Reanalysis Wind Research Articles

Spatial-temporal wave height forecast using deep learning and public reanalysis dataset

A novel data-driven model is proposed for efficient spatial–temporal significant wave height forecast in West Pacific using deep learning. A multivariate 3-layer LSTM-based architecture following a matrix-to-matrix mapping fashion is applied to establish the underlying spatial–temporal relationship between wind components and significant wave height field for long-term and ocean-scale forecasts. A parallel modeling strategy is employed to reduce the computational expense in which the forecasting domain is flexibly partitioned into finite windows and trained independently as a sub-model. A 30-year hindcast wave dataset released by EMC (Environmental Modeling Center) Operational Wave Models associated with NCEP Climate Forecast System Reanalysis and Reforecast (CFSRR) wind data is used to drive and test the model. A rectangle sea region covering over 9.33 × 106 km2 in West Pacific is regarded as a modeling domain. The data-driven model is validated against a 12-year period historical hindcast data, and global errors are carefully discussed. The forecasting performance of the established model on tropical cyclone wave events is investigated in terms of both spatial pattern and temporal variance of significant wave height. The extreme significant wave height, rough sea occurrence, as well as seasonal and annual characteristics of significant wave height in the West Pacific are carried out using the proposed model and compared with the original hindcast results. The data-driven model shows promising potential to accurately capture the ambiguous patterns and features that are variant in both spatial and temporal dimensions and has obvious superiority over numerical wave modeling in terms of computational efficiency.

The Natal Bight Coastal Counter-Current: A modeling study

Output from a high-resolution ocean model, a wind reanalysis and a particle tracking tool are used to improve our understanding of the shelf circulation in an embayment off South Africa's east coast, known as the KwaZulu-Natal Bight. This region spans across roughly 140 km of coastline and is located between 29°S and 30°S. It is influenced by the strong, south-westward flowing Agulhas Current on its offshore edge, while its shelf is dominated by weak and variable currents. On the KwaZulu-Natal Bight's shelf, realistic high-resolution model simulations indicate the presence of a mean north-eastward flow: the Natal Bight Coastal Counter-Current. The mean surface circulation depicts a Natal Bight Coastal Counter Current stretching along the 50 m isobath from the southern to the northern section of the KwaZulu-Natal Bight while progressively becoming narrower and weaker northwards. The mean vertical structure of this counter current extends throughout the water column and at its origin, it almost connects with the Agulhas Undercurrent. In this region, the Natal Bight Coastal Counter-Current is about 20 km wide and has an average speed of 20 cm/s at its core, which may exceed 100 cm/s during individual events. The passage of southward propagating anticyclonic eddies offshore of the Agulhas Current are associated with a southward flow along the southern KwaZulu-Natal Bight region and the interruption of the otherwise north-eastward shelf currents. While the circulation in the KwaZulu-Natal Bight is primarily driven by perturbations at the Agulhas Current front, there is also some indication of a direct wind-driven influence in coastal waters, inshore of the 50 m isobath and north of 29.5°S. Virtual particle tracking experiments show that the Natal Bight Coastal Counter Current may increase connectivity between Marine Protected Areas within the KwaZulu-Natal Bight, where the current greatly increases the water retention. This may trap nutrients from coastal origins on the shelf, together with any suspended particles such as larvae. Therefore, the Natal Bight Coastal Counter-Current has the potential to increase the suitability of this habitat for larval settlement.

Typhoon Wave Simulation Responses to Various Reanalysis Wind Fields and Computational Domain Sizes

A fully coupled tide-surge-wave model was developed to study the influence of different computational domains on typhoon wave characteristics in the waters surrounding Taiwan. Three typhoons were selected as study cases: Meranti, Malakas, and Megi, which successively impacted Taiwan in September 2016. Superposition of the CFSV2 winds blended with ERA5 winds onto the tide-surge-wave model yielded optimum simulations of typhoon waves. Storm wave responses along the eastern shelf of Taiwan resulting from three typhoons were examined in four model domains. The first domain (D01) was primarily situated in the region where giant waves were generated. The second domain (D02) covered an area extending from 114° E to 130° E and 19° N to 29° N. The third domain (D03) southwardly included the entire Bashi Channel, from longitudes of 111° E to 135° E and latitudes of 18° N to 30° N. The fourth domain (D04) was the largest among the four computational domains; it extended from longitudes of 105° E to 140° E and latitudes of 15° N to 31° N. The simulated sea state responses indicated that the smaller computational domains were inadequate for typhoon-driven storm wave computation purposes, although the areas of D01 and D02 reached approximately 0.75 and 1.38 million km2, respectively, encompassing all of Taiwan Island and adjacent waters. Our results suggest that utilizing at least D03 or a larger model domain (e.g., D04) is essential to account for the remote wind effect of typhoons on wave simulations in Taiwanese waters.

