Global Ocean Wind Research Articles

Global Ocean Wind Speed Retrieval From GNSS Reflectometry Using CNN-LSTM Network

Ocean surface winds play an essential role in regulating the earth’s weather and climate, and the Cyclone GNSS (CYGNSS) mission launched in 2016 is designed specially to monitor the ocean wind speed. In this study, an innovative model is developed based on a deep learning method to retrieve the ocean wind speed by making full use of the spatiotemporal information of CYGNSS observations. The proposed model named CNN-LSTM is established based on two modules, i.e., the Convolution Neural Network (CNN) module that extracts the spatial features around the Specular Point (SP) from a Two-Dimensional matrix of delay-Doppler Map (DDM) and the Long Short-Term Memory (LSTM) module which extracts the temporal features over a time series. The performance of the ocean wind speed derived from CNN-LSTM is assessed with the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5) products. The results show that the wind speed derived from CNN-LSTM reveals an accuracy of 1.34 m/s in terms of root mean square error (RMSE) values, showing an improvement of about 36.8%, 14.6%, 6.3%, when compared to the official retrieval algorithm called Minimum Variance Estimator (MVE), Multilayer Perceptron (MLP) net, and the CNN, respectively, confirming the feasibility and effectiveness of the designed method. Among all the experiments in this study which apply machine learning-based algorithms, the wind speed achieved by CNN-LSTM presents the smallest RMSE value. Furthermore, the error analyses of the wind speed retrieval in spatial and temporal scale are also discussed, which indicate the robust performance of CNN-LSTM model. The results show that the CNN-LSTM model proposed in this study contributes to offering efficient processing of Global Navigation Satellite Systems Reflectometry (GNSS-R) observations and fully exploits the capabilities of high-accurate ocean wind speed retrieval on a global scale.

GNSS reflectometry global ocean wind speed using deep learning: Development and assessment of CyGNSSnet

GNSS Reflectometry (GNSS-R) is a novel remote sensing technique for the monitoring of geophysical parameters using reflected GNSS signals from the Earth's surface. Ocean wind speed monitoring is the main objective of the recently launched Cyclone GNSS (CyGNSS), a GNSS-R constellation of eight microsatellites, launched in late 2016. In this study, the capability of deep learning, especially, for an operational wind speed data derivation from the measured Delay-Doppler Maps (DDMs) is characterized. CyGNSSnet is based on convolutional layers for the feature extraction from bistatic radar cross section (BRCS) DDMs, along with fully connected layers for processing ancillary technical and higher-level input parameters. The best architecture is determined on a validation set and is evaluated over a completely blind dataset from a different time span than that of the training data to validate the generality of the model for operational usage. After a data quality control, CyGNSSnet results in an RMSE of 1.36 m/s leading to a significant improvement by 28% in comparison to the officially operational retrieval algorithm. The RMSE is the lowest among those seen in the literature for any conventional or machine learning-based algorithm. The benefits of the convolutional layers, the advantages and weaknesses of the model are discussed. CyGNSSnet offers efficient processing of GNSS-R measurements for high-quality global ocean winds.

Calibration and Cross Validation of Global Ocean Wind Speed Based on Scatterometer Observations

AbstractGlobal ocean wind speed observed from seven different scatterometers, namely, ERS-1, ERS-2, QuikSCAT, MetOp-A, OceanSat-2, MetOp-B, and Rapid Scatterometer (RapidScat) were calibrated against National Data Buoy Center (NDBC) data to form a consistent long-term database of wind speed and direction. Each scatterometer was calibrated independently against NDBC buoy data and then cross validation between scatterometers was performed. The total duration of all scatterometer data is approximately 27 years, from 1992 until 2018. For calibration purposes, only buoys that are greater than 50 km offshore were used. Moreover, only scatterometer data within 50 km of the buoy and for which the overpass occurred within 30 min of the buoy recording data were considered as a “matchup.” To carry out the calibration, reduced major axis (RMA) regression has been applied where the regression minimizes the size of the triangle formed by the vertical and horizontal offsets of the data point from the regression line and the line itself. Differences between scatterometer and buoy data as a function of time were investigated for long-term stability. In addition, cross validation between scatterometers and independent altimeters was also performed for consistency. The performance of the scatterometers at high wind speeds was examined against buoy and platform measurements using quantile–quantile (Q–Q) plots. Where necessary, corrections were applied to ensure scatterometer data agreed with the in situ wind speed for high wind speeds. The resulting combined dataset is believed to be unique, representing the first long-duration multimission scatterometer dataset consistently calibrated, validated and quality controlled.

