Ocean Science www.ocean-sci.net 1812-0784 1812-0792 3 2 2007 10.5194/os-3-259-2007 http://www.ocean-sci.net/3/259/2007/ http://www.ocean-sci.net/3/259/2007/os-3-259-2007.html http://www.ocean-sci.net/3/259/2007/os-3-259-2007.pdf 259 271 2007-05-24 Improved near real time surface wind resolution over the Mediterranean Sea A. Bentamy abderrahim.bentamy@ifremer.fr H.-L. Ayina P. Queffeulou D. Croize-Fillon V. Kerbaol Institut Français pour la Rechercher et l'exploitation de la MER (IFREMER), DOPS, BP 70, 20280 Plouzané, France BOOST Technologies, 115 rue Claude Chappe, 29280 Plouzané, France Several scientific programs, including the Mediterranean Forecasting System Toward Environmental Predictions (MFSTEP project), request high space and time resolutions of surface wind speed and direction. The purpose of this paper is to focus on surface wind improvements over the global Mediterranean Sea, based on the blending near real time remotely sensed wind observations and ECMWF wind analysis. Ocean surface wind observations are retrieved from QuikSCAT scatterometer and from SSM/I radiometers available at near real time at Météo-France. Using synchronous satellite data, the number of remotely sensed data available for each analysis epoch (00:00 h; 06:00 h; 12:00 h; 18:00 h) is not uniformly distributed as a function of space and time. On average two satellite wind observations are available for each analysis time period. The analysis is performed by optimum interpolation (OI) based on the kriging approach. The needed covariance matrixes are estimated from the satellite wind speed, zonal and meridional component observations. The quality of the 6-hourly resulting blended wind fields on 0.25° grid are investigated trough comparisons with the remotely sensed observations as well as with moored buoy wind averaged wind estimates. The blended wind data and remotely wind observations, occurring within 3 h and 0.25° from the analysis estimates, compare well over the global basin as well as over the sub-basins. The correlation coefficients exceed 0.95 while the rms difference values are less than 0.30 m/s. Using measurements from moored buoys, the high-resolution wind fields are found to have similar accuracy as satellite wind retrievals. Blended wind estimates exhibit better comparisons with buoy moored in open sea than near shore. Atlas, R. M., Hoffman, R. N., Bloom, S. C., Jusem, J. C., and Ardizzone, J.: A multiyear global surface wind velocity dataset using SSM/I wind observations., Bull. Am. Meteorol. Soc., 77, 869–882, 1996. Liu, W. T., Tang, W., and Polito, P. S.: NASA Scatterometert provides global ocean-surface wind fields with more structures than numerical weather prediction, Geophys. Res. Lett., 25(6), 761–764, 1998. Bauer, E.: Characteristic frequency distributions of remotely sensed in situ and modeled wind speeds, Int. J. Climatol., 16, 1087–1102, 1996. Bentamy, A., Quilfen, Y., Gohin, F., Grima, N., Lenaour, M., and Servain, J.: Determination and validation of average field from ERS-1 scatterometer measurements, Global Atmos. Ocean Sys., 4, 1–29, 1996. Bentamy, A., Grima, N., and Quilfen, Y.: Validation of the gridded weekly and monthly wind fields calculated from ERS-1 scatterometer wind observations, The Global Atmosphere and Ocean System, 6, 373–396, 1998. Bentamy, A., Queffeulou, P., Quilfen, Y., and Katsaros, K.: Ocean surface wind fields estimated from satellite active and passive microwave instruments, IEEE Trans. Geosci. Remote Sens., 37, 2469–2486, 1999. Bentamy, A., Katsaros, K. B., Alberto, M., Drennan, W. M., and Forde, E. B.: Daily surface wind fields produced by merged satellite data. Gas Transfer at Water Surfaces, Geophysical Monograph 127, Copyright 2002 by the American Geophys. Union, 343–349, 2002. Bentamy A., Ayina, H. L., and Queffeulou, P.: Investigation of the accuracy of the gridded satellite wind fields over the Mediterranean Sea: Comparisons with ECMWF wind estimates, MFSTEP report 3330, http://www.bo.ingv.it/mfstep/WP3/, 