigation process, with respect to the original formulation. The best performing of the seven new wind bias mitigation factors, that based on the linear least square regression of the squared wind speed (LLSRE), has been implemented in the operational sea level forecast chain of the Tide Forecast and Early Warning Centre of the Venice Municipality (CPSM), to provide support to the operation of the MO.SE. barriers in Venice.

The distribution and variability of surface winds in the seas and straits in the Maritime Continent, or the Indonesian seas, are investigated using scatterometer wind measurements and reanalysis wind data. This study focuses on the detailed climatology of surface winds in the Indonesian seas and has significant implications for a better understanding of regional climate, meteorological disasters, and offshore wind resource. The monsoon winds predominantly blow over the seas along the routes formed by the coastlines of the large islands and the chains of the small islands. The wind speeds are persistently strong along these routes, and the major direction of the variability in winds is well aligned with these routes. The wind jets are formed in the central part of the routes. The interisland gaps and channels form wind jets of various scales. The Australian winter monsoon is stronger and more persistent than the East Asian winter monsoon in the Indonesian seas. The onset of each monsoon differs by approximately 40 days between the east and west of the Indonesian seas. The amplitudes and spatial extents of diurnally varying winds dominate along the northern coasts of Java Island and the small islands to the east, along the northwest and northeast coasts of Borneo, along the coast of southern Sulawesi Island, and to the southwest of Papua, and vanish along the centre lines of the monsoon routes. Sea areas with a large amplitude of diurnally varying wind are much the same throughout the year. The amplitude of diurnally varying wind has an annual maximum in September in the southern Indonesian seas, possibly in correspondence with the annual maximum of the temperature difference between land and sea. [ABSTRACT FROM AUTHOR]

The variational model inversion (VAR) method for synthetic aperture radar (SAR) wind retrieval based on the Bayesian theory can overcome the limitations 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 article, we propose a new wind retrieval method by applying the optimal interpolation (OI) theory to construct a formula that considers the SAR information, background information coming from the numerical prediction model, and their associated well-characterized errors. The retrieved wind vector can be acquired by the analytic solution of the OI formula. The results from the simulation data show that the error of the OI-retrieved wind is smaller than that of background wind in all considered cases; in particular, the accuracy of the OI-retrieved wind speed is significantly improved. Experiments on the Sentinel-1 SAR data show that the root mean square error of the OI-retrieved wind speed and direction are 1.4 m/s and 35°, respectively. Compared with other methods, the retrieved wind speed accuracy of the OI method is similar to that of the VAR method but higher than that of the direct wind retrieval 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 results indicate that the OI method can be effectively applied to SAR wind retrieval.

Short-term biological responses to sea surface wind field over mesoscale eddies were investigated using hourly ocean color chlorophyll-a (chl-a) concentration data from the Geostationary Ocean Color Imager (GOCI), scatterometer wind data, and satellite sea surface temperature (SST) data. Four warm eddies were identified from SST fronts, by subjectively fitting ellipses to selected points, and anticyclonically rotating current vectors were estimated from sequential chl-a images. Scatterometer data confirmed wind speed strengthening by approximately 30% over anticyclonic eddies, regardless of wind direction, caused by stability changes in the marine atmospheric boundary layer. The modified wind speed field produced a characteristic pattern of wind stress curl and Ekman pumping (EKP) over the eddies, showing positive and negative values on the left and right sides of the upwind direction, respectively. The EKP field was divided into three components: eddy current-induced, relative vorticity gradient-induced, and crosswind SST-induced terms. Short-term changes in the chl-a concentration showed a positive relation with EKP variations over most eddies. SST-induced EKP played a significant role in the eddies for 76% of the total variations, which is much higher than that in other oceans. This study emphasizes the significant effect of SST distribution and the biological responses to changes in an EKP field in relation to air–sea interactions and feedback processes in the East Sea (Sea of Japan). [ABSTRACT FROM AUTHOR]

