1. http://coaps.fsu.eduhttp://coaps.fsu.edu/~bourassa bourassa@coaps.fsu.edu The Florida State University MARCDAT2 Oct. 2005. 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

2. http://coaps.fsu.eduhttp://coaps.fsu.edu/~bourassa bourassa@coaps.fsu.edu The Florida State University MARCDAT2 Oct. 2005. 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

3. http://coaps.fsu.eduhttp://coaps.fsu.edu/~bourassa bourassa@coaps.fsu.edu The Florida State University MARCDAT2 Oct. 2005. 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.

4. http://coaps.fsu.eduhttp://coaps.fsu.edu/~bourassa bourassa@coaps.fsu.edu The Florida State University MARCDAT2 Oct. 2005. 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)

5. http://coaps.fsu.eduhttp://coaps.fsu.edu/~bourassa bourassa@coaps.fsu.edu The Florida State University MARCDAT2 Oct. 2005. 5 Example VOS and Buoy Observations Dec. Average from 1988-1997 Buoys VOS

6. http://coaps.fsu.eduhttp://coaps.fsu.edu/~bourassa bourassa@coaps.fsu.edu The Florida State University MARCDAT2 Oct. 2005. 6 Example Latent and Sensible Heat Fluxes

7. http://coaps.fsu.eduhttp://coaps.fsu.edu/~bourassa bourassa@coaps.fsu.edu The Florida State University MARCDAT2 Oct. 2005. 7 Example VOS and Buoy Observations Dec. Average from 1988-1997 VOS Buoys

8. http://coaps.fsu.eduhttp://coaps.fsu.edu/~bourassa bourassa@coaps.fsu.edu The Florida State University MARCDAT2 Oct. 2005. 8 Example Latent Heat Fluxes (January 1998 and August 1999

9. http://coaps.fsu.eduhttp://coaps.fsu.edu/~bourassa bourassa@coaps.fsu.edu The Florida State University MARCDAT2 Oct. 2005. 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

10. http://coaps.fsu.eduhttp://coaps.fsu.edu/~bourassa bourassa@coaps.fsu.edu The Florida State University MARCDAT2 Oct. 2005. 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

11. http://coaps.fsu.eduhttp://coaps.fsu.edu/~bourassa bourassa@coaps.fsu.edu The Florida State University MARCDAT2 Oct. 2005. 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

12. http://coaps.fsu.eduhttp://coaps.fsu.edu/~bourassa bourassa@coaps.fsu.edu The Florida State University MARCDAT2 Oct. 2005. 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.

13. http://coaps.fsu.eduhttp://coaps.fsu.edu/~bourassa bourassa@coaps.fsu.edu The Florida State University MARCDAT2 Oct. 2005. 13 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 X X  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

14. http://coaps.fsu.eduhttp://coaps.fsu.edu/~bourassa bourassa@coaps.fsu.edu The Florida State University MARCDAT2 Oct. 2005. 14 Comparison Data  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 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)

15. http://coaps.fsu.eduhttp://coaps.fsu.edu/~bourassa bourassa@coaps.fsu.edu The Florida State University MARCDAT2 Oct. 2005. 15 Validation with SeaWinds on QuikSCAT Means (FSU3 minus Scatterometer)  Standard deviation of QSCAT and FSU3 differences  Zonal pseudostress (upper left)  Meridional pseudostress (upper right)  Wind speed (lower left) -50 -25 -15 -5 5 15 25 50 -4 -2 -1.5 -1.0 -0.75 -0.5 -0.25 1 2 -50 -25 -15 -5 5 15 25 50 ZonalMerid Spd m 2 s -2 ms -1

16. http://coaps.fsu.eduhttp://coaps.fsu.edu/~bourassa bourassa@coaps.fsu.edu The Florida State University MARCDAT2 Oct. 2005. 16 Validation with SeaWinds on QuikSCAT Means (FSU3 minus Scatterometer)  Standard deviation of QSCAT and FSU3 differences  Zonal pseudostress (upper left)  Meridional pseudostress (upper right)  Wind speed (lower left) -50 -25 -15 -5 5 15 25 50 -4 -2 -1.5 -1.0 -0.75 -0.5 -0.25 1 2 -50 -25 -15 -5 5 15 25 50 Spd MeridZonal m 2 s -2 ms -1

