1. http://coaps.fsu.edu/~bourassahttp://coaps.fsu.edu/~bourassa/ bourassa@coaps.fsu.edu Surface Observations in The Southern Ocean, Variability, and the Consequences on Fluxes Mark A. Bourassa Center for Ocean-Atmospheric Prediction Studies, and Department of Meteorology The Florida State University http://coaps.fsu.edu/ bourassa@coaps.fsu.edu With help from Shawn R. Smith

2. http://coaps.fsu.edu/~bourassahttp://coaps.fsu.edu/~bourassa/ bourassa@coaps.fsu.edu The Florida State University Southern Ocean Working Group 2 Outline  Observing Systems and Surface Products (emphasis on winds)  Historical data (ships and buoys)  Satellites  Numerical Weather Prediction (reanalyses)  Validation and Planning Tool: Research Ships and Buoys  Validation of flux products and related variables  Calibration/Validation of satellite sensors  New: input in satellite sensor design.  High Frequency Fluxes  Uncertainties in High Frequency Fluxes  Recent Developments in Flux Modeling

3. http://coaps.fsu.edu/~bourassahttp://coaps.fsu.edu/~bourassa/ bourassa@coaps.fsu.edu The Florida State University Southern Ocean Working Group 3 Zonal Wind Stress  Averages from Hellerman and Rosenstein (the classic), COADS, SOC, and ECMWF (all for different periods).

4. http://coaps.fsu.edu/~bourassahttp://coaps.fsu.edu/~bourassa/ bourassa@coaps.fsu.edu The Florida State University Southern Ocean Working Group 4 Four Net Heat Fluxes  Net heat fluxes climatologies from SOC, COADS, NCEP/NCAR Reanalysis, and ECMWF (all for different periods).

5. http://coaps.fsu.edu/~bourassahttp://coaps.fsu.edu/~bourassa/ bourassa@coaps.fsu.edu The Florida State University Southern Ocean Working Group 5 Latent Heat Fluxes  Averages (from different periods) from SOC, COADS, NCEPR, and ECMWF.

6. http://coaps.fsu.edu/~bourassahttp://coaps.fsu.edu/~bourassa/ bourassa@coaps.fsu.edu The Florida State University Southern Ocean Working Group 6 Sensible Heat Fluxes  Fields are radically different in areas of ice, due to different assumptions.  NCEPR fluxes are exaggerated, but the input values (temperature and humidity differences) are OK: reasonable fluxes can be calculated.

7. http://coaps.fsu.edu/~bourassahttp://coaps.fsu.edu/~bourassa/ bourassa@coaps.fsu.edu The Florida State University Southern Ocean Working Group 7 Net Long Wave Radiative Surface Fluxes  Fluxes from Langley (satellite based), COADS, NCEPR, and ECMWF (all for different periods).

8. http://coaps.fsu.edu/~bourassahttp://coaps.fsu.edu/~bourassa/ bourassa@coaps.fsu.edu The Florida State University Southern Ocean Working Group 8 COADS Climatological Average December long-term (1960-93) mean of (Q S - Q)W, i.e., saturation specific humidity at sea surface temperature minus specific humidity, times wind speed (g kg -1 m s -1 ), based on the average of PACS enhanced year-month-1° box means. http://www.cdc.noaa.gov/coads/egs_paper_fig1.html

9. http://coaps.fsu.edu/~bourassahttp://coaps.fsu.edu/~bourassa/ bourassa@coaps.fsu.edu The Florida State University Southern Ocean Working Group 9 Average Monthly Number of Observations (COADS: 1988-1997)  Ship Coverage on a 1x1  grid.  Buoy Coverage Average Monthly Number of Observations

10. http://coaps.fsu.edu/~bourassahttp://coaps.fsu.edu/~bourassa/ bourassa@coaps.fsu.edu The Florida State University Southern Ocean Working Group 10 Seasonal Coverage (DJF Based on COADS)  The quantity of ship data is insufficient for seasonal averages, except in isolated regions.  There are also a large number of buoy observations (or mislabeled ships).  Annual means based in in situ data have uncomfortably large uncertainty!

