Preliminary results from NWP impact studies are indicating that upper-tropospheric wind information provided by tracking motions in sequences of geostationary satellite water vapor imagery can positively influence forecasts on regional scales, and possibly on global scales as well. The data are complimentary to cloud-tracked winds by providing data in cloud-free regions, as well as comparable in quality. First results from GOES-8 winds are encouraging, and further efforts and model impacts will be directed towards optimizing these data in numerical weather prediction (NWP). Assuming successful launches of GOES-J and GMS-5 satellites in 1995, high quality and resolution water vapor imagers will be available to provide nearly complete global upper-tropospheric wind coverage

Velden, Christopher S.

English

NASA Technical Reports Server

'(NASA-CR-199789) IMPACT OF N96-16148
GEOSTATIONARY SATELLITE WATER VAPOR
CHANNEL DATA ON WEATHER ANALYSIS
AND FORECASTING (Wisconsin Univ.) Unclas
6 p
G3/A7 0084505
V
https://ntrs.nasa.gov/search.jsp?R=19960008982 2020-06-16T05:56:34+00:00Z
Aooendix 3. Abstract from the 14th AMS Conf. on Weather Analysis and
' Forecasting, Dallas, TX, January 1995, pp.307-312. ^^ B-
= - •--"-- __—*——-— //V/ - V /
NASA-CR-199789
IMPACT OF GEOSTATIONARY SATELLITE WATER VAPOR CHANNEL DATA
ON WEATHER ANALYSIS AND FORECASTING
Christopher S. Velden*
University of Wisconsin
Madison, Wisconsin
1. INTRODUCTION
Recent studies have demonstrated the
feasibility of deriving wind information from
animated sequences of geostationary water
vapor imagery in order to supplement
conventional observations (Laurent 1993; Velden
1993; Holmlund 1993). Further work has
examined the impact of these data on numerical
weather prediction (NWP), and results suggest
useful and complimentary upper-tropospheric
wind information can be provided by these
observations (Velden et al. 1993, Velden et at.
1994).
Development of algorithms at CIMSS to
extract quantitative wind information from
geostationary satellite water vapor data has
progressed to the stage of quasi-operational
testing and demonstrations. Full-disk wind sets
derived from two different satellites (GOES-7 and
Meteosat-3) were routinely produced on a twice-
daily basis in March-April 1994, and delivered to
NMC and ECMWF for assimilation into research
versions (^  their respective global assimilation
systems. In addition, the wind sets were included
in real-time assimilations using the CIMSS
regional-scale model to assess finer-scale
impacts on NWP. Preliminary impact results
included here are encouraging, and the full
assessment along with case study evaluations
will be presented at the conference.
With the recent successful launch of GOES-
8, analysts and forecasters in the U.S. can
expect satellite observations and products of
* Corresponding author address: Christopher S.
Velden. Univ. of Wisconsin-SSEC, 1225 W.
Dayton St., Madison, Wisconsin 53706.
unprecedented quality. One big improvement will
be realized in the widely-utilized water vapor
channel. The combination of higher resolution
and reduced signal-to-noise will lead to an
improved qualitative presentation for subjective
analyses, but also should translate into superior
quantitative products such as water vapor
tracked winds. At the time of writing, GOES-8
imagery was just becoming available. First results
of tracking GOES-8 water vapor features are
reported on in this paper, with further findings and
applications to NWP to be presented at the
conference.
2. BACKGROUND
Wind vectors derived from water vapor
imagery are generated following the same
general philosophy as with cloud drift wind
tracking. Targets (structures in the imagery) are
identified and tracked from successive images to
derive motions (vectors), which are then assigned
heights based on a best match between
radiances (brightness temperatures) and a first
guess temperature profile. In most cases, this
height assignment will be representative of the
layer of moisture being tracked, since the
individual pixel radiances are achieved through
contributions of energy over the depth of the
attendant weighting function. Most vectors are
assigned tropospheric pressure-heights in the
200-500 mb range, with a peak near 300-350 mb.
As a final step, an objective quality control
procedure is invoked once the wind vector field
has been derived (Hayden and Velden 1991).