17. http://coaps.fsu.eduhttp://coaps.fsu.edu/~bourassa bourassa@coaps.fsu.edu The Florida State University MARCDAT2 Oct. 2005. 17 Observational Uncertainty Observational Error + Sampling Error Sept. 1992 July 1985 0 6 12 18 24 30 36 m 2 s -2

18. http://coaps.fsu.eduhttp://coaps.fsu.edu/~bourassa bourassa@coaps.fsu.edu The Florida State University MARCDAT2 Oct. 2005. 18 Observational Uncertainty Observational Error + Sampling Error March 1983 0 6 12 18 24 30 36 m 2 s -2

19. http://coaps.fsu.eduhttp://coaps.fsu.edu/~bourassa bourassa@coaps.fsu.edu The Florida State University MARCDAT2 Oct. 2005. 19 Uncertainty in Background Zonal 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 m 2 s -2 0 5 10 15 20 25 30 35 40 m 2 s -2

20. http://coaps.fsu.eduhttp://coaps.fsu.edu/~bourassa bourassa@coaps.fsu.edu The Florida State University MARCDAT2 Oct. 2005. 20 Uncertainty in Background Zonal 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 0 5 10 15 20 25 30 35 40 m 2 s -2

21. http://coaps.fsu.eduhttp://coaps.fsu.edu/~bourassa bourassa@coaps.fsu.edu The Florida State University MARCDAT2 Oct. 2005. 21 Uncertainty in the Background Zonal 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) 0 6 12 18 24 30 36 0 5 10 15 20 25 30 35 40 m 2 s -2

22. http://coaps.fsu.eduhttp://coaps.fsu.edu/~bourassa bourassa@coaps.fsu.edu The Florida State University MARCDAT2 Oct. 2005. 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

23. http://coaps.fsu.eduhttp://coaps.fsu.edu/~bourassa bourassa@coaps.fsu.edu The Florida State University MARCDAT2 Oct. 2005. 23 Validation with SeaWinds on QuikSCAT Standard Deviations  Standard deviation of QSCAT and FSU3 differences  Zonal pseudostress (upper left)  Meridional pseudostress (upper right)  Wind speed (lower left) Zonal Merid Spd 0 5 10 15 20 25 30 35 40 0 0.3 0.6 0.9 1.2 1.5 1.8 2.1 2.4

24. http://coaps.fsu.eduhttp://coaps.fsu.edu/~bourassa bourassa@coaps.fsu.edu The Florida State University MARCDAT2 Oct. 2005. 24 Validation with SeaWinds on QuikSCAT Standard Deviations  Standard deviation of QSCAT and FSU3 differences  Zonal pseudostress (upper left)  Meridional pseudostress (upper right)  Wind speed (lower left) 0 5 10 15 20 25 30 35 40 0 0.3 0.6 0.9 1.2 1.5 1.8 2.1 2.4 ZonalMerid Spd

25. http://coaps.fsu.eduhttp://coaps.fsu.edu/~bourassa bourassa@coaps.fsu.edu The Florida State University MARCDAT2 Oct. 2005. 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

26. http://coaps.fsu.eduhttp://coaps.fsu.edu/~bourassa bourassa@coaps.fsu.edu The Florida State University MARCDAT2 Oct. 2005. 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

27. http://coaps.fsu.eduhttp://coaps.fsu.edu/~bourassa bourassa@coaps.fsu.edu The Florida State University MARCDAT2 Oct. 2005. 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

28. http://coaps.fsu.eduhttp://coaps.fsu.edu/~bourassa bourassa@coaps.fsu.edu The Florida State University MARCDAT2 Oct. 2005. 28 Previous state of the art (same scale) Wave data greatly improve match to observations.