A Neural Network Multiparameter Algorithm for SSM/I Ocean Retrievals

Abstract

A new empirical multiparameter Special Sensor Microwave/Imager (SSM/I) retrieval algorithm based on the neural network approach, which retrieves wind speed, columnar water vapor, and columnar liquid water simultaneously using only SSM/I brightness temperatures, is compared with existing global SSM/I retrieval algorithms. In terms of wind speed retrieval accuracy, the new algorithm systematically outperforms all algorithms considered under all weather conditions where retrievals are possible with an algorithm rms error of 1.0 m/s under clear and 1.3 m/s under clear plus cloudy conditions. It also generates high wind speeds with acceptable accuracy. This improvement in accuracy is coupled with increased areal coverage with obvious benefits for operational applications. With respect to columnar water vapor and columnar liquid water, the new algorithm reproduces the results of existing algorithms closely. The new algorithm has been tested and accepted for operational use at the National Centers for Environmental Prediction (NCEP) producing a positive impact on forecast winds through assimilation into NCEP's numerical weather prediction models.

Introduction

We evaluate an empirical neural network (NN) algorithm that simultaneously retrieves several geophysical parameters over the ocean from brightness temperature (BT) measurements acquired by the Special Sensor Microwave Imager (SSM/I) (Hollinger et al., 1990) with primary emphasis on retrieving wind speed. This recently developed NN-based algorithm (OMBNN3), which retrieves wind speed, columnar water vapor, and columnar liquid water (see http://polar.wwb.noaa.gov/winds/), is described elsewhere Krasnopolsky et al. 1996, Krasnopolsky et al. 1997, Krasnopolsky et al. 1999. Here we present a comparison of this algorithm with other global SSM/I wind speed, W (m/s), columnar water vapor, V (mm), and cloud liquid water, L (mm), retrieval algorithms. We also examine different BT retrieval flags and their relation to different forms of atmospheric moisture, an important topic that is usually ignored. We also present various wind speed retrieval error analyses.

We describe global SSM/I retrieval algorithms that were considered in this study. Our new multiparameter empirical approach and the NN-based SSM/I retrieval algorithm, OMBNN3 (the corresponding FORTRAN code is available upon request at: [email protected]), are briefly described. We compare OMBNN3 with existing algorithms and perform error analyses using different retrieval flags. Finally, we discuss some operational applications of the geophysical fields that are retrieved by the new algorithm and present a recent example. OMBNN3 has been running as the operational SSM/I retrieval algorithm in the data assimilation system at the National Centers for Environmental Prediction since April 1998.