Near Real Time Blended Surface Winds

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1 Near Real Time Blended Surface Winds I. Summary To enhance the spatial and temporal resolutions of surface wind, the remotely sensed retrievals are blended to the operational ECMWF wind analyses over the global oceans. The blending method aims to provide 6-hourly gridded wind speed, zonal component, meridional component, wind stress and the corresponding components at global scale. The spatial resolution of the resulting wind fields is 0.25 in longitude and latitude. The remotely sensed wind observations are derived from near real time measurements performed by Seawinds scatterometer onboard QuikSCAT satellite and by the three Special Sensor Microwave Imager (SSM/I) onboard DMSP satellites F13, F14, and F15. ECMWF analyses are available at synoptic time (00h:00; 06h:00; 12h:00; 18h:00) on a regular latitude-longitude grid of size ECMWF analyses are interpolated in space and time over each satellite swath occurring within 3 hours from the synoptic time. The differences are evaluated at each scatterometer and radiometer wind cell of about 0.25 resolution. The former are used through an objective method to estimate global wind fields retaining first ECMWF-QuikSCAT wind differences in swath regions, and in the temporal and/or spatial QuikSCAT unsampled areas, available and valid observed differences between ECMWF and SSM/I are used. SSM/I retrieved surface wind speed is considered as regionalized variable. It is related on average to QuikSCAT wind speed through a linear relationship determined on one hand from QuikSCAT and buoy and on other hand from SSM/I and buoy comparison results (Bentamy et al, 2002). More details about data, objective method, computation algorithm may be found in (Bentamy et al, 2006). Wind fields are calculated at global scale over oceans, excluding sea ice areas. In order to determine the location of sea ice, the IFREMER/CERSAT daily sea ice concentration is used. This parameter is estimated for both Arctic and Antarctic from the daily brightness temperature maps from SSM/I. The Artist Sea ice (ASI) algorithm, developed at the University of Bremen (Germany), tested and validated for various situations, is used. For more details, see Ezraty et al. (2006). Data are freely available at IFREMER/CERSAT : ftp://ftp.ifremer.fr/ifremer/cersat/products/gridded/psi-concentration/

2 The blended wind fields are calculated during the period April 2004 until April They are considered as data test that will provide useful insight for near real time production of high space and time resolutions at global and regional scales. Data are available at IFREMER and freely distributed upon request (Contact : and ). The data files are in NetCDF format supported by several scientific softwares. This study is performed within MFSTEP ( and MERSEA ( projects. The main of this document is to provide the main characteristics of these new wind field estimates in terms of accuracy and spatial and temporal features at global and local scales. Details about remotely sensed data and the objective method used to estimate the blended wind fields may be found on MERSEA web site : ( ) II. Accuracy of Blended Wind Fields The quality of the 6-hourly blended wind speed, zonal and meridional components is estimated through comparisons with near surface winds derived from QuikSCAT scatterometer wind observations and from moored buoy measurements. Even though QuikSCAT retrieval winds are used to compute the blended estimates and assimilated in ECMWF numerical model since 22d January 2002 (Hersbach et al, 2004), the comparison procedure aims mainly to investigate how the objective method retains scatterometer observations. The comparison with buoy winds is tricky too. Indeed, buoy measurements are assimilated into the numerical model. The goal is to meet the accuracy results derived from comparison between remotely sensed wind observations and buoy wind measurements determined by several authors (Ebutchi et al, 2002) 2.1 Buoy Comparisons More than 190 moored buoys are used: 8 moorings located off the French and England coasts and maintained by UK Met-Office and/or Météo-France (ODAS hereafter, Figure 1a), 10 buoys provided by Puertos d El Estado located off shore and near shore of Spain (EPPE hereafter, Figure 1b), 96 buoys are provided by the National Data Buoy Center (NDBC) and