Australian blocking impacts on ocean surface waves

We assess the impact of split-flow blocking in the Australian region on global ocean wind waves using 30 years of high-resolution wave data from the CAWCR wave model hindcast. Statistically significant anomalies in significant wave height (Hs), peak wave period (Tp) and zonal wave energy flux (CgE) are found to covary with local variations in surface zonal wind induced by blocking at 140° E. Strong negative anomalies in these wave fields highlight the role of blocking in promoting calm wave conditions around the Australian coast. A relatively weak but notable remote signal also appears in the Northern Hemisphere extratropics, suggesting a link to the Australian region via tropical forcing. The occurrence of daily low (below the 5th percentile) and high (above the 95th percentile) wave conditions also varies contemporaneously with blocking at 140° E. Considering the ability of global circulation model forecasts to skilfully predict the blocking index out to at least 2 weeks lead time, our results point to blocking as a possible source of predictable wind-wave variability from sub-weekly to seasonal timescales.

Space of solutions to ocean surface wind measurement using scatterometer constellations

Satellite wind vector data are integral to atmospheric models and forecasts, but current measurement limitations make some synoptic, mesoscale activities difficult to observe. Using miniaturized electronics and advanced deployable mechanisms, new satellite wind scatterometers may be possible that increase spatial, temporal, and wind resolution and coverage. We propose a simple parametric model of the space of satellite wind scatterometer designs, their performances, and the transformation between design and performance, together called a solution space model. We explore two applications of this model: understanding how advances have expanded the design space and searching for alternative approaches to satellite wind scatterometry. Recent advances enable a greater capability-to-volume ratio, which enables constellations of small, low-cost scatterometers that co-operate in a variety of modes. We present two example concepts for constellations of co-operative satellite wind scatterometers. We estimate that a constellation of CubeSat scatterometers may affordably measure global ocean vector winds every three hours.

Accuracy Assessment of HY-2A Scatterometer Wind Measurements During 2011–2017 by Comparison With Buoys, ASCAT, and ERA-Interim Data

Chinese satellite HY-2A, carrying a Ku-band scatterometer (HSCAT), was launched in August 2011. This letter validates wind vectors retrieved by the HSCAT during October 2011–July 2017 by comparing with wind data recorded by global moored buoys, the Advanced Scatterometer (ASCAT), and ERA-Interim. HSCAT wind data are collocated with moored-buoy observations made by the National Data Buoy Center, tropical atmosphere ocean/triangle trans-ocean buoy network, pilot research moored array in the Tropical Atlantic, and research moored array for African–Asian–Australian monsoon analysis and prediction. Only buoys located offshore are selected. Their spatial and temporal differences are limited to 25 km and 30 min. The results show that the HSCAT wind speed has bias of 0.09–0.28 m/s and root-mean-square error (RMSE) of 1.24–1.52 m/s, which satisfies the mission specification of less than 2 m/s. The HSCAT wind direction has bias of 0.8°–0.9° and RMSE of 21.5°–25.8°, which is close to the mission specification of less than 20°. The HSCAT wind retrievals are overestimates at wind speeds lower than 5 m/s. The RMSEs of the HSCAT wind speed and direction decrease with increasing wind speed. Wind vectors from HSCAT and ASCAT are compared using spatial and temporal windows of 0.1° and 10 min. Results show the consistency of the wind speed; RMSEs of wind speed and direction are 1.50 m/s and 22.90°. The comparison of HSCAT and ERS-Interim wind data shows that the HSCAT provides global ocean wind data at the level required for data application.