11. http://coaps.fsu.edu/~bourassahttp://coaps.fsu.edu/~bourassa/ bourassa@coaps.fsu.edu The Florida State University Southern Ocean Working Group 11 Standard Deviation of daSilva clim. Octobers

12. http://coaps.fsu.edu/~bourassahttp://coaps.fsu.edu/~bourassa/ bourassa@coaps.fsu.edu The Florida State University Southern Ocean Working Group 12 Strengths and Weakness of In Situ Observing System  Strengths:  Observes key variables in calculating surface turbulent fluxes.  Atmospheric temperature and atmospheric humidity.  Moored buoys – excellent temporal sampling  Research ships & buoys– very high quality, high temporal resolution observations.  A excellent source of comparison/validation data.  Currently not ingested by models.  Weaknesses  Ships – poor spatial and temporal sampling.  Buoys – terrible spatial sampling.  Questionable SSTs.  Radiation data is rarely available.

13. http://coaps.fsu.edu/~bourassahttp://coaps.fsu.edu/~bourassa/ bourassa@coaps.fsu.edu The Florida State University Southern Ocean Working Group 13 Satellite Coverage  Geosynchronos Satellites  Polar Orbiting Satellites  Vector Winds  Scatterometers: ERS-1/2, NSCAT, SeaWinds, (ASCAT)  Polarametric Radiometer: WindSat  Wind Speed  Scatterometers: ERS-1/2, NSCAT, SeaWinds, (ASCAT)  Polarametric Passive Radiometer: WindSat  Non-Polarametric Passive Radiometers: e.g., SSM/I  Active Radiometers: TMI,

14. http://coaps.fsu.edu/~bourassahttp://coaps.fsu.edu/~bourassa/ bourassa@coaps.fsu.edu The Florida State University Southern Ocean Working Group 14 South Pacific Surface Winds Based on Daily NSCAT Fields  Vector motion is based on gridded 10m wind from NSCAT alone.  Arrow lengths are proportion to speed.  Arrows are advected with the 10m winds.  Background color is vorticity.

15. http://coaps.fsu.edu/~bourassahttp://coaps.fsu.edu/~bourassa/ bourassa@coaps.fsu.edu The Florida State University Southern Ocean Working Group 15 Southern Ocean Examples

16. http://coaps.fsu.edu/~bourassahttp://coaps.fsu.edu/~bourassa/ bourassa@coaps.fsu.edu The Florida State University Southern Ocean Working Group 16 SeaWinds Daily (22 hour) Coverage Ascending NodeDescending Node April 25, 2000 From Paul Chang (NOAA/NESDIS): http://manati.wwb.noaa.gov/quikscat/

17. http://coaps.fsu.edu/~bourassahttp://coaps.fsu.edu/~bourassa/ bourassa@coaps.fsu.edu The Florida State University Southern Ocean Working Group 17 SeaWinds Daily (22 hour) Coverage Ascending NodeDescending Node Feb. 2, 2000 From Paul Chang (NOAA/NESDIS): http://manati.wwb.noaa.gov/quikscat/

18. http://coaps.fsu.edu/~bourassahttp://coaps.fsu.edu/~bourassa/ bourassa@coaps.fsu.edu The Florida State University Southern Ocean Working Group 18 QSCAT Coverage of Southern Ocean Example of Daily Coverage

19. http://coaps.fsu.edu/~bourassahttp://coaps.fsu.edu/~bourassa/ bourassa@coaps.fsu.edu The Florida State University Southern Ocean Working Group 19 Coverage by Two SeaWinds Scatterometers

20. http://coaps.fsu.edu/~bourassahttp://coaps.fsu.edu/~bourassa/ bourassa@coaps.fsu.edu The Florida State University Southern Ocean Working Group 20

