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The Available Winds (1990-2005)

This document explores different sources of surface wind fields for climate studies, including surface measurements, ship and buoy data, models, and satellite measurements. It also highlights the challenges and limitations associated with each data source.

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The Available Winds (1990-2005)

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  1. The Available Winds (1990-2005) R. A. Brown 2003 U. ConcepciÓn

  2. Sources of Surface Wind Fields • From Surface Measurements • Ships • Buoys • From Models • GCM (with HOC PBLs) • UW Similarity Model (with OLE) • From Satellites R. A. Brown 2003 U. ConcepciÓn

  3. Sources of Surface Wind Fields • From Surface Measurements • Ships • Buoys • From Models • GCM (with HOC PBLs) • UW Similarity Model (with OLE) • From Satellites R. A. Brown 2003 U. ConcepciÓn R.A. Brown, 1997, 2000

  4. Satellite Measurements • Scatterometer Algorithms • Correlated to global buoy data • Correlated to global NMC surface winds • Radiometer Algorithms (SSM/I) Altimeter, etc • Correlated to buoys • Lidar Measurements (Satellite) • Doppler return • Point measurement: Averaging problem R. A. Brown 2003 U. ConcepciÓn R.A. Brown, 1997, 2000

  5. The Need for Winds R. A. Brown 2003 U. ConcepciÓn

  6. Practical Aspects of Wind Measurements Surface ‘Truth’ Ship winds: Sparse and inaccurate (except Met. Ships). Buoy winds: Sparse; a point; tilt; variable height; - miss high winds and low wind directions. Radiosondes:Very sparse. Quality? GCM winds: Bad physics in PBL Models; Toolow high winds, too high low winds. Resolution coarse (getting better). Satellite winds: Lack good calibration data. Resolution (”). R. A. Brown 2003 U. ConcepciÓn

  7. Ways to improve Wind accuracy for Climate Studies Problem Remedy Date ? AccuracyBetter surface obs. (2010) Better satellite Model functions (2003) Improve Surface WindError in GCMs Increase Resolution to meters in the PBL 2025 Better PBL parameterizations Analytic Similarity Theory 2003 LES & CFD Numerical Models 2010 SparsityMore buoys (2010) More Satellite Data 1991 - Satellite Data Sparsity SSM/I (1988)  + WindSat (2003-) QuikScat(1996 ) + SeaWinds 2003  Lidar Troposphere Winds 2009 R. A. Brown 2003 U. ConcepciÓn

  8. There exists an Opportunity (for satellite data): • There are no satellite determined winds in the PBL • There are few measurements of winds in the PBL in situ • Measurements from sondes and buoys incur large errors due to turbulence & OLE • The fluxes (air-surface) require boundary layer winds • Climate Analyses have been made on extremely poor climatology data R. A. Brown 2003 U. ConcepciÓn R. A. Brown 1/2001

  9. R. A. Brown 2003 U. ConcepciÓn

  10. The Exciting Results of Remotely Sensing Winds R. A. Brown 2003 U. ConcepciÓn

  11. Involvement with PBL Winds --- Bob Brown • PhD Thesis: A secondary flow model for the PBL, advisor: Bob Fleagle ’69 --- The nonlinear PBL solution. “the end of diffusion models” • AIDJEX Project: Get winds over Arctic Ocean ’74 --- Birth of PBL model w/OLE explicitly. Nonlinear similarity solution. • SeaSat Satellite: JPL: Show that U10 to 0.1 m/s from 800km impossible ’78 ---- “unexpected, but correlations exist!” • Publish: The Scatterometer as an Anemometer. J. Geophys. Res., 88, C3, 1663-1673, 1983 --- “U10 to 1 m/s.” • PI: Scatterometers (SASS, ERS1/2, NSCAT, QuikScat, SeaWinds; Lidars: LAWS, Sparkle, ?; Radiometers; SSMI, SARs; Radarsat data; 1978 - present “It’s a great job & someone has to do it.”

  12. Sources of Surface Wind Fields for Climate Studies • From Surface Measurements • Ships • Buoys • From Models • GCM (with K-theory PBLs) • UW Similarity Model (with OLE) • From Satellites • Scatterometers • SAR, Altimeter, SSMI, WindSat…. • Lidar? R. A. Brown 2003 U. ConcepciÓn

  13. Sources of Tropospheric Wind Fields for Climate Studies • From Surface Measurements • Radiosondes • Radar • From Models • GCM • From Satellites • Lidar R. A. Brown 2003 U. ConcepciÓn

  14. Summary: Satellite Sensor Measurement of Surface Winds SensorCharacteristics Visible, IR Cloud motion Microwave Scatterometer Active radar, 25km resolution 1500km swath; sees thru clouds wind vectors w/multiple looks Radiometer Passive radar; 25km res. 1500km swath; decays in clouds, vapor. Possibility: vector w/multi-looks Altimeter Active radar, narrow swath SAR Active radar, 100m res. 1500km swath; wind speed. Lidar (2005) R. A. Brown 2003 U. ConcepciÓn