Procedures to derive wind sets have become fully
automated (Merrill et al. 1991; Holmlund 1993),
and it has been demonstrated these data sets
can be created on a time scale commensurate
with real time operations.
Evaluation of the water vapor winds has
centered on two approaches; statistical validation
against rawinsondes, and model impacts. This
paper focuses on the latter. Earlier studies have
shown the water vapor tracked wind estimates to
be comparable in quality to upper-tropospheric
operational cloud tracked winds (Laurent 1993).
with RMS values of 6.5-8 m/sec, and speed
biases of (ess than 1 m/sec compared to
collocated rawinsondes (Vekten 1993; Velden et
at. 1994). It was also demonstrated by Velden et
al. 1994 that horizontal coverage is
complimentary to wind information obtained
through cloud tracking, and there is good
consistency between observations derived from
different satellites.
A first attempt to assess the potential
importance of the water vapor tracked wind
information on NWP was reported in Velden et al.
1993. In their study, a barotroptc hurricane track
forecast model was used to examine the impact
of the winds on the analyses of Atlantic hurricane
steering currents, and subsequent track
forecasts. Modest improvements in track
forecasts (relative to operational runs) were noted
with the inclusion of the water vapor winds into
the deep layer mean wind analyses which
initialized the model. This was demonstrated by
the fact that eleven out of fourteen 72 hr
forecasts were improved, with a decrease in the
mean forecast error of 8.2%.
3. IMPACT ON REGIONAL AND GLOBAL
ANALYSES AND FORECASTS
Wind sets produced by CIMSS for a trial
period in March-April 1994 (as described in
Velden et al. 1994) were transmitted to NMC for
model impact tests. NMC conducted the exercise
using a T62 version of the global spectral model
(similar to T126 AVN/MRF). Forecasts produced
from analyses initialized with the water vapor
winds are compared to operational forecasts in
order to assess model impact. Shown in Table 1
are comparisons of results from 72-h forecasts
for a 15-day subset of the trial period. The
forecast fields are verified against model
analyses. Two latitude-dependent domains are
considered in order to examine regional impacts.
Table 1. Impact of water vapor motion wind data
on 72-h forecasts from the NMC global spectral
model during a 15-day trial period in March,
1994.
60N-60S
200mb vector rms (mis)
200mb acpsi
200mb acv
opn
12.00
.809
.741
20N-20S
200mb vector rms 10.51
200mb acpsi .654
200mb acv .541
fsp
exp exp/opn
11.86 9/6
.815 9/6
.742 8/7
10.22 9/6
.681 12/3
.550 8/7
opn = operational runs
exp = experimental runs with water vapor winds
fsp = frequency of superior predictions
acpsi = anomaly correlation of stream function
acv = anomaly correlation of v-component
Considering first the near-global domain, the
impacts are not dramatic, as might be expected
since the wind coverage over the mid latitudes is
limited. It should also be noted that the wind sets
produced by CIMSS cover only a portion of the
western hemisphere, while the verification
statistics are global, further limiting the potential
impact. Despite these caveats, the impacts are
slightly positive in all parameters examined. 9 of
the 15 forecasts were improved, with a slight
reduction in the overall 200 mb wind vector rms.
Greater impact is seen in the tropical band, a
region of sparse conventional data, and abundant
water vapor wind coverage. While still modest, a
more substantial gain in forecast accuracy is
noted, especially considering the rotational
component of the wind (psi). For this particular
parameter, 12 of the 15 forecasts were improved.
These results suggest the winds are contributing
to a better definition of circulations or wave-like
features in the ITCZ region.
The water vapor wind sets were also provided
to ECMWF for their evaluation. While
acknowledging the impressive coverage of the
winds (Graeme Kelly, personal communication),
ECMWF had not yet accomplished a rigorous
model impact test at the time of this writing.
However, plans are to evaluate the impact of the
winds using a new 3D-VAR version of their global
model, and these results will be presented at the
conference.