21. http://coaps.fsu.edu/~bourassahttp://coaps.fsu.edu/~bourassa/ bourassa@coaps.fsu.edu The Florida State University Southern Ocean Working Group 21 Are Two Scatterometers Better Than One?  Example of gridded fields showing Hurricane Fabian (2003)  Single scatterometer uses observations from a 24 hours, and a 72 hours for background  Duo scatterometer uses observations from a 10 hour period and background from 30 hours  Averaging period will vary for orbits that are not in phase  Note the improved shape and speed in Fabian SeaWinds on QuikSCAT SeaWinds on QuikSCAT and Midori

22. http://coaps.fsu.edu/~bourassahttp://coaps.fsu.edu/~bourassa/ bourassa@coaps.fsu.edu The Florida State University Southern Ocean Working Group 22 QSCAT Only Field Minus Duo Scatterometer Field  18+ ms -1 difference around the center of Fabian.  The area of low wind speeds off of Newfoundland is more sharply defined in the duo-scatterometer case, resulting in large differences in this region.  The large yellow area of near zero differences has similar input in the two products. Vector Wind Speed Differences (ms -1 )

23. http://coaps.fsu.edu/~bourassahttp://coaps.fsu.edu/~bourassa/ bourassa@coaps.fsu.edu The Florida State University Southern Ocean Working Group 23 Problems with Sampling: Tropics  The QSCAT-only fields (left) show a great deal of sampling-related variability in rapidly evolving features, such as hurricane Fabian.  The combined scatterometer fields (right) also suffer from this problem.

24. http://coaps.fsu.edu/~bourassahttp://coaps.fsu.edu/~bourassa/ bourassa@coaps.fsu.edu The Florida State University Southern Ocean Working Group 24 Strengths of Weakness of Satellite Observing System  Strengths:  Vector winds (if there are appropriate satellites)  Scalar wind speeds  Sea Surface Height (SSH)  Sea Surface Temperature (SST)  Outgoing Radiation (at the top of the atmosphere)  Spatially and temporally complete fields can be made.  Weaknesses  Vector wind observations in peril!  Near surface air temperature  Near surface humidity  Radiation at the ocean’s surface  Precipitation (it’s just too variable)  Weaknesses are usually dealt with by incorporating model data, or assimilating surface data into models.

25. http://coaps.fsu.edu/~bourassahttp://coaps.fsu.edu/~bourassa/ bourassa@coaps.fsu.edu The Florida State University Southern Ocean Working Group 25 Testing the NCEP Reanalysis-1  Surface pressures are derived from surface winds observed from the SeaWinds scatterometer.  To build confidence in our pressures, they are first compared to observations from the Tropical Prediction Center.  The remaining examples are typical of the Southern Ocean.

26. http://coaps.fsu.edu/~bourassahttp://coaps.fsu.edu/~bourassa/ bourassa@coaps.fsu.edu The Florida State University Southern Ocean Working Group 26 Scatterometer-Derived Pressures TS Keith Statistics Best Track: 20.8N 94.9W 988 mb 70 mph QSCAT: 20.75N 94.75W 989.9 mb Development of this technique was inspired by Patoux and Brown (2001)

27. http://coaps.fsu.edu/~bourassahttp://coaps.fsu.edu/~bourassa/ bourassa@coaps.fsu.edu The Florida State University Southern Ocean Working Group 27 Surface Pressure Methodology Overview  Calculations involving SeaWinds winds are performed on a satellite grid then transferred onto 0.25° earth-aligned grid  Geostrophic vorticity can be calculated from background: (  A ij ) g = (  f j ) -1  2 p ij + (  j /  f j 2 )  p ij /  y  Satellite vorticity adjusted to a geostrophic value:  Rotate 18° AC, amplify 1.5 (Brown and Zeng, JAM, 1994)  Gradient wind adjustment  The two geostrophic vorticity values are blended using a variational method (Harlan and O’Brien, JGR, 1986; Zierden et al., JGR, 2000)