Southern annular mode impacts on global ocean surface waves

We assess the impact of the Southern Annular Mode (SAM) on global ocean wind waves using 30 years of wave data from a wave model hindcast that is forced with high resolution surface winds from the NCEP-CFSR reanalysis. Our primary focus is on the wave field and swell influence in the Southern Hemisphere, and we apply our analysis to each of the four temperate-zone seasons comprising March–May (MAM), June–August (JJA), September-November (SON), and December–February (DJF). Statistically significant anomalies in significant wave height (Hs), peak wave period (Tp) and zonal wave energy flux (CgE) are found to covary with local variations in surface zonal wind induced by the low and high polarity phases of the SAM.The signature of the SAM in ocean surface waves extends beyond local wind-generated forcing in the Southern Hemisphere extratropics to remote forcing in the Northern Hemisphere extratropics during DJF, with the associated atmospheric signal resembling a tropically-forced Rossby wave train. This has a significant impact on surface waves in the central North Pacific and North Atlantic oceans. The El Niño Southern Oscillation (ENSO) acts to weaken the link between the SAM and Northern Hemisphere winter climate, with the zonal wind / divergence anomalies amplifying over the tropical Pacific Ocean when we exclude El Niño and La Niña years from the analysis. This remote link to the SAM thus occurs independent of ENSO via SAM-driven changes to the tropical atmospheric circulation, suggesting that the SAM may provide a useful predictive signal for the Northern Hemisphere during DJF. The SAM also influences the occurrence of daily low (below the 5th percentile) and high (above the 95th percentile) wave conditions, emphasizing that the SAM may be a valuable source of predictable wave variability from sub-weekly to seasonal timescales. Future work should explore wave data suitable for trend analysis, considering the positive trend in the SAM over recent decades.

On the determination of global ocean wind and wave climate from satellite observations

Three extensive global wind speed and wave height datasets (altimeter, radiometer, model reanalysis) are analysed to investigate the global wind speed and wave height climate. Despite the fact that these datasets have all been carefully calibrated, they show systematic differences in wind speed. At high latitudes both altimeter and radiometer winds are biased high compared to buoy measurements. Altimeter winds are more impacted than radiometer winds. Based on the assumptions that altimeter winds respond primarily to the surface wave spectrum mean squared slope and radiometer winds respond primarily to the surface wave spectrum dissipation, it is shown that the observed differences are a result of changes in atmospheric stability. An analysis which accounts for differences in air and water temperatures describes the observed differences with surprising accuracy. Based on this analysis corrections to both altimeter and radiometer winds are proposed which account for the influence of atmospheric stability. It is also shown that satellites preferentially measure at particular local times of day. As winds have a diurnal variation in magnitude, this preferential measurement time can also bias statistical values obtained from such satellite systems.

Use of an optimum interpolation method to construct a high-resolution global ocean surface vector wind dataset from active scatterometers and passive radiometers

ABSTRACTThis study presents a new 0.25° gridded 6-hourly global ocean surface wind vector dataset from 2000 to 2015 produced by blending satellite wind retrievals from five active scatterometers (QuikSCAT, ASCAT-A, ASCAT-B, OSCAT, and HY-2A), nine passive radiometers (four SSM/I sensors, two SSMIS sensors, TMI, AMSR-E, and AMSR2) and one polarimetric radiometer (WindSat) with reanalysis from the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) employing an optimum interpolation method (OIM). The accuracy of this wind product is determined through various comparisons with buoy measurements, NCEP/NCAR reanalysis and the cross-calibrated multi-platform (CCMP) winds. The comparisons indicate that OIM winds agree well with buoys, showing a root-mean-squared difference of 1.32 m s−1 for wind speed and 24.73° for wind direction over 0–30 m s−1 wind speed range. And the quality of OIM winds is improved significantly relative to NCEP/NCAR reanalysis and can be comparable with CCMP winds. Furthermore, OIM winds can reveal abundant small-scale features that are not visible in reanalysis data. In addition, the wind speed and direction retrievals of most satellites are proved to play an important role in generating the high-quality product, but the procedure for including HY-2A winds and WindSat wind directions should be further explored.

