All-Weather Wind Vector Measurements from Intercalibrated PowerPoint Presentation
Thomas Meissner. Lucrezia Ricciardulli. Frank Wentz. IGARSS 2011. Vancouver, BC, Canada . July 26, 2011 . Outline. Passive (radiometer: WindSat) vs active (scatterometer: QuikSCAT) wind speed retrievals: . ID: 392722Embed code:
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All-Weather Wind Vector Measurements from Intercalibrated Active and Passive Microwave Satellite Sensors
Thomas MeissnerLucrezia RicciardulliFrank Wentz
Vancouver, BC, Canada
July 26, 2011Slide2
Passive (radiometer: WindSat) vs active (scatterometer: QuikSCAT) wind speed retrievals:
Surface emissivity versus radar backscatter.
Ocean Surface Emissivity Model.
Overview: RSS WindSat version 7 ocean products.
WindSat all-weather wind speeds.
Improved QuikSCAT Ku2011 geophysical model
Rain impact study.
Selected storm case: Hurricane Katrina.
Conclusion: active vs passive - strength +weaknesses.Slide3
Passive vs Active Wind Speeds
Passive (radiometer)Sees change in emissivity of wind roughened sea surface compared with specular surfaceLow winds: Polarization mixing of large gravity waves.High winds: Emissivity of sea foam.Radiative Transfer Model (RTM) function for wind induced surface emissivity.
Active (scatterometer)Sees backscatter from the Bragg-resonance of small capillary waves.Geophysical Model Function (GMF) for wind induced radar backscatter.
Challenge 1: High Wind Speeds (> 20 m/s)
Passive (radiometer)Lack of reliable ground truth. (buoys, NWP) for calibration and validation.Tropical cyclones: High winds correlated with rain (challenge 2).
Lack of reliable ground truth. (buoys, NWP) for calibration and validation.
Tropical cyclones: High winds correlated with rain (challenge 1).
Loss of sensitivity (GMF saturates).Slide5
Challenge 2: Wind Speeds in Rain
Passive (radiometer)Rainy atmosphere attenuates signal.Emissivity from rainy atmosphere has similar signature than from wind roughened surface.Scattering from rain drops is difficult to model.
Rainy atmosphere attenuates signal.
Backscatter from rainy atmosphere has similar signature than from wind roughened surface.
Scattering from rain drops is difficult to model.
Splash effect on surface.
Rain flagging difficult for single frequency sensor.Slide6
Ocean Surface Emissivity Model
Crucial part of Radiative Transfer Model (RTM). Physical basis of passive wind retrieval algorithm.Dielectric constant of sea water.Wind induced sea surface emissivity.Derived from WindSat and SSM/I TB measurements.Winds < 20 m/s:Buoys.NWP.Scatterometer.Winds > 20 m/s: HRD wind analysis (hurricanes).SFMR data.
T. Meissner + F. Wentz, IEEE TGRS 42(9), 2004, 1836 - 1849
. Meissner + F. Wen
Ocean Surface Emissivity Model (cont.)
Measured minus computed WindSat TB as function of SST (x-axis) and wind speed (y-axis).Slide8
Overview: RSS Version 7 Ocean Products
SSMISF8, F10, F11, F13, F14 ,F15, F16, F17
V7 releasedV7 release in progress
Intercalibrated multi-platform suite.
100 years of combined satellite data
RSS WindSat Version 7 Ocean Products
Optimized swath width by combining for and aft looks at each band.
Resolution + Required Channels
≥ 6.8 GHz
≥ 10.7 GHz
≥ 18.7 GHz
≥ 37.0 GHz
speed through rain
Liquid cloud water
New in V7 Radiometer : Winds Through Rain
Version 6: Rain areas needed to be blocked out.Version 7: Rain areas have wind speeds. C-band (7 GHz) required: WindSat, AMSR-E, GCOM Possible with only X-band (11 GHz): TMI, GMI. Residual degradation in rain.Slide11
WindSat Wind Speed Algorithms
No-rain algorithm (≥10.7 GHz, 32 km res.)Physical algorithm.Trained from Monte Carlo simulated TB. Based on radiative transfer model (RTM).Wind speed in rain algorithms (≥ 6.8 GHz, 52 km res.) Statistical or hybrid algorithmsTrained from match-ups between measured TB and ground truth wind speeds in rainy conditions.Utilizes spectral difference (6.8 GHz versus 10.7 GHz) in wind/rain response of measured brightness temperatures.Same method is used by NOAA aircraft step frequency microwave radiometers (SFMR) to measure wind speeds in hurricanes.