28. http://coaps.fsu.edu/~bourassahttp://coaps.fsu.edu/~bourassa/ bourassa@coaps.fsu.edu The Florida State University Southern Ocean Working Group 28 Methodology Gradient Wind Adjustment  Inspired by Patoux and Brown (2001)  Gradient Wind Formula: V g = V(1+V/fR) = V (1+Ro)  It can be shown (Endlich, MWR 1961) that: Ro = 1/fV 2 [(u 2  v/  x - v 2  u/  y) - uv(  u/  x -  v/  y)]  Advantages  Much more accurate than assuming scatterometer winds are geostrophic.  Requires only scatterometer observations.  Conditions of applicability  Valid at greater than ~8 from the Equator  Larger errors with tight, rapidly evolving systems

29. http://coaps.fsu.edu/~bourassahttp://coaps.fsu.edu/~bourassa/ bourassa@coaps.fsu.edu The Florida State University Southern Ocean Working Group 29 Methodology Variational Method  Cost function: F(p ij,  ij, ij ) = ∑ i ∑ j [(  A ij ) g -  ij ] + K  /2 [(  * ij ) g -  ij ] 2 + K E /2 G ij  Minimization of the cost function becomes the problem of solving:  2 p ij - (  j /f j )  p ij /  y -K(p ij - p 0ij ) =  f j (  * ij ) g  Computational cost:  Old approaches required approximately full time use of one 300 MHz (i.e., slow) CPU.  Our new approach is more efficient, and CPU speeds have substantially improved.

30. http://coaps.fsu.edu/~bourassahttp://coaps.fsu.edu/~bourassa/ bourassa@coaps.fsu.edu The Florida State University Southern Ocean Working Group 30 Example Pressures from QSCAT Winds  Ku-2001 model function  60% of the QSCAT-1 uncertainties (Bourassa et al. 2002, JGR-O, in press).  Rain flags are ignored.  NCEP Reanalysis provides a background to initialize the pressure field and update boundary conditions.  Validation is ongoing. The greatest source of error (uncertainty) in pressures (rather than pressure gradients) appears to come from errors in the background field.

31. http://coaps.fsu.edu/~bourassahttp://coaps.fsu.edu/~bourassa/ bourassa@coaps.fsu.edu The Florida State University Southern Ocean Working Group 31 Southern Hemisphere Examples (day 1)  SeaWinds shows surface circulation.  NCEP/NCAR Reanalysis has a very weak inverted trough.

32. http://coaps.fsu.edu/~bourassahttp://coaps.fsu.edu/~bourassa/ bourassa@coaps.fsu.edu The Florida State University Southern Ocean Working Group 32 Southern Hemisphere Examples (day 4)  Storm is in the correct location, but the reanalysis’ storm is far too weak.

33. http://coaps.fsu.edu/~bourassahttp://coaps.fsu.edu/~bourassa/ bourassa@coaps.fsu.edu The Florida State University Southern Ocean Working Group 33 Southern Hemisphere Examples (day 8)  Relatively good agreement.

34. http://coaps.fsu.edu/~bourassahttp://coaps.fsu.edu/~bourassa/ bourassa@coaps.fsu.edu The Florida State University Southern Ocean Working Group 34 Southern Hemisphere Examples (day 8)  However, upstream, the same old problems exist.

35. http://coaps.fsu.edu/~bourassahttp://coaps.fsu.edu/~bourassa/ bourassa@coaps.fsu.edu The Florida State University Southern Ocean Working Group 35 Southern Hemisphere Examples (days 11 and 12)  It starts again. Unfortunately, these examples are typical of the NCEP/NCAR’s accuracy in the Southern Ocean.

