The algorithm has extended the range of wind speeds over which useful
retrievals can be obtained. It not only improves the accuracy of
the wind speed retrievals, especially at high wind speeds (without bias
correction), but makes available three additional fields. Table
1 indicates the importance of the inclusion of water vapor, liquid water,
and SST in retrieval algorithms on the accuracy of wind speed.
The GSW is the original linear algorithm, and the GSWP algorithm contains
the water vapor correction suggested by Petty (1993). The RMS error
statistics of the OMBNN3 algorithm, which takes into account also the liquid
water and SST influences, are lower than those of the GSWP algorithm over
all wind speeds, and especially at wind speeds > 15 m/s.
|Algorithm||Algorithm RMSE||Total RMSE||W > 15 m/s RMSE|
|Multiple Linear Regression & Linear Approximation (GSW)||1.4 (1.8)||1.8 (2.1)||(2.7)|
|Multiple Linear Regression & Nonlinear Approximation (GSWP)||1.3 (1.6)||1.7 (1.9)||(2.6)|
|Multiple Nonlinear Regression & Nonlinear Approximation (GS)||1.4 (2.2)||1.8 (2.5)||(2.7)|
|Physically-Based (PB)||1.3 (1.8)||1.7 (2.1)||(2.6)|
|Neural Network (OMBNN3)||1.0 (1.3)||1.5 (1.7)||(2.3)|
Fig. 2 shows the wind speed difference (buoy minus satellite wind speeds) characteristics (retrieval errors in wind speed) as functions of three other parameters: columnar water vapor, columnar liquid water and sea surface temperature for three algorithms GSW, GSWP and OMBNN3. Including nonlinear water vapor correction in GSWP reduced the bias and its dependence on the water vapor concentration (and partly on SST which is closely related to water vapor); however, it did not reduce its dependence on the liquid water concentration. This correction also did not significantly improve the standard deviation of the differences. The OMBNN3 algorithm, with its simultaneous multi-parameter retrievals, reduced the bias and its dependence on all three other parameters together with a significant improving the standard deviation of the differences.
A multi-parameter empirical ocean algorithm for SSM/I retrievals
by: Krasnopolsky, V.M., W.H. Gemmill, and L.C. Breaker, 1998,Technical Note, OMB Contribution No. 154, NOAA/NCEP/EMC, Canadian Journal of Remote Sensing, in press
New Transfer Function for SSM/I Based on an Expanded Neural Network Architecture
by: Krasnopolsky VM, WH Gemmill & LC Breaker, 1996, NOAA/NCEP/EMC technical note, OMB Contribution No. 137 , Camp Springs, MD, 38 pp.