SATELLITE wind scatterometer instruments are active remote

Transcription

1 2340 IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, VOL. 10, NO. 5, MAY 2017 Improved Use of Scatterometer Measurements by Using Stress-Equivalent Reference Winds Jos de Kloe, Ad Stoffelen, and Anton Verhoef Abstract Numerical weather prediction (NWP) and buoy ocean surface winds show some systematic differences with satellite scatterometer and radiometer wind measurements, both in statistical results and in local geographical regions. It is possible to rescale these reference winds to remove certain aspects of these systematic differences. Space-borne ocean surface winds actually measure ocean surface roughness, which is related more directly to stress. Air mass density is relevant in the air sea momentum transfer as captured in the stress vector. Therefore, apart from the already common neutral wind correction for atmospheric stratification, also a mass density wind correction is investigated here to obtain a better correspondence between satellite stress measurements and buoy or NWP winds. The bicorrected winds are called stressequivalent winds. Stress-equivalent winds do not strongly depend on the drag formulation used and provide a rather direct standard for comparison and assimilation in user applications. This paper presents details on how this correction is performed and first results that show the benefits of this correction mainly in the extratropical regions. Index Terms Meteorology, radar remote sensing, sea surface, stress measurement. I. INTRODUCTION SATELLITE wind scatterometer instruments are active remote sensing instruments emitting microwave radiation and detecting its scattering (σ 0 ) from the ocean surface [1], [2]. This backscatter is mainly modulated by the ocean surface roughness, which in turn is directly related to surface wind stress. Similarly, the microwave emission from the ocean surface is modulated by the cm-scale roughness of the ocean. These measurements do not depend on atmospheric properties, such as surface layer (SL) stability and air mass density. Wind stress is relevant for the air sea interaction, forcing of ocean models, wave models, and surge models, and thus, a large scatterometer user community is interested in using scatterometer satellite stress measurements [3]. However, for validation purposes, it is practical to use the widely available and World Meteorological Organization standard 10-m winds from moored buoys or numerical weather models. Weather models are closely moni- Manuscript received June 30, 2016; revised November 30, 2016 and March 1, 2017; accepted March 16, Date of publication April 26, 2017; date of current version May 24, This work was supported by the Wind, Ice, and Temperature at the Sea Surface Project, funded by the Copernicus Marine Environment Monitoring Service. (Corresponding author: Jos de Kloe.) The authors are with the Royal Netherlands Meteorological Institute, De Bilt 3731, The Netherlands ( kloedej@knmi.nl; ad.stoffelen@knmi.nl; anton.verhoef@knmi.nl). Color versions of one or more of the figures in this paper are available online at Digital Object Identifier /JSTARS tored by in situ wind measurements, among others, and their parameterizations are continuously improved to correctly simulate the 10-m wind, rather than the wind stress. Currently, wind stress inputs to ocean models are obtained by using SL parameterizations of the atmospheric drag. The verification of these parameterizations is difficult as calibrated in situ surface stress measurements are not widely available. Uncertainties or differences in stress parameterizations are large and typically 30% [4]. In order to allow for accurate verification, most satellite surface wind producers prefer to relate to a wind vector measurement valid at this reference height of 10 m ( u 10 ) instead of to a wind stress vector measurement ( τ). For scatterometers, the backscatter is related to the wind vector at 10 m (u 10 ) using a geophysical model function (GMF). Because of the complexity of the relation between the ocean-modulated scattering and wind speed and direction this GMF is derived empirically, by comparing a large amount of scatterometer data with corresponding buoy and Numerical weather prediction (NWP) model data, combined with additional measurements like aircraft campaign data and symmetry considerations. It must be noted that the GMFs that are currently in use have been designed to only depend on properties of the measurements that are combined to perform wind retrieval, i.e., normalized radar cross section (NRCS or σ 0 ), look angle, incidence angle, wavelength, and polarization of the different radar measurements. We choose not to allow any NWP model or SL model dependence in the GMFs that we have designed since SL models are rather uncertain as already mentioned. This prevents dependence on any particular SL model, which otherwise could lead to undesired effects like correlation between modeling errors and scatterometer winds that are propagated to user applications, such as NWP data assimilation. This approach has two obvious limitations. First, the GMF is derived to be valid at 10 m above the surface, but the backscatter modulation itself happens at the rough ocean surface. This implicitly assumes a certain given shape for the vertical wind profile in the atmospheric SL, which is used to translate the ocean roughness to a wind vector valid at 10 m above the surface. If the GMF is derived from a global dataset, this profile shape corresponds to a global average of these profile shapes. Since the actual profiles vary depending on stable, neutral, or unstable boundary layer conditions, this will have an effect on the measured 10-m wind. In a global statistical analysis, it will manifest itself as an increase in standard deviation when This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see

