Simulating Cloud Motion Vectors From Global Circulation Model Data For
Use in OSSEs: A Preliminary, But Useful, Algorithm For Application to
Current NASA/NOAA OSSE Projects.

October 18, 2001

Christopher O'Handley

G.D. Emmitt

Steven Greco

Simpson Weather Associates

809 E. Jefferson St.

Charlottesville, VA 22902

# 

# 

# **1. Introduction**

*a) Overview*

The purpose of this report is to describe the method we have developed
for producing realistic cloud motion wind vectors (CMV) for use in
OSSEs. Currently, such CMV are obtained by simply using the locations of
all observed CMV for the time of interest. The numerical model wind at
each location/height is assigned to the CMV at that location, and a
gaussian random error is added to each report. This approach has two
major shortcomings. First, it may result in CMV in areas where the model
has no clouds. Second, there are no correlated errors (such as the slow
bias discussed below) in the resulting CMV.

To assure realistic impact assessments using OSSEs, our objective is to
produce simulated cloud motion vectors (CMV) that exhibit both random
and correlated errors similar to those occurring in practice. We will
describe the procedures currently used to produce operational CMV, their
spatial distributions, and the magnitudes and sources of errors. We then
describe our approach for simulating CMV and their errors using
numerical prediction data. We close with a description of future work
that is needed to evaluate the resulting winds and further develop the
techniques. Note that this paper deals only with CMV from visible and
infrared imagery; future work will address the need to simulate CMV from
water vapor imagery.

*b) Operational cloud motion wind algorithms*

The following description, based primarily on Nieman et al (1997),
corresponds most closely to the procedures used to derive GOES CMV at
NESDIS.

Generally, a set of 3 infrared (IR) images, at 30 minute intervals, is
used to derive CMV. The first step is an image registration analysis
between the three images, using the middle image as the starting point.
Although the images are navigated and registered prior to receipt by
NESDIS, registration errors greater than 2 km (equal to a 1 m/s speed
error over 30 minutes) are encountered on a regular basis. The
registration correction involves an examination of as many landmarks as
possible on the images; the individual shifts are then averaged to
produce a single correction in *x* and *y* for the image set. Only
corrections which exceed 2 km are actually applied, which suggests that
the residual registration error is less than 2 km in x and y. However,
because of variations in the number of usable landmarks, there are times
when the residual error exceeds 2 km. Also, since a single correction is
applied across the whole image, there is no way to account for
rotational shifts or other nonlinear types of error.

# The second step is target identification. A grid of approximately 60x60 km is used to search the first image of a triplet for sufficiently non-homogeneous cloud patterns. The algorithm uses spatial-coherence analysis to identify the highest (brightest) cloud in the scene, and find the region of strongest brightness gradient. The cloud is identified as a target if the gradient exceeds a certain magnitude. While any type of cloud can be tracked, scenes with multiple cloud decks are eliminated because these make cloud height difficult to determine.

Once a target has been identified, the cloud top height is determined.
Several methods are available. The simplest, which is used as a back-up,
is the infrared window approach, in which the coldest 20% of the sample
is assumed to represent the cloud top temperature, and height is
determined by comparison with a temperature profile from a numerical
forecast. More advanced techniques, such as the CO~2~ slicing and H~2~O
intercept methods (Nieman et al, 1993), are used when possible, and are
particularly beneficial in cases where the target cloud is
semi-transparent (and thus susceptible to contamination by surface
radiation). Both methods use information from multiple channels and
require both cloudy and clear-sky radiances. Though more accurate, these
techniques also rely on information from numerical forecasts to estimate
radiances. The current GOES satellites have sensors which allow use of
the H~2~O intercept, whereas 1993-era GOES sensors required use of CO~2~
intercept. Recently, techniques have also been developed that enable
estimation of cloud base heights for low-level (below 700 mb) cloud
tracers, as it is known that such clouds generally move with the wind
speed close to cloud base.