36. http://coaps.fsu.edu/~bourassahttp://coaps.fsu.edu/~bourassa/ bourassa@coaps.fsu.edu The Florida State University Southern Ocean Working Group 36 Numerical Weather Prediction  Reanalyses   NWP reanalyses use a single package of parameterizations and assimilation to reprocess a long time series of data.  Advantages:  Uniform methodology resulting in spatially and temporally complete fields.  Information can be propagated in time.  Disadvantages:  The model is only as good as it’s physics and the data assimilated.  Surface physics, cloud physics, and radiative transfer are all relatively weak points in modeled physics.  A large fraction of surface data is rejected because of inconsistencies with the model.  This fraction is greater in data sparse regions.  The type of coverage (satellite and in situ) changes substantially with time.

37. http://coaps.fsu.edu/~bourassahttp://coaps.fsu.edu/~bourassa/ bourassa@coaps.fsu.edu The Florida State University Southern Ocean Working Group 37 High Quality In Situ Data Are Valuable  Validation and Planning Tool: Research Ships and Buoys  Validation of flux products and related variables  Calibration/Validation of satellite sensors  New: input in satellite sensor design.  Research ships and research buoys provide much finer quality of data than VOS.  These observations have been used to test the quality of NCEP Reanalysis surface fields (winds, SST, air temperature, and humidity) and surface turbulent fluxes.  Winds and turbulent heat fluxes were found to be greatly biased!  Much better sensible and latent heat fluxes could be calculated using NCEP winds, temperatures, and humidities, along with better transfer coefficients.

38. http://coaps.fsu.edu/~bourassahttp://coaps.fsu.edu/~bourassa/ bourassa@coaps.fsu.edu The Florida State University Southern Ocean Working Group 38 Validation Example: SeaWinds on QSCAT Wind Speed  QSCAT-1 Model Function  Preliminary results  2 months of data  Observations from eight research vessels  < 25 km apart, < 20 minutes apart.  Uncertainty was calculated using PCA, assuming ships and satellite make equal contributions to uncertainty. Likely to be unflagged rain contamination

39. http://coaps.fsu.edu/~bourassahttp://coaps.fsu.edu/~bourassa/ bourassa@coaps.fsu.edu The Florida State University Southern Ocean Working Group 39 Validation Example: SeaWinds on QSCAT Wind Direction  Preliminary results  Same conditions as the previous plot.  Correctly selected ambiguities are within 45  of the green line or the corners.  Red dashed lines indicates 180  errors.  Yellow dashed lines indicate 90  errors.  Statistics are for correctly selected ambiguities.

40. http://coaps.fsu.edu/~bourassahttp://coaps.fsu.edu/~bourassa/ bourassa@coaps.fsu.edu The Florida State University Southern Ocean Working Group 40 Assessment of Small Scale Variability in Surface Vector Winds - An Estimate of Beam to Beam Variability of Vector Winds Within a Vector Wind Cell Mark A. Bourassa Dept. of Meteorology, The Florida State University, and The Center for Ocean-Atmospheric Prediction Studies

41. http://coaps.fsu.edu/~bourassahttp://coaps.fsu.edu/~bourassa/ bourassa@coaps.fsu.edu The Florida State University Southern Ocean Working Group 41 The Problem  The different looks within a vector wind cell do not occur at the same time. The winds can and do change between looks, and short water waves respond very rapidly to those changes.  These changes can be thought of as appearing as noise in the observed backscatter. When individual footprints are averaged over sufficient space/time (space in this case), the variability due to smaller scale processes can be greatly reduced.  If we have smaller footprints or have a longer time between looks, then this apparent noise will increase.  The looks are also not at identical locations within the ocean vector wind cell. That is not yet considered within this study.

