QuikSCAT Data Research Articles

Transformative Urban Changes of Beijing in the Decade of the 2000s

The rapid economic growth, the exodus from rural to urban areas, and the associated extreme urban development that occurred in China in the decade of the 2000s have severely impacted the environment in Beijing, its vicinity, and beyond. This article presents an innovative approach for assessing mega-urban changes and their impact on the environment based on the use of decadal QuikSCAT (QSCAT) satellite data, acquired globally by the SeaWinds scatterometer over that period. The Dense Sampling Method (DSM) is applied to QSCAT data to obtain reliable annual infrastructure-based urban observations at a posting of ~1 km. The DSM-QSCAT data, along with different DSM-based change indices, were used to delineate the extent of the Beijing infrastructure-based urban area in each year between 2000 and 2009, and assess its development over time, enabling a physical quantification of its urbanization which reflects the implementation of various development policies during the same time period. Eventually, as a proxy for the impact of Beijing urbanization on the environment, the decadal trend of its infrastructure-based urbanization is compared with that of the corresponding tropospheric nitrogen dioxide (NO2) column densities as observed from the Global Ozone Monitoring Experiment (GOME) instrument aboard the second European Remote Sensing satellite (ERS-2) between 2000 and 2002, and from the SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY aboard of the ESA’s ENVIronmental SATellite (SCIAMACHY /ENVISAT) between 2003 and 2009. Results reveal a threefold increase of the yearly tropospheric NO2 column density within the Beijing infrastructure-based urban area extent in 2009, which had quadrupled since 2000.

Comparison of QuikSCAT, WRF and buoy ocean surface wind data off Valparaiso Bay, Chile

Abstract The winds that affect the surface of the ocean are also important for a vast array of activities, either operational or scientific, hence the importance of being able to adequately predict this quality. Because of the preceding fact, a Weather Research and Forecasting model was used to perform simulations at the surface of the Ocean, for winds derived from different boundary conditions (NCEP-CFSR, ERA-Interim and NCEP-FNL) and configured with different spatial resolution (25, 5 and 1 km), with the objective of evaluating which of these data sets delivers the more precise wind simulation at the surface of the ocean. A comparative analysis was performed between the different outputs of the WRF model, QuikSCAT satellite and in situ observations of a buoy installed off the central coast of Chile. The results showed that the WRF model, overestimates the wind magnitudes, across all boundary conditions or spatial resolution. Additionally, depending on the in situ wind magnitude (>6 ms−1), the model predicts adequately wind magnitude and direction. Spatial comparisons were performed between QuikSCAT and WRF outputs at the Chilean coast to evaluate any possible differences. The modeled winds showed a tendency to be stronger than those measured by Satellites and the bigger differences appeared closer to the shore. The wavelet coherence and phase analysis, confirmed that the model delivers precise wind information for frequencies greater than the daily cycle. Finally, the results of the simulation produced by the ERA-Interim analysis showed lower errors in terms of temporal and spatial variability of surface winds.

Scatterometer-derived long-term (2000 to 2017) variability in snowmelt and freeze in river basins of the Indian Himalayas

We present the scatterometer-derived snowmelt and freeze variability data for three river basins in the Indian Himalayas (the third pole)—knowledge about which is important for studying the climate and water resources of the area. We document the timing and duration of snowmelt/freeze across the Indian Himalayas. The analysis includes data from QuikSCAT (2000 to 2009), Oceansat Scatterometer (OSCAT) (2010 to 2013), Advanced Scatterometer (ASCAT) (2014 to 2016), and SCATSAT (2017). We utilize Ku-band HH-polarized data from QuikSCAT, OSCAT, and SCATSAT and C-band VV-polarized data from ASCAT and use the adaptive/variable threshold method based on winter mean and standard deviation. The analysis is based on the sensitivity of the backscatter coefficient to the presence of liquid water content in the snow caused due to melting. Past air temperature data available for seven Himalayan stations are used to validate the methodology. The melt/freeze detection is limited to areas where snow is available in that year. The majority of melt onset in the study area takes place in April. Melt onset for higher-elevation zones for the Indus, Ganges, and Brahmaputra basins varies from April 19 to May 20, April 17 to April 30, and April 7 to April 28, respectively. Major freeze onset for these basins is in October. The outputs generated from the analysis will be available on Bhuvan portal under the National Information System for Climate and Environment Studies program of Indian Space Research Organisation.