Assessment of wind products obtained from multiple microwave scatterometers over the China Seas

Sea surface winds (SSWs) are vital to many meteorological and oceanographic applications, especially for regional study of short-range forecasting and Numerical Weather Prediction (NWP) assimilation. Spaceborne scatterometers can provide global ocean surface vector wind products at high spatial resolution. However, given the limited spatial coverage and revisit time for an individual sensor, it is valuable to study improvements of multiple microwave scatterometer observations, including the advanced scatterometer onboard parallel satellites MetOp-A (ASCAT-A) and MetOp-B (ASCAT-B) and microwave scatterometers aboard Oceansat-2 (OSCAT) and HY-2A (HY2-SCAT). These four scatterometer-derived wind products over the China Seas (0°–40°N, 105°–135°E) were evaluated in terms of spatial coverage, revisit time, bias of wind speed and direction, after comparison with ERA-Interim forecast winds from the European Centre for Medium-Range Weather Forecasts (ECMWF) and spectral analysis of wind components along the satellite track. The results show that spatial coverage of wind data observed by combination of the four sensors over the China Seas is about 92.8% for a 12-h interval at 12:00 and 90.7% at 24:00, respectively. The analysis of revisit time shows that two periods, from 5:30–8:30 UTC and 17:00–21:00 UTC each day, had no observations in the study area. Wind data observed by the four sensors along satellite orbits in one month were compared with ERA-Interim data, indicating that bias of both wind speed and direction varies with wind speed, especially for speeds less than 7 m/s. The bias depends on characteristics of each satellite sensor and its retrieval algorithm for wind vector data. All these results will be important as guidance in choosing the most suitable wind product for applications and for constructing blended SSW products.

Madden Julian Oscillation impacts on global ocean surface waves

We assess the impact of the tropical Madden Julian Oscillation (MJO) on global ocean wind waves using 30 years of wave data from a wave model hindcast that is forced with high resolution surface winds from the NCEP–CFSR reanalysis. We concentrate on the boreal winter season when the MJO has its greatest amplitude and is potentially a source of predictable wave impacts at intra-seasonal lead times. Statistically significant anomalies in significant wave height (Hs), peak wave period (Tp) and zonal wave energy flux (CgE) are found to covary with the intra-seasonal variation of surface zonal wind induced by the MJO as it traverses eastward from the western tropical Indian Ocean to the eastern tropical Pacific. Tp varies generally out of phase with Hs over the life cycle of the MJO, indicating that these MJO-wave anomalies are locally wind-generated rather than remotely generated by ocean swell.Pronounced Hs anomalies develop on the northwest shelf of Australia, where the MJO is known to influence sea level and surface temperatures, and in the western Caribbean Sea and Guatemalan–Panama Seas with enhanced wave anomalies apparent in the vicinity of the Tehuantepec and Papagayo gaps. Significant wave anomalies are also detected in the North Pacific and North Atlantic oceans in connection with the MJO teleconnection to the extratropics via atmospheric wave propagation. The impact in the north Atlantic stems from induction of the high phase of the North Atlantic Oscillation (NAO) about 1 week after MJO convection traverses the Indian Ocean, and the low phase of the NAO about one week after suppressed convection traverses the Indian Ocean. Strong positive Hs anomalies maximize on the Northern European coast in the positive NAO phase and vice versa for the negative NAO phase. The MJO also influences the occurrence of daily low (below the 5th percentile) and high (above the 95th percentile) wave conditions across the tropics and in the North Pacific and North Atlantic, emphasizing that the MJO may be a valuable source of intra-seasonal predictability of surface wave variability.

Assessment of the global ocean wind energy resource

Against a background of an environmental and resources crisis, the exploitation of renewable and clean energy can effectively alleviate the energy crisis and contribute to emission reduction and environmental protection, thus promoting sustainable development. This study aims to develop a grade classification map of the global ocean wind energy resource based on CCMP (cross-calibrated, multi-platform) wind field data for the period 1988–2011. We also calculate, for the first time, the total storage and effective storage of wind energy across the global ocean on a 0.25°×0.25° grid. An optimistic increasing long-term trend in wind power density was found. In addition, the global ocean wind energy resource was analyzed and regionalized by considering the temporal and spatial distributions of wind power density, wind energy levels, and effective wind speed, as well as through a consideration of wind energy storage and the stability and long-term trends of wind power density. This research fills a gap in our knowledge in this field, and provides a reference point for future scientific research and development into wind energy resources such as wind power generation, water pumping, and wind-heating.

Development of Geophysical Model Functions for Oceansat-2 Scatterometer

This letter deals with the development of geophysical model functions (GMFs) specific to Oceansat-2 Scatterometer (OSCAT) for the operational retrieval of global ocean surface vector winds from OSCAT. It makes use of OSCAT-measured radar backscatter and the surface analysis winds at 10-m neutral-stability height from the European Centre for Medium-Range Weather Forecasts (ECMWF) model for the period from November 2009 to April 2010. The OSCAT-specific GMF has been used for retrieving the vector solutions from backscatter data as well as for the directional ambiguity removal. The procedures and the conditions incorporated for the same are presented in this letter.

