The latest version of the automated cloud motion vector software has yielded significant improvements in the quality of the GOES cloud-drift winds produced operationally by NESDIS. Cloud motion vectors resulting from the automated system are now equal or superior in quality to those which had the benefit of manual quality control a few years ago. The single most important factor in this improvement has been the upgraded auto-editor. Improved tracer selection procedures eliminate targets in difficult regions and allow a higher target density and therefore enhanced coverage in areas of interest. The incorporation of the H2O-intercept height assignment method allows an adequate representation of the heights of semi-transparent clouds in the absence of a CO2-absorption channel. Finally, GOES-8 water-vapor motion winds resulting from the automated system are superior to any done previously by NESDIS and should now be considered as an operational product

Hayden, Christopher M.

Menzel, W. Paul

Nieman, Steve

Velden, Christopher S.

Wanzong, Steve

English

NASA Technical Reports Server

^(NASA-CR-199772) UPGRADES TO THE N96-16147
NOAA/NESDIS AUTOMATED CLOUD-MOTION
VECTOR SYSTEM (Wisconsin Univ.)
4 p Unclas
G3/47 0084504
https://ntrs.nasa.gov/search.jsp?R=19960008981 2020-06-16T05:56:39+00:00Z
Appendix 6. Abstract from the 8th AMS Conf. on Satellite Meteorology, Atlanta,
GA, January 1996.
1.1
NASA-CR-199772
Upgrades to the NOAA/NESDIS Automated Cloud-Motion Vector System
x/w $ - TrySteve Nieman \ W. Paul Menzel 2, Christopher M. Hayden 2,Steve Wanzong 1 and Christopher S. Velden 1
1
 - Cooperative Institute for Meteorological Satellite Studies
Madison, Wisconsin
2
 - NOAA/NESDIS
Madison, Wisconsin
I. Introduction
In the past, cloud-motion vectors (CMVs) were
produced within NESDIS in a highly interactive way
requiring a large investment in manpower. Operators
displayed loops of geostationary imagery within several
sectors of the full disk, selected suitable targets on the
imagery, and followed them manually through
subsequent images. Heights and vector displacements
were computed automatically and displayed graphically
over the imagery for inspection, but height assignment
methodology was inaccurate when applied to semi-
transparent cirrus. Operators often made judgments
on the height of tracers based on their experience, and
manual cloud heights were often substituted into the
final product.
In 1992, the Cooperative Institute for
Meteorological Satellite Studies (CIMSS) delivered the
first version of the automated CM V software package to
NESOIS. It was now possible to produce a full disk
wind set without manual intervention. Suitable tracers
were automatically selected within the first image of the
loop and heights were assigned using several methods.
Implementation of the CO2-slicing algorithm for
assigning heights to semi-transparent features (Menzel
et a I.. 1983) enabled more accurate height assignment
without operator input. The tracking of features
through the subsequent imagery was automated using
the auto-correlation technique (Merrill et al., 1991). It
was no longer necessary for operators to manually loop
through imagery and "point and click* on features.
Finally, an automated quality-control (QC) algorithm,
the auto-editor (Hayden , 1993), was developed.
Initially, the auto-editor was supplemented with manual
editing. Normal operational procedure involved running
the automated software, plotting the results, manually
deleting vectors where appropriate, and then manually
adding vectors to improve coverage.
Since 1992, CIMSS and NESDIS have made
several changes to all aspects of the automated CMV
software. The most significant of these changes
involves a new tracer selection procedure, the
introduction of the H2O-intercept height assignment
technique, and full automation with a new version of
the auto-editor. This latest version of the software has
been running on GOES-8 data in Washington D.C.
since the fall of 1994. Results have shown that, even
without the benefit of manual QC, the automated
GOES-8 cloud-drift winds are superior to any previous
NESDIS CMV product.
II. Tracer Selection
The initial implementation of the tracer
selection algorithm was very basic. For each target
domain, the highest pixel brightness values were found,
and local gradients were computed around those
locations. Any of those gradients that were large