Sea surface wind (SSW) is a crucial parameter for meteorological and oceanographic research, and accurate observation of SSW is valuable for a wide range of applications. However, most existing SSW data products are at a coarse spatial resolution, which is insufficient, especially for regional or local studies. Therefore, in this paper, to derive finer-resolution estimates of SSW, we present a novel statistical downscaling approach for satellite SSW based on generative adversarial networks and dual learning scheme, taking WindSat as a typical example. The dual learning scheme performs a primal task to reconstruct high resolution SSW, and a dual task to estimate the degradation kernels, which form a closed loop and are simultaneously learned, thus introducing an additional constraint to reduce the solution space. The integration of a dual learning scheme as the generator into the generative adversarial network structure further yield better downscaling performance by fine-tuning the generated SSW closer to high-resolution SSW. Besides, a model adaptation strategy was exploited to enhance the capacity for downscaling from low-resolution SSW without high-resolution ground truth. Comprehensive experiments were conducted on both the synthetic paired and unpaired SSW data. In the study areas of the East Coast of North America and the North Indian Ocean, in this work, the downscaling results to 0.25° (high resolution on the synthetic dataset), 0.03125° (8× downscaling), and 0.015625° (16× downscaling) of the proposed approach achieve the highest accuracy in terms of root mean square error and R-Square. The downscaling resolution can be enhanced by increasing the basic blocks in the generator. The highest downscaling reconstruction quality in terms of peak signal-to-noise ratio and structural similarity index was also achieved on the synthetic dataset with high-resolution ground truth. The experimental results demonstrate the effectiveness of the proposed downscaling network and the superior performance compared with the other typical advanced downscaling methods, including bicubic interpolation, DeepSD, dual regression networks, and adversarial DeepSD.

A wave buoy commonly used for measurements in marine environments is very useful for measurements on the sea surface wind and waves. However, it is constantly exposed to external forces such as typhoons and the risk of accidents caused by ships. Therefore, the installation and maintenance charges are large and constant. In this study, we developed a system for monitoring the sea surface wind using marine radar to provide spatial and temporal information about sea surface waves at a small cost. The essential technology required for this system is radar signal processing. This paper also describes the analytical process of using it for monitoring the sea surface wind. Consequently, developing this system will make it possible to replace wave buoys in the near future.

The Chinese HY-2D satellite was launched on 19 May 2021, carrying a Ku-band scatterometer. Together with the operating scatterometers onboard the HY-2B and HY-2C satellites, the HY-2 series scatterometer constellation was built, constituting different satellite orbits and hence opportunity for mutual intercomparison and intercalibration. To achieve intercalibration of backscatter measurements for these scatterometers, this study presents and performs three methods including: (1) direct comparison using collocated measurements, in which the nonlinear calibrations can also be derived; (2) intercalibration over the Amazon rainforest; (3) and the double-difference technique based on backscatter simulations over the global oceans, in which a geophysical model function and numerical weather prediction (NWP) model winds are needed. The results obtained using the three methods are comparable, i.e., the differences among them are within 0.1 dB. The intercalibration results are validated by comparing the HY-2 series scatterometer wind speeds with NWP model wind speeds. The curves of wind speed bias for the HY-2 series scatterometers are quite similar, particularly in wind speeds ranging from 4 to 20 m/s. Based on the well-intercalibrated backscatter measurements, consistent sea surface wind products from HY-2 series scatterometers can be produced, and greatly benefit data applications.

