Editorial Type:
Article
Article Type:
Research Article

Objectively Derived Daily “Winds” from Satellite Scatterometer Data

P. J. Pegion Center for Ocean–Atmospheric Prediction Studies, The Florida State University, Tallahassee, Florida

Search for other papers by P. J. Pegion in
Current site
Google Scholar
PubMed Close
,
M. A. Bourassa Center for Ocean–Atmospheric Prediction Studies, The Florida State University, Tallahassee, Florida

Search for other papers by M. A. Bourassa in
Current site
Google Scholar
PubMed Close
,
D. M. Legler Center for Ocean–Atmospheric Prediction Studies, The Florida State University, Tallahassee, Florida

Search for other papers by D. M. Legler in
Current site
Google Scholar
PubMed Close
, and
J. J. O’Brien Center for Ocean–Atmospheric Prediction Studies, The Florida State University, Tallahassee, Florida

Search for other papers by J. J. O’Brien in
Current site
Google Scholar
PubMed Close
Print Publication:
01 Sep 2000
DOI:
https://doi.org/10.1175/1520-0493(2000)128<3150:ODDWFS>2.0.CO;2
Page(s):
3150–3168
Restricted access

Abstract

An objective technique is used to create regularly gridded daily “wind” fields from NASA scatterometer (NSCAT) observations for the Pacific Ocean north of 40°S. The objective technique is a combination of direct minimization, and cross validation with multigridding. The fields are created from the minimization of a cost function. The cost function is developed to maximize information from the observational data and minimize smoothing. Three constraints are in the cost function: a misfit to observations, a smoothing term, and a misfit of the curl. The second and third terms are relative to a background field. The influence of the background field is controlled by weights on the smoothing constraints. Weights are objectively derived by the method of cross validation. Cross validation is a process that removes observations from the input to the cost function and determines tuning parameters (weights) by the insensitivity of the removed observations to the output field. This method is computationally expensive; thus the technique of multigridding is incorporated into cross validation. Multigridding solves for the weights by cross validation on a coarse grid, then these weights are used to determine pseudostress on the original fine grid. This allows for the practical application of cross validation with only modest computational resources required.

Daily pseudostress fields are generated on a 1° × 1° resolution grid for the NSCAT period. These objectively derived fields are compared to independent data sources (NCEP and Florida State University winds). The kinetic energy of the NSCAT fields exceeds that of the independent NCEP reanalysis and is similar to observations. Pseudostresses for the equatorial cold tongue region (15°S–15°N, 180°–90°W) are extracted from the objectively derived NSCAT fields and a complex empirical orthogonal function (CEOF) analysis is performed. The analysis shows a large amount of variability in intraseasonal timescales for the Southern Hemisphere trade winds. This variability is supported by in situ observations.

Corresponding author address: Mark A. Bourassa, Center for Ocean–Atmospheric Prediction Studies, The Florida State University, Tallahassee, FL 32306-2840.

Email: bourassa@coaps.fsu.edu

Abstract

An objective technique is used to create regularly gridded daily “wind” fields from NASA scatterometer (NSCAT) observations for the Pacific Ocean north of 40°S. The objective technique is a combination of direct minimization, and cross validation with multigridding. The fields are created from the minimization of a cost function. The cost function is developed to maximize information from the observational data and minimize smoothing. Three constraints are in the cost function: a misfit to observations, a smoothing term, and a misfit of the curl. The second and third terms are relative to a background field. The influence of the background field is controlled by weights on the smoothing constraints. Weights are objectively derived by the method of cross validation. Cross validation is a process that removes observations from the input to the cost function and determines tuning parameters (weights) by the insensitivity of the removed observations to the output field. This method is computationally expensive; thus the technique of multigridding is incorporated into cross validation. Multigridding solves for the weights by cross validation on a coarse grid, then these weights are used to determine pseudostress on the original fine grid. This allows for the practical application of cross validation with only modest computational resources required.