2 DE KLOE et al.: IMPROVED USE OF SCATTEROMETER MEASUREMENTS BY USING STRESS-EQUIVALENT REFERENCE WINDS 2341 comparing to other wind information, and this is easily mistaken to be an increased random measurement error. However, locally the effects are systematic. This effect is corrected for by converting reference buoy or NWP wind data to equivalent neutral winds and to use these to derive the GMF. Equivalent neutral winds are obtained by computing surface roughness parameters from the 10-m NWP winds or buoy winds and then subsequently use these surface parameters to compute the 10-m wind assuming neutral atmospheric stratification. Since stability information and stability parameterizations are adequately known in general, equivalent neutral NWP and buoy winds provide a physically more consistent comparison to scatterometer-derived winds than the unscaled 10-m reference winds. This, for example, is how the well-known CMOD5.n GMF was derived [5], [6], [7]. Section II gives some more details on how this equivalent neutral wind conversion is performed. Similarly, the backscatter modulation or ocean roughness on the cm scale mainly depends on the way the wind interacts with the surface, so on wind surface stress, rather than on near surface wind speed. In fact, at a given wind speed cold air will generate more impact on the ocean surface than warm air would, due to the higher air mass density. Therefore, it would be physically more consistent when air mass density was taken into account when comparing scatterometer or radiometer winds to buoys or NWP model winds. Moreover, variations in air mass density over the globe are rather predictable and conversion to a scaled 10-m wind, taking out the surface effect of air mass density, is expected to be favorable for scatterometer or radiometer wind comparisons. Section III will summarize the details needed to do this so called stress-equivalent wind conversion. It must be noted that the scatterometer or radiometer wind results are not altered or changed in any way after a GMF has been chosen and applied for wind inversion. The scatterometeror radiometer-retrieved winds are, in our view, best described by stress-equivalent winds. Only the reference NWP or buoy winds should be rescaled to simulate stress-equivalent winds. We note that the GMF is being refitted using these adapted stress-equivalent winds as input reference. By doing so, the full benefit of this change can be seen in the statistical results when comparing reference data (like NWP, buoy) with scatterometer wind results. At the European organisation for exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility (OSI SAF) work is ongoing to include these effects in a next GMF for C-band scatterometry, called CMOD7 [8]. Section V will show examples of local improvement when stress-equivalent reference winds are compared to ASCAT scatterometer results. Finally, some conclusions are presented in Section VII. II. EQUIVALENT NEUTRAL WIND CONVERSION The wind retrieved from satellite measurements is fitted to the surface wind at 10-m altitude. This is an integral part of the GMF fit procedure. The model reference wind at 10 m (u 10 ) is designed to be (on average) close to the true wind as measured by Fig. 1. Schematic view of different SL wind profiles, which for identical surface wind give different 10-m wind. This shows how the actual 10-m wind may differ from the neutral wind if the atmospheric stability is not neutral (just for illustration, not calculated from any SL model). buoys, among others. These 10-m winds are translated to an equivalent-neutral wind by using a SL model. Equivalent-neutral winds are physically more consistent with satellite winds and a better resource for comparison. This process is called neutral wind conversion and has been applied since the first spaceborne scatterometers [9]. First the reference buoy or NWP model wind is translated to the near surface wind using a realistic SL wind profile estimated from local buoy measurements or NWP model parameters, respectively, and then translated to 10 m again using an average (neutral) wind profile in order to make the satellite wind comparisons independent of the atmospheric stability conditions. This process is illustrated in a schematic way in Fig. 1. The Neutral wind conversion scales the reference NWP or buoy wind to the surface wind, depending on stable (red line) or unstable (blue line), and then up to 10 m again using a neutral profile (green line). This ensures that the reference wind is better suitable to be used as reference to be compared to the satellite surface wind measurements. To convert the NWP reference wind to neutral wind the SL model and supporting data must be used, which is part of the model code to avoid inconsistencies. Portabella and Stoffelen [4] found that distributions of air-sea temperature difference at the NWP model scale are different from those measured locally at buoys; this is compensated for by tuning of the SL model in order to provide consistent 10-m winds in all air stability conditions [10]. Similarly, the LKB method is known to work well locally for buoy data [11], The two SL models are compared by Portabella and Stoffelen in [4]. In this study the NWP model reference wind at 10 m (u 10 ) was converted using a stand-alone version of what is used in the ECMWF model. This model estimates the atmospheric stability near the ocean surface using a form of the Monin Obukhov stability theory, in which a Charnock relation is used to calculate the roughness length. The exact details are not repeated here for brevity, and can be found in [12]. The ECMWF Fortran implementation (called OCFLX) was used to calculate the u 10n