The long-term trends of sea surface wind are of great importance to our understanding of the effects of climate change on the marine environment. In the northern South China Sea (SCS), the long-term changes in coastal sea surface wind are not well-understood. Based on the latest reanalysis (ERA5) data from 1979 to 2019, our analysis showed a decreasing trend in the annual mean wind speed in the coastal area and an increasing trend in the open sea. There was a significant weakening trend in the easterly wind component in the coastal and continental shelf areas, whereas there was an increasing trend in the northerly wind component in the open sea. The Mann–Kendall mutation analysis suggested that there were significant changes in the wind speed and frequency of strong wind. Significant correlations were found between the variation of the wind field and El Niño–Southern Oscillation by wave coherence analysis. The strengthening of the wind stress curl was an important factor for the enhancement of coastal upwelling along the coast of the northern SCS. The wind field plays an important role in modulating the climatic change of significant wave height.

Southeast Asia (SEA) climate shows a large range of variability scales, from extreme events to interannual variability. Understanding its answer to climate change is of primary scientific and socio-economic importance. IPCC 5th assessment report however pointed the lack of knowledge in regional climate change and its impact in SEA. In particular little is known about the answer of wind to climate change. The impact of climate change on sea surface wind speed is investigated here, examining changes in daily and extreme event scales, interannual variability and climatological average between the twentieth and twenty-first centuries. For that the RegCM4 regional model was used to perform a dynamical downscaling of CMIP5 simulations done with CNRM-CM5 global climate model under RCP4.5 and RCP8.5 hypothesis. Comparisons with QuikSCAT satellite data show that the downscaled simulations perform overall better than the global simulations. Both models produce regionally and seasonally contrasted results in terms of daily wind speed answer to climate change. Global simulations produce mostly weak and non-significant changes, only suggesting an intensification of northeast winter monsoon in the northern SEA. Conversely regional downscaled simulations suggest from March to November in the northern South China Sea and Pacific regions a significant weakening of wind speed from climatological to daily scales (summer monsoon to extreme values), for regions and periods of initially strong values, associated with a 40–50% decrease of tropical cyclones frequency. These changes result from the increase of mean meridional south to north sea level pressure gradient and decrease its daily variability. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) is widely used to detect sea surface wind. It has been found that the signals of co-polarization SAR tend to become saturated under high wind-speed conditions. Also, low signal-to-noise ratios of cross-polarization SAR have been observed under low wind-speed conditions. In this letter, a typhoon wind-field retrieval method is developed by compositing the wind speeds retrieved from dual-polarized SAR images. This letter then applies the proposed method to eight typhoons (four from the Canadian RADARSAT-2 satellite and four from the Chinese Gaofen-3 satellite). The results show that the method is successful in regard to fine typhoon wind structure retrieval, with a root-mean-square error of wind direction of 12° and a wind speed of 1.7 m/s, against 12 oceanic buoy measurements in the South China Sea. [ABSTRACT FROM AUTHOR]

An improved version of the Local-Gradient-Modified (LG-Mod) algorithm for Sea Surface Wind (SSW) directions retrieval by means of Synthetic Aperture Radar (SAR) images is presented. A “local” multi-scale analysis of wind-aligned SAR patterns is introduced to improve the LG-Mod sensitivity to SAR backscattering modulations, occurring locally with various spatial wavelengths. The Marginal Error parameter is redefined, and the adoption of the Directional Accuracy Maximization Criterion (DAMC) allows for the novel Multi-Scale (MS) LG-Mod to automatically select the local processing scale that may be regarded as optimal for pattern enhancement, once a discrete set of scales has been already fixed. Hence, this optimal scale successfully gives evidence to guarantee the best achievable local direction estimation. The assessment of the MS LG-Mod is carried on both simulated SAR images and a Sentinel-1 (S-1) dataset, consisting of 350 Interferometric Wide Swath Ground Range Multi-Look Detected High-Resolution images, which cover the region of the Gulf of Maine. In the latter case, the removal of artifacts and non-wind features from SAR amplitudes is mandatory before directional estimations. In situ wind observations gathered by the National Oceanic and Atmospheric Administration National Data Buoy Center (NOAA NDBC) are exploited for validation. The findings obtained from S-1 data confirm the ones from simulated patterns. The MS LG-Mod analysis performs better than each single-scale one in terms of both percentages of reliable directions and directional Root Mean Square Error (RMSE) values achieved.

