Water vapor imagery from GOES satellites has been available for over a decade. These data are used extensively, mainly in a qualitative mode, by forecasters in the United States (Weldon and Holmes, 1991). Some attempts have been made at quantifying the data by tracking features in time sequences of the imagery (Stewart et al., 1985; Hayden and Stewart, 1987). For a variety of reasons, applications of this approach have produced marginal results (Velden, 1990). Recently, METEOSAT-3 (M-3) was repositioned at 50W by the European Space Agency, in order to provide complete coverage of the Atlantic Ocean. Data from this satellite are being transmitted to the U.S. for operational use. Compared with the GOES satellite, the M-3 has a superior resolution and signal-to-noise ratio in its water vapor channel, which translates into improved automated tracking capabilities. During a period in 1992 which included the Atlantic hurricane season, water vapor tracking algorithms were applied to the M-3 data in order to evaluate the coverage, accuracy and model impact of the derived vectors. Data sets were produced during several tropical cyclone cases, including Hurricane Andrew. In this paper, the M-3 water vapor wind sets are assessed, and their impact on a hurricane track forecast model is examined

Aberson, Sim

Franklin, James

Nieman, Steve

Velden, Christopher

English

NASA Technical Reports Server

(MASA-CR-199769) T R A C K I N G  MOTIONS M96-1614'3 
FROPI SATELLITE WATER VAPOR I M A G E R Y :  
QUANTffAYIVE APPLICATIQNS TO 
HURRICAME SWAG# FORECASTING Uncl a s  
<Wisconsin U n i v , )  4 p 
https://ntrs.nasa.gov/search.jsp?R=19960008977 2020-06-16T05:56:30+00:00Z
Append ix  4. A b s t r a c t  from t h e  2 0 t h  AMS Conf .  o n  H u r r i c a n e s  a n d  T r o p i c a l  
M e t e o r o l o g y ,  San  A n t o n i o ,  TX, May 1993, pp.193-196. 
TRACKING MOTIONS FROM SATELLITE WATER VAPOR IMAGERY: 
QUANTITATIVE APPLICATIONS TO HURRICANE TRACK FORECASTING ***p ~ $ 4  /v/; 'GB f 
Christopher Velden and Steve Nieman ,,,N -q?- r L  
Cooperative Insti tute for  Meteorological Satel l i te  Studies , P L ~ P ? ~ ? / N J  
1 
c 3 
I Madison, Wisconsin 693 0 
Sim Aberson and James Franklin 
NOAA/AOML, Hurricane Research Division 
M i a m i ,  Florida 
1. INTRODUCTION 
-- - 
Water vapor imagery from GOES satellites has been 
available for over a decade. These data are used 
extensively, mainly in a qualitative mode, by forecasters in 
the United States (Weldon and Holmes 1991). Some 
attempts have been made at quantifying the data by 
tracking features in time sequences of the imagery (Stewart 
et al. 1985; Hayden and Stewart 1987). For a variety of 
reasons, applications of this approach have produced 
marginal results (Velden 1990). Recently, METEOSAT-3 
(M-3) was repositioned at SOW by the European Space 
Agency, in order to provide complete coverage of the 
Atlantic Ocean. Data from this satellite are being 
transmitted to the U.S. for operational use. Compared with 
the GOES satellite, the M-3 has a superior resolution and 
signal-to-noise ratio in its water vapor channel, which 
translates into improved automated tracking capabilities. 
During a period in 1992 which included the Atlantic 
hurricane season, water vapor tracking algorithms were 
applied to the M-3 data in order to evaluate the coverage, 
accuracy and model impact of the derived vectors. Data 
sets were produced during several tropical cyclone cases, 
including Humcane Andrew. In this paper, the M-3 water 
vapor wind sets are awressed, and their impact on a 
hurricane track forecast model is examined. 
2. STATISTICAL EVALUATION OF THE M-3 WINDS 