Wind characteristics in the North and Baltic Seas from the QuikSCAT satellite

ABSTRACTThe QuikSCAT mission provided valuable daily information on global ocean wind speed and direction from July 1999 until November 2009 for various applications including numerical weather prediction, ocean and atmospheric modelling. One new and important application for wind vector satellite data is offshore wind energy, where accurate and frequent measurements are required for siting and operating modern wind farms. The greatest advantage of satellite observations rests in their extended spatial coverage. This paper presents analyses of the 10 year data set from QuikSCAT, for the overview of the wind characteristics observed in the North and Baltic Seas, where most of Europe's offshore wind farms operate and more will be constructed. Significant issues in data availability are identified, directly related to the flagging schemes. In situ observations from three locations in the North Sea are used for comparisons. Mean biases (in situ minus satellite) are close to zero for wind speed and ‐2.7° for wind direction with a standard deviation of 1.2 m s − 1 and 15°, respectively. The impact of using QuikSCAT and in situ measurements extrapolated to 10 m for wind power density estimations is assessed, accounting for possible influences of rain‐contaminated retrievals, the sample size, the atmospheric stability effects and either fitting the Weibull distribution or obtaining the estimates from the time series of wind speed observations.Copyright © 2012 John Wiley & Sons, Ltd.

Improved Hurricane Ocean Vector Winds Using SeaWinds Active/Passive Retrievals

The SeaWinds scatterometer, onboard the QuikSCAT satellite, infers global ocean vector winds (OVWs); however, for a number of reasons, these measurements in hurricanes are significantly degraded. This paper presents an improved hurricane OVW retrieval approach, known as Q-Winds, which is derived from combined SeaWinds active and passive measurements. In this technique, the effects of rain are implicitly included in a new geophysical model function, which relates oceanic brightness temperature and radar backscatter measurements (at the top of the atmosphere) to the surface wind vector under both clear sky and in the presence of light to moderate rain. This approach extends the useful wind speed measurement range for tropical cyclones beyond that exhibited by the standard SeaWinds Project Level-2B (L2B) 12.5-km wind vector algorithm. A description of the Q-Winds algorithm is given, and examples of OVW retrievals are presented for the Q-Winds and L2B 12.5-km algorithms for ten hurricane overpasses in 2003-2008. These data are also compared to independent surface wind vector estimates from the National Oceanic and Atmospheric Administration Hurricane Research Division's objective hurricane surface wind analysis technique known as H*Wind. These comparisons suggest that the Q-Winds OVW product agrees better with independently derived H^ Wind analysis winds than does the conventional L2B OVW product.

Estimated global ocean wind power potential from QuikSCAT observations, accounting for turbine characteristics and siting

For the first time, global ocean usable wind power is evaluated for modern offshore turbine characteristics including hub height, usable portion of the wind speed distribution, and siting depth. Mean wind power increases by 30%, 69%, and 73% within the tropics and Northern and Southern Hemisphere extratropics, respectively, between hub heights of 10 m and 100 m. A turbine with a cut‐out speed of 25 m s−1 (30 m s−1) within the Northern Hemisphere storm track harvests between 55% (82%) and 85% (>98%) of available power. Within this region, a 2–3 m s−1 change in cut‐out speed can result in a 5–7% change in usable power. Eighty meter wind power accumulates at a rate of 20–45 MW km2 m−2 per meter depth increase from the shore to the shelf break. Beyond the shelf break, wind power accumulates at a slower rate (<12 MW km2 m−2 m−1). The combined impact of all three characteristics on available wind power is assessed for three technology tiers: existing, planned, and future innovations. Usable percent of 80 m available global ocean wind power ranges from 0.40% for existing to 2.73% for future envisioned turbine specifications. Offshore wind power production is estimated using three offshore wind turbine power curves, three ocean depth limits and two siting densities. Global offshore wind power is as much as 39 TW (54% of onshore) and is maximized for the smallest and least powerful of the three turbine specifications evaluated.