In this paper, we presented a method for retrieving sea surface wind speed (SSWS) from Sentinel-1 synthetic aperture radar (SAR) horizontal-horizontal (HH) polarization data in extra-wide (EW) swath mode, which have been extensively acquired over the Arctic for polar monitoring. In contrast to the conventional algorithm, i.e., using a geophysical model function (GMF) to retrieve SSWS by spaceborne SAR, we introduced an alternative retrieval method based on a GMF-guided neural network. The SAR normalized radar cross section, incidence angle, and wind direction are used as the inputs of a back propagation (BP) neural network, and the output is the SSWS. The network is developed based on 11,431 HH-polarized EW images acquired in the marginal ice zone (MIZ) of the Arctic from 2015 to 2018 and their collocated scatterometer wind measurements. Verification of the neural network based on the testing dataset yields a bias of 0.23 m/s and a root mean square error (RMSE) of 1.25 m/s compared to the scatterometer wind data for wind speeds less than approximately 30 m/s. Further comparison of the SAR retrieved SSWS with independent buoy measurements shows a bias and RMSE of 0.12 m/s and 1.42 m/s, respectively. We also analyzed the uncertainty of the retrieval when reanalysis model wind direction data are used as inputs to the neural network. By combining the detected sea ice cover information based on SAR data, sea ice and marine-meteorological parameters can be derived simultaneously by spaceborne SAR at a high spatial resolution in the Arctic.

Temporal and spatial distribution characteristics of sea surface wind in Guangdong's coastal areas were analyzed with data from four offShore observational stations between 2012 and 2015. The results are shown as follows: (I) The probability distribution of wind speed was basically consistent with Gaussian distribution characteristics; winds of Beaufort force 6 or higher were observed mainly in far offshore stations from October to March. (2) The probability distribution of wind direction was represented well by Weibull distribution. The deviation of wind direction of far station was relatively small for it was mainly controlled by monsoon over the South China Sea, while the near offShore station had a relatively large diurnal variation because of the influence of local synoptic systems such as sea-land breeze. (3) There were significant seasonal differences in wind speed and direction observed by different offShore observational stations. In strong wind seasons, the deviation of wind direction was relatively small while the deviation of wind speed was relatively large, and vice versa. In contrast with Class I station, the other three stations exhibited approximately normal distribution of wind direction and wind speed deviations. ( 4) Wind direction diurnal variation was moderate in windy periods, while it was obvious in relatively lower speed conditions. The deviation of wind speed in windy periods was generally greater because it was influenced by mesoscale weather systems for 10-20 h, and the influence was complicated, resulting in greater local differences in wind speed. [ABSTRACT FROM AUTHOR]

We analyzed the frequency distribution characteristics of wind speeds occurring at different offshore sites within a range of 0-200 km based on the sea surface wind data captured via buoys and oil platforms located along the east coast of Guangdong Province. The results of the analysis showed that average wind speed measured for each station reached a maximum in winter while minima occurred in summer, corresponding to obvious seasonal variation, and average wind speed increased with offshore distance. The prevailing wind direction at the nearshore site is the easterly wind, and the frequency of winds within 6-10 m s-1 is considerable with that of winds at > 10 m s-1. With the increase of the offshore distance, the winds were less affected by the land, and the prevailing wind direction gradually became northerly winds, predominately those at > 10 m s-1. For areas of shorter offshore distance (< 100 km), surface wind speeds fundamentally conformed to a two-parameter Weibull distribution, but there was a significant difference between wind speed probability distributions and the Weibull distribution in areas more than 100 km offshore. The mean wind speeds and wind speed standard deviations increased with the offshore distance, indicating that with the increase of the wind speed, the pulsation of the winds increased obviously, resulting in an increase in the ratio of the mean wind speed to the standard deviation of wind speed. When the ratio was large, the skewness became negative. When a relatively great degree of dispersion was noted between the observed skewness and the skewness corresponding to the theoretical Weibull curve, the wind speed probability distribution could not be adequately described by a Weibull distribution. This study provides a basis for the verification of the adaptability of Weibull distribution in different sea areas. [ABSTRACT FROM AUTHOR]