During the spring of 1992, M-3 wind sets were 
routinely produced on a daily basis (around 12 UTC) at 
CIMSS. The domain of the wind sets covered the eastern 
U.S. and the western N o d  Atlantic ocean basin (Fig. 1). 
The purpose of this exercise was to assess the horizontal 
and vertical coverage of the winds, and evaluate their 
accuracy. M-3 water vapor targets were selectively chosen 
brightness temperature threshold value which was not to 
be ex& in the hrget selection step of the wind- 
tracking algorithm. 
The data set presented in Fig. 1 is typical of the data 
sets produced during the exercise. From a purely 
qualitative point of view, the horizontal coverage of the 
wind vectors shown in Fig. 1 is quite good in compuison 
to conventional observrbiom routinely available over the 
western North Atlantic basin. It was found that the vertical 
distribution of the assigned vector pressweheights was 
typically in the range of 200-500mb, with a maximum near 
350mb. It was also clearly demonstmed for future 
wnsiderations that the wind sets could be created on 
McIDAS (or VDUC at NMC) in a time scale 
commensurate with red time operations. 
to be constraiaed to cloud-free areas in the imagery. This 
was accomplished by activating an empiricdlydetennhed Fig. 1. Typical water vapor wind se t  coverage. 
" Comparisons between the M-3 wind vectors and 
collocated rawinsondes (within 1 .O degree) were compiled 
and are presented in Table 1. The winds are Plso evaluated 
against the collocated first guess forecast (in this case the 
Aviation model 12lt forecast). Both vector speed bias and 
RMS were computed and compared. The data sets are also 
stratified to reflect comparisons with selected rawinsondes 
in relatively remote areas. 
Table 1.Statistical evaluation of M-3 
water vapor motion winds 
1) Versus eastern US/western Atlantic rawinsondes 
(N==981) 
H20 Winds NMC Forecast 
Speed Bias (m/s) -0.9 -1.8 
Vector RMS (m/s) 6.5 5.5 
2) Versus Bermuda rawinsonde (Na31) 
H2O Winds NMC Forecast 
Speed Bias (m/s) -0.8 -2.1 
Vector RMS (m/s) 6.2 6.4 
3) Versus Guadeloupe rawinsonde (N-50) 
HZ0 Winds NMC Forecast 
Speed Bias (m/s) -0.7 -1.9 
Vector RMS (m/s) 5.6 5.9 
Overall, the M-3 vector speed bias (wind vector minus 
rawinsonde) is -0.9 mlsec, which is about 1 mlsec better 
than the first guess forecast. The RMS error, however, is 
1 mlsec higher than the first guess. The superior 
performance of the 6rst guess RMS error can be explained 
by the fact that most of the comparisons were over the 
eastern United States, an area where the model has been 
properly initialized with abundant rawinsonde data. 
Examination of the relatively remote Bermuda and 
Guadeloupe rawinsonde comparisons, however, show the 
RMS error of the M-3 winds slightly below that of the 6rst 
guess. The speed bias is also much improved over that of 
the first guess. 
3. IMPACT ON NUMERICAL HURRICANE TRACK 
FORECASTS 
Another way of quantitatively evaluating new data 
types is through model impact studies. In our investigation, 
a humcane track forecast model (VICBAR, DeMaria et al. 
1992) is used to test the sensitivity of the water vapor wind 
data on numerical humcane track forecasts. VICBAR is a 
nested, spectral barotropic model that has been N n  in 
near-real time at the NowAtlantic Oceanographic and 
Meteorological Laboratory Humcane Raaearch Division 
(AOMLHRD) for the past few yeus. The initirl condition 
for the forecast model is a vertically averaged (mass 
weighted) deeplayer mun wind over the 850-200mb 
depth of the troposphere, with an added tropical cyclone 
bogus. The buotmpic forecast model uses the W o w -  