3 2342 IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, VOL. 10, NO. 5, MAY 2017 wind fields used in this study and its outputs have been tested satisfactorily against the operational ECMWF model outputs of equivalent-neutral winds. The corrections discussed in this paper only consider wind speed. However, also wind direction varies with altitude, and also this relation depends on the presence of stable or unstable atmospheric conditions. This wind turning effect currently may lead to wind direction biases in the order of 10, and may partially be explained by too much turbulent mixing in the boundary layer of current NWP models[13]. However, this turning does not stop at 10-m altitude, and continues to the surface, causing a difference between surface stress direction and 10-m wind vector direction (see [13, Figs. 2 and 7]). This turning shifts sign on both hemispheres, and since the GMF is a global average, it is not taken into account in the GMF. A future correction could compensate for this effect, taking known atmospheric stability and latitude into account. III. STRESS-EQUIVALENT WIND CONVERSION In the current paper a simplified version of the stress calculation is used in which wind surface stress is taken to be proportional to the square of the neutral wind speed u 2 10n, the local air mass density ρ and a drag coefficient C D that contains all remaining complexities (it may depend on sea state, sea surface temperature (SST), and also on the surface wind itself, etc.). Often the neutral stability drag coefficient is used for C D, but we have chosen to use a drag coefficient that relates stressequivalent wind to wind stress (see Section VI for details). This relation has been used to convert the model reference neutral winds u 10n to so-called stress-equivalent winds (u 10s ). To rescale the model reference equivalent-neutral wind at 10 m (u 10n ) to stress-equivalent wind, it must be translated to the corresponding surface wind stress for actual air mass density (as defined by the NWP model), in order to be representative of the generation of the cm-scale roughness by air sea momentum exchange. This is identical to what was done in [14] and [15]. Subsequently, it is rescaled with the global average air mass density, and scaled back again to 10 m, using the same SL wind profile. This process is illustrated in Fig. 2. The starting point for computing the stress-equivalent wind conversion is the equivalent-neutral wind u 10n as described in Section II. From this the wind stress vector τ can be calculated [16] as τ = ρc D u 10n u 10n. (1) The current scatterometer GMF relations have been derived using reference input winds without taking the air density into account. However, backscatter is closely related to surface wind stress and not only to surface wind speed. This means that the GMF-derived retrieved scatterometer winds implicitly take a global average air density and drag coefficient into account. An obvious improvement in the use of scatterometer winds should be to implement use of more realistic air density values. Air density can easily be calculated from surface Fig. 2. Schematic view of different surface winds that give identical surface stress for different air mass densities. This shows how the actual 10-m wind may differ from the scatterometer wind if the air mass density differs from the average air mass density. (just for illustration, not calculated from any SL model). parameters available in NWP models (pressure, temperature, and humidity). A common formulation is [17] T v = ( q)T [K] (2) ρ air = p [kg/m 3 ] (3) RT v in which R = [J/kg/K] is a constant obtained by combining the universal gas constant with the molecular weight of dry air; T v [K] is the virtual temperature; q is the specific humidity; T [K] is the 10-m temperature; ρ air [kg/m 3 ] is the air density; and p [N/m 2 ] is the surface atmospheric pressure. Using this actual air density the corrected reference wind can be calculated as follows: ρair u 10s = u 10n ρ air. (4) In which ρ air is a global average air density value above the ocean, and is taken to be the constant value of [kg/m 3 ] Since C D is implicitly incorporated in the GMF, the use of u 10n or u 10s implies that C D implicitly only depends on the input variables of wind speed and direction, which may be an oversimplification. This is further discussed in Section VI. The effect of air mass density correction shows a clear local variability, as well as a seasonal dependency as is shown in Figs. 3 and 4. IV. DETAILS ON DATASETS USED FOR THIS WORK The results presented in this paper are based on the OSI SAF operational near-real-time (NRT) wind product of the ASCAT instrument on Metop-A produced by KNMI [18]. From the 25- km product, the level 2 data in NetCDF format were used for the full year These scatterometer measurements have been associated with NWP reference winds taken from the ECMWF ERA-Interim model.

