The research supported by this contract and directed Activities in the inversion and interpretation of data produced by the Nimbus-7 scanning multichannel microwave radiometer (SMMR) are reported. There were five principal subjects: (1) modeling of the emissivity of foam patches on the ocean surface; (2) inversion of radiometric data by a multidimensional algorithm; (3) an operational water vapor retrieval algorithm; (4) inference of Antarctic firm accumulation rates; and (5) inference of water vapor over the Arctic sea ice

Baumann, W. T.

Bigelow, T. S.

Rosenkranz, P. W.

Staelin, D. H.

Xu, X. R.

English

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https://ntrs.nasa.gov/search.jsp?R=19850019158 2020-03-20T19:18:09+00:00Z
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FINAL REPORT
NASA Contract NAS5-22929
covering the period
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	 February 1, 1976 - April 30, 1984
submitted by:
D. H. Staelin (principal investigator),
W. T. Baumann, T. S. Bigelow,
P. W. Rosenkranz, Xu Xi-ru
(IAS1-CR- 175293) 1TROSP8PRIC PRONTIL ZONE 	 1185-27469
STUDIES Pinal Report, 1 Feb. 1976 - 30 1pr.
1984 (Massachusetts last, of Tech.) 8 p
8C 102/fiF ly 1	 CSCL 04B	 Unclas
G3/47 22640
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10June 15, 1984
Research Laboratory of Electronics
Massachusetts Institute of Technolcgy
Cambridge, Massachusetts 02139
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Introduction
•
	
	 The research supported by this contract was directed
toward inversion and interpretation of data produced by the
Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR).
There were five principal subjects: 1) modeling of the
emissivity of foam patches on the ocean surface; 2) inversion
•	 of radiometric data by a multidimensional algorithm; 3) an
operational water vapor retrieval algorithm; 4) inference of
Antarctic firn accumulation rates; 5) inference of water
vapor over the Arctic sea ice.
1. Ocean foam emissivity
Foam on the ocean was modeled as a dielectric slab with a
dielectric constant which varied with height above the ocean
surface. An example of these computations is given in
figure 1, which plots emissivity at 37 GHz vertical and
horizontal polarization as a function of incidence angle,
with foam thickness as a parameter. Curves for 0, .5 and
2 cm thickness are shown. This computation assumed a flat
surface under the foam. The emissivity of a surface roughened
by depressions due to floating bubbles was also computed, and
is plotted in figure 2, at 37 and 19 GHz. Three curves are
shown for each polarization; two computations for rms rough-
ness of .026 and .1. cm, and measurements of foam reported by
Stogryn (1972). The angular variation of the calculations
for .1 cm is similar to the measurements, suggesting that
small-scale roughness is important. This wcrk is described
in an M.S. thesis by Bigelow (abstract appended).
2. Multidimensional inversion
In the case of SMMR, the data has three dimensions:
scan number (varies with distance parallel to the satellite
•	 track), distance along a scan, and channel number (or
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Surface Emissivity
Figure
	
1	 Calculated surface emissivity vs. incidence angle
for 100% foam cover for oarameters indicated.
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Figure	 2	 Lomparison of calcslatco cmisslvity with surface
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frequency and polarization). The antenna beam width is
approximately proportional to wavelength. In the operational
SMMR data processing, brightness temperatures for each channel
are binned at resolutions comparable to or larger than the
beamwidth. As a result, longer wavelength brightness tempera-
tures are not available at the desired resolution of some of
the geophysical parameters, to be used in estimating them.
This circumstance motivated work on a new retrieval algorithm.
Our solution was to do the inversion in the spatial Fourier
transform domain. This approach allowed the combination of
data from all channels of the instrument to estimate each
parameter; furthermore, spatial correlations of the geophysical
parameters and the antenna gain pattern were accounted for.
This algorithm is described in a series of papers by Rosenkranz
and Baumann (1980) and Rosenkranz (1981-82) (abstracts are
appended).
These advantages of a multidimensional algorithm were
expected to be most significant in situations where the
structure of weather patterns was Highly developed -- for
example, in tropical storms. Unfortunately, because of the
typically very heavy precipitation in these storms, the
inversion problem is very nonlinear. Figure 3 and table 1
compare estimates of maximum wind speed made by the multi-
dimensional algorithm operating on SMMR data with estimates
from observers, for several tropical storms. There is some
correlation, except for the two storms Tess and Faye (points
2, 3 and 5), although the variability is underestimated.
3.	 Operational water vapor algorithm
For operational use, MIT supplied to GSFC an algorithm
for estimation of water vapor from the 18, 21 and 37 GHz
SMMR brightness temperatures. The estimate is of the .form
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Figure 3. Comparison of SMMR-derived and conventional
estimates of maximum wind speed in tropical storms.
-7-
Table 1.- Key to Figure 3.
STORM	 TIME AND DATE OF SMMR OBSERVATION
1) KENDRA 1618Z 30 OCT. 78
2) TESS 1403Z 1 NOV. 78
3) TESS 1421Z 2 NOV. 78
4) TC-21-78 1311Z 21 NOV. 78
5) FAYE 0258Z 5 JUL. 79
6) DOLORES 1854Z 17 JUL. 79
7) DOLORES 1932Z 19 JUL, 79
8) DAVID 0243Z 26 AUG. 79
9) DAVID 0358Z 30 AUG. 79
10) FREDERIC 0253Z 1 SEPT. 79
11) LOLA 0143Z 5 SEPT, 79
12) 0I4EN 0247Z 25 SEPT, 79
13) OWEN 1536Z 25 SEPT, 79
14) ROGER 1517Z 5 OCT. 79
15) TIP 0236Z 11 OCT. 79
16) TIP 0248Z 17 OCT. 79
,t.
-8-
V = V  + aV* + b (V*) 2
where
V* _ d i (TBi - Toi)
i
and Vo , a, b, d i , Toi are constant coefficients. After tuning
of the coefficients a and b at GSFC, retrievals agreed with
radiosonde measurements to an rms accuracy of 1 precip. mm .
4. Antarctic firn accumulation rates
A study of the brightness temperatures measured by SMMR
over the Antarctic firn showed that they could be related to
snow accumulation rate, given in situ measurements from a
small number of bore sites and surface temperatures. This
work is described in an unpublished manuscript by Staelin and
Xu.
5. Arctic water vapor
The brightness temperature spectrum of the Arctic sea ice
varies with age, temperature and other factors. It was observed,
however, that the brightness temperature at 21 GHz departed from
a smooth curve drawn through the other frequencies. This
spectral feature was attributed to atmospheric water vapor
and the SMMP, brightness temperatures were used to map water
vapor over the ice. These maps showed increases in water
vapor in the vicinity of large polynyas.
I


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