enough and not too close to one another were assigned
as target locations. In practice, the necessary gradient
threshold was almost always met and the targeting grid
was very coarse. The result was an extremely
widespread and uniform field of targets (figure 1).
* Corresponding author address: Steve Nieman, Univ.
of Wisconsin - Madison, Space Science and
Engineering Center, Madison, Wl, 53706.
Figure 1. Typical target distribution resulting from
GOES-7 imagery and the old tracer selection
algorithm
In the new tracer selection algorithm,
gradients are computed for each pixel within the
targeting box and the maximum gradients undergo a
pixel brightness check to ascertain whether they should
be considered further. The pixel brightness check is
more a means of filtering out clouds of certain levels
than quality control.
All prospective targets now also undergo a
spatial-coherence analysis (Coakley and Bretherton,
1982). Data within the target domain is divided into as
many 3x3 boxes as possible and means and standard
deviations of the 9 points within these boxes are
computed. Boxes having standard deviations below a
given threshold are deemed to be 'coherent' and are
input to a 2- dimensional cluster analysis scheme to
obtain the mean brightness for any coherent clusters
(i.e. signals). Presently two checks are employed to
filter out unwanted targets. First, only two coherent
signals are allowed. The presence of more than 20
percent of the coherent 3x3 boxes outside of the two
largest clusters represents a multi-deck scene. Cloud-
height diagnosis for multi-deck scenes is extremely
difficult (Menzel et al , 1983) so they are eliminated.
Second, scenes are not allowed to be too coherent. If
more than 80 percent of the total number of 3x3 boxes
within the target domain meet the standard deviation
threshold, the scene is deemed to be too coherent and
it fails to be a target. Scenes which are that coherent
typically represent uniform coastal features.
As a result of the spatial-coherence filtering,
the total number of targeting attempts within a given
image can be increased significantly without any
increase in the overall time necessary to process
winds. The extra time used to complete a spatial-
coherence analysis for all prospective targets is offset
by the time saved by not having to height assign and
track undesirable targets. The field of resulting targets
from the new scheme (figure 2) shows a higher density
of tracers in desirable locations, and almost none in the
large clear areas that the previous scheme had
targeted.
Figure 2. Typical target distribution resulting from
GOES-8 imagery and the new tracer selection
algorithm
III. H2O-lntercept Height Assignment
As mentioned, the development of the CO2-
slicing algorithm was a major step in the automation of
wind production. This is the preferred method for
assigning heights to semi-transparent clouds, but the
lack of a CO2-absorption channel on the current series
of GOES imagers has necessitated the implementation
of the H2O-intercept algorithm. Comparisons of the
two methods have demonstrated that the H2O-intercept
algorithm is an adequate replacement (Nieman et. al,
1993).
The algorithm is predicated on the fact that the
radiances for two spectral bands vary linearly with
cloud amount. Radiances from the infrared window
and H2O-absorption bands are measured and
compared to calculations of radiances for both of these
bands for opaque clouds at varying levels in an
atmosphere specified by a numerical prediction of
temperature and humidity profiles. Measured and
calculated radiances will agree for clear-sky and
opaque cloud conditions. The cloud-top height is
inferred from the linear extrapolation of measured
radiances onto the opaque cloud calculations.
The measured radiances used to infer the
linear relationship between the two bands are the
average radiances for the cluster of clearest (warmest)
fields of view and the cluster of cloudiest (coldest)
fields of view within the observational area. If the
calculated water-vapor radiance for clear sky is less
than the measured water-vapor radiance, the
calculated water-vapor radiances are adjusted to agree
with the measured and the difference is attributed to an
inaccurate transmittance used in the computation of
clear-column radiance. Calculated warm radiances
that are greater than measured are not adjusted since