4 DE KLOE et al.: IMPROVED USE OF SCATTEROMETER MEASUREMENTS BY USING STRESS-EQUIVALENT REFERENCE WINDS 2343 Fig. 3. Expected wind correction when taking air mass density derived from the ERA interim model in to account. This is identical to the difference u 10s u 10n. Global map showing the average effect for January Only locations with a valid scatterometer wind where included in the averaging. Fig. 4. Same as Fig. 3 but now for July The neutral wind correction method needs model information for the humidity, temperature, Charnock parameter, and SST as input. These values are extracted at forecast steps of 3 up to 15 hours for each model run and linearly interpolated to the scatterometer times and locations (except for the SST that is only available at analysis time). The stress-equivalent wind correction method needs model information for the humidity, SST, and sea surface pressure to calculate the air mass density. Also these values are extracted at forecast steps of 3 up to 12 hours for each model run and linearly interpolated to the scatterometer times and locations. Before the statistical results were created, the data flagged 1 to be (partially) over ice or land were discarded. In addition data marked with the KNMI QC flag, too low NRCS signal or too high noise level were discarded [18]. V. STRESS-EQUIVALENT WIND COMPARISON RESULTS To show the benefit of applying the stress-equivalent wind conversion to NWP reference winds, a latitude dependence of the average of the difference between the scatterometer wind and the model reference winds (u 10, u 10n, and u 10s )isshown in Fig. 13. For neutral winds this clearly shows that almost everywhere the model wind speeds are closer to the scatterometer 1 The flags used to discard data are described in the ASCAT Wind Product User Manual [18], section 5.2, page 14. The data with these Fortran bits set in the quality code are discarded: bit 14 (some_portion_of_wvc_is_over_ice) bit 15 (some_portion_of_wvc_is_over_land) bit 17 (knmi_quality_control_fails) bit 20 (any_beam_noise_content_above_threshold) bit 22 (not_enough_good_ sigma0_for_wind_retrieval). wind speeds (i.e., the difference is on average closer to zero) than uncorrected NWP winds. For stress-equivalent winds this shows that the model wind speeds are closer to the scatterometer wind speeds in the storm track region (above 45 and below 60 ). In other regions, especially the (sub)tropics, there seems to be a small increase in distance between model and scatterometer winds, possibly due to local ERA-interim bias. As a reference, the difference between u 10s and u 10n, which corresponds exactly to the air density correction to the NWP wind, is plotted as a black line. Only data points on locations that have a valid scatterometer wind where included, so all land and ice has been removed from this curve. The predicted correction explains up to 0.2 m/s average bias between scatterometer and ERA model in the higher latitude regions. This is clearly significant when compared to the wind bias observed between latitudes of 65 southupto75 north. Closer to the poles and to the ice edges, the large-scale correction remains favorable, but biases remain, up to 1 m/s or more, so other systematic model errors may become more dominant there. It should be noted that these regions with very high bias have very little data, so will not contribute much to the density plots shown in Figs. 16 and 17. Similar plots in which winter and summer months are selected are given in Figs. 14 and 15. These clearly show that the effect is significant in summer and winter. Although a clear seasonal variation can be seen, there still is a latitude-dependent systematic bias caused by the pole-to-equator air temperature difference that should be taken into account. In fact, the seasonal effect in the air density correction clearly resembles the actual large-scale wind bias. In the northern hemisphere winter months (JFM) the bias in the northern hemisphere storm track region increases, and so does the air density correction in this region. The same happens in the southern hemisphere winter months (JAS) in the southern hemisphere storm track region. When looking at the geographical distribution of the stressequivalent wind correction, it is very clear that in polar regions and especially near the ice edge and the coast of Greenland and Canada (Newfoundland) in winter time, the difference between scatterometer and reference NWP wind decreases when the reference wind is converted from neutral wind to stressequivalent wind. This local effect is shown in Figs. 5, 6, and 7. The expected wind correction from the air density correction effect for this area and month is given in Fig. 8. These are regions that often show air mass density well above the average, especially in winter time. On the other hand, this difference increases in the tropics, where air mass density is relatively low, but the equivalent-neutral ERA-interim winds were already close to ASCAT stress-equivalent winds. An example is shown is Figs. 9, 10, and 11. The expected wind correction from the air density correction effect for this area and month is given in Fig. 12. Verspeek et al. [8] show that ERA-interim winds are biased low by about 0.1 m/s w.r.t. ECMWF operational winds, probably due to their smoothness. Moreover, in triple collocation, ECMWF operational winds are better calibrated w.r.t. moored buoys than ERA-interim winds. Taking these additional results into account, stress-equivalent winds appear beneficial.