Global ocean wind power sensitivity to surface layer stability

Global ocean wind power has recently been assessed (W. T. Liu et al., 2008) using scatterometry‐based 10 m winds. We characterize, for the first time, wind power at 80 m (typical wind turbine hub height) above the global ocean surface, and account for the effects of surface layer stability. Accounting for realistic turbine height and atmospheric stability increases mean global ocean wind power by +58% and −4%, respectively. Our best estimate of mean global ocean wind power is 731 W m−2, about 50% greater than the 487 W m−2 based on previous methods. 80 m wind power is 1.2–1.5 times 10 m power equatorward of 30° latitude, between 1.4 and 1.7 times 10 m power in wintertime storm track regions and >6 times 10 m power in stable regimes east of continents. These results are relatively insensitive to methodology as wind power calculated using a fitted Weibull probability density function is within 10% of power calculated from discrete wind speed measurements over most of the global oceans.

An Ocean Surface Wind Vector Model Function for a Spaceborne Microwave Radiometer

Surface wind vector measurements over the oceans are vital for scientists and forecasters to understand the Earth's global weather and climate. In the last two decades, operational measurements of global ocean wind speeds were obtained from passive microwave radiometers (Special Sensor Microwave/ Imagers); and over this period, full ocean surface wind vector data were obtained from several National Aeronautics and Space Administration and European Space Agency scatterometry missions. However, since SeaSat-A in 1978, there have not been other combined active and passive wind measurements on the same satellite until the launch of Japan Aerospace Exploration Agency's Advanced Earth Observing Satellite-II in 2002. This mission provided a unique data set of coincident measurements between the SeaWinds scatterometer and the Advanced Microwave Scanning Radiometer (AMSR). The AMSR instrument measured linearly polarized brightness temperatures (TB) over the ocean. Although these measurements contained wind direction information, the overlying atmospheric influence obscured this signal and made wind direction retrievals not feasible. However, for radiometer channels between 10 and 37 GHz, a certain linear combination of vertical and horizontal brightness temperatures causes the atmospheric dependence to cancel and surface parameters such as wind speed and direction and sea surface temperature to dominate the resulting signal. In this paper, an empirical relationship between AMSR T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</sub> 's (specifically A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">.</sup> T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BV</sub> - T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BH</sub> ) and surface wind vectors (inferred from SeaWinds' retrievals) is established for three microwave frequencies: 10, 18, and 37 GHz. This newly developed wind vector model function for microwave radiometers can serve as a basis for wind vector retrievals either separately or in combination with active scatterometer measurements.

A satellite-derived climatology of global ocean winds

An interactive Climatology of Global Ocean Winds (COGOW) is presented based on 5 years (August 1999–July 2004) of QuikSCAT satellite measurements of wind speed and direction 10 m above the sea surface. This climatology provides the first high spatial resolution, observationally based, online atlas of ocean winds. Users can retrieve climatological wind maps and wind statistics, both in tabular and graphical form, from the COGOW web-based atlas. The global coverage of these data provides highly accurate information about the wind statistics in regions of the world ocean that are sparsely sampled by ships and buoys. A case study of the recovery of the vessel Ehime Maru off the Hawaiian Island of Oahu is presented to demonstrate the usage and value of COGOW. Evidence of air–sea interactions, one of many wind phenomena visible within COGOW, is discussed to further familiarize users with COGOW. Finally, the utility of COGOW with regard to various operational and research communities is summarized.

Nonlinear Effects of Variable Winds on Ocean Stress Climatologies

Abstract Variability in surface winds at subseasonal time scales can affect the estimates of the mean and seasonal stress over the ocean, owing to the nonlinear dependence of stress on wind speed. A global ocean wind product that merges the European Centre for Medium-Range Weather Forecasts (ECMWF) fields with satellite and in situ data is used to assess the nonlinear effects of wind variability, in particular synoptic signals (taken here to include all periods of <6 days), on estimates of the mean and the seasonal cycle in zonal and meridional stress. Climatologies based on the period March 1988–February 1999 are considered. Synoptic effects are most pronounced at mid- and high latitudes, where they can amount up to 20% of the mean or seasonal stress. Uncertainties in stress values associated with synoptic wind errors are assessed by comparing estimates from merged-data winds to those from original ECMWF winds. Differences in synoptic winds contribute noticeably to the stress differences. Outside the Tro...