Wind velocity and wind curl variability over the Black Sea from QuikScat and ASCAT satellite measurements

Measurements of QuikScat for 2000–2009 and ASCAT scatterometers for 2009–2016 are used to describe the variability of the Black Sea wind velocity and wind curl. High-resolution (12.5 km) scatterometry data provide a possibility to investigate several small-scale features (“hot points”) of the wind field in the basin related to the topographic effects. They include: the gap winds near the Bosphorus, the Kerch Strait, and Tuapse; tip jets at the south and west of the Crimean peninsula, several capes of the Anatolian coast; the Kolkheti valley winds; wind shadow zones at the eastern Turkey coast and the western Crimean coast. Driving factors and directional variability of wind in these “hot points” is discussed.QuikScat and ASCAT data are used to describe seasonal and interannual variability of wind characteristics in 2000–2016. Comparison with in-situ data shows that QuikScat noticeably overestimates wind magnitude during low winds conditions (<2 m/s). ASCAT winds are generally lower than QuikScat, but the ASCAT wind curl is higher. The amount of low winds values (<2 m/s) in the ASCAT data is 6% of the total data, and it is 1% in the QuikScat data. Overestimation of low winds decreases wind gradients near the wind shadows, which is the most possible reason of the underestimation of the wind curl in the QuikScat dataset.Wind curl has a different sign and a completely different interannual variability in the western and eastern parts of the sea. Near the eastern coast of the basin, high-resolution satellite measurements reveal three powerful small-scales maximums of cyclonic vorticity. These maximums are related to the wind jets flowing around topographic obstacles in the vicinity of the Kerch Strait, in the mountain gap near Tuapse, and from the Kolkheti valley. They are observed throughout the whole year, but are largest in winter. The contribution of these maximums to the overall cyclonic vorticity is essential (30% in the QuikScat data and 50% in the ASCAT data). In the western part of the basin, the wind curl is negative (anticyclonic) on average, with the highest anticyclonic curl observed in summer. Winds are decreasing in 2000–2016 in agreement with the data on the 20 century. At the same time the basin-average wind curl rises due to its increase in the eastern Black Sea. We speculate that these trends are associated with the displacement of the Siberian Anticyclone to the west. The related rise of pressure over the eastern Europe correlates significantly with the wind curl variability on interannual time scales.

Snowmelt detection from QuikSCAT and ASCAT satellite radar scatterometer data across the Alaskan North Slope

The timing of seasonal snowmelt in high-latitude tundra has implications ranging from local biological productivity to global atmospheric circulation, yet remains difficult to quantify, particularly at large spatial scales. Snowmelt detection in such remote polar environments is possible using satellite-based microwave scatterometers, such as NASA’s QuikSCAT. QuikSCAT measured scattering in Ku-band, which is sensitive to snowmelt signals, from 1999 until the antenna failed in 2009. The Advanced Scatterometer (ASCAT) (2006–2021 (projected) operational), which operates at C-band, may be able to extend the QuikSCAT record, but existing techniques fail to adequately monitor tundra environments. Here, we designed a departure threshold algorithm to produce a consistent 15-year time series of melt onset for the tundra of the Alaskan North Slope, using the overlap period for the enhanced resolution datasets to calibrate the ASCAT melt detection record against QuikSCAT. We produced a time series of day of year of melt onset for 4.45 km x 4.45 km grid cells on the Alaskan North Slope from 2000–2014. Time series validation with in situ mean daily air temperature produced mean R2 values of 0.75 (QuikSCAT) and 0.72 (ASCAT). We qualitatively observed a difference between early-season melt, which occurred rapidly and was driven by strong wind events, and more typical melt, which occurred gradually along a latitudinal gradient. We speculate that future melt timing will have greater frequency of early-season onset as climate change destabilizes the high-latitude atmosphere.