  15. The Struggle to get (and keep getting) Satellite Wind Sensors R. A. Brown 2003 U. ConcepciÓn

  16. Conclusions * The US has 1 active wind vector satellite. * The NASDA/NASA SeaWinds satellites have been plagued with bad luck in duration * Active wind sensors cost a lot more than chemistry, passive sensors * ESA has led the way in active wind sensors --- the scatterometers and the lidars R.A. Brown 2000 R. A. Brown 2003 U. ConcepciÓn

  17. The Search for Surface Truth R. A. Brown 2003 U. ConcepciÓn

  18. Practical Aspects of Wind Measurements Surface ‘Truth’ Limits Ship winds: Sparse and inaccurate (except Met. Ships). Buoy winds: Sparse; a point; tilt; variable height; - miss high winds and low wind directions. GCM winds: Bad physics in PBL Models; Toolow high winds, too high low winds. Resolution coarse (getting better). Satellite winds: Lack good calibration data. Resolution (”). 11-99, 5/00 RAB R. A. Brown 2003 U. ConcepciÓn

  19. Poor Surface ‘Truth’ Buoys • No ocean measurements > 23 m/s • Tilting in High Seas • Displacement Height Effects • Blocking effects of ship • Height effects of masts Meteorology Ships R.A. Brown, 1997, 2000 R. A. Brown 2003 U. ConcepciÓn

  20. CONCLUSIONS 3 • Buoy winds are not good surface truth • GCM PBL models still have wrong physics, too-low winds • The oV saturates (due to white water) @ U10 ~ 35 m/s • But the oH saturates at U10 ~ 65 m/s • The PMF/scat data has better resolution than GCMs. • Scatterometer derived pressure fields can de-alias winds, and correct (smooth) o single or small area anomalies R.A. Brown PORSEC 2000 R. A. Brown 2003 U. ConcepciÓn

  21. CONCLUSIONS 3 • Buoy winds are not good surface truth except in narrow ranges • GCM PBL models still have wrong physics, too-low winds • The oV saturates (due to white water) @ U10 ~ 35 m/s • But the oH saturates at U10 ~ 65 m/s • The scatterometer pressure MF has better resolution than GCMs. • Scatterometer derived pressure fields can de-alias winds, and correct (smooth) o single or small area anomalies R. A. Brown 2003 U. ConcepciÓn

  22. GCM Wind Model Function SAR footprint GCM footprint 60-100 km Scat footprint 25 km Caveats to getting a good U10: * Synoptic times only * Smoothing built in * No OLE in PBL (diffusive coefficient used for advection) Buoy 8-min averages ~ 5-km R. A. Brown 2003 U. ConcepciÓn

  23. Why a PBL (planetary boundary layer) model? • The satellite measures the mean density of the capillaries and short gravity waves on the ocean surface. There is no good theory relating this to anything geophysically worthwhile. • There exists a raw empirical parameterization between surface roughness and near surface winds (for over flat, smooth land surface). • There is a nonlinear analytic solution of the PBL in a rotating frame of reference (but it contains OLE). RAB, 5/2000 R. A. Brown 2003 U. ConcepciÓn

  24. The Scatterometer R. A. Brown 2003 U. ConcepciÓn

  25. CONCLUSIONS • The surface layer relation, hence U10 {u*(o ) }works well • There is almost no surface truth --- buoy or GCM surface winds --- with U10 > 25 m/s • The U10 model function can be extrapolated to about 35 m/s • There are indications that o responds to the sea state for U10>40 m/s. (H-pol > 60?) • Winds > 30 m/s need an asterisk indicating a kluge • The Model function still misses very high winds ‘99 R. A. Brown 2003 U. ConcepciÓn

  26. CONCLUSIONS • The PBL relation VG{U10 [u*(o )] } works well • Scatterometer derived pressure fields can be used to choose the best direction ambiguity, substitute an average U10 or a very high wind. • Similarly, the pressure fields can be used to correct (smooth) o single or small area (rainy) anomalies (a kluge with an *) • GCM PBL models still have wrong physics, too-low winds, too low pressure gradients ‘99

  27. Programs and Fields available http://pbl.atmos.washington.eduQuestionsto rabrown, neal or jerome@atmos.washington.edu • Direct PBL model: PBL_LIB. (1975) An analytic solution for the PBL flow with rolls, U(z) = f( P, To , Ta , ) • The Inverse PBL model: Takes U10 and calculates surface pressure fields P (U10 , To , Ta , ) (1986) • Pressure fields directly from the PMF: P (o) along all swaths (2003) • Surface stress fields from PBL_LIB corrected for stratification effects along all swaths (2004)

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