water equations, with the forecast storm track defermined 
from the location of the relative v d c i t y  miuimum on the 
innermost model mesh. This forecrsting system has been 
usedtoevaluabimp.ctsofotherdatatypes(F~and 
DeMaria 1992; Veldaa et d. 1992) 
Wind wb were produced daily at l2UTC during 
several Adantic tropical cyclonea in 1992. For the model 
impact evaluation, foncLsts were included bas& on the 
following cri- 1) storm within the domain of germated 
wind set, and 2) storm intensity of tropicd storm strength 
or better. There were 19 forecpst cases, from 4 different 
storms (iiluding Hurricane AaQew) that met these 
criteria (Fig. 2). 
Fig. 2. Storm tracks, and center positions when 
forecasts were made (circles) in this study. 
Table 2 shows the results of forecssts which were 
initidizcd with opezationally available data plus the water 
vapor wind dara, computd to control runs which were 
initidizcd on + d y  available data only. 
Considerhrg all of the cclses together, the results show 
modest i m p m v m  to the foncasts with the inclusion of 
the water vapor wind data. The improvement in mean 
forecast error (MFI in Table 2) ranges from 1.8% at 24h 
to 8.2% at 72h, however, after an ad..stment is made for 
the serial correlation between foreclst cases ( F e  and 
DeMaria 1992), none of the improvements were found to 
be significant at the 95% confidence level. Another 
measure of forecast impact is the firtquency of improved 
forecasts (FW), which simply shows the percentnge of 
forecasts which m l t e d  in some improvement when the 
water vapor data were included. The FF at 72h indicates 
that 79% (11 out of 14) of the foreat& were improved. 
Table 2. Impact of METEOSAT water vapor winds on 
VICBAR t ropical  cyclone track forecasts. The 
following verif ication s t a t i s t i c s  are valid for  
forecasts which included the water vapor winds: 
rider of forecasts (N), effective number of 
independent forecasts (N*), and the mean forecast 
error (MFE) re la t ive  t o  best  track verif ication.  
Also given are comparisons with the control 
forecasts: the  mean forecast imprwsment (MFI), 
expressed i n  both kilaneters and as a percent 
re la t ive  t o  the  control forecast error, the 
standard deviation of the improvsments (SDI), the 
number of improved forecasts (IF), the frequency 
of improved forecasts (PIP) expressed as a 
percent, and whether the forecast improvements are 
s t a t i s t i c a l l y  significant a t  the 95% confidence 
level  (SIG). 
Forecast 
Interval  MFE HFI MPI SDI IF FIF SIG 
(hl N I* (Loo) (km) Z (km) , / X (y/n) 
ue a nohble improvemeat at 24 a d  48h ovet thc suuple 
pnsent#l in Table 2. The FW m .Is0 impwed. The 
vduw at 72h m nctrly unchanged since only one crst 
was deleted i k m  the o @ d  sample. 
I t i s o f i n b n a t t o ~ t b t ~ i o n o f f c l r e c u d  
d i f f e a e n c e s i a t h e ~ e d ~ e , i n o r d e r t o f u l l y  
apprr&btheFWnatlta.T.bie4ahowsthedistribution 
of the 7% forecua difltiaeacea. Of the 13 cases, two 
f o ~ w e r s m b M y ~ b y t h e ~ a f t h e  
water vapor winds. One of these poor tmrcurbr was a 
H r n r i ~ B o M i e c M 8 , P v h i c h s a m d t o ~ a t ~ i n  
puc~Bonnie'strackdidnotfollowthecbepkya 
~ w d i a t h e m o d d f o r e c r s t . R . t h e r , i t ~ a  
~ - k y e r t l o w , i n a d h . e c t i o n q U i t e d 1 8 1 ~ ~ t h e  
deeplayer fiow. 'Ille water vrpor winds provide 
nmmmmts in the 200-SOOmb layer, thus afhsiq tbc 
upperputofthedeeplayermcrnwindfIow6dd.Inlhis 
case, the control analysis 8pproxim8tui the dullow-kyer 
flow marc closely, rad as a consequence, the VICBAR 