Soil Moisture and Vegetation Water Content Retrieval Using QuikSCAT Data

Climate change and hydrological cycles can critically impact future water resources. Uncertainties in current climate models result in disagreement on the amount of water resources. Soil moisture and vegetation water content are key environmental variables on evaporation and transpiration at the land–atmosphere boundary. Radar remote sensing helps to improve our estimate of water resources spatially and temporally. This work proposes a backscattered power formulation for the Ku-band. Li et al. (2010) retrieved soil moisture and vegetation water content values using Windsat data and simultaneous collocated QuikSCAT backscattered power are used to estimate different parameters of backscatter formulation. These parameters are used to estimate soil moisture and vegetation water content using QuikSCAT power everywhere and every day during the summer season. The 2-folded cross validation method is used to evaluate the performance of soil moisture and vegetation water content retrieval. A relatively large correlation is observed between vegetation water content using WindSat and QuikSCAT data in land classes of Evergreen Needleleaf, Evergreen Broadleaf, Deciduous Broadleaf, and Mixed Forests. Similarly, the retrieved soil moisture using QuikSCAT in areas with bare surface fraction of greater than 60% shows relatively high correlation with WindSat values. QuikSCAT satellite collects data over land globally almost every day. Therefore, QuikSCAT data can be used to generate a global map of soil moisture and vegetation water content daily from 2000 to 2009.

Differential patterns of surface winds over Bay of Bengal during the various phases of Indian northeast monsoon derived from QuikSCAT data, 1999-2008

Differential patterns of surface winds over Bay of Bengal during the various phases of Indian northeast monsoon derived from QuikSCAT data, 1999-2008

RapidScat Cross-Calibration Using the Double Difference Technique

RapidScat is a National Aeronautics and Space Administration (NASA) Ku-Band scatterometer that was operated onboard the International Space Station between September 2014 and August 2016 when the mission effectively ended after an irrecoverable instrument failure. A unique non-Sun-synchronous orbit facilitated global contiguous geographical sampling between the ±56° latitude. For the first time, such an orbit enabled an overlap with other scatterometers flying in Sun-synchronous orbits. The double-difference technique was developed and successfully used for microwave radiometer calibration at the Remote Sensing Laboratory at the University of Central Florida, USA. This paper presents the extension of the double difference methodology to scatterometry. The methodology has been adopted for the cross-instrument calibration between RapidScat and QuikScat scatterometers simultaneously orbiting the Earth on-board two independent satellite platforms. The double-difference technique was deployed to compare measurements from these two scatterometers, as a more accurate alternative to the classic single difference approach. The work summarized in this paper addressed a cross-calibration algorithm developed and applied to RapidScat and QuikScat data in the period from January 2015 to March 2016. The initial results of the statistical analysis and biases between the two scatterometers are presented. Calculated biases may be used for measurement correction and reprocessing.

Evaluating Outer Tropical Cyclone Size in Reanalysis Datasets Using QuikSCAT Data

The present study examines the fidelity of outer tropical cyclone (TC) size and wind field structure in four atmospheric reanalysis datasets to evaluate whether reanalyses can be used to derive a long-term TC size dataset. Specifically, the precision and accuracy of reanalysis TC size for the North Atlantic (NA) and western North Pacific (WNP) basins are analyzed through comparison with a recently developed QuikSCAT TC size dataset (2000–09). Both outer TC size and structure in reanalyses closely match QuikSCAT data as revealed by strong correlations, similar standard deviations, and generally small biases. Of the TC size metrics examined, the radii of 6–8 m s−1 winds in the NA and radii of 6–10 m s−1 winds in the WNP are generally most comparable to QuikSCAT data. Compared to WNP TCs, NA TC size and structure are represented with greater fidelity. Among the four reanalyses examined, the National Centers for Environmental Prediction Climate Forecast System Reanalysis and the Japan Meteorological Agency Japanese 55-year Reanalysis represent TC size and structure with the greatest fidelity for both basins. Differences between reanalysis and QuikSCAT TC size increase with increasing QuikSCAT TC size in both basins and with decreasing TC latitude in the WNP. Finally, comparison of the distribution of reanalysis TC size during the satellite era with the distribution of QuikSCAT TC size suggests that reanalysis TC size is represented with reasonable fidelity throughout the satellite era and, thus, may be useful for constructing a multidecadal TC size dataset.