The fourth step is the actual tracking of target clouds from image to
image. The position of the target on image 2 is estimated using the wind
predicted by a numerical forecast model. A correlation procedure is then
used to search the second image; if the maximum correlation exceeds a
pre-defined threshold, the target is identified and the resulting 30
minute shift defines the first CMV. This step is repeated using images 2
and 3, which yields a second CMV. If the difference between the two
vectors is sufficiently small (generally about 5 m/s), an average CMV is
computed from the two vectors, and the resulting vector is passed on to
the quality control system.

While the above steps are fairly similar among the various CMV producers
(Butterworth, 1997a), the quality control procedure which follows
differs considerably (Holmlund et al, 2001). The most advanced,
completely automated procedures are implemented at EUMETSAT and NESDIS.
The NESDIS technique, known as the auto-editor, involves two steps of a
fully three-dimensional recursive filter objective analysis. In the
first step, a background analysis is produced using a numerical forecast
along with all available non-CMV observations. This is followed by a
second step which incorporates the CMV. During this process, a penalty
function is used to re-assign the heights of the CMV to the level of
best fit if necessary (the adjustment is limited to 150 mb), and winds
which differ greatly from the resulting analysis are flagged. At
EUMETSAT, the individual CMV vectors are put through a series of
consistency tests that primarily compare them to other CMV reports,
though one test involves a model forecast field. Both methods also yield
quality control flags for each CMV.

*c) Known errors in CMV*

Recent comparison studies between CMV and co-located radiosondes
indicate that rms error values of CMV increase with height, ranging from
about 3-4 m/s below 700 mb, to 4-6 m/s between 400 and 700 mb, and 6-8
m/s at higher levels (Tomassini et al, 1999). It should be noted that
these are not absolute accuracies, as radiosondes themselves have errors
ranging from roughly 2 m/s (low levels) to 4 m/s (high levels). More
troubling is a persistent slow bias in CMV, particularly at higher wind
speeds. Some studies have found the slow bias to be as large as 5-6 m/s
in uncorrected CMV. This error is so important (jetstream wind speeds
are already underforecasted by numerical models) that NESDIS actually
implements a "slow bias correction" as part of their auto-editor. All
CMV whose speed exceeds 10 m/s are augmented with 7% of the forecast
wind speed at that location. The corrected CMV that result from the
auto-edit procedure still exhibit a slow bias of approximately 1-2 m/s,
depending on windspeed.

While the magnitudes of CMV errors have been well defined, the exact
causes of the errors are somewhat uncertain. It has been suggested that
the slow bias may be due to non-representative cloud motions, i.e. that
the tracer clouds, generally cirrus, do not move with the winds near jet
level. This has not been conclusively shown, although it is well known
that other clouds do not move with the wind at cloud top (the height at
which the CMV is assigned). For instance, tropical cumulus have been
shown to move with the wind near cloud base, and there is often a 1-2
m/s difference between the winds at cloud base and cloud top (Peslen et
al, 1986). It would seem that other explanations could account for the
slow bias. In particular, the presence of sub-visual cirrus clouds above
the target clouds could contaminate the height assignment, making the
target cloud appear higher. Where the windspeed increases upward, the
effect would be to place a slower CMV higher up in the atmosphere, in a
region with faster windspeed -- thus introducing a slow bias. This will
be examined in more detail below.

More generally, the height assignment process is susceptible to numerous
errors. In the case of semi-transparent cloud targets, contamination by
upwelling surface radiation can corrupt the height assignment. The CO~2~
and H~2~O techniques help circumvent this problem, but at the expense of
introducing information from numerical forecast models. In certain
situations, this presumably could lead to large errors in height
assignment. Other sources of error include image registration shifts,
inaccuracies in the correlation algorithms used to identify and track
cloud tracers, and cloud development and dissipation.