3 located off and near U.S coasts (Figure 1c), 66 buoys of the TAO array located in the equatorial Pacific (Figure 1d), and 13 buoys of the PIRATA network located in the equatorial Atlantic (Figure 1e). NDBC, ODAS, and TAO provide hourly winds, while EPPE and PIRATA are 3-hourly and 10 minutes sampled, respectively. Furthermore, buoy winds are measured at various heights. Therefore, the raw buoy wind speeds are converted to the standard height, 10 meters, using logarithmic profile and assuming atmospheric neutral stability. For comparison purposes, the valid 10m buoy winds occurring 3 hours from the synoptic time (00h:00; 06h:00; 12h:00; 18h:00) are arithmetically averaged. At each buoy location and for each 6 hours epoch all available and valid blended estimates within a radius of 0.25 are selected and averaged. Then, the former are compared to the 6-hourly averaged buoy wind estimates. Figure 2 and 3 illustrate the comparison results for wind speed and direction, respectively. Table 1 summarizes the related statistical parameters. In general speaking, blended wind speed and direction compare well with buoy estimates. The wind speed correlation coefficients range from 0.80 to The rms difference (buoy minus blended) values are less than 2m/s. One can notice that the comparisons do not exhibit any systematic biases for wind speed and direction at the five buoy arrays. The mean difference values are about 3% of buoy mean wind speed estimated from the whole buoys and during the comparison period (January 2005 July 2005). The lowest correlation values of wind speed and direction are found at TAO and PIRATA areas, while the highest values are found at ODAS region. The large number of low wind speeds (more than 20% of TAO buoy winds are less than 5m/s) and the poor sampling scheme of polar satellites are the main reasons of low correlation values found over the tropical areas. Excluding TAO and PIRATA buoy wind speed less than 3m/s, yields to rms wind speed difference decreasing of about 16%. Figure 4 illustrates an example of wind speed distribution and sampling scheme impact on the buoy and blended comparisons. At the PIRATA equatorial location (35 W 0 N) the buoy (in green) and blended (in blue) wind speed time series (during February 2005) exhibit less good agreement than at higher latitude location (38 W 15 N). One can notice that through this example (Figure 4) the blended winds retrieve fairly well the remotely sensed wind observations (shown in red symbols). The comparison results found at NDBC and EPPE regions are quite similar to previous results related to QuikSCAT wind observation accuracy. However, as indicated in Figure 1, these comparisons involve off-shore as well as near-shore buoys. For instance, when considering NDBC buoys moored off-shore ( distance from land higher than 250 km), the wind speed and

4 direction correlation values increase to 0.94 and 1. 90, respectively. The rms difference values are about 1.50m/s for wind speed, and 17 for wind direction. For NDBC buoys located nearshore (distance from land less than 30km), the wind speed and direction correlations are 0.86 and 1.64, respectively. It is noticeable, that the mean difference values of near-shore buoy and blended wind estimates are quite low (about 0.15m/s for wind speed and 6 for wind direction)) and the corresponding standard deviations are about 1.80m/s and 22. The accuracy of the blended satellite wind data is also investigated for high wind conditions. The sampling length of such events from buoy measurements is generally quite poor. Indeed, less than 4% of buoy wind speeds (most are from NDBC and ODAS networks) exceed 15m/s, during the period January July As expected, the correlation between buoy and blended wind speed estimates decreases for such wind condition. However it is still statistically significant and reaches The bias does not exceed 0.42m/s and the standard deviation is less than 2.50m/s. 2.2 Remotely Sensed Wind Comparisons This section summarizes some results characterizing the comparison between QuikSCAT and SSM/I wind and the resulting 6-hourly satellite blended wind estimates. It aims to highlight how the blended analysis retrieves the remotely sensed wind observations. Figure 5 shows an example of QuikSCAT wind observations and 6-hourly gridded wind field estimated for the first epoch of 13 th March It indicates that the main wind patterns observed by the scatterometer are clearly restored by the blended analysis. The comparison of the remotely sensed and satellite blended winds are investigated at various oceanic regions. Table 2 summarizes the comparisons results. Results related to satellite observation and ECMWF analysis comparisons are shown too. On average, the bias between satellite observation and blended wind analysis is very low. The RMS values do not exceed 0.40m/s. To further investigate the quality of the resulting blended wind fields, the statistical distributions of wind speed, zonal wind component, and meridional component are estimated at several oceanic regions during January Figure 6 shows PDFs estimated from data located within latitudinal interval centered on 55 S, 0 N, and 55 N. ECMWF (in black), blended (in blue), and satellite (in red) agree well in basic structure. For the variables and at the three regions blended winds retrieve remotely sensed observations. Wind speed exhibits Weibull distribution characteristics (in blue dashed line) with different factor. The shape