Antarctic ice-sheet near-surface snowmelt detection based on the synergy of SSM/I data and QuikSCAT data

Microwave radiometer SSM/I data and scatterometer QuikSCAT data have been widely used for the ice-sheet near-surface snowmelt detection based on their sensitivity to liquid water present in snow. In order to improve the Antarctic ice-sheet near-surface snowmelt detection accuracy, a new Antarctic ice-sheet near-surface snowmelt synergistic detection method was proposed based on the principle of complementary advantages of SSM/I data (high reliability) and QuikSCAT data (high sensitivity) by the use of edge detection model to automatically extract the edge information to get the distribution of Antarctic snowmelt onset date, snowmelt duration and snowmelt end date. The verification result shows that the proposed snowmelt synergistic detection method improves the detection accuracy from about 75% to 86% based on AWS (Automatic Weather Stations) Butler Island and Larsen Ice Shelf. The algorithm can also be applied to other regions, which provides methodological support and supplement for the global snowmelt detection.

Near-surface wind variability over the broader Adriatic region: insights from an ensemble of regional climate models

Over the past few decades the horizontal resolution of regional climate models (RCMs) has steadily increased, leading to a better representation of small-scale topographic features and more details in simulating dynamical aspects, especially in coastal regions and over complex terrain. Due to its complex terrain, the broader Adriatic region represents a major challenge to state-of-the-art RCMs in simulating local wind systems realistically. The objective of this study is to identify the added value in near-surface wind due to the refined grid spacing of RCMs. For this purpose, we use a multi-model ensemble composed of CORDEX regional climate simulations at 0.11° and 0.44° grid spacing, forced by the ERA-Interim reanalysis, a COSMO convection-parameterizing simulation at 0.11° and a COSMO convection-resolving simulation at 0.02° grid spacing. Surface station observations from this region and satellite QuikSCAT data over the Adriatic Sea have been compared against daily output obtained from the available simulations. Both day-to-day wind and its frequency distribution are examined. The results indicate that the 0.44° RCMs rarely outperform ERA-Interim reanalysis, while the performance of the high-resolution simulations surpasses that of ERA-Interim. We also disclose that refining the grid spacing to a few km is needed to properly capture the small-scale wind systems. Finally, we show that the simulations frequently yield the accurate angle of local wind regimes, such as for the Bora flow, but overestimate the associated wind magnitude. Finally, spectral analysis shows good agreement between measurements and simulations, indicating the correct temporal variability of the wind speed.

The Potential for Estimating Snow Depth With QuikScat Data and a Snow Physical Model

Active microwave remote sensing is a promising tool for global snow water equivalent (SWE) mapping. However, many studies have shown that more information is needed to estimate the SWE accurately. A very important problem is characterizing the snow grain size and quantitatively separating the effects of grain size and snow mass on the backscattering magnitude. In this letter, QuikScat backscattering coefficient data are used to estimate snow depth, with the snow grain size, density, and temperature estimated from the snow thermal model, driven by the Global Land Data Assimilation System forcing data. Considering the spatial resolution and the incident angle of the enhanced resolution QuikScat data, the estimation is applied to flat farm land and grass land. The snow thermal simulated snow grain size was found to be well correlated with the QuikScat measurements of the effective scattering coefficient, and the relationship between them is calibrated using data from one site in 2008–2009. Then, this calibrated relationship is used to estimate the snow depth at other sites. The results show that the snow thermal model simulated grain size can be used to improve the snow depth estimation from active microwave remote sensing.

Turbulent kinetic energy of the ocean winds over the Kuroshio Extension from QuikSCAT winds (1999–2009)

AbstractWe investigate mesoscale turbulence (10–1000 km) in the ocean winds over the Kuroshio Extension ( N– N, E– E) using the QuikSCAT data set (November 1999 to October 2009). We calculate the second (Djj) and third‐order structure functions (Djjj) and the spatial variance (Vj) as a function of scale r ( denotes, respectively, the longitudinal (divergent) and transverse (vortical) component). The most interesting results of the analysis follow. Although both and measure the turbulent kinetic energy (TKE), we find that is the more robust measure. The spatial variance density (dVj/dr) has a broad peak near 450 km (close to the midlatitude Rossby radius of deformation). On interannual time scales, TKE correlates well with the El Niño 3.4 index. According to turbulence theory, the kinetic energy cascades downscale (upscale) if (also skewness ) is negative (positive). Our results for the Kuroshio Extension are consistent with a downscale cascade (indicating convergence dominates). Furthermore, classical turbulence theory predicts that and independent of r; however, we find SL varies strongly with r, from −4 at small scales to −0.3 at large scales. This nonclassical behavior implies strong‐scale interaction, which we attribute to the rapid, and sometimes explosive, growth of storms in the region through baroclinic instability. Finally, we find that ST (a measure of cyclonic/anticyclonic asymmetry) is positive (cyclonic) and also varies strongly with r, from 4 at small scales to 0.5 at large scales. New turbulence models are needed to explain these results, and that will benefit Weather Prediction and climate modeling.