*d) CMV data coverage*

At present, CMV are produced from the following 5 satellites: GOES-W
(135 W), GOES-E (75 W), METEOSAT (0 W), GMS (140 E), and INSAT (80 E).
Generally, winds are produced within about 45-60° from the satellite
sub-point. This results in nearly global coverage within about 45° of
the equator. However, the exact types of winds (infrared/visible/water
vapor), as well as the techniques and quality control procedures, differ
among the producers. For comparison, in 1993 there were 4 satellites
producing CMV (the above minus INSAT), which resulted in a gap of 30-45°
in longitude in the Indian Ocean region.

*e) CMV use in data assimilation*

Because of the differences in types of winds produced, methods used, and
resulting accuracies, the major weather prediction centers have put in
place severe restrictions on the use of CMV in their data assimilation
systems. For example, at ECMWF (Butterworth 1997b), winds from INSAT are
not used, nor are any visible winds above 700 mb. Also rejected are IR
winds over land that are north of 20° N, and IR winds over land below
500 mb that are south of 20° N. Furthermore, all high-density winds are
thinned to one observation per (approximately) 1.25° box at each model
level; the observations are chosen on the basis of the quality control
flags. Note that other types of restrictions are applied to water vapor
and sounder winds, which are not discussed here. At NCEP, INSAT winds
are not used, GMS winds are used everywhere, and GOES and Meteosat winds
are used everywhere south of 20° N, and only over the ocean elsewhere.
Note that CMV use in data assimilation is constantly being evaluated and
modified, so this description may not be 100% accurate.

# **2. Data**

For this study, simulated cloud track winds were produced using data
from an ECMWF T213 spectral model 30-day forecast, which serves as a
"nature run" for the NPOESS OSSE project. The data was available on a
one half degree latitude/longitude grid at 31 pressure levels from 1000
to 100 mb, every 6 hours, from 06 UTC 2/5/1993 to 00 UTC 3/6/1993.
Parameters that were used include cloud fraction, cloud liquid water
content, temperature, and u and v wind components. Low-level cloud
fractions (below 800 mb) were corrected to eliminate the global
under-estimation of low cloud in the nature run (Masutani et al, 1999).

**3. Methods and Results**

*a) Cloud-motion winds: target selection*

At each model grid point within 60° of a satellite sub-point, a
"satellite cloud view" (SCV) is calculated by applying cloud overlap
assumptions to the model cloud fractions. For contiguous cloud layers
(those in successive model layers), maximum overlap is assumed, i.e. the
cloud layers are considered vertically stacked. As an example, assume
that the model cloud fraction is 40% in the higher of 2 layers, and 50%
in the next layer. The SCV of the first layer is 40%. Applying maximum
overlap, 40% of the cloud in the second layer is considered obscured,
leaving an additional SCV of 10% in that layer. If the second layer has
less cloud than the higher layer, none of it can be seen. For
non-contiguous cloud layers, i.e. cloudy layers separated by at least
one clear layer, random overlap is applied. Using the above example, the
SCV in the lower layer could range from 10% to 50%. If the higher layer
has 40% and the lower layer 30%, then the SCV in the lower layer could
range from 0-30%, i.e. all of the cloud might be seen. The random
overlap essentially states that non-contiguous cloud layers are
considered to be completely unrelated dynamically.

The SCV profile is then examined from the top down to determine if a
suitable cloud tracer exists. Any layer with a SCV cloud fraction of
between 0.05 and 0.25 is assumed to be a potential tracer, and the first
such level encountered is chosen as the tracer for that location. It is
assumed that if the SCV is less than 0.05, there is insufficient cloud
for tracking. On the other hand, if SCV exceeds 0.25, the cloud is
assumed to be more stratiform in nature, with insufficient edges or
brightness variations for tracking to be successful. This approach is
based on the general targeting algorithms described earlier, which rely
on inhomogeneities in the cloud field. The exact SCV range is somewhat
arbitrary, and has been adjusted to give "reasonable" distributions of
CMV. Further work is needed to evaluate the CMV distributions that
result. In particular, these values have been adjusted to give CMV
totals that are close to 1993 levels; current operational CMV totals are
much higher.

