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presentation_meissner.pptx presentation_meissner.pptx Presentation Transcript

  • IGARSS 2011
    Vancouver, BC, Canada
    July 26, 2011
    All-Weather Wind Vector Measurements from Intercalibrated Active and Passive Microwave Satellite Sensors
    Thomas Meissner
    Lucrezia Ricciardulli
    Frank Wentz
  • Outline
    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 function.
    Validation.
    High winds.
    Rain impact study.
    Selected storm case: Hurricane Katrina.
    Conclusion: active vs passive - strength +weaknesses.
  • Passive vs Active Wind Speeds
    Passive (radiometer)
    Sees change in emissivity of wind roughened sea surface compared with specular surface
    Low 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.
    Calibration
    Ground truth:
    Buoy, NWP wind speeds
  • 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).
    Active (scatterometer)
    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).
  • 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.
    Active (scatterometer)
    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.
  • 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
    T. Meissner + F. Wentz, IEEE TGRS, under review
  • Ocean Surface Emissivity Model (cont.)
    Measured minus computed WindSat TB as function of SST (x-axis) and wind speed (y-axis).
  • Overview: RSS Version 7 Ocean Products
    Intercalibrated multi-platform suite.
    100 years of combined satellite data.
    Climate quality.
    DMSP SSM/I, SSMIS
    F8, F10, F11, F13, F14 ,F15, F16, F17
    TRMM TMI
    AMSR-E, AMSR-J
    WindSat
    V7 released
    V7 release in progress
    QuikSCAT
  • RSS WindSat Version 7 Ocean Products
    Optimized swath width by combining forand aft looks at each band.
  • 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.
  • 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 algorithms
    Trained 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.
    Radiometer winds in rain:
    T. Meissner + F. Wentz, IEEE TGRS 47(9), 2009, 3065 - 3083
  • 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.
    L=0.2 mm
    W=15 m/s
    H-Wind Algo
    (tropical cyclones)
    No-Rain Algo
    SST=28oC
    SST
    Global Rain Algo
    Wind Speed
    Liquid Water
  • WindSat Wind Speed Validation
    2-dimensional PDF: WindSat versus CCMP (cross-calibrated multi-platform) wind speed.
    Rain free and with rain.
  • WindSat Wind Validation at High Winds (1)
    Renfrew et al. QJRMS 135, 2009, 2046 – 2066
    Aircraft 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.
  • 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.
  • Improved QuikSCAT Ku2011 GMF: Development
    The GMF relates the observed backscatter ratio σ0 to wind speed w and direction φat 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.
  • Ku2001 versus Ku2011
    Greenland Aircraft Flights
    Ku2001Ku2011
    Ku2001Ku2011
    A0
    A2
  • Rain Impact: WindSat/QuikSCAT vs Buoys
    Table shows WindSat/QuikSCAT – Buoy wind speed as function of rain rate (5 years of data)
  • 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 events
    Retrieve rain rate which is needed to perform rain correction
  • 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.
  • WindSat all-weather wind
    QuikSCAT Ku 2011 wind
    Hurricane Katrina08/29/2005 0:00 Z
    HRD analysis wind
    WindSat rain rate
  • Active vs Passive - Strength + Weaknesses
    WindSat and QuikSCAT V7 Data Sets available on www.remss.com
    + +very good
    + slightly degraded
    strongly degraded / impossible
    Assessment based on operating instruments:
    Polarimetric radiometer (WindSat).
    Single frequency scatterometer (QuikSCAT, ASCAT, Oceansat).