Spatial Variability of Random Error and Biases in the FSU3 Winds

1. Spatial Variability of Random Error and Biases in the FSU3 Winds Mark A. Bourassa, Shawn R. Smith and Jim O’Brien Help from Paul Hughes and Robert Banks Center for Ocean-Atmospheric Prediction Studies, The Florida State University The Florida State University

2. Goals • Estimate biases and uncertainties in FSU3 gridded products • FSU3 Winds are fluxes are • Objectively derived based on in situ data • 1x1° grid over water • Fluxes: stress, sensible heat, latent heat • Flux related fields: Pseudostress components, air temperature, atmospheric humidity, scalar wind speed • SSTs from Reynolds • Objectively estimate uncertainty based largely on the in situ data • Determine spatially and temporally varying uncertainty fields • Assess preliminary results through comparison to a satellite derived product The Florida State University

3. Historical Background • FSU monthly winds are based solely on in situ observations • They are used nationally and internationally for many applications. • ENSO forecasting is a common application. • Problems were becoming evident in the old (subjective) FSU winds. • The new FSU wind products (FSU2 and FSU3) was developed to better handle the problems. • Replacement of subjective FSU winds with new objective technique • Objective re-analyzed Pacific pseudo-stress fields (FSU2) are 1978-2004 • Operational objective quick-look fields began in Feb. 2002 • FSU3 winds and fluxes are expected to be released soon. The Florida State University

4. The ‘New’ in ‘New FSU’ • Objective gridding technique (original FSU winds were subjectively drawn). • Fast • Consistent • Independently weight observations from different types of platforms • The weights for different types of platforms are determined objectively. • FSU2 classified platforms as VOS, buoy, or ‘not to be used.’ • FSU3 classifies observations as VOS, moored buoy, drifting buoy, or ‘not to be used.’ • FSU3 uses a state of the air flux model (see poster for details) The Florida State University

5. Example VOS and Buoy Observations Dec. Average from 1988-1997 Buoys VOS The Florida State University

6. Example Latent and Sensible Heat Fluxes The Florida State University

7. Example VOS and Buoy Observations Dec. Average from 1988-1997 VOS Buoys The Florida State University

8. Example Latent Heat Fluxes(January 1998 and August 1999 The Florida State University

9. Considerations For Gridded Fields • There are several problems that must be overcome • Filling the gaps • A good approximation • Must have realistic spatial trends • Removing the edge effects due to overlapping ship tracks (or buoy chains) • Poor techniques will introduce too much spurious divergence/curl • Ocean models are highly sensitive to divergence/curl • Avoid excessive smoothing • Filter out bad data The Florida State University

10. The Gridding Process • The gridding technique (Bourassa et al. 2005, JCLIM (FUS2)) is an improvement from our variational method (Pegion et al. 2000, MWR) applied to scatterometer winds • Three types of constraints on the solution field • Misfits to observations, for each type of observational platform • RMS sum of misfits to each pseudostress component • Misfit to curl of background field • A penalty function with respect to the background field • The data to which these constraints are applied are scaled to be dimensionally consistent • The background fields are based on the observational data. • The weights for constraints are objectively estimated through cross validation The Florida State University

11. Standard Deviation of Differences in Monthly Zonal Pseudostress • The relatively poor results in the Western Pacific (around 179E and 15N) are due to a data void. New FSU Winds Old FSU Winds The Florida State University

12. Future FSU3 Wind and Flux Products • 1 degree rather than 2 degree grid spacing. • Product includes fluxes and flux-related variables. • Additional basins • Indian Ocean: nearly finished • North and Tropical Atlantic Ocean: nearly finished • North Pacific Ocean: expected in late 06 or early 07 • Radiative Fluxes • Later flux data sets will include Bill Rossow’s ISCCP radiative fluxes. The Florida State University

13. X X Consideration in the Calculation of Uncertainty • Several factors contribute to uncertainty in gridded products • Observational uncertainty (Kent, Taylor, and Challenor, IJRS, 19) • Sampling-related uncertainty • Gaps in the observations • Variability at higher frequencies than can be resolved with the observations • Results in non-uniform error characteristics if the averaging volume is too small • Biases are important, but difficult to assess without comparison data. • Geophysical difference should be considered • Representation error: • Data from one location is not a perfect match to other locations • Propagation of error from nearby grid points • Errors related to non-linearity in parameterizations of fluxes The Florida State University

14. Comparison Data • The validation data are from two scatterometers: • SeaWinds on QSCAT (53 months) • The QSCAT data are gridded on a half degree grid. • A similar objective method is applied (Pegion et al. 2003 MWR) • Advantages of scatterometer data • Much better temporal sampling • Spatial sampling is much more uniform • ~92% of the ice free oceans covered each day. • The daily number of SeaWinds observations is approximately equal to the annual number of ship and buoy observations that enter the GTS data stream. • QSCAT winds are very accurate (Bourassa et al. 2003 JGR) • Differences from in situ winds (Bourassa et al. 2005 JCLIM) • Equivalent neutral winds - stability influences • Current relative winds • Wave modifications to stress are at least partially accounted for The Florida State University