the low measurement may be the result of cloud
contamination.
IV. Automated Quality Control
An important improvement to the automated
wind production system came with the new version of
the automated quality control algorithm (Hayden, 1993)
shown in figure 3. The basic tool of the objective editor
remains the 3-dimensional recursive filter objective
analysis developed at CIMSS. This system has
undergone an extensive upgrade with attention to
improving quality control, especially the recursive-filter-
flag (RFF), which is appended to each datum following
the analysis. Differences in vector root-mean square
error (VRMS) with respect to rawinsonde observations
throughout the range of acceptable RFF flags
amounted to 0.4 ms*1 in the old algorithm. The new
configuration shows an expanded range of 1.1 ms'1
from the lowest acceptable RFF values to the highest.
Thus, the resultant quality flag on the final winds
should now be more meaningful to the user.
Figure 3. The objective editing system
Numerous suggestions from the operational
community have found their way into the latest version.
The pressure reassignment is now constrained to 150
hPa by the algorithm. A small percentage of the
vectors (< 5 percent) are eliminated by this added
constraint. Also, the revised editor has tightened the
tropopause check to a threshold lapse rate of less than
0.5 K per 25 hPa above 300 hPa in hopes that it will be
less likely to re-assign heights to some stratospheric
value.
The revised editor contains an adjustment for
the well documented slow bias of CMVs. The quarterly
statistical summaries of the ECMWF routinely show
this feature, with respect to both wind measurements
and coincident speeds of the ECMWF forecast. For
GOES-7 winds produced with the old automated
system, the bias could reach -5 ms'1 at higher wind
speeds. The bias is also present in comparisons with
the NMC aviation forecasts, which are used in the
derivation and quality control of GOES CMVs. To
mitigate this slow bias, the revised editing procedure
now increments each vector with 7 percent of the speed
of the forecast, interpolated to its reassigned level,
provided that the forecast wind speed is greater than 10
ms'1. Philosophically, this is no different from the bias
corrections routinely applied to radiance measurements
to achieve agreement with forward calculations.
V. Results
The quality of CMVs is traditionally assessed
through collocation with rawinsonde (RAOB)
observations. More elaborate verification procedures
involve the diagnosis of the impact the inclusion of a
CMV dataset can have on a numerical model forecast.
RAOB statistics are more useful for a fixed measure of
product quality over a period of time. Therefore, that is
what we present here.
Figure 4 shows verification statistics for
GOES-7 cloud-drift winds (G7CD), GOES-8 cloud-drift
winds (G8CD) and GOES-8 water-vapor motion winds
(G8WV). VRMS and bias statistics are shown. All
statistics are monthly means resulting from collocation
to RAOBs within 2 degrees latitude and longitude and
25 hPa at 0000 UTC and 1200 UTC daily. All CMVs
are the product of some version of the automated wind
software. Operational G7CD winds still undergo a
manual QC phase in which operators delete some
vectors; the statistics represent conditions before those
deletions occurred. G8CD and G8WV winds do not
undergo go any manual QC.
I *
Figure 4. Monthly statistics for VRMS (upper
curves) and bias (lower curves) for 3 NESDIS
products
Statistics for the G7CD winds begin in
November of 1992, shortly after the initial
implementation of the automated software. During that
first winter, VRMS and bias were high, mainly because
the auto-editor was mistakenly not being run at 0000
UTC daily. This was corrected in the early spring of
1993 and the statistics improved. The winter of 1993-
94 was much better in terms of VRMS and bias. The
old auto-editor was still being run with a few minor
modifications and it seemed to do a reasonable job.
There was however, still some degradation in the CMVs
early in the season. The unedited winds (not shown)
tend to show an increase in VRMS as the mean wind
speed increases during the winter. With the advent of
the first auto-editor this was greatly reduced. The
entire year of 1994 was the best ever for automated
cloud-drift wind production as VRMS remained
consistently at or just above 7ms'1. In early
September 1994 the wind software was shifted from the