Once the target has been located, the T213 model u and v wind components
are recorded as the true wind associated with the CMV at that location.
An example of the distribution of CMV produced using this method is
shown in Figure 1. Panels a and b contain the column totals of opaque
and cirrus cloud (SCV), which show the distribution of clouds and
weather systems. Panels c-f show the locations of the CMV in 4 different
layers, and Table I lists the number of CMV for this case, statified by
height and cloud fraction. The total number of CMV is about 4 times
larger than the number of true CMV for the same time period, and the
vertical distribution is too heavily skewed to the low layers. It is
assumed that inclusion of cirrus targets would increase the number of
high level CMV while reducing those at lower levels; this remains for
future work. However, the spatial distribution of the simulated CMV is
reasonably similar to observed distributions of CMV in 1993, especially
if the simulated vectors are randomly thinned to the same total number.
This is demonstrated in Figure 2 (note that these figures are 12 hours
apart).

*b) Modeling of Slow Bias*

As indicated earlier, comparative studies between CMV and radiosonde
winds have revealed the presence of a slow bias which increases with
wind speed. It has been suggested that these types of errors exhibit
spatial correlations which could have a strong negative impact on
numerical weather prediction (Butterworth, 1997b; Holmlund et al, 2001).
Unfortunately, no studies have clearly documented such a spatial
correlation, though it seems reasonable to suspect that the bias
patterns might be correlated along the path of jet streams.

The cause of the slow bias is not known. However, it has been attributed
to non-representative cloud motions near the jet stream or, in the
specific case of water vapor winds, to improper height assignment or
edge effects on cloud tracking (Daniels et al, 2000). Our attempt to
simulate a slow bias is based on the idea that a slow bias may be the
result of contamination of the cloud height estimate by thin or
sub-visual cirrus clouds. If present, such clouds would make the target
cloud appear to be colder than it really is, and thus higher in the
atmosphere. Since winds generally increase upward throughout most of the
troposphere, this would effectively introduce a slow bias by making a
lower, slower-moving cloud appear to be at a higher height, where
windspeeds are faster.

Our approach is as follows. For each profile where a cloud target is
found, we determine if thin cirrus cloud is likely to be present above
the target cloud. If such cirrus is predicted, then the pressure
difference δ*p* between the highest cirrus and the target cloud is
calculated. δ*p* is multiplied by a gaussian random deviate (between
0-1), and the resulting value δ*p~adj~* is used to adjust the pressure
of the target cloud upward to a new apparent pressure (*p~app~*). The
model wind speed at this new level is the equivalent of the true, or
radiosonde speed. However, the wind speed at the original level is still
applied to the CMV. This introduces a "cold bias" error which may or may
not also be a negative wind speed error, depending on the vertical
profile of wind speed.

As it turns out, this procedure does produce a slow bias. Table II shows
the resulting bias values for all CMV above 700 mb, for the case shown
in Figure 1. The speed bias is tabulated two ways, with respect to the
true or radiosonde speed (BIAS1) and then with respect to the CMV speed
(BIAS2). BIAS1, which is equivalent to the way the values are usually
reported in the literature, shows a strong slow bias that increases
dramatically with increasing wind speed. The bias values for speeds
above 30 m/s greatly exceed any published statistics, which may indicate
that we are allowing vertical adjustments that are too large. However,
it should be noted that the sample size is relatively small. BIAS2 shows
a much more constant slow bias across all speed ranges. This differs
from the recent findings of Lalaurette and Garcia-Mendez (2000), who
plotted the slow bias for winds above 400 mb versus CMV speed. They
found a linear increase in the slow bias from 0 m/s at 15 m/s to --5 m/s
at 45 m/s. Unfortunately, we have not seen other observational studies
in which the slow bias is tabulated based on CMV speed for comparison.
Our overall simulated slow bias of --2.15 m/s is reasonably close to
recently published values (Tomassini et al, 1999)