Statistical post-processing of reanalysis wind speeds at hub heights using a diagnostic wind model and neural networks

Abstract. The correct representation of wind speeds at hub height (e.g., 100 m above ground) is becoming more and more important with respect to the expansion of renewable energy. In this study, a post-processing of the wind speed of the regional reanalysis COSMO-REA6 in Central Europe is performed based on a combined physical and statistical approach. The physical basis is provided by downscaling wind speeds with the help of a diagnostic wind model, which reduces the horizontal grid point spacing by a factor of 8 compared to COSMO-REA6 and considers different vertical atmospheric stabilities. In the second step, a statistical correction is performed using a neural network, as well as a generalized linear model based on different variables of the reanalysis. Although only a few measurements by masts or lidars are available at hub height, an improvement of the wind speed in the root-mean-squared error of almost 30 % can be achieved. A final comparison with radiosonde observations confirms the added value of combining the physical and statistical approaches in post-processing the wind speed.

Geomorphology of sand dunes in the Taklamakan Desert based on ERA5 reanalysis data

The characteristics of the wind regime and the development of dune morphology in the Taklamakan Desert have been the focus of scholars. In this study, we used ERA5 reanalysis wind data at a height of 10 m from 1979 to 2019 to examine the sand transport and wind regime characteristics of the entire Taklamakan Desert. We combined the two latest dune development modes, i.e., bed instability and elongation modes, and determined that: (1) Taklamakan Desert is overall characterized by a lower wind energy environment; the wind and sand activity intensities are highest in the eastern part of the desert, with a decreasing trend from the east to the west. The wind direction in the eastern region shows unimodal characteristics due to the backward easterly wind; the wind regime gradually becomes complicated from the northeast to the southwest; (2) the morphological alignments of dunes can be predicted by such modern wind regimes combined with the latest dune development modes. Large longitudinal dunes in the middle of the desert can be predicted with the elongation mode, whereas secondary transverse dunes, which derive from these composite dunes, can be predicted using the bed instability mode. Collectively, this paper fills the gap of the lack of uniform and standardized macro-scale wind data in the Taklamakan Desert, and the two modes of dune development derived from ERA5 reanalysis data can match the morphology and trend of most modern dunes.

Coastal dunes migration over the Itaúnas district - Espírito Santo, humid tropical coast of Southeast Brazil

The migration of transgressive coastal dunes over urban and farming areas is commonly observed in the semi-arid northern coast of the Brazilian northeast. However, it is unusual in the humid tropical coast of southeastern Brazil. This paper aims to investigate the aeolian dynamics and the drivers of the burial process over the Itaúnas District, Espírito Santo State landscape, by transgressive coastal dunes. Methodological procedures consist of remote sensing techniques, geoprocessing, wind regime simulations, and mathematical calculations of migration rate parameters. Satellite images were georeferenced using ArcGIS 10.3 software and wind regime information was simulated using HYSPLIT software using wind reanalysis data. The results suggest a sediment transport rate of 44.98 kg/m/s, wind drift potential of 123.96 v.u. and dune migration rate estimated at 4.0 m/yr. It was found that the northern and western sectors of the studied dunefield have the highest migration rates towards the main access road, water resources and urban area. The values obtained are considered significant because it is a coastal segment subjected to a humid tropical climate.

Linking the long-term variability in global wave energy to swell climate and redefining suitable coasts for energy exploitation

The sustainability of wave energy linked to the intra- and inter-annual variability in wave climate is crucial in wave resource assessment. In this study, we quantify the dependency of stability of wave energy flux (power) on long-term variability of wind and wave climate to detect a relationship between them. We used six decades of re-analysis wind and simulated wave climate in the entire globe and using two 30-yearly periods, we showed that not only the previously suggested minimum period of 10 years for wave energy assessment appears to be insufficient for detecting the influence of climate variability, but also the selection period for wave energy assessment can lead to an over/underestimation of about 25% for wave power. In addition, we quantified the dependency of rates of change of wave power, wind speed and wave parameters and showed that the change in wave power is mainly a function of change in swell wave climate globally. Finally, we redefined the suitability of global hotspots for wave energy extraction using intra-annual fluctuation, long-term change, and the available wave power for the period of six decades. The results highlight the importance of climate variability in resource assessment, sustainability, and prioritizing the hotspots for future development.