Comparison of Remotely Sensed Wind Data over Sulawesi and Maluku Islands Sea Areas

&lt;p class="TTPKeywords"&gt;&lt;span&gt;In order to obtain accurate prediction of ocean wind energy, long term data are needed. However, one data sources might not able to provide long duration data. Therefore, the data need to be combined with other sources of data. However, before combining the data, it is important to compare and validate them to confirm their accuracy. In the present study, wind speed data collected by QuikScat and SSM/I (SSMIS) missions are compared and analyzed. QuikScat data were collected by a satellite with the same name, while Special Sensor Microwave Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data are processed and offered by Remote Sensing System (RSS). SSM/I (SSMIS) are passive microwave radiometers carried onboard Defense Meteorological Satellite Program (DMSP). For the comparison, 5 (five) arbitrary positions over Sulawesi and Maluku islands sea areas are chosen for the analyses. For the evaluation purposes, beside time series of daily data from several chosen positions in research location, several statistical parameters are also computed and compared such as mean, standard deviation, root mean square (RMS), correlation coefficient. &lt;/span&gt;&lt;/p&gt;

Comparison of Remotely Sensed Wind Data over Sulawesi and Maluku Islands Sea Areas

&lt;p class="TTPKeywords"&gt;&lt;span&gt;In order to obtain accurate prediction of ocean wind energy, long term data are needed. However, one data sources might not able to provide long duration data. Therefore, the data need to be combined with other sources of data. However, before combining the data, it is important to compare and validate them to confirm their accuracy. In the present study, wind speed data collected by QuikScat and SSM/I (SSMIS) missions are compared and analyzed. QuikScat data were collected by a satellite with the same name, while Special Sensor Microwave Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data are processed and offered by Remote Sensing System (RSS). SSM/I (SSMIS) are passive microwave radiometers carried onboard Defense Meteorological Satellite Program (DMSP). For the comparison, 5 (five) arbitrary positions over Sulawesi and Maluku islands sea areas are chosen for the analyses. For the evaluation purposes, beside time series of daily data from several chosen positions in research location, several statistical parameters are also computed and compared such as mean, standard deviation, root mean square (RMS), correlation coefficient. &lt;/span&gt;&lt;/p&gt;

ASCAT and QuikSCAT Azimuth Modulation of Backscatter Over East Antarctica

For most land and ice surfaces, the measured radar backscatter at the spatial resolution of a wind scatterometer is insensitive to the azimuth angle. However, for regions of East Antarctica, the backscatter strongly depends on the azimuth angle. This relationship between backscatter and azimuth angle is often modeled with a Fourier series. Although previous work has separately examined the data from QuikSCAT, a Ku-band scatterometer, and the Advanced Scatterometer (ASCAT), a C-band scatterometer, this letter compares the two on the same high-resolution grid. We find that, although QuikSCAT has superior azimuth angle coverage (due to its measurement geometry) compared to ASCAT, both are suitable to estimate the radar backscatter azimuth angle modulation of East Antarctica. The ASCAT data exhibit a much larger azimuth modulation than the QuikSCAT data. This is attributed to the different wavelengths of the microwave signal: At C-band (5.7 cm), East Antarctica has features that show radar backscatter to be more dependent on azimuth angle than at Ku-band (2.2 cm). This letter also examines the ASCAT and QuikSCAT azimuth modulation over previously identified regions of wind glaze. Although azimuth modulation is expected to be minimal over wind glaze, we find the wind-glaze regions to contain more structure than previously suggested.