5 2344 IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, VOL. 10, NO. 5, MAY 2017 Fig. 5. Average difference between ASCAT-A windspeed and ERA interim u 10 for the month of January 2012 in the North-Atlantic region. Fig. 9. Average difference between ASCAT-A windspeed and ERA interim u 10 for the month of January 2012 in the Tropical-Atlantic region. Fig. 6. Same as Fig. 5 but now using u 10n as reference. Fig. 10. Same as Fig. 9 but now using u 10n as reference. Fig. 7. Same as Fig. 5 but now using u 10s as reference. Fig. 11. Same as Fig. 9 but now using u 10s as reference. Fig. 8. Expected air density correction effect. Same as Fig. 3 but zoomed to the same area as the above Figs. 5, 6, and 7. The systematic local effect, up to 0.5 m/s is clearly visible. Local systematic errors in NWP models are of diverse origin. Sandu et al. [13] report about the lack of mixing in the stable marine SL, while Lin et al. [19] report about large uncertainties in moist convection. Moreover, scatterometers provide a wind relative to the ocean surface, while NWP model and buoy winds are relative to a fixed earth reference, such that ocean currents Fig. 12. Expected air density correction effect. Same as Fig. 3 but zoomed to the same area as the above Figs. 9, 10, and 11. The systematic local effect, up to 0.2 m/s is clearly visible. are ignored in the latter. Many of these errors depend on the climatological regime and are visible as modulations in Figs. 13, 14, and 15. Scatterometer errors, like the viscosity effect that is related to SST (as already mentioned in [9]), are not a likely explanation,

6 DE KLOE et al.: IMPROVED USE OF SCATTEROMETER MEASUREMENTS BY USING STRESS-EQUIVALENT REFERENCE WINDS 2345 Fig. 13. Latitude dependence of the difference between scatterometer u 10s and ECMWF ERA-Interim winds u 10 (blue line), u 10n (green line), and u 10s (red line) in (m/s) for ASCAT-A 25 km L2 product (full year 2012). The air density correction to the wind corresponds exactly to the difference between u 10s and u 10n, and is plotted as a black line. The dotted line displays the amount of available winds for each latitude bin. Fig D histogram of the difference between scatterometer u 10s and ECMWF ERA interim u 10n (m/s) for ASCAT-A 25-km L2 product (full year 2012, abs(lat)>45) against air mass density (kg/m 3 ). Fig. 14. Same as Fig. 13, but now only the data from January, February, and March 2012 is included. Fig. 15. Same as Fig. 13, but now only the data from July, August, and September 2012 is included. Fig. 17. Same as Fig. 16 but now the difference between scatterometer u 10s and ECMWF ERA interim stress-equivalent wind u 10s (m/s) for ASCAT-A 25-km L2 product (full year 2012, abs(lat)>45) against air mass density (kg/m 3 ). since this effect is much stronger for Ku-band than it is for C-band [20] and spatially smooth. The air mass density dependence of this wind speed difference can convincingly be shown if data are selected in the latitude regions above 45 and below 45. This is shown in Figs. 16 and 17. Fig. 16 shows a two-dimensional (2-D) histogram of the wind speed difference between scatterometer winds (u 10,se,scat ) and reference neutral model winds (u 10n,nwp ). The data distribution clearly is skewed. If the data is divided in a range of air density bins, the average wind speed difference in each bin (as overplotted by the black line in the figure) clearly forms a line that deviates from the vertical, meaning that a relation between wind speed difference and air mass density is present. The red line is the result of a linear regression fit to the black data points. The slope and intercept as well as the correlation between the black points and the fitted red line are displayed in the white text at the bottom right of the plot. The amount of selected wind results included in the plot are displayed in the white text at

