Tuesday, 11 January 2000
Statistical regressions and Neural Nets methods have been used for many
geophysical remote sensing applications. An evaluation of these methods
versus a physical inversion approach for retrieval of atmospheric
temperature and moisture profiles from a combination of Advanced
Microwave Sounding Unit (AMSU) and Microwave Humidity Sounder (MHS)
measured brightness temperatures is presented here and evaluated for the
combined AMSU and MHS, AMSU alone, and AMSU 50 GHz sounding channels.
The statistical inversion methods include a linear and a quadratic
regression in brightness temperature. These approaches assume that the
geophysical parameters of interest which determine the observables can
be represented by a linear combination of those observables. The weights
corresponding to each of the observables are calculated using a training
set of geophysical state vectors and their corresponding radiance
vectors. In this application, the geophysical state vector consists of
atmospheric profiles of temperature, water vapor, and cloud liquid
water, as well as surface skin temperature and spectral surface
emissivity. The radiance vector is composed of a number simulated
brightness temperatures which are calculated at the AMSU and MHS
frequencies using the Optimal Spectral Sampling (OSS) forward radiative
transfer model developed by AER, Inc. This radiative transfer model
assumes a one dimensional, homogenous, non-scattering atmosphere with
only oxygen, water vapor, and liquid cloud water droplets as the
absorbers. In addition to providing radiance vectors for the regression
training set, this forward model is also used in a physical inversion
algorithm. The physical inversion algorithm used here is the Unified
Retrieval (UR) developped by AER, Inc. The physical inversion method
attempts to accommodate non-linear effects by solving the inversion
using through Gauss-Newton iteration. In addition to the requirement of
providing retrieved temperature and moisture profiles, it is necessary
to retrieve additional parameters (stated above) because one or more of
the AMSU/MHS channels is sensitive to each parameter and those
parameters can not be sufficiently specified a priori. When the
non-linear effects of water vapor and cloud water are significant, the
physical iterative method should outperform the statistical regression
method, but when non-linear effects are small, the two retrieval methods
should perform equally well. It is an objective of this work to
illustrate under what conditions and to what degree the various
algorithms differ.
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