The other issue regarding the slow bias is horizontal correlation of the
errors. Figure 3 shows those CMV that have a slow bias greater than --1
m/s (panel a) and those that have a fast bias greater than +1 m/s (panel
b). Clearly this procedure does introduce concentrated areas of slow
bias, in regions where cirrus clouds are found (compare to Figure 1).
Unfortunately, there are no published studies showing spatial
distributions of bias errors in operational CMV for comparison. However,
observational studies have shown that the slow bias is predominantly a
mid-latitude phenomenon, and this is reflected in the spatial
distributions shown in Figure 3.

*c) Image Registration Errors*

As noted earlier, NESDIS applies a registration correction to each image
triplet if the calculated registration errors exceed 2 km. However, the
accuracy of the correction depends on the number and distribution of
landmarks, which can vary considerably. Thus, errors exceeding 2 km may
be fairly common. In addition, rotational and non-linear errors cannot
be eliminated using the current approach. Since a 2 km shift in 30
minutes introduces a wind speed error of 1.1 m/s at nadir (and more near
the edge of the image), this is obviously a significant source of
correlated error that must be included in simulated CMV.

The main difficulty in simulating these errors is the spherical nature
of the earth. A simple west-east registration shift will introduce only
zonal wind errors, though the magnitude of the error will increase
toward the edges of the image. However, a north-south shift will
introduce both longitudinal and zonal wind components, with the ratio of
the components changing depending on latitude and longitude. To quantify
the distribution and magnitude of wind errors across the CMV domain, map
projection equations are used. We have used the equations for the
equatorial azimuthal orthographic projection:

*x = cos(lat)sin(lon)* (1)

y *= sin(lat)* (2)

The resulting projection, shown in Figure 4, is not exactly the
satellite projection, but it is very close.

The procedure to estimate wind errors introduced by a registration shift
is as follows. First, an *x*,*y* shift (*δx*, *δy*) is randomly assigned
for the image as a whole. This value is assumed to be valid at nadir.
Then, at the location of each cloud target, the following is done.
First, equations (1) and (2) are used to calculate the unshifted *x* and
*y* positions of the point based on the true latitude and longitude;
this location is recorded as *x~true~* and *y~true~*. Next, the shifted
longitude position *x~shift~* is determined from *x~true~* + δ*x*, and a
new longitude is back-calculated from equations (1) and (2). This is
repeated for latitude, resulting in a new (or apparent) latitude and
longitude for the location. Finally, the differences between the true
and apparent latitudes and longitudes are used to determine the distance
shifts in km, which yields a wind speed which corresponds to the image
registration error.

Figure 5 shows the estimated wind errors resulting from registration
shifts of 2 km over 30 minutes. A due eastward shift (panel a) causes
westerly winds across the domain, with speeds of 1-2 m/s. The wind speed
is constant in latitude, but increases toward the western and eastern
edges of the domain. A northward shift (panel b) causes a symmetric
pattern, with southerly winds predominating near the center and
increasing zonal components toward the corners. Total wind speeds can
exceed 3 m/s at the corners of the map. A combined northward/eastward
shift (panel c) causes a more complicated pattern, with wind speeds
greater than 2 m/s over significant parts of the domain and maxima
exceeding 5 m/s in the SW and NE corners

**4. Discussion/Future Work**

We have produced simulated CMV whose spatial distribution appears
broadly similar to operational CMV from 1993. However, the vertical
distribution is too highly skewed to lower altitudes, and the total
number of vectors is too large by a factor of 4. Several items need to
be explored to fix these problems. First, we need to examine the cloud
overlap calculation, particularly the method used to identify thin
(sub-visual) cirrus clouds. After this, we will need to add cirrus
targets into the tracking algorithm. Operational CMV producers do track
cirrus clouds, and these make up a significant source of CMV. It is
presumed that using cirrus targets would give us a more realistic
vertical distribution of CMV; it might also change the slow bias results
significantly. We will need to modify the algorithm to simulate CMV of
the density that are currently produced, which greatly exceeds the
levels of 1993. This will involve testing different cloud fraction
ranges for the target cut-off, and examining the spatial patterns of CMV
that result.