Wind variability over a large lake with complex topography: Lake of the Woods

Lake of the Woods spans two geological regions: the Precambrian Shield and the Glacial Lake Agassiz Lakebed. The lake watershed’s complex topography influences wind speeds and directions. We study wind variability over Lake of the Woods using reanalysis data and wind observations at seven sites. A wind variability index is proposed as a measure of lake-wide spread in wind direction. Our results show that the directional variability depends on wind speeds, with higher variability at lower speeds. The wind variability also changes throughout the year; it is more pronounced in spring (Apr-Jun) and fall (Sep-Oct) and less pronounced during winter (Dec-Jan). Observed wind directions are 60% more variable than reanalysis wind, indicating a need for higher-resolution reanalysis to resolve local wind effects due to topographic changes. Based on the statistical hierarchical clustering method, our analysis shows that geographically variable wind can be grouped into three regions to represent the predominant wind directions accurately. Clustering results also suggest spatial coverage gaps in the existing meteorological observation network. We reassess the existing network and propose a new meteorological observation site in the southeast basin near Morson, Ontario. Our study is of significance for water quality management because the wind is the key driver of water circulation, transport, and mixing in the lake, and accurate wind measurements are necessary to better constrain the underpinning physical, biological, and biochemical processes in mechanistic ecological models.

Assessment of combined wind and wave energy in the tropical cyclone affected region:An application in China seas

Marine renewable energy resources are important for clean energy development. Research on the combined assessment of wind and wave resources, especially in tropical cyclone (TC) affected area is still rare. This study is to assess the potential combined wind and wave energy in the coastal waters of China, where TCs frequently occurred. The study utilizes the blended wind field with a parametric TC model based on the ECMWF reanalysis wind data from 1979 to 2013 for assessing potential wind energy, and the waves generated by the same wind data using the FVCOM-SWAVE model for assessing wave energy. The combined wind and wave energy density and annual power generation are analyzed. The potential energy from wind and waves at 15 nearshore locations along China east coasts are estimated. The results show that the frequency and intensity of TCs have a considerable impact on the distribution of density and stability of wind and wave energy, especially in the southeastern coastal areas of China. Suggestions of site selection of co-located wind and wave farm in the coastal waters of China are given. The research outcome provides a useful guidance for the selection of potential sites and design of wind and wave energy converters along the China coasts.

An adversarial learning approach to forecasted wind field correction with an application to oil spill drift prediction

Reanalysis wind fields are obtained by correcting the numerically forecasted wind fields based on observation data (i.e., either remote sensing or in-situ observations, or both). Although they are more accurate than forecasted wind fields, reanalysis wind fields tend to have time latencies because they can only be released after the observations are obtained. In order to produce accurate estimates of wind fields in a more timely manner, we develop an adversarial learning approach to correcting forecasted wind fields to be close to reanalysis wind fields. The adversarial learning approach is conducted by an adversarial ConvLSTM network (ACLN) framework that consists of a corrector and a discriminator. The corrector aims at comprehensively capturing both spatial and temporal characteristics of a sequence of forecasted wind fields and producing a corrected forecast wind field for the final field in the sequence. The discriminator tries to distinguish corrected forecast wind field from its corresponding reanalysis wind field. The training of ACLN is alternate between the corrector and the discriminator in an adversarial fashion. The adversarial training mechanism enhances the corrector’s representational power. Additionally, the corrector exploits a residual learning architecture that effectively learns the differences between forecasted wind fields and the corresponding reanalysis wind fields. In this scenario, the well trained corrector requires neither reanalysis wind fields nor observations such that it can correct forecasted wind fields in a timely manner. Furthermore, corrected forecast wind fields are employed for oil spill drift prediction. Extensive experiments validate the effectiveness of the proposed ACLN framework in forecasted wind field correction along with oil spill drift prediction. Compared with ECMWF numerical forecasts, the ACLN achieves an average reduction of 6.2%, 6.9%, and 10.6% in RMSE, MAE, and MAPE, respectively. Compared with a basic drift prediction method, the ACLN based prediction method reduces the error by about 5000 m in the Sanchi oil spill accident. The source codes are available at https://github.com/liyongqingupc/ACLN-WindFieldCorrection, providing a baseline for correcting forecasted wind fields.