15. Validation with SeaWinds on QuikSCATMeans (FSU3 minus Scatterometer) Zonal • Standard deviation of QSCAT and FSU3 differences • Zonal pseudostress (upper left) • Meridional pseudostress (upper right) • Wind speed (lower left) Merid m2s-2 -50 -25 -15 -5 5 15 25 50 -50 -25 -15 -5 5 15 25 50 Spd ms-1 -4 -2 -1.5 -1.0 -0.75 -0.5 -0.25 1 2 The Florida State University

16. Validation with SeaWinds on QuikSCATMeans (FSU3 minus Scatterometer) • Standard deviation of QSCAT and FSU3 differences • Zonal pseudostress (upper left) • Meridional pseudostress (upper right) • Wind speed (lower left) Zonal Merid -50 -25 -15 -5 5 15 25 50 m2s-2 -50 -25 -15 -5 5 15 25 50 Spd ms-1 -4 -2 -1.5 -1.0 -0.75 -0.5 -0.25 1 2 The Florida State University

17. Observational UncertaintyObservational Error + Sampling Error Sept. 1992 July 1985 0 6 12 18 24 30 36 m2s-2 m2s-2 0 6 12 18 24 30 36 The Florida State University

18. Observational UncertaintyObservational Error + Sampling Error March 1983 0 6 12 18 24 30 36 m2s-2 0 6 12 18 24 30 36 m2s-2 The Florida State University

19. 0 5 10 15 20 25 30 35 40 m2s-2 Uncertainty in BackgroundZonal Pseudostress (Sept. 1992) • Uncertainty including observational and representation errors (upper left) • Total uncertainty in background: observational, representation, and sampling (upper right). • Standard deviation of differences with QSCAT (53 months) 0 6 12 18 24 30 36 0 6 12 18 24 30 36 m2s-2 m2s-2 The Florida State University

20. 0 5 10 15 20 25 30 35 40 Uncertainty in BackgroundZonal Pseudostress (July 1985) • Uncertainty including observational and representation errors (upper left) • Total uncertainty in background: observational, representation, and sampling (upper right). • Standard deviation of differences with QSCAT (53 months) 0 6 12 18 24 30 36 m2s-2 0 6 12 18 24 30 36 m2s-2 m2s-2 The Florida State University

21. m2s-2 0 5 10 15 20 25 30 35 40 Uncertainty in the BackgroundZonal Pseudostress (March 1983) • Uncertainty including observational and representation errors (upper left) • Total uncertainty in background: observational, representation, and sampling (upper right). • Standard deviation of differences with QSCAT (53 months) m2s-2 0 6 12 18 24 30 36 m2s-2 0 6 12 18 24 30 36 The Florida State University

22. Conclusions • The fields produced by this objective technique are vastly superior in quality to previous subjectively derived fields. • Accuracy is improved • spurious forcing related to noise in the observational pattern should be greatly reduced • Technique for determining uncertainty for pseudostress components is working well. • Spatially and temporally variable uncertainty • Patterns and magnitudes are similar to those from FSU3/scatterometer comparison. • Remaining to do: • Code determination of final uncertainty via functional. • Add fluxes and other flux-related variables. • Uncertainties, relative to scatterometer fields, are typically in the range of 0.005 to 0.015Nm-2 The Florida State University

23. Validation with SeaWinds on QuikSCATStandard Deviations • Standard deviation of QSCAT and FSU3 differences • Zonal pseudostress (upper left) • Meridional pseudostress (upper right) • Wind speed (lower left) Merid Zonal 0 5 10 15 20 25 30 35 40 0 5 10 15 20 25 30 35 40 Spd 0 0.3 0.6 0.9 1.2 1.5 1.8 2.1 2.4 The Florida State University

24. Validation with SeaWinds on QuikSCATStandard Deviations Zonal • Standard deviation of QSCAT and FSU3 differences • Zonal pseudostress (upper left) • Meridional pseudostress (upper right) • Wind speed (lower left) Merid 0 5 10 15 20 25 30 35 40 0 5 10 15 20 25 30 35 40 Spd 0 0.3 0.6 0.9 1.2 1.5 1.8 2.1 2.4 The Florida State University

25. Mean Differences in Monthly Zonal Pseudostress (FSU minus scatterometer) • The patterns in the zonal winds are related to the currents. Scatterometer winds are current relative, whereas the FSU winds are earth relative. New FSU Winds Old FSU Winds The Florida State University

26. Mean Differences in Monthly Meridional Pseudostress (FSU minus scatterometer) • The Meridional differences indicate that neither the subjective or objective FSU winds capture the strong Meridional convergence about the ITCZ. New FSU Winds Old FSU Winds The Florida State University

27. Standard Deviation of Differences in Monthly Meridional Pseudostress • The meridional standard deviations have a local maximum about the ITCZ and in the area of the SPCZ. • The VOS observation pattern is easily identifiable in the subjective FSU winds. New FSU Winds Old FSU Winds The Florida State University

28. Previous state of the art(samescale) Wave data greatly improve match to observations. The Florida State University