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Surface Marine Wind Retrieval in Non-Precipitating Regions. Nonlinear regression (2D-Var) approach Radarsat-1 synthetic aperture radar (SAR) Validation using ship and buoy winds SAR error estimates Conclusions. Harold Ritchie, Richard Danielson, and Michael Dowd - PowerPoint PPT Presentation
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Surface Marine Wind Retrievalin Non-Precipitating Regions
• Nonlinear regression (2D-Var) approach• Radarsat-1 synthetic aperture radar (SAR)• Validation using ship and buoy winds• SAR error estimates• Conclusions
Harold Ritchie, Richard Danielson, and Michael DowdCanadian Meteorological Centre and Dalhousie University
The assimilation of SAR datainto models depends partly on
• whether errors in SAR-wind information can be quantified
• the identification of conditions for which SAR data improve marine wind forecasts.
Errors can be explicitly quantified using nonlinear regression.These may be related to physical processes (e.g., wave tilt,precipitation impact) or satellite characteristics (e.g., beammode, incidence angle).
wind gusts (darker)
Regression Approach
Regression Approach
])([1yexy CMODα
xexbx
SAR backscatter cross section SAR errors
Numerical Model winds Model errors
Regression Approach
Hersbach (2003), Vachon and Dobson (2000) SAR backscatter cross section SAR errors
Numerical Model winds Model errors
Radarsat-1 incidence angle bias correction
])([1yexy CMODα
xexbx
• CMOD is first used to remove the incidence angle dependence of the SAR obs ( ). This allows R to be positive definite.
• J is generally a function of the estimated winds (x) and the unknown error covariances (R and B).
• Here, error covariances are assumed to decay exponentially with a length scale of 150 km and B error variances are fixed at 1 m2/s2 (only R varies).
]~)([]~)([),( yxRyxRxR 1T CMODCMOD|ln|J
][][ b1Tb xxBxx |ln|B
Regression Approach
y~
• Polar orbiting every 100 minutes at ~800 km• C-band SAR (5-cm wavelength; horizontally polarized)• First ScanSAR to use multiple beam modes to obtain ~50-m resolution over swaths of ~400 km• We employ 609 acquisitions from June 2004 to July 2005 at 6.4-km resolution
Radarsat-1 SAR
Backscatter (dB)
400-m SAR Acquisition (Koch 2004 smoothing)
• masking over land
Backscatter (dB)
400-m SAR Acquisition (Koch 2004 smoothing)
• masking over land
• along beam seams
Backscatter (dB)
400-m SAR Acquisition (Koch 2004 smoothing)
• masking over land
• along beam seams
• over sea ice
Backscatter (dB)
400-m SAR Acquisition (Koch 2004 smoothing)
• masking over land
• along beam seams
• over sea ice
• where retrieved wind speed would be less than 3 m/s or greater than 33 m/s
Backscatter (dB)
800-m SAR Acquisition (Koch 2004 smoothing)
• masking over land
• along beam seams
• over sea ice
• where retrieved wind speed would be less than 3 m/s or greater than 33 m/s
Backscatter (dB)
1.6-km SAR Acquisition (Koch 2004 smoothing)
• masking over land
• along beam seams
• over sea ice
• where retrieved wind speed would be less than 3 m/s or greater than 33 m/s
Backscatter (dB)
3.2-km SAR Acquisition (Koch 2004 smoothing)
• masking over land
• along beam seams
• over sea ice
• where retrieved wind speed would be less than 3 m/s or greater than 33 m/s
Backscatter (dB)
6.4-km SAR Acquisition (Koch 2004 smoothing)
• masking over land
• along beam seams
• over sea ice
• where retrieved wind speed would be less than 3 m/s or greater than 33 m/s
Ship and Buoy ValidationGTS ship/buoy obs(CDC web archive)
Ship and Buoy ValidationGTS ship/buoy obs(CDC web archive)
• vertical adjustment to 10-m using Walmsley (1988) or logarithmic profile requires obs heights (WMO Pub 47)
Ship and Buoy ValidationGTS ship/buoy obs(CDC web archive)
• vertical adjustment to 10-m using Walmsley (1988) or logarithmic profile requires obs heights (WMO Pub 47)
• taken within 90 min of an acquisition
Ship and Buoy ValidationGTS ship/buoy obs(CDC web archive)
• vertical adjustment to 10-m using Walmsley (1988) or logarithmic profile requires obs heights (WMO Pub 47)
• taken within 90 min of an acquisition
• valid within a radius of 5-50 km, depending on proximity to land
Retrieval Example
yPrecipitation
Region
SAR Backscatter (dB)
Retrieval Example
SAR Backscatter (dB) Normalized by CMOD
y~yPrecipitation
Region
Retrieval Example
Normalized by CMOD
xb and CMOD(xb)
15-km Hourly Model Winds
y~
Retrieval Example
Retrieval
xb and CMOD(xb)
15-km Hourly Model Winds
and CMOD( )x̂ x̂
Error Estimates
SAR error variance is reduced (as expected)
Errors appear Gaussian
Error Estimates
Errors appear Gaussian
Wind speed (and direction)
errors are unchanged
Error Estimates
Wind Speed (m/s)
Retrieval
(mean / std)
Error
(bias / std)
Number of Collocations
Precip Region 11.0 / 4.4 0.6 / 3.4 1542
No Precip 9.5 / 3.1 0.2 / 2.1 1542
Wind Speed (m/s)
Retrieval
(mean / std)
Error
(bias / std)
Number of Collocations
High Incidence 7.8 / 3.2 0.0 / 2.2 3520
Low Incidence 7.7 / 3.0 0.0 / 2.5 3580
• Precip regions have higher error standard deviation (with slightly stronger wind speeds)
• Low incidence angle regions (with no precip) have higher error standard deviation
Conclusions
• If errors in ship and buoy obs can be neglected, then the regression approach permits a distinction between errors with and without precipitation and at high and low incidence angles.
• A more sophisticated approach considers ship and buoy errors (which may be larger than corresponding SAR or model errors). The B and R error covariance matrices can also be improved.
Radarsat-1 Incidence Angle Bias
• Retrieved wind speeds can be biased at near and far range (Monaldo et al. 2001)
• We obtain wind speeds that are more consistent with numerical model winds by multiplying SAR data by = 1 + 0.005 (Incidence Angle – 30)
Spatial Error Correlation relative to ship and buoy wind speed and backscatter (using CMOD)
CMOD (C-band model) empiricallyrelates wind and Braggscattering from waves.