Geoscience Reference
In-Depth Information
The data from the new generation of polar-orbiting weather satellites with
Advanced Microwave Sounding Unit (AMSU) enhance the possibility of
monitoring the state of the atmosphere as well as the dynamic and
thermodynamic atmospheric processes. In particular, AMSU data enables to
determine temperature and moisture profiles for use in numerical weather
prediction and climate models. The AMSU is the operational microwave
sounder aboard the National Oceanic and Atmospheric Administration's
(NOAA) polar-orbiting satellites. The first AMSU-A was launched on the
NOAA-15 satellite, 13 May 1998, and measures outgoing radiation from the
earth's surface and/or atmosphere in 15 spectral regions (four “window”
channels at 23.8, 31.4, 50.3 and 89 GHz and 11 temperatures sounding channels
from 52.8 to 58 GHz). These temperature sounding channels are used to derive
atmospheric temperature profiles from the surface to an altitude of about 45
km in most situations. The window channels receive energy primarily from
the surface and the boundary layer, and are used for deriving the products such
as total precipitable water, cloud liquid water, snow cover, sea ice concentration,
and precipitation rate. The India Meteorological Department (IMD) has shown
the positive impact of these products in their limited area model. Impact study
was carried out using high-resolution (80 km) TOVS temperature-humidity
profile data, locally derived from High Resolution Picture Transmission (HRPT)
station, IMD, New Delhi (Bhatia et al., 1999). Preliminary results have also
been obtained using advanced ATOVS Processing Package (AAPP) installed
at IMD, New Delhi for retrieval of temperature and moisture profile from
AMSU data using neural networks.
In the recent years neural networks have been used in an increasing number
of meteorological applications and proved successful in the development of
computationally efficient inversion methods for retrievals of atmospheric profile
data (Motteler et al.,1995). Stogryn et al. (1994) presented their work by using
fully connected, feed-forward neural networks to retrieve ocean surface wind
speed based on the Special Sensor Microwave Imager (SSM/I) on board the
Defense Meteorological Satellite Program (DMSP) satellites. Yang et al. (1997)
used a neural network to estimate soil temperature. Hsieh and Tang (1998)
reviewed the obstacles to adapting the neural network technique to
meteorological and oceanographic prediction and data analysis. Butler et al.
(1996) obtained good results with a neural network to retrieve temperature
profiles from the Defense Meteorological Satellite Program Special Sensor
Microwave Temperature instrument (DMSP SSM/T-1). The brightness
temperature measured by a radiometer ( T B ) is a description of spectral radiance
measured by the radiometer in temperature units using the Rayleigh-Jeans
approximation of the Plank function (commonly at microwave frequencies as
there is a linear relationship between radiances and T B . The atmospheric
temperature ( T A ) is then retrieved from the radiance measured by the radiometer
(or the T B ) with an adequate retrieval method. In the present study, temperature
profiles ( T A ) have been retrieved by two different retrieval schemes: ANN and
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