2005. Bourassa, M. A., Legler, D. M., O'Brien, J. J., and Smith, S. R.: SeaWinds validation with research vessels, J. Geophys Res., 108(C2), 3019, doi:10.1029/2001JC001028, 2003. Boutin, J., Etcheto, J., Rafizadeh, M., and Bakker, D. C. E.: Comparison of NSCAT, ERS 2 active microwave instrument, special sensor microwave imager, and Carbon Interface Ocean Atmosphere buoy wind speed: Consequences for the air-sea CO2 exchange coefficient, J. Geophys. Res., 104, 11 375–11 392, 1999. Brody, L. R. and Nestor, M. J. R.: Regional Forecasting Aids for the Mediterranean Basin (Handbook for Forecasters in the Mediterranean, Part 2).Naval Research Laboratory, 7 Grace Hopper Avenue, Monterey, California, 93943-5502, 178 pp, 1980. Chen, G.: An Intercomparison of TOPEX, NSCAT, and ECMWF Wind Speeds: Illustrating and understanding Systematic Discrepancies, Mon. Wea. Rev., 132, 780–792, 2003. Chin, T. M., Milliff, R. F., and Large, W. O.: Basin scale, high-wavenumber sea surface wind fields from a multiresolution analysis of scatterometer data, J. Atmos. Ocean. Technol., 15, 741–763, 1998. Crapolicchio, R., Lecomte, P., and Hersbach, H.: Assimilation of reprocessed ERS scatterometer data into ECMWF weather analysis on the Mediterranean Sea, Adv. Geosci., 2, 327–329, 2005. Ebuchi, N., Graber, H. C., and Caruso, M. J.: Evaluation of wind vectors observed by QuikSCAT/SeaWinds using ocean buoy data, J. Atmos. Oceanic Technol., 19, 2049–2062, 2002. Gaffard, C. and Roquet, H.: Impact of ERS-1 scatterometer wind data on the ECMWF 3D-Var assimilation system, Tech Memorandum no 217, ECMWF, 1995. Goodberlet, M. A., Swift, C. T., and Wilkerson, J. C.: Remote sensing of ocean surface winds with the Special Sensor Microwave/Imager, J. Geophys. Res., 94, 14 547–14 555, 1989. JPL: QuikScat science data product user's manual (version 2.0). Jet Propulsion Laboratory Publ. D-18053, Pasadena, CA, 84pp, available online at http://podaac.jpl.nasa.gov/quikscat, 2001. Komen, G. J., Cavaleri, L., Donelan, M., Hasselmann, K., Hasselmann, S., and Janssen, P. A. E. M.: Dynamics and Modelling of Ocean Waves, Cambridge University Press, 532 p, 1994. Liu, W. T., Katsaros, K. B., and Businger, J. A.: Bulk parameterization of air-sea exchanges of heat and water vapor including the molecular constraints at the interface, J. Atmos. Sci., 36, 1722–1735, 1979. Long, D. E. and Mendel, J. M.: Identifiability in wind estimation from wind scatterometer measurements, IEEE Trans. Geosci. Remote Sens., GE-29, 268–276, 1991. Meissner, T., Smith, D., and Wentz, F. J.: A 10-year intercomparison between collocated SSM/I oceanic surface wind speed retrievals and global analyses, J. Geophys. Res., 106(C6), 11 731–11 742, 2001. Mestas-Nunez, A. M., Chelton, D. B., Freilich, M. H., and Richman, J. G.: An evaluation of ECMWF-based climatological wind stress fields, J. Phys. Oceanogr., 24, 1532–1549, 1994. Milliff, R. F., Large, W. G., Monel, J., Danabasoglua, G., and Chin, T. M.: Ocean general circulation model sensitivity to forcing from scatterometewrinds, J. Geophys. Res., 104, 11 337–11 358, 1999b. Monahan, A. H.: The Probability Distribution of Sea Surface Wind Speeds Part II: Data set Intercomparison and Seasonal Variability, J. Climate, 19(4), 521–534, 2006. Picket, M. H., Tang, W., Rosenfeld, L. K., and Wash, C. H.: QuikSCAT satellite comparisons with nearshore buoy wind data off the U.S. west coast, J. Atmos. Oceanic Technol., 20, 1869–1979, 2003. Portabella, M. and Stoffelen, A.: A comparison of KNMI quality control and JPL rain flag for SeaWinds, Can. J. Rem. Sens., 28(3), 424–430, 2002. Queffeulou, P.: Wave height variability over the Mediterranean Sea, using altimeter data, Proceedings of the Fifth International Symposium on Ocean Wave Measurements and Analysis, Waves 2005, Madrid, 3–7 July 2005. Sobieski, P. W., Craeye, C., and Bliven, L. F.: Scatterometric signatures of multivariate drop impacts on fresh and salt water surfaces, Int. J. Rem. Sens., 20, 2149–2166, 1999.