All-Weather Wind Vector Measurements from Intercalibrated Active and Passive Microwave Satellite Sensors Thomas Meissner Lucrezia Ricciardulli Frank Wentz IGARSS 2011 Vancouver, BC, Canada July 26, 2011 IGARSS 2011 Vancouver, BC, Canada July 26, 2011

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 (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 o Low winds: Polarization mixing of large gravity waves. o High winds: Emissivity of sea foam. Radiative Transfer Model (RTM) function for wind induced surface emissivity. Passive (radiometer) Sees change in emissivity of wind roughened sea surface compared with specular surface o Low winds: Polarization mixing of large gravity waves. o 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. 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 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). 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). 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. 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. 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. 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, 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 DMSP SSM/I, SSMIS F8, F10, F11, F13, F14,F15, F16, F17 DMSP SSM/I, SSMIS F8, F10, F11, F13, F14,F15, F16, F17 TRMM TMI AMSR-E, AMSR-J QuikSCAT WindSat V7 released V7 release in progress V7 released V7 release in progress Intercalibrated multi-platform suite. 100 years of combined satellite data. Climate quality. Intercalibrated multi-platform suite. 100 years of combined satellite data. Climate quality.

RSS WindSat Version 7 Ocean Products Optimized swath width by combining for and aft looks at each band. Product Resolution + Required Channels ≥ 6.8 GHz 52 km ≥ 10.7 GHz 31 km ≥ 18.7 GHz 24 km ≥ 37.0 GHz 10 km SSTYes No Wind speed no rain Yes No Wind speed through rain YesNo Wind direction NoYesNo Water vaporYes No Liquid cloud water Yes Rain rateNo Yes

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. 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. 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, Radiometer winds in rain: T. Meissner + F. Wentz, IEEE TGRS 47(9), 2009,

WindSat All-Weather Wind Speeds Liquid Water SST Wind Speed No-Rain Algo Global Rain Algo H-Wind Algo (tropical cyclones) L=0.2 mm SST=28 o C W=15 m/s

WindSat Wind Speed Validation 2-dimensional PDF: WindSat versus CCMP (cross- calibrated multi-platform) wind speed. Rain free and with rain. 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 – 150 measurements during 5 missions. – Wind vectors measured by turbulence probe. – Adjusted to 10m above surface. Renfrew et al. QJRMS 135, 2009, 2046 – 2066 – Aircraft observations taken during the Greenland Flow Distortion Experiment, Feb + Mar – 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: 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. 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: 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 σ 0 to wind speed w and direction φ at the ocean’s surface. To develop the new GMF we used 7 years of QuikSCAT σ 0 collocated with WindSat wind speeds (90 min) and CCMP (Atlas et al, 2009) wind direction. – WindSat also measures rain rate, used to flag QuikSCAT σ 0 when developing GMF. – We had hundreds of millions of reliable rain-free collocations, with about 0.2% at winds greater than 20 m/s. The GMF relates the observed backscatter ratio σ 0 to wind speed w and direction φ at the ocean’s surface. To develop the new GMF we used 7 years of QuikSCAT σ 0 collocated with WindSat wind speeds (90 min) and CCMP (Atlas et al, 2009) wind direction. – WindSat also measures rain rate, used to flag QuikSCAT σ 0 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 Ku2001 Ku2011 A0A0 A2A2 Greenland Aircraft Flights

Rain Impact: WindSat/QuikSCAT vs Buoys Table shows WindSat/QuikSCAT – Buoy wind speed as function of rain rate (5 years of data) Rain RateWindSat All-WeatherQuikSCAT 2011 BiasStd. Dev.BiasStd. Dev. no rain light rain 0 – 3 mm/h moderate rain 3 – 8 mm/h heavy rain > 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 events – Retrieve rain rate which is needed to perform rain correction 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. 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.

Hurricane Katrina 08/29/2005 0:00 Z WindSat all-weather wind HRD analysis wind QuikSCAT Ku 2011 wind WindSat rain rate

Active vs Passive - Strength + Weaknesses Assessment based on operating instruments: – Polarimetric radiometer (WindSat). – Single frequency scatterometer (QuikSCAT, ASCAT, Oceansat). Assessment based on operating instruments: – Polarimetric radiometer (WindSat). – Single frequency scatterometer (QuikSCAT, ASCAT, Oceansat). Condition Passive WindSat V7 Active QuikSCAT Ku2011 GMF Wind speed no rain low – moderate winds + no rain high winds + + rain +  Wind direction moderate – 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