Efforts to develop display routines for overlaying gridded and nongridded data sets are discussed. The primary objective is to have the capability to review global patterns of winds and lidar samples; to zoom in on particular wind features or global areas; and to display contours of wind components and derived fields (e.g., divergence, vorticity, deformation, etc.). Current considerations in support of a polar orbiting shuttle lidar mission are discussed. Ground truth for a shuttle lidar experiment may be limited to fortuitous alignment of lidar wind profiles and scheduled rawinsonde profiles. Any improvement on this would require special rawinsonde launches and/or optimization of the shuttle orbit with global wind measurement networks

Emmitt, G. D.

Houston, S.

English

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https://ntrs.nasa.gov/search.jsp?R=19860005334 2020-03-20T16:44:10+00:00Z
4INFLUENCE OF COHERENT MESOSCALE STRUCTURES
ON SATELLITE-BASED DOPPLER LIDAR
WIND MEASUREMENTS
NASA Contract #: NAS8-35597
Monthly Progress Report
for
November 1985
Submitted by: G. D. Emmitt and S. Houston
Simpson Weather Associates, Inc.
809 E. Jefferson Street
Charlottesville, VA 22902
(804) 979-3571
(NASA-CR-1764;1) INFIUEHCE CF CCHEHENI	 N86-14804
MESOSCALE STRULIVILS Ch SATELLITE-SASEU
uGFPLEF LIDAh WINE MEASUREMENTS Monthly
Progress Retort (Siapscn Weather Associates,	 Unclas
Inc.) 13 p HE AJ2/hF ACI	 CSC.. 34b G3/47 u5uJe
A. During November we have continued our efforts to develop display routines
for overlaying gridded and non-gridded data sets. The primary objective
is to have the capability to review global patterns of winds and lidar
samples; to zoom in on particular wind features or global areas; and to
display contours of wind components and derived fields (e.g. divergence,
vorticity, deformation, etc.). Figures 1 and 2 are examples of our zoom
product used to evaluate a polar orbiting shuttle lidar mission. As can
be seen in Figure 2, the density of cusps (i.e. locations of best wind
measurements) is not very great. Ground truth for a shuttle lidar
experiment may be limited to fortuitous alignment of lidar wind profiles
and scheduled rawinsonde profiles. Any improvement on this would require
special rawinsonde launches and/or optimization of the shuttle orbit with
global wind measurement networks.
We are now able to display the ECMWF data in a variety of map projections
using the NCAR graphics package. For example, the u component for the
global area bounded by longitude 0 0 and -1800 is contoured in Figure 3.
Figure 4 is a blowup of a subarea of Figure 3 which includes the USA.
The vector display of ECMWF 1000 mb winds for this subarea is provided in
Figure 5a (1000 mb) and 5b (500 mb). We are now ready to overlay the
lidar derived wind fields on the ECMWF fields and to produce difference
fields that can be then used in follow-on model impact studies.
Because the u and v error potentials in the lidar winds are a function of
distance from the orbital ground track, it is logical that there may be
some peculiar patterns in the derivative fields (divergence, vorticity,
etc.). Using a control wind field with a constant divergence (du/dx =
dv/dy = 10 5 sec-1), the error patterns for divergence are shown in
Figures 6a and 6b). The errors form a repetitive pattern but are only
10-30% of the improved divergence field values. Further evaluation of
this issue used a case from AVEVAS II (see June 85 progress report). In
Figures 7a-i the lidar derived fields (with and without random noise) are
compared to the AVEVAS II fields. The general patterns are preserved
while in detail there are some differences that could be considered
significant for some applications. However, considering the potential
for errors, it is encouraging to see the overall agreement.
To illustrate the dependency of the lidar winds and derivative fields
upon the satellite perspective, the same AVEVAS II wind divergence field
(Figure 7a) is sampled with the satellite moving along the northern
boundary of the model domain (Figures 8a and 8b). Once again the general
patterns are preserved while details appear to be related to the
satellite perspective.
Use of the derivative fields to detect error patterns has been helpful
and will became a standard method of evaluating the MPA during its
development.
B. Presently, no serious contract scheduling problems are anticipated.
C. During December we expect to get our first lidar/ECMWF wind comparisons.
Our goal is to provide GLAS with an error algorithm that can be used in a
series of impact studies that have been discussed but not formally
proposed.
1
i's:
4A*	
—
Current considerations of shuttle or space station lidar experiments
require us to shift our efforts from the "free-flyer" simulations to
those peculiar to a low orbit, short-term proof-of-concept mission.
Specifically we will focus upon:
1) the "two-pass" wind algorithm for use with the shuttle mission, and
2) determining the optimum scan angle, scan rate, and PRF for a 28.5 0,
500 km AGL orbit.
D. Total cumulative costs as of 1 December 1985	 $118554.26
Total costs for reporting month of November 1985	 7244.90
Estimated costs to complete contract 	 $212227.00
2 of physical completion (1st and 2nd years) 	 56%
4
J
List of Figures
Figure 1. Doppler lidar shot pattern for the shuttle in a polar orbit.
Figure 2. Shuttle tracks and lidar shot pattern (sinusoidal) for two c
secutive orbits over the U.S. The map projection is the Lam.
conformal conic projection.
Figure 3. Contours %J the ECMWF 500 mb East-West wind component field YLvuw
the November 10, 1979, 00 GMT model run. The data extends from
1800 to 00 longitude and from the south pole to the north pole
(grid - 1.8750). The inner :-*oxed area is the boundary of wind
data extracted over the United States. This area is approximately
1300W to 60OW longitude and 200N to 55ON latitude. The contour
interval - 5.0 m s
-l t solid lines - positive u-component= dashed
lines - negative u-component.
Figure 4. Contours of the subset of data of over the U.S. (see Figure 3;.
Figure 5. a. Wind vectors for 1000 mb ECMWF winds over the U.S.
b. Wind vectors for 500 mb ECMWF winds over the U.S.
Figure 6. a. Divergence fields for space-based Doppler lidar estimated
winds. Input wind field du/dx - dv/dy - 100 x 10-6
 sec-1
Output wind field is gridded it x 11 ooverlaplping (300 km)2
areas. Contour intervals: 0.5 x 10-6 sec .
b. Same as 6a, except random noise added to input wind field.
Figure 7. a. Divergence calculated for 900 mb LAMPS winds for AVEVAS II
(see June 1985 report).
b. ''atellite estimated divergence using 900 mb LAMPS winds.
c. Satellite estimated divergence using 900 mb LAMPS winds.
Random noise is included in the input data.
d. Same as 7a except for vorticity.
e. Same as 7b except for vorticity.
f. Same as 7c except for vorticity.
g. Same as 7a except for deformation.
h. Same as 7b except for deformation.
i. Same as 7c except for deformation.
Figure S. a. Same as 7b except satellite track is over the northern
portion of the wind fiele.
b. Same as 7c and with satellite over the northern part of the
network.
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Influence of coherent mesoscale structures on satellite-based Doppler lidar wind measurements

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