surface marine wind retrieval in non-precipitating regions

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.