Improving Multiyear Ice Concentration Estimates With Reanalysis Air Temperatures

Multiyear ice (MYI) characteristics can be retrieved from passive or active microwave remote sensing observations. One of the algorithms that combine both observations to identify partial concentrations of ice types (including MYI) is the Environment Canada Ice Concentration Extractor (ECICE). However, cycles of warm-cold air temperature trigger wet-dry cycles of the snow cover on MYI surface. Under wet snow conditions, anomalous brightness temperature and backscatter, similar to those of first-year ice (FYI), are observed. This leads to misidentification of MYI as being FYI, hence decreasing the estimated MYI concentration suddenly. The purpose of this paper is to introduce a correction scheme to restore the MYI concentration under this condition. The correction is based on air temperature records. It utilizes the fact that the warm spell in autumn lasts for a short period of time (a few days). The correction is applied to MYI concentration retrievals from ECICE using an input of combined QuikSCAT and AMSR-E data, acquired over the Arctic region in a series of autumn seasons from 2003 to 2008. The correction works well by replacing anomalous MYI concentrations with interpolated ones. For September of the six years, it introduces over 0.1×10 6 km 2 MYI area, except for 2005. Due to the regional effect of warm air spells, the correction could be important in the operational applications where ice concentrations are crucial on small scale and mesoscale.

Remote Sensing Observations of the Winter Yellow Sea Warm Current Invasion into the Bohai Sea, China

Using ASCAT, QuikSCAT, and MODIS data, we analyzed the sea surface wind field, temperature, salinity, and chlorophyll concentrations in the mixed zone between the Bohai Sea and Yellow Sea in the winter (the period of winter 2013 included December 2013 and January-February 2014) from 2002 to 2013. We found that the intrusion of the Yellow Sea Warm Current into the Bohai Sea occurred three times in the winters of 2007 (strongest), 2004, and 2013 (weakest) during this 12-year period. We present detailed validation of the intrusion in 2013. This study shows that the intrusion of the Yellow Sea Warm Current into the Bohai Sea occurred when the wind speed, sea surface temperature, and salinity were above (or close to) the multiyear average and the chlorophyll concentration was less than the multiyear average.

Numerical simulations of SAR microwave imaging of the Brazil current surface front

Abstract This paper analyzes the hydrodynamic and atmospheric instability modulation mechanisms which influence the Brazilian Current's (BC) thermal front signature in Synthetic Aperture Radar (SAR) images. Simulations were made using the M4S SAR imaging model. Two SAR images of the Brazilian Southeastern Coast depicting the BC's thermal front were selected including a VV (ASAR/Envisat) and a HH polarization (RADARSAT-1) image. Conditions of current shear and divergence were reproduced for the fronts imaged, using in situ (Acoustic Doppler Current Profilers) current velocities. Wind velocity fields were simulated based on QuikSCAT data. Results showed that SAR imaging of the BC front may be influenced both by atmospheric instabilities and hydrodynamic modulations. The first mechanism prevailed on the RADARSAT image and the latter on the ASAR/Envisat image. When atmospheric instabilities prevailed, the contribution of shear and divergence was almost negligible. When hydrodynamic modulations prevailed, a better agreement between the simulated responses and SAR image responses was obtained by inforcing a reduction of 88% in the relaxation rate, and higher divergence values, of the order of 10-4 s-1. Results indicate that, for some specific cases, local increases in shear and divergence may allow the detection of the BC thermal front.

Climatology of North Sea wind energy derived from a model hindcast for 1958–2012

Model-based wind speed data derived from the coastDat2 data set for the North Sea were used to assess wind power potential considering both spatial and temporal variability. The atmospheric part of coastDat2 was simulated with the regional climate model COSMO-CLM 4.8. The quality of the used wind speed data is analysed by comparison with buoy and QuikSCAT data. To determine where an offshore power plant can be cost-effectively developed, the distribution of the possible production dependencies on the offshore distance is one of the more important factors. A synthetic power function was used to convert the model-derived wind speeds at a height of 100m to wind power. The data were analyzed for the period of 1958–2012, and the results obtained for the decadal and spatial variability were mapped. The site related summaries are discussed.The inter-annual to decadal variability can reach up to 5% from the multi-decadal mean and therefore plays an important role in wind energy; wind power estimates based on short observational time series, particularly from the late 1990s, may exhibit high biases. The up-scaling from wind speeds at a height of 10m using conventional power laws may result in similar biases. On inter-annual to decadal time scales, synergies are not expected from the different arrays in the North Sea, i.e., a decrease in the power output of an array may not be balanced by another.