5 factor values (resp. scale) are (resp. 2.90), 6.42 (resp. 3.23), and (resp. 2.91) At 55 N, Equator, and 55 S, respectively and are in good agreement with Bauer (1996) results. The difference between observed and Weibull quantiles at probability 0.95 is very small stating the goodness of the fit. The main departure is found at the equator. At 55 N and 55 S, the zonal wind is skewed eastward due to seasonal wind structure in northern area and to westerly wind in southern region. At the equator, the zonal component is slightly skewed westward due to trade winds. At the three regions, the zonal component derived from satellite observations as well as from blended have broad pick near mean value. The meridional component is quite symmetric in northern and southern area, and skewed northward with broad pick at the equator. The remotely sensed and blended wind distributions have pronounced picks than ECMWF, due to the longitudinal variation. Furthermore, the occurrence of high winds (great than 15m/s) is quite same for observed satellite and blended data. For instance, AT 55 N, the percentage of high wind condition from the two sources is about 20%, while is about 16% for ECMWF. For winds higher than 20m/s, the number drops to 2.1% for satellite and blended products, and to about 1% for ECMWF. Similar results are found at the equatorial and southern locations. However, on average, at 55 S ECMWF provides higher winds than satellite and blended estimates. References Bentamy A., H-L Ayina, P. Queffeulou, D. Croize-Fillon ; 2006 : Improved Near Real Time Surface Wind Resolution over The Mediterranean Sea. Submitted to Ocean Journal. Bentamy A., K B. Katsaros, M. Alberto, W. M. Drennan, E. B. Forde, and H. Roquet, 2003 : Satellite Estimates of wind speed and latent heat flux over the global oceans, J. Climate, 16, Bentamy A., K B. Katsaros, M. Alberto, W. M. Drennan, E. B. Forde, 2002 : Daily surface wind fields produced by merged satellite data. American Geophys. Union, Ebuchi N., H. C. Graber, and M. J. Caruso, 2002 : Evaluation of wind vectors observed by QuikSCAT/SeaWinds using ocean buoy data, J. Atmos. Oceanic Tech., Vol 19,

6 Ezraty R., F. Girard-Ardhuin, J.F. Piollé, L. Kaleschke and G. Heygster, 2006 : Sea ice concentration and drift in the Central Arctic estimated from Special Sensor MIcrowave (SSM/I). User s manual, version 2.0. Available at Hersbach, H., Stoffelen, A., and de Haan, S.: The improved C-band geophysical model function CMOD5, Proceeding of the Envisat & ERS Symposium Salzburg (A), 6 10 September 2004.

7 Table 1 : Statistical parameters characterizing the comparisons between 6-hourly Buoy and blended wind speed and direction estimates Wind Speed Wind Direction Length Bias Std Cor. Bias Std Cor ODAS EPPE NDBC TAO PIRATA Table 2 : Statistical parameters characterizing the comparisons between satellite observation and blended analysis on one hand, and between satellite observations and ECMWF wind analysis on another hand. Bias and Rms stand for mean and root mean square of differences, respectively. Cor is the correlation coefficient. Mediterranean Sea North Atlantic Tropical Atlantic Wind Speed Zonal Comp. Meridion al Comp. Blended ECMWF Blended ECMWF Blended ECMWF Bias Rms Cor Bias Rms. Cor. Bias Rms. Cor

8 Figure 1: Buoy networks used to validating blended winds :a) ODAS; b) EPPE; c) NDBC; d) TAO; e) PIRATA Figure 2: Comparisons between 6 hourly averaged buoy and blended winds speeds

9 Figure 3: Comparisons between 6 hourly averaged buoy and blended winds directions Figure 4 : Time series of wind speed derived from PIRATA buoys (in green), blended (in blue), from ECMWF in black, and from QuikSCAT observations (red symbols) at the buoy locations : 0 N- 35 W, and 15 N 35 W.

10 Figure 5 : Example of Blended wind speed (in color) and direction field over the Mediterranean Sea for 12 th March 2006 at 00h:00 (top). Figure in bottom shows the QuikSCAT observations used to estimate the blended wind vectors. Figure 6: Frequency densities of remotely sensed (in red), blended (in blue), and ECMWF (in black) wind speed (a, d, and g), zonal wind component (b, e, h), and meridional component (c, f, i), estimated at three latitudes: 55 N (top), Equator (middle), and 55 S (bottom)

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