It is of interest to also examine the impact of
the winds on regional scales. To accomplish this,
forecast impact tests were conducted using the
CIMSS regional assimilation system (CRAS),
which is an adaptation of the Australian Bureau of
Meteorology operational regional forecast model
(Leslie et al. 1985). The model analysis covers a
North American and adjacent waters domain at
150 km horizontal resolution, with 39 vertical
levels. The forecast model operates with 20
vertical levels. Within the model package, a 3-D
variational wind-mass adjustment routine is
activated in areas lacking in mass information
(i.e. oceanic regions). This routine essentially
spreads the relative wealth of wind information
onto the mass field through gradient balance
adjustments. Experimental forecasts (EXP)
including the water vapor winds are compared to
control runs (CON) which contain only
operationally available data.
An example of data impact on the model
analysis and forecast fields is illustrated in Figs. 1
and 2, respectively. Shown in Fig. 1 is a plot of
the 275-325 mb water vapor winds (large vectors)
for 00 UTC on 22 March 1994. It should be noted
that winds were only produced over marine
areas. The vectors are overlain on a 300 mb
model wind analysis difference field (EXP -
CON), indicated by the smaller barbs (a barb
length of one grid point interval equals 8 m/sec).
As can be seen, differences at this particular
level are modest, but can be as great as 6 m/sec
such as in the trough off of the east coast of the
U.S.
These analysis differences translate into 48-h
forecast differences shown in Fig. 2. The 48-h
EXP forecast 300 mb height field is overlain to
show the large-scale pattern. In order to highlight
the differences, in this figure a barb length of one
grid point interval equals 5 m/sec. Fig. 2 shows
small differences cover most of the domain, with
notable differences in several locations including
the EXP forecast tendency to amplify the ridge
over the central U.S.
Model impact results from CRAS forecasts of
19 cases from the March/April 1994 trial period
reveal a trend similar to that found with the NMC
impact study. In general, a modest but positive
improvement in RMS and bias statistics of upper-
level height and wind fields are noted. An
example of the forecast impact (72 hr) in terms of
RMS at 400 mb is given in Fig. 3. In this figure, a
comparison of CON and EXP forecast RMS
errors (versus rawinsondes) is presented as a
CON minus EXP value (meters) for each case.
Positive model forecast impacts (reduced RMS
errors as indicated by cases above the 0 line)
produced by the addition of the water vapor
winds to the analyses are indicated in 12 of the
19 cases. Only 3 cases exhibit a degraded
forecast, and 4 had virtually no impact. These
results are fairly representative for mandatory
levels between 200 and 500 mb, where the
greatest impact would be expected due to the
vertical distribution of the data.
The impact of the winds on the regional
model forecasts appears to be highly case
dependent. While the absolute forecast error
improvements are not large in a domain-wide
RMS sense, regional forecasts may be more
significantly affected. Qualitative examination of
the cases run with the CRAS model indicates
greatest positive impact occurs with active
patterns in the eastern Pacific (i.e. upper-level
circulations or jet streaks). These active periods
correspond to some of the positive impact peaks
in Fig. 3. Often these systems are not adequately
defined by operationally available data (i.e.
aircraft reports or cloud tracked winds). For an
example, see Vekten et al. (1994).
4. PRELIMINARY DATA FROM GOES-8
At the time of writing, data from GOES-8 were
becoming available as part of system check-out.
Although limited, observations were collected for
several time periods in August 1994.
Qualitatively, the GOES-8 water vapor imagery
appears to be superior to the GOES-7 imagery,
due to improved resolution (8 km vs. 16 km) and
signal-to-noise. Wind algorithms were applied to
extract water vapor vectors to assess (in a
preliminary sense) and compare to GOES-7
Fig. 1. Plot of 275-325 mb water vapor winds (large barbs, full stick = 5 m/sec and flag = 25 m/sec)
over 300 mb CRAS wind analysis difference (EXP minus CON) field (arrow length of one grid length
interval = 8 m/sec) for 00 UTC 22 March 1994.
Fig. 2. Difference field (EXP - CON) from two CRAS 48 hr forecasts of 300 mb wind (arrow length of
one grid interval = 5 m/sec) initialized at 00 UTC 22 March 1994. Also shown (contours in meters) is
the 300 mb EXP height forecast.