IBM 4381 mainframe to IBM rs6000 workstations. This
did not adversely affect the quality of the product and
greatly decreased the amount of time necessary to
complete a wind set. The new auto-editor also became
operational at this time (although the bias adjustment
was not yet being performed) and the quality of the
G7CD product was as good or better than it had ever
been for the next four months. In late December of
1994 the auto-editor began to apply a bias adjustment
of 5%. This change was immediately apparent as the
bias dropped almost 1 ms"1 and VRMS values
improved by approximately 0.15 ms'1. Experimentation
with the G8CD product at CIMSS suggested that even
better results could be attained with a bias adjustment
of 7% and that change was implemented for the G7CD
product in the early summer of 1995. The bias fell well
below 1 ms'1 at that time and VRMS values were
below 7 ms"1.
G8CD wind production began in Washington
in October of 1994. The GOES-8 products are not yet
operational so they do not benefit from data acquisition
priority and backup procedures that are routine for
GOES-7. Thus the mean statistics are for a widely
varying number of wind sets depending on availability,
ranging from 60% to 90%. The initial implementation
of the G8CD product included the new tracer selection
procedures, the H2O-intercept method in place of the
CO2-slicing algorithm and the new auto-editor using a
bias adjustment of 7%. The VRMS and bias for the
G8CD product have been routinely superior to those for
the G7CD product by about 0.5 ms"1.
G8VW wind production began in Washington
in December of 1994 using the same software that
produces the G8CD winds. New tracer selection
procedures are not enabled for the G8WV product
since clear air targets in areas of relatively weak water-
vapor gradients are desired. A simple water-vapor
brightness temperature comparison scheme is used for
height assignment (the inclusion of window channel
data during height assignment offers little in clear air
since the water vapor channel can penetrate only to
about 500 hPa). The bias for the G8WV winds is
comparable to the G8CD winds, while the VRMS
appears to be consistently greater by 0.5 to 1.0 ms"1.
Even so, VRMSs of 7.5ms are greatly superior to any
water-vapor wind product from previous geostationary
satellites, and it is felt that the inclusion of numerous
vectors in clear air will complement the coverage
provided by the G8CD product.
VI. Summary
The latest version of the automated cloud
motion vector software has yielded significant
improvements in the quality of the GOES cloud-drift
winds produced operationally by NESDIS. Cloud
motion vectors resulting from the automated system
are now equal or superior in quality to those which had
the benefit of manual quality control a few years ago.
The single most important factor in this improvement
has been the upgraded auto-editor. Improved tracer
selection procedures eliminate targets in difficult
regions and allow a higher target density and therefore
enhanced coverage in areas of interest. The
incorporation of the H2O-intercept height assignment
method allows an adequate representation of the
heights of semi-transparent clouds in the absence of a
CO2-absorption channel. Finally, GOES-8 water-vapor
motion winds resulting from the automated system are
superior to any done previously by NESDIS and should
now be considered as an operational product.
References
Coakley, J. and F. Bretherton, 1982: "Cloud cover from
high resolution scanner data: detecting and allowing
for partially filled fields of view". J. Geophys. Res.,87,
C7, 4917-4932.
Hayden, C.M., 1993: Recent research in the automated
quality control of cloud motion vectors at CIMSS
/NESDIS. Second International Wind Workshop,
Tokyo, Japan, 1993, EUM P 14
ISSN 1023-0416,219-226.
Menzel, W.P., W.L Smith and T.R. Stewart, 1983:
Improved cloud motion wind vector and altitude
assignment using VAS. J. dim. Appl. Meteor., 22,
377-384.
Merrill, R.T., W.P. Menzel, W. Baker, J.Lynch, and E.
Legg, 1991: A report on the recent demonstration of
NOAA's upgraded capability to derive cloud motion
satellite winds. Bull. Amer. Meteor. Soc., 72,
372-376.
Nieman, S., J. Schmetz and W. P. Menzel, 1993: A
comparison of several techniques to assign heights
to cloud tracers. J. Appl. Meteor, 32, 1559-1568.


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