Spaceborne GNSS-R Wind Speed Retrieval Using Machine Learning Methods

This paper focuses on sea surface wind speed estimation using L1B level v3.1 data of reflected GNSS signals from the Cyclone GNSS (CYGNSS) mission and European Centre for Medium-range Weather Forecast Reanalysis (ECMWF) wind speed data. Seven machine learning methods are applied for wind speed retrieval, i.e., Regression trees (Binary Tree (BT), Ensembles of Trees (ET), XGBoost (XGB), LightGBM (LGBM)), ANN (Artificial neural network), Stepwise Linear Regression (SLR), and Gaussian Support Vector Machine (GSVM), and a comparison of their performance is made. The wind speed is divided into two different ranges to study the suitability of the different algorithms. A total of 10 observation variables are considered as input parameters to study the importance of individual variables or combinations thereof. The results show that the LGBM model performs the best with an RMSE of 1.419 and a correlation coefficient of 0.849 in the low wind speed interval (0–15 m/s), while the ET model performs the best with an RMSE of 1.100 and a correlation coefficient of 0.767 in the high wind speed interval (15–30 m/s). The effects of the variables used in wind speed retrieval models are investigated using the XGBoost importance metric, showing that a number of variables play a very significant role in wind speed retrieval. It is expected that these results will provide a useful reference for the development of advanced wind speed retrieval algorithms in the future.

Wind resource assessment and techno-economic analysis of wind energy and green hydrogen production in the Republic of Djibouti

The ever increasing energy demand of the Republic of Djibouti leads to the diversification of energy sources. While a few studies have explored the prospects of green hydrogen production from wind energy in developing countries and particularly in Africa, the economic risk analysis of wind power production for electricity generation and green hydrogen production has not been assessed for African countries. This study evaluates for the first time the potential of wind energy for electricity and green hydrogen production in the Republic of Djibouti. In this study, wind speed characteristics were analyzed using wind data measured at five meteorological stations from 2015 to 2019. The techno-economic analysis of five wind farms with a total capacity of 450 MW is performed. Levelized cost of energy production (LCOE), levelized cost of green hydrogen production (LCOH), sensitivity analysis, Monte Carlo simulation and economic performance indicators are presented. Results reveal that the annual wind speed varies between 5.52 m/s and 9.01 m/s for the five sites. ERA5 wind reanalysis indicates that the seasonal variability of wind is stable between different years. The proposed wind farms estimate 1739 GWh per year of electrical energy with LCOE ranging from 6.94 to 13.30 US cents/kWh, which is less than the locale electricity tariff. The production cost of green hydrogen was competitive with LCOH ranging from 1.79 to 3.38 US $/kg H2. The sensitivity analysis shows that the most relevant parameters in the economic analysis are the initial investment cost, the interest rate and the factor capacity.

Climatology and variability of wind speeds along the southwest coast of India derived from Climate Forecast System Reanalysis winds

AbstractWind climate along the southwest coast of India (Kerala coast) has been analysed using 41 years of Climate Forecast System Reanalysis (CFSR) winds to delineate long‐term trends and variability. The study reveals significant decreasing trends in annual mean wind speeds, of the order of −2.0 to −2.5 cm·s−1·year−1, for the period 1979–2019. Southwest monsoon has contributed the highest weakening trends (−3.0 to −4.0 cm·s−1·year−1) due to significant decrease in the southwesterly/westerly wind speeds. The wind climate and trends during the northeast monsoon and premonsoon seasons have been characterized largely by the prevalence of sea breeze and shamal–makran wind systems. We find that the intensity of both sea breeze and shamal–makran wind systems reduces from north to south along the Kerala coast, which is in consistent with the earlier studies. Decadal oscillations in wind speeds, driven by the decadal variability in large‐scale atmosphere–ocean circulations, are evident. Recent changes in the frequency of occurrence of Indian Ocean Dipole in the Indian Ocean have determined the overturning trends since 2010, and resulted in increasing trends in the current decade along the central and northern coasts of Kerala. Furthermore, interannual variability in wind speeds has been linked to the El Niño–Southern Oscillations (ENSO) and Indian Ocean Dipole (IOD).