7 2346 IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, VOL. 10, NO. 5, MAY 2017 the bottom left of the plot, both in absolute number and as percentage relative to the global amount of available data resulting from the latitude selection that was applied. If the same exercise is done for reference stress-equivalent winds (u 10s ) the data distribution is more regular, and the over plotted line is clearly closer to the vertical. The slope changes from for u 10n to for u 10s. This does not explain all correlation between air density and wind speed difference between model and scatterometer. Other factors that may contribute could for example be the low resolution of the ERAinterim model, which may cause extra differences. A similar comparison to the most recent operational ECMWF model may confirm this, but is out of scope of the current study. From earlier triple collocation studies [21] it is known that NWP model errors are clearly larger than the scatterometer measurement error due to lacking atmospheric turbulence in the NWP model on the 25-km scale. Also, systematic NWP model errors have been reported, e.g., in stable atmospheric stratification [13], near moist convection [19], and due to ocean currents, for example [22]. Some of these deficiencies may be correlated to air mass density variations, although not directly related to it. Therefore, it is encouraging to note that the air-mass-density dependency of differences between scatterometer and ERA-interim stress-equivalent winds is reduced with respect to similar differences in equivalent-neutral winds. VI. DRAG COEFFICIENT DEPENDENCE ON WIND The above results depend on the assumption that there is a quadratic functional relation between wind stress and stressequivalent wind speed. This may not be correct if the drag coefficient C D has a relation to other parameters, such as sea state, as in the ECMWF model. Since stress-equivalent winds may be used to analyse the forcing of ocean, wave and storm surge models, we provide some further information in this section on the relationship between stress-equivalent winds and wind stress in the ECMWF model context. For example Edson et al. [23] present COARE3.5 with measured evidence for a close to linear relation between mean drag coefficient values for wind speeds between 5 and 20 m/s (see [23, Fig. 6]). The ERA-interim model displays similar behavior for low winds, but no saturation of the drag coefficient for high wind speeds. Fig. 18 shows the result when accumulating the drag coefficient as used by the wave model against the stress-equivalent wind speed from the atmospheric model in a 2-D histogram for 1 year of model data (2012). There clearly is a strong almost linear dependency between both parameters in this model. The substantial spread in the drag coefficient is due to sea-state dependence. If the zero offset is ignored, this would result in a relation between wind stress and wind that is more close to a cubic function. Equation 1 would then change into τ = ρc D u 10n 2 u 10n. (5) Fig D histogram of drag coefficient taken from the ECMWF ERAinterim wave model against the stress-equivalent wind speed taken from the ECMWF ERA-interim atmospheric model (full year 2012). And (4) would change into ( ) 1 ρair 3 u 10s = u 10n. (6) ρ air In this equation, C D is a different drag relation compared to the earlier mentioned C D, from which the wind speed dependence has been removed. This suggests that further improvements could be possible. VII. CONCLUSION From a theoretical point of view it is clear that air mass density should be taken into account when satellite near-surface wind measurements are compared to reference winds from NWP or buoy winds. A correction for air mass density is tested in this manuscript that should be globally unbiased for scatterometer winds, but provides an improved large-scale latitudinal dependency. For higher latitudes this is also clearly visible in the presented dataset, where a comparison to stress-equivalent winds reduced a large-scale residual dependency of wind differences on air mass density. In the (sub)tropical regions the effect of stress-equivalent wind correction currently results in equal or larger wind speed differences between NWP model and scatterometer. Over all latitudes and among comparisons with real winds, equivalent-neutral winds and stress-equivalent winds, the latter shows indeed the smallest large-scale latitudinal dependency. Currently, the NRT KNMI scatterometer products contain uncorrected u 10 reference winds next to the scatterometer wind retrievals 2. The reprocessed KNMI Seawinds scatterometer product contains neutral u 10n reference winds, which suggests they would be a measurement of wind rather than wind stress. The reprocessed KNMI ASCAT scatterometer product contains stress equivalent u 10s reference winds. 2 The only exception at the moment is the near real time regridded L3 ASCAT product, available through the website of the Copernicus Marine Environment Monitoring Service (CMEMS): This product does contain the stress-equivalent reference winds.

