Four tasks related to the signal processing aspects of the severe-storms Doppler lidar program are discussed. The development of algorithms for windfield retrieval from Doppler lidar measurements are discussed. Signal processor installation on a CV-990 aircraft is discussed

Lee, R. W.

English

NASA Technical Reports Server

  
 
 
N O T I C E 
 
THIS DOCUMENT HAS BEEN REPRODUCED FROM 
MICROFICHE. ALTHOUGH IT IS RECOGNIZED THAT 
CERTAIN PORTIONS ARE ILLEGIBLE, IT IS BEING RELEASED 
IN THE INTEREST OF MAKING AVAILABLE AS MUCH 
INFORMATION AS POSSIBLE 
https://ntrs.nasa.gov/search.jsp?R=19820014977 2020-03-21T09:19:14+00:00Z
RESEARCH STUDY:
SEVERE STORMS DOPPLER LIDAR SIGNAL PROCESSING
Final Report
February 1982
Contract NAS8-33834
Prepared for the George C. Marshall Space
Flight Center, National Aeronautics and
Space Administration, Huntsville, Alabama
March 10, 1982
R. W. Lee	
A 
,<5'
co
N r v^ ^! N Q^
;1P V
 • vr
LASSEN Rff RESEARCH .
MANTON CALIFORNIA 96059
(NASA-(;H-IbI9VJ) RESEARCH STUDY: SEVERE
	 Nt$2-22851
STUUSS JOPELLR 11DAR SIGNAL EfiCCESSING
Rival ReEort (Lassen fieseaich) 5 p
HC A02 /14F A01	 CSCL 0 413	 Unclas
G3/47 19193
- -	
­ -4
I. Introduction
Contract NASS-33834 includes four tasks related to the signal
processing aspects of the severe-storms Doppler lidar program. Tasks A
and B involve the development of algorithms for windfield retrieval from
Doppler lidar measurements made during the severe storms program. Tasks C
and D provide for support during signal processor installation and in
post-flight data analysis.
This final report will summarize the work performed in support of
these tasks. Reference will be made to the detailed descriptions of this
work contained in the nineteen monthly progress reports generated during
the life of the contract.
II. Tasks A and B: Algorithm development and simulation
Task A called for the development of windfield retrieval algorithms
based upon least-squares surface fitting techniques. The presence of bad
or missing data points in the measurements requires an analysis method
with two important properties: it must be tolerant of large measurement
errors, and it must fill in gaps in the data. In addition the technique
should make the best possible use of weak measurements, and should 	
M
provide resultant wind fields on arbitrary analysis grid points. Task B
provided for evaluation of the proposed algorithms by use of windfield
and data-acquisition simulations.
Such simulated windfields were presented in MPR 4 (Nov. 10, 1980).
Numerical filtering of random data sets was used to produce flow fields
with realistic spatial spectra. These random fields were sampled with a
lidar simulation, with appropriate amounts of introduced measurement
noise. Examples of the degraded windfield estimates are shown in MPR 4.
Since the proposed analysis technique relies upon least-squares
operations with the measurement data set, it is important that realistic
estimates of measurement reliability be available. An editing algorithm
has been developed and described in MPR 5 (Dec. 10, 1980) to examine the
measurements and assign probable errors. This assignment is made on the
basis of signal strength, measurement context, and knowledge of the error
characteristics of the Doppler estimator. Examples shown in MPR 5
demonstrate the rather good agreement between the actual data errors (as
introduced into the synthetic wind field by the simulated lidar system)
and the error estimates produced by the editor.
-1-
Also shown in MPR 5 are the first results of windfield smoothing by
the use of quadratic surface fitting. The two scalar velocity
measurements are separately fitted to quadratic surfaces, with the
surface coefficients determined by local least-squares fitting. Such a
fitting process tends to eliminate weak or erroneous measurements, and
the continuous surface makes interpolation to any grid system trivial.
The next step was to make the surface-fitting process adaptive to
the quality of the data. Where measurement errors are low smoothing is
minimal (to preserve spatial resolution); where large errors are present
smoothing is increased, with spatial resolution compromised to the degree
necessary to achieve the desired velocity accuracy. Many examples of such
adaptive smoothing are shown in MPR 6 (Jan. !0, 1931). Useful velocity
fields are obtained in regions where the raw data was nearly impossible
to interpret.
Extension of the algorithm to compensation for advection is treated
(with examples) in MPR 7 (Feb. 10, 1981). Local estimates of average
translation velocity are used to estimate the location of measurement
points at a time common to the forward and aft measurements (which may
themselves be separated in time by 60 seconds). In the simulation studies
considerable success was obtained in correcting for advection, but with
field data considerable judgment must be used before applying such a
correction.
The final step in algorithm development was estimation of divergence
and circulation 'rom the velocity fields. It was found best to estimate
these parameters analytically from the coefficients of the quadratic
surfaces (see description and examples in MPR 8, Mar. 10, 1981).