42. http://coaps.fsu.edu/~bourassahttp://coaps.fsu.edu/~bourassa/ bourassa@coaps.fsu.edu The Florida State University Southern Ocean Working Group 42 The Approach  The following approach will overestimate the problem, because it approximates the variability in a slice through a footprint. Averaging in the second dimension should further reduce the uncertainty in the mean, and hence reduce the differences between means calculated at slightly different times.  Taylor’s hypothesis is used to convert spatial scale (in these examples 25, 20, 15, 10, and 5 km) to time scale.  Time scale = spatial scale / mean wind speed.  A maximum time scale of 40 minutes is used.  The weighting in space (translated to time) is equal to a Gaussian distribution, centered on the center of the footprint, and dropping by one standard deviation at the edge of the footprint.  Mean speeds and directions are calculated, and differences are calculated for time steps of 1 through 20 minutes.  Ideally, this variability would be negligible for the space time scales we are considering.

43. http://coaps.fsu.edu/~bourassahttp://coaps.fsu.edu/~bourassa/ bourassa@coaps.fsu.edu The Florida State University Southern Ocean Working Group 43 Example of Variability in 60s Averages for Various Difference In Time  Variance in wind speed differences (ms -1 ) as a function of the difference in time (minutes) for individual observations (one minute averages).  This is not what we are interested in, but it is a nice standard of comparison. 0 to 4 ms -1 4 to 8 ms -1 8 to 12 ms -1 12 to 16 ms -1 16 to 20 ms -1

44. http://coaps.fsu.edu/~bourassahttp://coaps.fsu.edu/~bourassa/ bourassa@coaps.fsu.edu The Florida State University Southern Ocean Working Group 44 Examples for 25km footprints  Variance in wind speed differences (left; ms -1 ) and directional differences (right; degrees) as a function of the difference in time (minutes).  High wind speeds have more variability in speed, but less in direction.  Directional variability for low wind speeds is very sensitive to the differences in time. 0 to 4 ms -1 4 to 8 ms -1 8 to 12 ms -1 12 to 16 ms -1 16 to 20 ms -1

45. http://coaps.fsu.edu/~bourassahttp://coaps.fsu.edu/~bourassa/ bourassa@coaps.fsu.edu The Florida State University Southern Ocean Working Group 45 Examples for 20km footprints  Variance in wind speed (left; ms -1 ) and direction (right; degrees) as a function of the difference in time (minutes). 0 to 4 ms -1 4 to 8 ms -1 8 to 12 ms -1 12 to 16 ms -1 16 to 20 ms -1

46. http://coaps.fsu.edu/~bourassahttp://coaps.fsu.edu/~bourassa/ bourassa@coaps.fsu.edu The Florida State University Southern Ocean Working Group 46 Examples for 15km footprints  Variance in wind speed (left; ms -1 ) and direction (right; degrees) as a function of the difference in time (minutes). 0 to 4 ms -1 4 to 8 ms -1 8 to 12 ms -1 12 to 16 ms -1 16 to 20 ms -1

47. http://coaps.fsu.edu/~bourassahttp://coaps.fsu.edu/~bourassa/ bourassa@coaps.fsu.edu The Florida State University Southern Ocean Working Group 47 Examples for 10km footprints  Variance in wind speed (left; ms -1 ) and direction (right; degrees) as a function of the difference in time (minutes). 0 to 4 ms -1 4 to 8 ms -1 8 to 12 ms -1 12 to 16 ms -1 16 to 20 ms -1

48. http://coaps.fsu.edu/~bourassahttp://coaps.fsu.edu/~bourassa/ bourassa@coaps.fsu.edu The Florida State University Southern Ocean Working Group 48 Examples for 5km footprints  Variance in wind speed (left; ms -1 ) and direction (right; degrees) as a function of the difference in time (minutes).  Speeds, for large wind speeds, are highly sensitive to the differences in observation time. 0 to 4 ms -1 4 to 8 ms -1 8 to 12 ms -1 12 to 16 ms -1 16 to 20 ms -1