We need to more carefully analyze the results of our slow bias
simulation. Observational studies frequently present the bias statistics
as a function of latitude and height; we have only looked at the entire
sample above 700 mb. We will also look at the spatial patterns to
determine how error magnitudes are correlated; this should also involve
an examination of the direction errors (observations suggest that there
is little directional component to the slow bias). This process should
help us determine if we are producing a slow bias for the wrong reason.

Many researchers have indicated that CMV are frequently contaminated by
surface radiation, which would tend to produce a warm, or low, bias in
the cloud heights. Seemingly, this would tend to produce a fast bias and
in fact at slower wind speeds, observed CMV do tend to show a slightly
positive bias (Lalaurette and Garcis-Mendez, 2000). We need to
incorporate a surface radiation component to our height adjustment,
probably only in cases where the target cloud is "thin". Potentially, a
single, thin target cloud could be affected by both a surface-induced
warm bias and a cirrus-induced cold bias, of varying magnitudes. It will
be interesting to see how these effects combine, and whether a slow bias
is still present in our simulated CMV.

Finally, our registration error simulator could be improved by
incorporating the map projection equations for the true satellite
projection. It is believed that this would lead to slightly larger
induced winds, especially near the edges of the domain. We may also want
to add the ability to create rotational image shifts, though this may
not be necessary as the current approach serves the purpose of
introducing wind errors that are correlated across the satellite domain.

**5) References**

Butterworth, P., 1997a: NWP on-line scientific note no, 2: Investigation
into satellite winds -- I. Production.

Butterworth, P., 1997b: NWP on-line scientific note no. 3: Investigation
into satellite winds -- II. Treatment at UKMO.

Daniels, J., C. Velden, W. Bresky and A. Irving, 2000: Status and
development of operational GOES wind products. *Proc. Fifth Intl. Winds
Workshop*, Lorne, Australia.

Holmlund, K., C.S. Velden, and M. Rohn, 2001: Enhanced automated quality
control applied to high-density satellite-derived winds. *Mon. Wea.
Rev*., **129**, 517-529.

Lalaurette, F. and A. Garcia-Mendez, 2000: Monitoring satellite winds at
ECMWF. *Proc. Fifth Intl. Winds Workshop*, Lorne, Australia

Masutani, M, K.A. Campana and S-K Yang, 1999: Note on cloud cover of the
ECMWF nature run used for OSSE/NPOESS project. Office note 427. NCEP.

Nieman, S.J., J. Schmetz and W.P Menzel, 1993: A comparison of several
techniques to assign heights to cloud tracers. *J. Appl. Met*., **32**,
1559-1568.

Nieman, S.J., W.P. Menzel, C.M. Hayden, D. Gray, S.T. Wanzog, C.S.
Velden, and J. Daniels, 1997: Fully automated cloud-drift winds in
NESDIS operations*. Bull. Amer. Met. Soc*., **78**, 1121-1133.

Peslen, C.A., S.E. Koch and L.W. Uccellini, 1986: The effect of the
arbitraty level assignment of satellite cloud motion wind vectors on
wind analyses in the pre-thunderstorm environment. *J. Clim. Appl.
Met*., **25**, 615-632.

Tomassini, M., G. Kelly and R. Saunders, 1999: Use and impact of
satellite atmospheric winds on ECMWF analyses and forecasts. *Mon. Wea.
Rev*., **127**, 971-986.