Algorithm descriptions (EDIT, ADVECT and SMOOTH) were delivered in
:March 1981. A description of these algorithms was presented in December
1981 at the 20th Conference on Radar Meteorology (Boston, American
Meteorological Society). A copy of this paper as printed in the
proceedings of the conference was appended to MPR 18 (Jan. 10, 1982).
III. Task C: Processor installation
Support for signal processor installation and systems integration
was provided on three occassions in 1981. During April Lassen Research
person«<. provided assistance during systems integration at Raytheon and
-2-
iAdvanced Computer in the Boston area. During June support was provided
for processor installation on the CV-990 aircraft at Ames Research
Center. This support included participation on the June 12 initial flight
of the system. Lassen Research personnel again visited Ames Research
Center during July to assist in the resolution of several data
interpretation problems. This support resulted in the discovery and
correction of software problems, as verified by the excellent data
acquired during flight 10.
IV. Task D: Analysis of flight data
This task was included in the contract since it was anticipated that
some revision of the analysis algorithms would be required when the
characteristics of the flight data were known. That this was the case was
confirmed by the first flight results (MPR 14, Sept. 10, 1981). These
examples contained velocity errors which were systematic. While the
algorithms developed earlier perform well with random data errors, they
are unable to correct systematic data errors.
The suspicions noted in MPR 14 that these systematic errors were due
to errors in the aircraft attitude measurements (and hence in the
pointing of the lidar beam relative to the flight vector) were confirmed
in MPR 15 (Oct. 10, 1981). Examples in MPR 15 demonstrate that the major
part of the systematic errors can be explained by errors in aircraft
drift angle on the order of a few tenths of a degree. The examples also
show clearly that the error is due to a delay of approximately two
seconds in the drift angle measurement.
The presence of such a delay in the drift angle measurement was
confirmed through an investigation of the data acquisition software
timing. It was found that about 75% of the systematic error could be
removed through interpolation of the drift-angle measurements to remove
the time delay (MPR 16, Nov. 10, 1981). Examples in MPR 16 show dramatic
improvement with the interpolation scheme, particularly when the aircraft
is experiencing turbulence (as in the boundary layer).
Also noted in MPR 16 are errors in the pointing angle of the laser
beam (due to a 0.84-second delay) and errors in the vertical elevation of
the beam. The elevation angle of the beam is nominally zero degrees, but
since delays are present in the aircraft attitude information the actual
-3-
-- --,... _...f
elevation angle may be in error by up to one degree. In addition there
appear to be biases in the elevation angles: perhaps -0.4 dcg in the aft
beam and ♦1.0 deg in the forward beam (estimated through terrain returns
from known topography, see MPR 16).
V. Recommendations for further work
Recommendations can be made in two areas. The first area includes
suggestions for changes in procedures on future data flights. Clearly an
effort should be made to revise system software to minimize the time
delay between the measurement of aircraft attitude and data acquisition.
Since this delay cannot be made equal to zero, it is further suggested
that the various important Engles be M19•Asured more frequently than once
per scan, so that interpolation for delay correction can be more
effective.
The second area concerns the data already taken. More accurate
interpolation techniq;.<s cs.,n be developed to compensate for time delays.
Such interpolation should also be developed to estimate the vertical
angle of the beam: while the vertical angle cannot be compensated for, it
should be known so that excessive vertical errors can be used to flap
questionable data. These interpolation technques take into account the.
fact that the data is not obtained at a uniform rate.
Finally, there is a limit to the accuracy of interpolation due to a
number of sources. Unknown and random delays are present in the angular
data due to the non-synchronous oampling of the INS unit relative to the
lidar data acquisition; unknown errors are present in the INS angles.
When aircraft dynamics are high (as in the turbulent boundary layer)
these second-order errors may reduce the utility of the data to near zero
even after interpolation. It is suggested that continuity of the mean
radial velocity be constrained to recover this data. This may be
accomplished by least-squares fitting of mean radial velocities to a
function with constrained smoothness, and adopting the resulting function
as the "true" mean radial velocity. Such a procedure may be defended by
arguments depending upon the spatial spectra of velocity fields.
-4-


Research study: Severe storms Doppler lidar signal processing

Abstract

Similar works

Full text

Available Versions

NASA Technical Reports Server