49. http://coaps.fsu.edu/~bourassahttp://coaps.fsu.edu/~bourassa/ bourassa@coaps.fsu.edu The Florida State University Southern Ocean Working Group 49 Comments  I am not convinced that this problem is negligible for direction at low wind speeds ( 16 ms -1 ). Particularly so for speeds in the 5 km footprint.  We may have to run an atmospheric model to see if considering the extra dimension helps.  The number of surface layer cells within a footprint (think of this as N when converting from a variance to an uncertainty in the mean) drops as the footprint decreases in area. I doubt that we are seeing the limit where N=1, so I that an atmospheric model will show decreased variability.  The boundary-layer height varies regionally, making the number of cells per area change regionally  The high wind speed & small footprint variability could be due to a relatively small number of observations used to calculate the means. I consider that combination to be the least reliable.  I can provide a table of number if someone want to test the impacts of these estimates of variability.

50. http://coaps.fsu.edu/~bourassahttp://coaps.fsu.edu/~bourassa/ bourassa@coaps.fsu.edu The Florida State University Southern Ocean Working Group 50 High Frequency Fluxes  What resolution in space and time is obtainable?  The answer to this question is highly dependent on the sampling of the observational system.  Temporal resolution is more difficult to achieve than temporal resolution.  For data-based products, this is the key constraint.  For NWP reanalyses, the quality of the modeled physics is also a constraining factor.  We can expect to have access to daily (or better) fields of stress, so long as there is at least one wide swath scatterometer in orbit.  Strengths and weaknesses of WindSat remain to be seen.  Current indications are not good for Southern Ocean  For energy fluxes, the availability of atmospheric temperature and moisture data might be the key constraint.  High quality SST is also essential for ALL energy fluxes.  The products must be internally consistent!

51. http://coaps.fsu.edu/~bourassahttp://coaps.fsu.edu/~bourassa/ bourassa@coaps.fsu.edu The Florida State University Southern Ocean Working Group 51 Graphical Example of AVHRR SST Snapshot Frontal Instabilities Wavelength: 50-100 km Cold Filament Width Scale: 10-30 km

52. http://coaps.fsu.edu/~bourassahttp://coaps.fsu.edu/~bourassa/ bourassa@coaps.fsu.edu The Florida State University Southern Ocean Working Group 52 SST During Hurricane Danielle  SSTs from two satellites (TMI on TRMM and AVHRR), and Reynolds’ blend of in situ and satellite SST.  Graphic from http://www.ssmi.com/hurricane/example_data_applications.html#sst_comparison

53. http://coaps.fsu.edu/~bourassahttp://coaps.fsu.edu/~bourassa/ bourassa@coaps.fsu.edu The Florida State University Southern Ocean Working Group 53 TMI on TRMM  Remote Sensing Systems (ssmi.com) uses radiometers (SSMI and TMI) to determine a wide range of parameters:  SST (  C)  Wind Speed (m/s)  Rain rate (mm/hr)  TRMM’s coverage of the tropics and subtropics is excellent.

54. http://coaps.fsu.edu/~bourassahttp://coaps.fsu.edu/~bourassa/ bourassa@coaps.fsu.edu The Florida State University Southern Ocean Working Group 54 Observed Surface Stresses  Preliminary data form the SWS2 (Severe Wind Storms 2) experiment.  The drag coefficients for high wind speeds are large and plentiful.  The atypically large drag coefficients are associated with rising seas.  Many models overestimate these fluxes.  Excellent empirical fit to means of these data and many other by P.K. Taylor & M. Yelland (2001).

55. http://coaps.fsu.edu/~bourassahttp://coaps.fsu.edu/~bourassa/ bourassa@coaps.fsu.edu The Florida State University Southern Ocean Working Group 55 Bourassa (2003) Surface Stress Model: Comparison to Observations  A modified version of Bourassa et al. (1999).  Modifies the wind speed by the orbital velocity.  Better accounts for the spread.  Does not match the four high wind speed points.  Surface turbulent fluxes respond to the shear on the RHS of the top equation.  Scatterometers appear to be similar, but the 0.8 is uncertain.

