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Oceanic Surface Wind in Satellite Retrievals, NCEP Reanalyses, GFS, and CFS. Sarah Marquardt, Wanqiu Wang, and Pingping Xie. Background. Surface wind is a critical variable in the coupling between atmosphere and ocean. It directly affects Latent and sensible heat fluxes Momentum flux

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Oceanic surface wind in satellite retrievals ncep reanalyses gfs and cfs

Oceanic Surface Wind in Satellite Retrievals, NCEP Reanalyses, GFS, and CFS

Sarah Marquardt, Wanqiu Wang, and Pingping Xie


Background
Background Reanalyses,

  • Surface wind is a critical variable in the coupling between atmosphere and ocean. It directly affects

    • Latent and sensible heat fluxes

    • Momentum flux

  • Substantial SST errors in the coupled models, such as CFS, are related to errors in surface wind

  • Validation of coupled atmosphere-ocean models needs

    • Global coverage of surface wind observations

    • Diagnosis of the reliability of observations


  • CFS 4-mo lead SST forecast mean error (K) Reanalyses,

    • Is the cold bias in equatorial Pacific due to erroneous surface winds?

    • What are the impacts of surface winds on the southeastern Pacific warm bias in addition to the known excessive local solar radiation?


    Objectives
    Objectives Reanalyses,

    • Estimate uncertainty of the surface wind fields in satellite retrievals

    • Examine the dependability of NCEP GDAS, CDAS1, CDAS2

    • Diagnose errors in surface wind fields in NCEP GFS and CFS


    Data 10 m wind
    Data (10-m wind) Reanalyses,

    • Satellite retrievals (2000-2007)

      • RSS QuikScat wind Version 3a

      • JPL QuikScat wind Product 109

      • RSS SSMI wind Version 6

      • NCDC Blended Winds Version 1.2

    • In situ observations (2000-2004)

      • FSU in situ wind FSU3 Version 1

    • Reanalyses (2000-2007)

      • GDAS (2002-2007)

      • CDAS1

      • CDAS2

    • NCEP model simulations (Version 2003)

      • GFS (2000-2007)

      • CFS 0 mo. lead, init. 21st 1999-2006



    Annual mean wind speed (m/s) Reanalyses,

    • Spatial distribution of all observations is very similar

    • NCDC winds are slightly faster in the tropics, SSMI winds are slightly slower in N.H.

    • Spread among retrievals is generally less than 0.4 m/s except for west coast of continents


    QuikScat vs FSU moored buoy Reanalyses,

    • Bias and rms error are smaller and correlation is higher in the tropics

    • - QuikScat wind speed mean error is generally less than 1 m/s in the tropics but can be over 1 m/s too high near the extratropics


    Uncertainty of Observations Reanalyses,

    2000-2004

    Satellite – FSU Moored Buoys

    • Bias and rms error are smaller and correlation is higher in the tropics

    • Mean wind speed error is about 0.5 m/s (9% of total) in the tropics and about 1.6 m/s outside the tropics

    • All satellite retrievals are comparable. RSS QuikScat slightly better in the tropics, SSMI is better in NH


    Dependability of ncep reanalyses
    Dependability of NCEP reanalyses Reanalyses,

    OBS = Mean of RSS, JPL, and SSMI


    Annual mean wind speed (m/s) Reanalyses,

    • GDAS wind matches observations very well

    • CDAS1 is too slow in the lower latitudes and CDAS2 is too fast in mid to high latitudes


    Seasonal mean wind vector (m/s) Reanalyses,

    • GDAS wind matches observations very well

    • CDAS1 (CDAS2) wind too slow (fast) in southeastern Pacific

    • CDAS1 and CDAS2 have wind direction errors along the coast


    Seasonal mean wind speed (m/s) Reanalyses,

    FMA ASO

    • All three NCEP products are too fast near the coast

    • Slow bias along the coast in CDAS1 and CDAS2



    Surface wind errors in ncep models
    Surface wind errors in NCEP models Reanalyses,

    OBS = Mean of RSS, JPL, and SSMI


    Annual mean wind speed (m/s) Reanalyses,

    • GFS and CFS winds are very similar

    • Both models are too weak north and south of the equator in the Pacific but too strong in eastern equatorial Pacific

    • GFS is too weak in the extratropics


    Seasonal mean wind vector (m/s) Reanalyses,

    • Some errors in wind direction exist along 30S in GFS in FMA

    • Wind direction is accurate along the coast in FMA but inaccurate in JJA. Model winds are easterly instead of southerly.


    Seasonal mean wind (m/s) Reanalyses,

    FMA ASO

    • GFS and CFS winds are too strong along the equator throughout the year

    • Wind differences are largest in ASO, the same months where the cold SST bias is largest in the CFS forecast


    Wind effects on SST Reanalyses,

    • Too strong easterlies persist through the 1 mo lead forecast

    • Cold SST bias increases in size and magnitude

    • Evolution of equatorial CFS wind speed indicates a cancellation of errors: SST induced westerlies cancel too strong easterlies


    CFS 4-mo lead SST forecast mean error (K) Reanalyses,

    • Too strong easterlies in GFS likely the reason for cold SST bias in the CFS forecast for the months of ASON

    • Impact of wind errors on SEP warm bias is not clear


    Vector Correlation Reanalyses,

    • Equation described by Crosby et al (1993)

    • CDAS1, CDAS2, GDAS: Vector correlation generally over 0.6, lowest correlations occur over the cold tongues of the eastern tropical Pacific and Atlantic

    • GFS: Vector correlation is highest in the tropics


    Summary
    Summary Reanalyses,

    • Uncertainty of time mean wind speed in satellite observations is about 0.5 m/s in the tropics and 1 m/s in the extratropics

    • GDAS winds are very accurate

    • CDAS1 is too slow in the tropics and CDAS2 is too fast in the mid latitudes; both have wind direction errors along the west coasts of South America and Africa

    • GFS and CFS winds are too strong in the Southeast Pacific and the eastern equatorial Pacific; there are wind direction errors along the west coast of South America.

    • Too strong easterlies in ASO in the GFS are associated with the eastern equatorial cold SST bias in the CFS forecast for ASON


    Additional figures
    Additional Figures Reanalyses,


    Vector Correlation = Reanalyses,

    Tr

    Covariance matrices of datasets 1 and 2

    Covariance matrix of dataset 1

    • Equation described by Crosby et al (1993)


    RMS differences of annual mean climatology Reanalyses,

    • RMS error between observations and GDAS is about 0.3 m/s

    • RMS errors are largest in the tropics and extratropics


    Seasonal mean wind pseudo stress Reanalyses,

    • GFS and CFS wind stress is too strong near the coast between equator and 25S

    • GFS is too strong in ASO along the equator while CFS is too weak

    • The too weak CFS wind speed in the equatorial Pacific is associated with the southeastern Pacific SST warm bias

    • Too strong easterlies in GFS likely the reason for cold SST bias in the CFS forecast for the months of ASON


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