Daily pseudostress fields are generated on a 1° × 1° resolution grid for the NSCAT period. These objectively derived fields are compared to independent data sources (NCEP and Florida State University winds). The kinetic energy of the NSCAT fields exceeds that of the independent NCEP reanalysis and is similar to observations. Pseudostresses for the equatorial cold tongue region (15°S–15°N, 180°–90°W) are extracted from the objectively derived NSCAT fields and a complex empirical orthogonal function (CEOF) analysis is performed. The analysis shows a large amount of variability in intraseasonal timescales for the Southern Hemisphere trade winds. This variability is supported by in situ observations.

Corresponding author address: Mark A. Bourassa, Center for Ocean–Atmospheric Prediction Studies, The Florida State University, Tallahassee, FL 32306-2840.

Email: bourassa@coaps.fsu.edu

Save
Cover Monthly Weather Review
Monthly Weather Review

  • Atlas, R., R. N. Hoffman, S. C. Bloom, J. C. Jusem, and J. Ardizzone, 1996: A multiyear global surface wind velocity dataset using SSM/I wind observations. Bull. Amer. Meteor. Soc.,77, 869–882.

  • ——, S. C. Bloom, R. N. Hoffman, E. Brin, J. Ardizzone, J. Terry, T. D. Bungato, and J. C. Jusem, 1999: Geophysical validation of NSCAT winds using atmospheric data and analyses. J. Geophys. Res.,104, 11 405–11 424.

  • Bentamy, A., Y. Quilfen, F. Gohin, N. Grima, M. Lenaour, and J. Servain, 1996: Determination and validation of average wind fields from ERS-1 scatterometer measurements. Global Atmos. Ocean Syst.,4, 1–29.

  • ——, B., H. Grima, and Y. Quilfen, 1998: Validation of the gridded weekly and monthly wind fields calculated from ERS-1 scatterometer wind observations. Global Atmos. Ocean Syst.,6, 373–396.

  • Bourassa, M. A., M. H. Freilich, D. M. Legler, W. T. Liu, and J. J. O’Brien, 1997: Wind observation from new satellite and research vessels agree. Eos, Trans. Amer. Geophys. Union,78, 597–602.

  • ——, D. M. Legler, J. J. O’Brien, J. N. Stricherz, and J. Whalley, 1998: High temporal and spatial resolution animations of winds observed with the NASA scatterometer, Preprints, 14th Int. Conf. on Interactive Information Processing Systems, Phoenix, AZ, Amer. Meteor. Soc., 556–559.

  • ——, L. Zamudio, and J. J. O’Brien, 1999: Noninertial flow in NSCAT observations of Tehuantepec winds. J. Geophys. Res.,104, 11 311–11 320.

  • Brandt, A., 1982: Guide to multigrid development. Multigrid Methods, W. Hackbusch and U. Trottenberg, Springer-Verlag, 220–312.

  • Caruso, M. J., S. Dickinson, K. A. Kelly, M. Spillane, L. J. Mangum, M. McPhaden, and L. D. Stratton, 1999: Evaluation of scatterometer winds using equatorial Pacific buoy observations. WHOI-99-10, 62 pp. [Available from Woods Hole Oceanographic Institution, Woods Hole, MA 02543.].

  • Chelton, D. B., and F. J. Wentz, 1986: Further development of an improved altimeter wind speed algorithm. J. Geophys. Res.,91, 14 250–14 260.

  • ——, M. H. Freilich, and S. K. Esbensen, 2000a: Satellite observations of the wind jets off the Pacific coast of Central America. Part I: Case studies and statistical characteristics. Mon. Wea. Rev.,128,.

  • ——, M. H. Freilich, and S. K. Esbensen, 2000b: Satellite observations of the wind jets off the Pacific coast Central America. Part II: Regional relationships and dynamical considerations. Mon. Wea. Rev.,128,.