In this paper, the capability of the advanced scatterometer (ASCAT) in measuring surface wind speed in coastal waters of China was first evaluated by comparing with in situ wind observations from 13 moored buoys during the period from July 2013 to December 2015. The scatterometer performs well in most coastal regions of China. However, a sharp reduction in wind speed retrieval accuracy is found in areas adjacent to the Yangtze River estuary. The mean bias and root-mean-square error between ASCAT wind speeds and buoy measurements reach up to 3.44 and ${\text{4.06 m}}\cdot {\text{s}}^{- 1}$ , respectively. This large overestimation of surface wind is also observed in Sentinel-1A synthetic aperture radar (SAR) derived wind speeds. The analysis of quasi-synchronous SAR images and sea surface temperature observations shows that the overestimation of the scatterometer or SAR winds is mainly caused by the effects of ships or ocean thermal fronts due to the complicated thermodynamic and hydrodynamic characteristics of the study area on radar backscatter signal. These effects should be taken into account or marked as flags in activities generating or using scatterometer or SAR wind products. [ABSTRACT FROM AUTHOR]

High-spatial-resolution wind fields derived from synthetic aperture radar (SAR) instruments are crucial to a wide range of applications. However, the spatial scale effect on wind speed retrieval accuracy has seldom been reported. For the purpose of understanding this issue, this letter makes a quality assessment of wind speed retrieval accuracy based on four commonly used C-band geophysical model functions (CMOD4, CMOD-IFR2, CMOD5, and CMOD5.N) at spatial resolutions ranging from 50 m to 50 km using Sentinel-1 interferometric wide (IW) swath mode images. Our results show that the CMOD5 function is the most effective among these functions, owing to a low root-mean-square error (RMSE) of 1.17 m/s and a bias of −0.28 m/s for wind speed retrieval at a spatial resolution of 500 m. It is further observed that the variance of wind speeds retrieved from copolarized SAR images decreases exponentially with the decrease of spatial resolutions. For Sentinel-1 IW mode images, the variance of wind speeds retrieved with CMOD5 decreases rapidly from 50 to 500 m, with a drop in RMSE of 40%, and thereafter levels off. Thus, a spatial resolution of 500 m, with the CMOD5 function, is recommended optimal in this letter, for wind speed retrieval using Sentinnel-1 IW mode data. [ABSTRACT FROM PUBLISHER]

ABSTRACT: We present a link between highly variable winter precipitation (e.g. occasional heavy snow) in the mountainous cities along the east coast of Korea and upper ocean heat content (OHC) in the western part of the East Sea (Japan Sea). After removing annual climatological cycle, OHC (as well as sea surface temperature, SST) variability coherent with the winter sea surface wind (SSW) is extracted using maximum covariance analysis (MCA). The first‐mode MCA pattern, explaining 68% of the total covariance, indicates that the stronger westerly (easterly) winter SSW links to larger (smaller) OHC in the upper 400 m or higher (lower) SST of the western part of the sea. Time series of the first‐mode MCA are comparable to winter precipitation variability at three local cities along the coast (Gangneung, Donghae, and Uljin) with significantly high correlation coefficients (0.84, 0.68, and 0.56, respectively). The links between OHC and SSW and between SSW and precipitation are discussed because zonal contrast of OHC drives zonal tilt of northerly monsoonal wind in winter by canonical turbulent momentum exchange within the marine planetary boundary layer (MPBL). The momentum exchange within the MPBL yields the interannual variability of SSW and thus, winter precipitation in the coastal cities. [ABSTRACT FROM AUTHOR]