Table I: Number of cloud track winds for each atmospheric layer, and
range of cloud fraction for those winds.

+-------------+-------------+-------------+-------------+-------------+
|             | ## CLO      |             |             |             |
|             | UD FRACTION |             |             |             |
+-------------+-------------+-------------+-------------+-------------+
| LEVEL       | 0-10%       | 10-20%      | 20-30%      | TOTAL       |
+-------------+-------------+-------------+-------------+-------------+
| high        | 29          | 67          | 52          | 148         |
+-------------+-------------+-------------+-------------+-------------+
| middle      | 359         | 633         | 255         | 1247        |
+-------------+-------------+-------------+-------------+-------------+
| low         | 1136        | 1319        | 369         | 2824        |
+-------------+-------------+-------------+-------------+-------------+
| pbl         | 685         | 460         | 107         | 1252        |
+-------------+-------------+-------------+-------------+-------------+
|             | 2209        | 2479        | 783         | 5471        |
+-------------+-------------+-------------+-------------+-------------+

Table II: Speed bias (m/s) for all simulated CMW above 700 mb. BIAS1 is
the bias as tabulated by radiosonde (true) speed, with NUM1 the number
of observations for that category. BIAS2 and NUM2 are the same, but
tabulated against the CMW speed.

+-------------+-------------+-------------+-------------+-------------+
| ###         | BIAS1       | NUM1        | BIAS2       | NUM2        |
| Speed range |             |             |             |             |
+-------------+-------------+-------------+-------------+-------------+
| 0-10 m/s    | 0.29        | 1215        | -2.41       | 1571        |
+-------------+-------------+-------------+-------------+-------------+
| 10-20 m/s   | -2.95       | 1126        | -1.77       | 992         |
+-------------+-------------+-------------+-------------+-------------+
| 20-30 m/s   | -5.23       | 378         | -2.43       | 241         |
+-------------+-------------+-------------+-------------+-------------+
| 30-40 m/s   | -8.65       | 127         | -2.29       | 58          |
+-------------+-------------+-------------+-------------+-------------+
| 40-50 m/s   | -10.59      | 21          | -1.48       | 5           |
+-------------+-------------+-------------+-------------+-------------+
|             | -2.19       | 2867        |             |             |
+-------------+-------------+-------------+-------------+-------------+
|             |             |             |             |             |
+-------------+-------------+-------------+-------------+-------------+

![](media/image1.wmf){width="4.4in" height="2.8125in"}Figure 1:
Simulated cloud track winds for 1200 UTC 2/6/1993. (a) Column total
cirrus (b) column total opaque cloud (c) Low-level CMV (below 850 mb)

![](media/image4.wmf){width="4.3in" height="3.254861111111111in"}Figure
1 (Continued): (d) CMV between 850 -- 500 mb (f) CMV between 500 -- 200
mb (f) CMV above 200 mb

![](media/image7.png){width="4.6in" height="3.1in"}

![](media/image8.wmf){width="4.5in" height="3.066666666666667in"}

Figure 2: Comparison between actual cloud track winds from 1993 and
simulated winds. (a) Actual CMV from 0000 UTC February 6, 1993 (b)
simulated CMV from Figure 1, randomly thinned to approximately the same
density as the observed winds.

![](media/image9.wmf){width="4.2in" height="3.1493055555555554in"}Figure
3: Locations of CMV above 700 mb that exhibit a bias greater than 1 m/s.
(a) slow bias (b) fast bias.

![](media/image11.wmf){width="4.15in" height="4.072916666666667in"}

Figure 4: Map projection used to simulate registration errors. Latitude
and longitude shown at 10° intervals.

![](media/image12.wmf){width="2.6659722222222224in" height="3.4in"}

Figure 5: Wind vectors resulting from image registration shifts of 2 km
in 30 minutes. (a) eastward shift (b) northward shift (c) northward and
eastward shift.