Retrieval of Atmospheric Temperature and
Moisture Profiles from Hyperspectral Sounding Data
Using a Projected Principal Components Transform and a Neural Network
William J. Blackwell
Lincoln Laboratory, Massachusetts Institute
of Technology
Abstract
A
novel statistical method for the retrieval of atmospheric temperature and
moisture (relative humidity) profiles has been developed and evaluated with
simulated clear-air hyperspectral sounding data. The
accuracies of the estimates produced by the algorithm meet or exceed (in some
cases by a factor of two) the accuracies of the estimates from traditional
iterated minimum variance retrieval techniques while requiring less
computation. The algorithm is implemented in two stages. First, a projected principal
components (PPC) transform is used to reduce the dimensionality of and
optimally extract geophysical profile information from the spectral radiance
data. Second, an artificial feedforward neural
network (NN) is used to estimate the desired geophysical parameters from the
projected principal components. The
performance of this method (henceforth referred to as the PPC/NN method) was
evaluated using simulated clear-air observations from the 2378-channel
Atmospheric InfraRed Sounder. Separate training and
validation profile data were selected from the NOAA88b radiosonde
set of approximately 7500 profiles. Surface, solar, and instrument effects were
modeled. It was found that the PPC/NN method has a number of advantages over
traditional statistical and physical/iterative hyperspectral
profile retrieval techniques. Neural-network estimates based on the PPC
transform were significantly more accurate than neural-network estimates
obtained with conventional principal components techniques, including the Karhunen-Loeve transform and the noise-adjusted principal
components transform. The retrieval accuracy of PPC/NN was also superior to
that of a principal components regression technique. One particularly
noteworthy result of the present work is a comparative study between the PPC/NN
method and an iterated minimum-variance (IMV) method. The temperature profile
retrieval accuracy of both methods is similar, but the relative humidity
profile retrieval accuracy of PPC/NN was greater than that of the IMV method at
all altitudes and substantially better near the surface.
Speaker’s Biographical Sketch
William
J. Blackwell received the B.S. degree in electrical engineering from the
Georgia Institute of Technology, Atlanta, in 1994, and the S.M. and Sc.D.
degrees in electrical engineering from the Massachusetts Institute of
Technology (MIT), Cambridge, in 1995 and 2002. He is currently a member of the
technical staff at MIT Lincoln Laboratory. Dr. Blackwell held a National
Science Foundation Graduate Research Fellowship from 1994 to 1997 and is a
member of Tau Beta Pi, Eta Kappa Nu, Phi Kappa Phi, and Sigma Xi, and is a
Senior Member of the IEEE. He is chapter chair of the Boston section of the
IEEE Geoscience and Remote Sensing Society (GRSS),
associate editor of the IEEE GRSS Newsletter, and a member of the organizing
committee for IGARSS 2008 to be held in Boston, MA. Dr. Blackwell serves on the science teams of
several NASA and NOAA satellite programs and has authored over 40 journal
articles, conference proceedings, and technical reports on atmospheric remote
sensing. He is co-author of the book “Neural Networks in Atmospheric Remote
Sensing,” to be published by Artech House in 2009.
DATE:
Wednesday, February 27,
2008
TIME:
12:00-12:10 pm-refreshments;
12:10-1:00 pm-seminar
LOCATION: 424
Benedum Hall