  • Chen, D., M. A. Cane, and S. E. Zebiak, 1999: The impact of NSCAT winds on predicting the 1997/1998 El Niño: A case study with the Lamont-Doherty Earth Observation model. J. Geophys. Res.,104, 11 321–11 328.

  • Chin, T. M., R. F. Milliff, and W. G. Large, 1998: Basin-scale, high-wavenumber sea surface wind fields from a multiresolution analysis of scatterometer data. J. Atmos. Oceanic Technol.,15, 741–763.

  • Elsner, J. B., and C. P. Schmertmann, 1994: Assessing forecast skill through cross validation. Wea. Forecasting,9, 619–624.

  • Freilich, M. H., and R. S. Dunbar, 1999: The accuracy of the NSCAT 1 vector winds: comparisons with National Data Buoy Center buoys. J. Geophys. Res.,104, 11 231–11 246.

  • Gonzales, A. E., and D. G. Long, 1999: An assessment of NSCAT ambiguity removal. J. Geophys. Res.,104, 11 449–11 458.

  • Graber, H. C., A. Bentamy, and N. Ebuchi, 1997: Evaluation of scatterometer winds with ocean buoy observations. Proc. Scatterometer Symp., Maui, HI, NASA, 152–153. [Available from Scatterometer Projects Office, Jet Propulsion Lab., Pasadena, CA 91109.].

  • Halpern, D., R. A. Knox, and D. S. Luther, 1988: Observations of 20-day meridional current oscillations in the upper ocean along the Pacific equator. J. Phys. Oceanogr.,18, 1514–1534.

  • Horel, J. D., 1984: Complex principle component analysis: Theory and examples, J. Climate Appl. Meteor.,23, 1660–1673.

  • Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc.,77, 437–471.

  • Kelly, K. A., S. Dickinson, and Z.-J. Yu, 1999: NSCAT tropical wind stress maps: Implications for improving ocean modeling. J. Geophys. Res.,104, 11 291–11 310.

  • Kutsuwada, K., 1998: Impact of wind/wind-stress field in the North Pacific constructed by ADEOS/NSCAT data. J. Oceanogr.,54, 443–456.

  • Lau, K. M., and P. H. Chan, 1988: Intraseasonal and interannual variations of tropical convection: A possible link between the 40–50 day oscillation and ENSO? J. Atmos. Sci.,45, 506–521.

  • Legeckis, R., 1977: Long waves in the eastern equatorial Pacific Ocean: A view from a geostationary satellite. Science,197, 1179–1181.

  • Legler, D. M., and J. J. O’Brien, 1985: Development and testing of a simple assimilation technique to derive average wind fields from simulated scatterometer data. Mon. Wea. Rev.,113, 1791–1800.

  • ——, I. M. Navon, and J. J. O’Brien, 1989: Objective analysis of pseudostress over the Indian Ocean using a direct-minimization approach. Mon. Wea. Rev.,117, 709–720.

  • ——, J. N. Stricherz, and J. J. O’Brien, 1997: TOGA pseudostress atlas 1985–1994: III, Indian Ocean. COAPS Rep. 97-3, 175 pp. [Available from COAPS/The Florida State University, Tallahassee, FL 32306.].

  • Madden, R. A., and P. R. Julian, 1971: Detection of a 40–50 day oscillation in the zonal wind in the tropical Pacific. J. Atmos. Sci.,28, 702–708.

  • Meyers, S. D., C. S. Jones, D. M. Legler, and J. J. O’Brien, 1994: The sensitivity of parametric variations in direct minimization techniques. Mon. Wea. Rev.,122, 1632–1639.

  • Michaelsen, J., 1987: Cross validation in statistical climate forecast models. J. Climate Appl. Meteor.,26, 1589–1600.

  • Milliff, R. F., T. J. Hoar, H. van Loon, and M. Raphael, 1999a: Quasi-stationary wave variability in NSCAT winds. J. Geophys. Res.,104, 11 425–11 426.