8 DE KLOE et al.: IMPROVED USE OF SCATTEROMETER MEASUREMENTS BY USING STRESS-EQUIVALENT REFERENCE WINDS 2347 The relation between 10-m reference wind and stressequivalent reference wind depends on the local density of the air and air stability and can amount to differences up to 1 m/s. Therefore, using uncorrected reference winds may lead to both random and systematic errors. For this reason KNMI plans to make more explicit what scatterometers actually measure by replacing all reference winds in our products with stress-equivalent winds abbreviated as u 10s by scaling equivalent-neutral winds with the local air density as presented in this paper. In ocean, wave or surge user applications, stress-equivalent winds, or NWP differences with satellite-based stress-equivalent winds may be used to improve the forcing. In our products, we use ECMWF winds and stresses as a reference and an analysis is provided of the ECMWF drag coefficient for user reference. ACKNOWLEDGMENT The authors would like to acknowledge the KNMI scatterometer team and the International Ocean Vector Winds Science team for fruitful discussions and contributions to the topic of stress-equivalent winds. They acknowledge EUMETSAT and ECMWF for the provision of the input data. REFERENCES [1] J. Figa-Saldaña et al., The advanced scatterometer (ASCAT) on the meteorological operational (MetOp) platform: A follow on for European wind scatterometers, Canadian J. Remote Sens., vol. 28, no. 3, pp , 2002, doi /m [2] D. B. Chelton and M. H. Freilich, Scatterometer-based assessment of 10-m wind analyses from the operational ECMWF and NCEP numerical weather prediction models, Monthly Weather Rev., vol.133,pp , 2005, doi: /MWR [3] G. L. Geernaert and W. J. Plant, Surface waves and fluxes: Volume I current theory, Environmental Fluid Mechanics, vol.7,newyork,ny, USA: Springer-Verlag, [4] M. Portabella and A. C. M. Stoffelen, On scatterometer ocean stress, J. Atmos. 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Figa-Saldana, On the quality of high-resolution scatterometer winds, J. Geophys. Res., vol. 116, 2011, Art. no. C10033, doi: /2010jc [22] K. A. Kelly et al., Ocean currents evident in satellite wind data. Geophys. Res. Lett., vol. 28, pp , [23] J. B. Edson et al., On the exchange of momentum over the open ocean, J. Phys. Oceanogr., vol. 43, no. 8, pp , 2013, doi /JPO- D Jos de Kloe was born on October 7, 1968, in The Netherlands. He received the M.Sc. degree in physics from Utrecht University, Utrecht, The Netherlands, in 1992, and the Ph.D. degree in high temperature plasma physics from Eindhoven University of Technology, Eindhoven, The Netherlands, in Since October 2000, he has been in the Royal Netherlands Meteorological Institute, De Bilt, The Netherlands, through diverse ESA and national projects. He has worked on building software for simulating scatterometer instruments, and software for (re)processing scatterometer, and wind lidar data. Ad Stoffelen was born in 1962, in The Netherlands. He received the M.Sc. degree in physics from the Technical University of Eindhoven, Eindhoven, The Netherlands, in 1987, and the Ph.D degree in meteorology on scatterometry at the University of Utrecht, Utrecht, the Netherlands, in He is working at the Royal Netherlands Meteorological Institute (KNMI) and responsible for the scatterometer wind products. He is also deeply involved in the European Space Agency ADM-Aeolus Doppler Wind Lidar mission. He currently leads a group on active satellite sensing at KNMI and is involved in topics from future missions and retrieval to 24/7 operations, user training and services. Anton Verhoef was born on December 10, 1964, in The Netherlands. He received the M.Sc. degree in physics and the Ph.D. degree in solid state physics from the Rijksuniversiteit Groningen, Groningen, The Netherlands, in 1989 and 1994, respectively. He is currently in the Royal Netherlands Meteorological Institute, De Bilt, The Netherlands, working on scatterometry processing software development, data validation, quality monitoring, and user services.