T. Meissner + F. Wen
WindSat All-Weather Wind Speeds
Blending between no-rain, global wind speed in rain and H-wind (tropical cyclones) algorithms. Depends on SST, wind speed and cloud water.Smooth transitions between zones.
Global Rain Algo
WindSat Wind Speed Validation
2-dimensional PDF: WindSat versus CCMP (cross-calibrated multi-platform) wind speed.Rain free and with rain.Slide14
WindSat Wind Validation at High Winds (1)
Renfrew et al. QJRMS 135, 2009, 2046 – 2066Aircraft observations taken during the Greenland Flow Distortion Experiment, Feb + Mar 2007.150 measurements during 5 missions.Wind vectors measured by turbulence probe.Adjusted to 10m above surface.Slide15
Improved QuikSCAT Ku2011 GMF: Purpose
Improvement at high wind speeds.
When RSS Ku2001 was developed (Wentz and Smith, 1999), validation data at high winds were limited.
GMF at high winds had to be extrapolated.
Analyses showed Ku2001 overestimated high winds.
WindSat wind speeds have been validated.
Confident up to 30 – 35 m/s.
Emissivity does not saturate at high winds. Good sensitivity.
Excellent validation at low and moderate wind speeds < 20 m/s (Buoys, SSM/I, CCMP, NCEP,…), > 20 m/s: Aircraft flights.
WindSat can be used as ground truth to calibrate new Ku-band scatterometer GMF.
Produce a climate data record of ocean vector winds.
Combining QuikSCAT with other sensors using consistent methodology.Slide16
Improved QuikSCAT Ku2011 GMF: Development
The GMF relates the observed backscatter ratio σ0 to wind speed w and direction φ at the ocean’s surface.To develop the new GMF we used 7 years of QuikSCAT σ0 collocated with WindSat wind speeds (90 min) and CCMP (Atlas et al, 2009) wind direction. WindSat also measures rain rate, used to flag QuikSCAT σ0 when developing GMF. We had hundreds of millions of reliable rain-free collocations, with about 0.2% at winds greater than 20 m/s.Slide17
Ku2001 versus Ku2011
Greenland Aircraft FlightsSlide18
Rain Impact: WindSat/QuikSCAT vs Buoys
Table shows WindSat/QuikSCAT – Buoy wind speed as function of rain rate (5 years of data)
0 – 3 mm/h
3 – 8 mm/h
> 8 mm/h
Rain Impact: WindSat/QuikSCAT/CCMP
Figures show WindSat – CCMP and QuikSCAT – WindSat wind speeds as function of wind speed and rain rate.5 years of data.No rain correction for scatterometer has been applied yet.With only single frequency (SF) scatterometer (QuikSCAT, ASCAT) it is very difficult to Reliably flag rain eventsRetrieve rain rate which is needed to perform rain correctionSlide20
Rain Impact on Scatterometer: Caveat
Rain impact depends on rain rate + wind speed:At low wind speeds: QuikSCAT wind speeds too high in rain.At high wind speeds: QuikSCAT wind speeds too low in rain.Important: Correct GMF at high wind speeds.Ku2001 wind speeds too high at high wind speeds.Accidental error cancellation possible in certain cases.Slide21
08/29/2005 0:00 Z
WindSat all-weather wind
HRD analysis wind
QuikSCAT Ku 2011 wind
WindSat rain rateSlide22
Active vs Passive - Strength + Weaknesses
Assessment based on operating instruments:Polarimetric radiometer (WindSat).Single frequency scatterometer (QuikSCAT, ASCAT, Oceansat).
ConditionPassiveWindSat V7ActiveQuikSCATKu2011 GMFWind speedno rainlow – moderate winds+ ++ +no rainhigh winds+ ++rain+Wind directionmoderate – high winds no - moderate rain+ ++ +low winds+high rain+Rain detection+ +
+ + very good
+ slightly degraded
strongly degraded / impossible
WindSat and QuikSCAT V7 Data Sets available on