The Modified Method of Reanalysis Wind Data in Estuarine Areas

High-quality wind field data are key to improving the accuracy of storm surge simulations in coastal and estuarine water. These data are also of great significance in studying the dynamic processes in coastal areas and safeguarding human engineering structures. A directional correction method for ECMWF reanalysis wind data was proposed in this paper based on the correlation with the measured wind speed and direction. The results show that the accuracies of wind speed and direction were improved after being modified by the correction method proposed in this paper. The modified wind data were applied to drive the storm surge model of the Yangtze Estuary for typhoon events, which resulted in a significant improvement to the accuracy of hindcasted water levels. The error of the hindcasted highest water levels was reduced by 16–19 cm.

Global Middle-Atmosphere Response to Winter Stratospheric Variability in SABER and MLS Mean Temperature

Abstract Satellite observations of middle-atmosphere temperature are used to investigate the short-term global response to planetary wave activity in the winter stratosphere. The focus is on the relation between variations in the winter and summer hemispheres. The analysis uses observations from Thermosphere–Ionosphere–Mesosphere Energetics and Dynamics (TIMED) Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) for 2002–21 and Aura Microwave Limb Sounder (MLS) for 2004–21, and reanalysis temperatures and winds from MERRA-2 for 2002–21. We calculate temporal correlations of the Eliassen–Palm flux divergence in the winter stratosphere with global temperature. Results show a robust perturbation extending to midlatitudes of the Southern Hemisphere (SH) stratosphere during Northern Hemisphere (NH) winter. An increase in wave forcing is followed by a decrease in temperatures over the depth of the stratosphere in the SH, peaking at a lag of 3 days. Summer mesospheric temperature perturbations of the opposite sign are seen in many winters. Comparable signals in the NH summer middle-atmosphere are present during some SH winters but are weaker and less consistent than those in the SH during NH winter. A diagnostic evaluation of the patterns of correlation, the mesospheric zonal winds, and the stability criteria suggests that the temperature perturbations in the midlatitude summer mesosphere are more closely associated with the summer stratosphere directly below than with the wave activity in the winter stratosphere. This suggests that the interhemispheric coupling in the stratosphere is driving or contributing to the coupling between the winter stratosphere and the summer mesosphere that has been reported in several investigations. Significance Statement There are many instances in which one part of the atmosphere is found to regularly respond to perturbations occurring in a distant region. In this study, we use observations to investigate one such pattern: temperature changes at high altitude (60–100 km) in the summer that follow dynamical changes near the winter pole at 40–60 km. Such analysis is useful to understand which physical processes contribute to the global connectivity and variability of the atmosphere.

Relative dominance of wind stress curl and Ekman transport on coastal upwelling during summer monsoon in the southeastern Arabian Sea

Coastal upwelling in the south eastern Arabian Sea (SEAS) that occurs and intensifies during the summer monsoon season (June–September) is assessed in terms of alongshore wind stress and its curl using ECMWF reanalysis (ERA 5) winds for the period 2016–2020. Critical role of wind stress curl and its spatio-temporal variability, with advancing monsoon in modulating upwelling along the coast, is studied using Ekman pumping and vertical velocities integrated from coast to offshore boundary of positive curl. From the analysis it is observed that the Ekman transport dominates the southern part of the region while Ekman pumping is stronger for the northern section of the study area. This study also helps to delineate the offshore extent of upwelling throughout the monsoon season with a strong yet narrow zone hugging the coast during July/August (peak monsoon) while a broad and diffused upwelling zone for the rest of the season. This spatio-temporal variability in coastal upwelling is dominated by wind stress curl and its significance was not yet explored well for the SEAS region. From the analysis, 2016 was observed to be strong upwelling, while 2019 was the weakest upwelling season as inferred from data on wind, sea surface temperature, sea level anomaly and chlorophyll-a concentration.