56. http://coaps.fsu.edu/~bourassahttp://coaps.fsu.edu/~bourassa/ bourassa@coaps.fsu.edu The Florida State University Southern Ocean Working Group 56 Bourassa (2003) Means & Uncertainties Comparison of Model to SWS2 Observations  Error bars indicate  3 standard deviations from the binned mean.  Each bin contains at least 10 observations.  The lower stress bins contain several hundred observations.  Greatly improved fit in the mid-range.  Consideration of sea state can greatly reduce random errors and biases.

57. http://coaps.fsu.edu/~bourassahttp://coaps.fsu.edu/~bourassa/ bourassa@coaps.fsu.edu The Florida State University Southern Ocean Working Group 57 High Resolution Fluxes  Physically-based models that have been shown to be good for stress and (different model) for turbulent heat fluxes have great promise.  Members SEAFLUX group are working on blending the best features of these models.  These models consider sea state and net radiative flux in addition to the usual parameters.  Given reasonable input data, it should be possible to calculate reasonable accurate surface turbulent fluxes applicable to small spatial and temporal scales.  The great difficulty is finding (producing) internally consistent input for these flux models.  Again, recent improvements in the our understanding of the physical links between these variables should lead to improved products.

58. http://coaps.fsu.edu/~bourassahttp://coaps.fsu.edu/~bourassa/ bourassa@coaps.fsu.edu The Florida State University Southern Ocean Working Group 58 Recommendations (1 of 3)  Of the current satellite instruments, only the scatterometer is capable of observing synoptic scale variability including winds around low pressure systems.  There are currently no plans for another US scatterometer mission. The loss of the existing instrument or one of the planned instruments will result in a very large gap in the observational record.  Daily average wind fields can be produced with one satellite provided that spatial variability in uncertainty is acceptable.  Two scatterometers could be used to produce much better quality daily and 12 hourly fields.  The quality of numerical weather prediction products for the Southern Ocean improved dramatically after assimilation of scatterometer data. Reanalyses that assimilate these data should be encouraged.  The continuity of scatterometer data (or equivalent) is essential.

59. http://coaps.fsu.edu/~bourassahttp://coaps.fsu.edu/~bourassa/ bourassa@coaps.fsu.edu The Florida State University Southern Ocean Working Group 59 Recommendations (2 of 3)  Uses for high quality in situ data (from research vessels and buoys)  High quality in situ wind speed data, for U 10 > 20 ms -1, is needed for the calibration of many satellite instruments. Observations of winds and waves from the Southern Ocean should be ideal for examining sea state influence on scatterometer winds and stresses. Very preliminary results suggest that scatterometer winds (and stresses in general) are sensitive to sea state, and that scatteromter- derived stresses are much less sensitive to sea state.  Gridded flux products can easily be evaluated with in situ data.  The variability observed from the in situ data can be used as design input for future satellite missions. For U 10 > 20 ms -1, this variability is large and appears to a significant source of error for some proposed sensor instruments (scatterometer, polarimetric radiometers, and perhaps SAR).  The data for research vessels (national and international) should be quality assured and archived.  Resupply vessels should be outfitted with automated sensors.

60. http://coaps.fsu.edu/~bourassahttp://coaps.fsu.edu/~bourassa/ bourassa@coaps.fsu.edu The Florida State University Southern Ocean Working Group 60 Recommendations (3 of 3)  There is two much variability in surface stress and net energy flux gridded products because of inconsistencies in the products.  For example, different SST product are used in products, and different time averaging is used in parameters that used in flux calculations.  Recommend the development of surface flux products for which the variables are all treated in an internally consistent manner.  Consider the physics that are of interest, thereby allowing the spatial and temporal characteristics of interest to be determined. Ask people providing the products used in the studies to identify what is needed to accurately identify variability on the scales of interest.  In other words, understand the apparent limits of the observing system, and work to insure there is an observing system that is adequate for the desired studies.