72HRFCST 400MB HEIGHTS
Fig. 3. Comparison of RMS errors (verified against rawinsondes) for CRAS model 72 hr forecasts of
400 mb heights for 19 cases in March/April 1994, expressed in terms of RMS(CON) minus
RMS(EXP). Positive impact of the water vapor winds (EXP) is indicated in cases with points above the
zero line. A few of the days contain multiple data sets (00 and 12 UTC).
Fig. 4. Example of the horizontal coverage of water vapor winds attainable from GOES-8, plotted in
knots over a GOES-8 water vapor image.
winds. An example of the typical coverage
achieved with GOES-8 is shown in Fig. 4. Wind
vector coverage is quite remarkable, and
indicates a high degree of success in obtaining
traceable features.
A limited inter-comparison of vector quality
(GOES-8 vs. GOES-7) is shown in Table 2. For 7
days in August, collocated (space and time) wind
vectors from each satellite were derived, quality-
controlled and are compared to collocated
rawinsondes. The results show a -10% reduction
in GOES-8 wind vector RMSE relative to GOES-
7. While preliminary and limited, these results are
quite encouraging. This assessment will continue
as GOES-8 data become routinely available, and
an update will be presented at the conference.
Table 2. Inter-comparison of GOES-8 and
GOES-7 water vapor motion wind vectors verified
against collocated rawinsondes for a 7 day period
in August, 1994. MUM = number of comparisons.
RMSE and BIAS are in m/sec.
RMSE BIAS NUM
GOES-7
GOES-8
6.96
6.32
0.10
0.09
494
494
5. SUMMARY
Preliminary results from NWP impact studies
are indicating that upper-tropospheric wind
information provided by tracking motions in
sequences of geostationary satellite water vapor
imagery can positively influence forecasts on
regional scales, and possibly on global scales as
well. The data are complimentary to cloud-
tracked winds by providing data in cloud-free
regions, as well as comparable in quality. First
results from GOES-8 winds are encouraging, and
further efforts and model impacts will be directed
towards optimizing these data in NWP. Assuming
successful launches of GOES-J and GMS-5
satellites in 1995, high quality and resolution
water vapor imagers will be available to provide
nearly complete global upper-tropospheric wind
coverage.
Acknowledgments
This work depended strongly on the efforts of
Steve Nieman and Steve Wanzong of CIMSS,
and Robert Aune of NOAA/NESOIS. Gratitude is
extended to Pete Kaplan of NMC for providing
the NMC model impact results. The support and
contributions of Paul Menzel and Kit Hayden are
also acknowledged. Funding for this effort is
being provided by NASA grant NAG8-974 and
NOAA grant 50WCNE-306075.
6. REFERENCES
Hayden, C.M.. and C.S. VekJen. 1991: Quality
control and assimilation experiments with
satellite-derived wind estimates. 9th AMS Conf.
Num. Wea. Pred., Baltimore, 19-23.
Holmlund, K., 1993: Operational water vapour
wind vectors from Meteosat imagery. 2nd
workshop on wind extraction from operational
satellite data, Tokyo, 77-84. Available from
EUMETSAT.
Laurent, H., 1993: Wind extraction from
Meteosat water vapor channel image data. J.
Appl. Meteor., 32,1124-1133.
Leslie, L.M., G.A. Mills, LW. Logan. D.J.
Gauntlett, G.A. Kelly, M.J. Manton, J.L
McGregor and J. Sardie, 1985: A high resolution
primitive equations model for operations and
research. Aust. Met. Mag., 33,11-35.
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.
Velden, C., 1993: Investigation of water vapor
motion winds from geostationary satellites. 2nd
workshop on wind extraction from operational
satellite data, Tokyo, 99-104. Available from
EUMETSAT.
Velden, C., S. Nieman, S. Aberson and J.
Franklin, 1993: Tracking motions from satellite
water vapor imagery: Quantitative applications to
hurricane track forecasting. 20th Conf. Hurr. and
Tropical Meteor., San Antonio TX. 193-196.
Velden, C., S. Nieman and S. Wanzong,
1994: Investigation of water vapor motion winds
from geostationary satellites. 7th Conf. Satellite
Meteor., Monterey CA, 360-363.


Impact of geostationary satellite water vapor channel data on weather analysis and forecasting

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