Extreme Wind Wave Climate off Jeddah Coast, the Red Sea

Climate change can give rise to significant changes in the intensity and frequency of extreme events. In the present study, extreme wave events off the central-eastern coast of the Red Sea, near the city of Jeddah, were investigated using a 39-year wave hindcast generated using WaveWatch III configured at a 3.3 km resolution forced with ERA5 reanalysis winds. The validated model outputs were used to derive the annual and seasonal climatology for the region at a few selected locations off the Jeddah coast. The study revealed robust temporal and spatial variability in the region, considering the influence of both northern and southern Red Sea waves that meet at the convergent zone. Generalized Pareto Distribution (GPD) and Generalized Extreme Value (GEV) were two models used for the estimation of extreme wave analysis in the study region. The extreme significant wave heights were estimated for 10, 25, 50, and 100-year return periods. For offshore locations, the estimated return periods using the GPD method were 3.27, 3.44, and 3.38 m, respectively. GPD with the POT method was the more suitable approach, as it produced reduced bias and RMSE. At all locations, linear trends show an increase during the summer, winter, and pre-summer periods. No significant increase in the Hs trend was observed at the selected locations near and off the coast. However, the decrease in trend observed at all locations during the pre-winter period was noticeable for the 99th percentile Hs.

Correction of ERA5 Wind for Regional Climate Projections of Sea Waves

This paper proposes a method to infer the future change in the wind-wave climate using reanalysis wind corrected to statistically match data from a regional climate model (RCM). The method is applied to the sea surface wind speed of the reanalysis ERA5 from the European Centre for Medium-Range Weather Forecasts. The correction is determined from a quantile mapping between ERA5 and the RCM at any given point in the geographical space. The issues that need to be addressed to better understand and apply the method are discussed. Corrected ERA5 wind fields are eventually used to force a spectral wave numerical model to simulate the climate of significant wave height. The correction strategy is implemented over the Adriatic Sea (a semi-enclosed basin of the Mediterranean Sea) and includes the present-day period (1981–2010) and the near-future period (2021–2050) under the two IPCC RCP4.5 and RCP8.5 concentration scenarios. Evaluation against observations of wind and waves gives confidence in the reliability of the proposed approach. Results confirm the evolution toward an overall decrease in storm wave severity in the basin, especially under RCP8.5 and in its northern area. It is expected that the methodology may be applied to other reanalyses, RCMs (including multi-model ensembles), or seas with similar characteristics.

A Neural Network Method for Retrieving Sea Surface Wind Speed for C-Band SAR

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.

Impacts of Climate Oscillation on Offshore Wind Resources in China Seas

The long-term stability and sustainability of offshore wind energy resources are very important for wind energy exploration. In this study, the Cyclostationary Empirical Orthogonal Function (CSEOF) method, which can determine the time varying spatial distributions and long-term fluctuations in the cyclostationary geophysical process, was adopted to investigate the geographical and temporal variability of offshore wind resources in China Seas. The CSEOF analysis was performed on wind speeds at 70 m height above the sea surface from a validated combined Quick Scatterometer (QuikSCAT) and Advanced Scatterometer (ASCAT) wind product (2000–2016) with high spatial resolution of 12.5 km, and Climate Forecast System Reanalysis (CFSR) wind data (1979–2016) with a grid size of 0.5° × 0.5°. The decomposition results of the two datasets indicate that the first CSEOF mode represents the variability of wind annual cycle signal and contributes 77.7% and 76.5% to the wind energy variability, respectively. The principal component time series (PCTS) shows an interannual variability of annual wind cycle with a period of 3–4 years. The second mode accounts for 4.3% and 4.7% of total wind speed variability, respectively, and captures the spatiotemporal contribution of El Niño Southern Oscillation (ENSO) on regional wind energy variability. The correlations between the mode-2 PCTS of scatterometer or CFSR winds and the Southern Oscillation Index (SOI) are greater than 0.7, illustrating that ENSO has a significant impact on China’s offshore wind resources. Moreover, the mode-1 or mode-2 spatial pattern of CFSR winds is basically consistent with that of scatterometer data, but CFSR underestimates the temporal variability of annual wind speed cycle and the spatial changes of wind speed related to ENSO. Compared with reanalysis data, scatterometer winds always demonstrate a finer structure of wind energy variability due to their higher spatial resolution. For ENSO events with different intensities, the impact of ENSO on regional wind resources varies with time and space. In general, El Niño has reduced wind energy in most regions of China Seas except for the Bohai Sea and Beibu Bay, while La Niña has strengthened the winds in most areas except for the Bohai Sea and southern South China Sea.