  • ——, W. G. Large, J. Morzel, G. Danabasoglu, and T. M. Chin, 1999b:Ocean general circulation model sensitivity to forcing from scatterometer winds. J. Geophys. Res.,104, 11 337–11 358.

  • Naderi, F. M., M. H. Freilich, and D. G. Long, 1991: Spaceborne radar measurements of wind velocity over the ocean—An overview of the NSCAT scatterometer system. Proc. IEEE,79, 850–866.

  • O’Brien, J. J., and R. D. Pillsbury, 1974: Rotary wind spectra in a sea breeze regime. J. Appl. Meteor.,13, 820–825.

  • Polito, P. S., W. T. Liu, and W. Tang, 1997: Correlation-based interpolation of NSCAT wind data. Proc. NASCAT Scatterometer Symp., Maui, HI, NASA, 30–32. [Available from Scatterometer Projects Office, Jet Propulsion Lab., Pasadena, CA 91109.].

  • Philander, S. G. H., D. Halpern, R. Legeckis, L. Miller, C. Paul, R. Watts, R. Weisberg, and M. Wimbush, 1985: Long waves in the equatorial Pacific Ocean. Eos, Trans. Amer. Geophys. Union,66, 154–156.

  • Pierson, W. J., Jr., 1990: Examples of, reasons for and consequences of poor quality of wind data for the marine boundary layer: Implications for remote sensing. J. Geophys. Res.,95, 13 313–13 340.

  • Putman, W. M., D. M. Legler, and J. J. O’Brien, 2000: Interannual variability of synthesized FSU and NCEP–NCAR reanalysis pseudostress products over the Pacific Ocean. J. Climate,13, 3003–3016.

  • Rasmusson, E. M., and T. H. Carpenter, 1982: Variations in tropical sea surface temperature and surface wind fields associated with the Southern Oscillation/El Niño. Mon. Wea. Rev.,110, 354–384.

  • Servain, J., J. N. Stricherz, and D. M. Legler, 1996: TOGA pseudo-stress atlas 1985–1994. Vol. I. Tropical Atlantic. Centre ORSTOM, Plouzané, France, 158 pp.

  • Shanno, D. F., and K. H. Phua, 1980: Remark on Algorithm 500—A variable method subroutine for unconstrained nonlinear minimization. ACM Trans. Math. Software,6, 618–622.

  • Siefridt, L., B. Barnier, D. M. Legler, and J. J. O’Brien, 1998: 5-day average winds over the north-west Atlantic from ERS1 using a variational analysis. Global Atmos. Ocean Syst.,5, 317–344.

  • Stricherz, J. N., D. M. Legler, and J. J. O’Brien, 1997: TOGA pseudo-stress atlas 1985–1994: II, Tropical Pacific Ocean. COAPS Rep. 97-2, 177 pp. [Available from COAPS/The Florida State University, Tallahassee FL 32306.].

  • Tang, W., and W. T. Liu, 1996: Objective interpolation of scatterometer winds. JPL Tech. Rep. 96-19, 16 pp. [Available from Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109.].

  • Verschell, M. A., M. A. Bourassa, D. E. Weissman, and J. J. O’Brien, 1999: Model validation of the NASA Scatterometer winds. J. Geophys. Res.,104, 11 359–11 374.

  • Wahba, G., and J. Wendelberger, 1980: Some new mathematical methods for variational objective analysis using splines and cross-validation. Mon. Wea. Rev.,108, 1122–1143.

  • Wikle, C. K., R. F. Millif, and W. G. Large, 1999: Surface wind variability on spatial scales from 1 to 1000 km observed during TOGA COARE. J. Atmos. Sci.,56, 2222–2231.

  • Wilks, D. S., 1995: Statistical Methods in Atmospheric Sciences: An Introduction. International Geophysics Series, Academic Press, 467 pp.

  • Zeng, L., and G. Levy, 1995: Space and time aliasing structure in monthly mean polar-orbiting satellite data. J. Geophys. Res.,100, 5133–5142.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 106 28 3
PDF Downloads 59 25 1