modes control forcast without the winds was closa to the 
&sewed storm track. The nison for the other poor 
forecat has not ye2 bem idmtiki. 
Table 4. Distribution of 72-h forecasts relative 
t o  control forecasts for cases east  of 7W. 
Forecast differences (bn) 
From an exuninrtion of fig. 2, it is evident tfiot 
several of the s c l d  cases arc relatively close to the U.S. 
mninlarad. It is reasonable to assume that in these cases, 
the VICBAR model was relatively well initi.lized by the 
nearby conventiond (operPto11.1) & base, limiting the 
poteatixl for the satellite data to have a positive impact. On 
theothexhand,sQrmswellouttoaersh~uldmrktbettet 
cadidates for forec~a  improvement. To trat this 
hypothesis, the sample was shrtifibd to include only those 
cases earit of 70W. -The neults ofthese 13 crses ue ahown 
in Table 3. While still not significant, the MH pemmtages 
Table 3. Same as Table 2, except only cases east 
of 70W. 
Forecast 
Interval  MFE MPI MPI SDI IF PIP SIG 
(h) N N* (Loo) (km) X (ba) # X (y/n) 
200 100 0 100 200 
BETTER W R S  
On a more positive note, Table 4 shows that 10 of the 
13 72h foream wau improvemats over the amtml 
forecasts, with 4 of than bcing notable improveanent8 
(defined hers as grtda than 100km). Three of the d l y  
improved foncurte d during Hurricane Aadrsw's 
intendon with m qpedevd cyclonic  MOIL An 
exampleis ahowniaFig. 3, h m  12 UT 19 August 1992. 
A t ~ t i m s ~ e ~ d c i r c u t t i o n w 8 s ~ t o t h e  
a o a h o f ~ r a d q u i t e c v i d . a s i n t h e w 8 t c r ~  
hagay. The derived water vapor winds a # u d  the 
circuktion, as showninKg. 1. Duringthemxt72h, Fig. 
3 s h o w s d u t A o d r c w ' s ~ k w y o n t y ~ . & d ; e d b y  
the @cvd low, abifiing it NNW for a short time 
befonAwfrswcscrpbdits inf l~aadturnedlmxeto 
the weat. The VICBAR control forecuff from lhis time 
recurved Andrew to the mrlh and avatdly  to the 
n o a h a s t m ~ Q t h e ~ c ~ y x o f t h a q p m ~ o n  
the dsep layer mean sD#ring tlow. Although the turn to the 
westwae notpredicted, theVICBAR foreou*rmdtwith 
wrterv .porwindd .b t t spadOdwi thmchl tas  
curvature, and an improved lmgcamge fkmaat. 
Fig.  3. Example showing impact of water vapor 
winds on a VICBAR forecas t  of Andrew's t rack 
4. SUMMARY 
The recent availability of METEOSAT data over the 
western Atlantic Ocean has led to an effort to extract 
quantitative information from the water vapor channel. 
Data sets containing vectors derived from Pnimntwl water 
vapor imagery were produced during 1992 using the 
CIMSS automated wind derivation algorithm. A statistical 
evaluation of the vectors rev& that the water vapor 
winds (relative to collocated rawinso&) show a reduced 
speed bias compared to the collocated 6rst guess forecast 
values. The vector RMS errors are larger (by about 1 
mlsec) than the first guess over the eastern U.S., but 
become slightly lower than the first guess at remote 
tocations (e.g., Bermuda and Gwdeloupe). 
The wind sets were also demomtrated to have a 
slightly positive impact on biuotropic numerical hurricane 
track forwasts (VICBAR model), although the d t s  were 
not swistically sigdicant at the 95% level. Except for a 
couple of examples, the pnlimirury d t s  sam to 
suggest that the positive impact is mukmizod on cases well 
offshore and away from data-rich regions, as would be 
expected from intuitive reasoning. Most of the cams near 
the U.S. coast showed negligible or slightly negative 
impact. The most notable forecast improvements occurred 
during Hurricane Andrew's intcxaction with an upper-level 
low, which was wellaptwed by the water vapor winds. 
Overall, 7% track forecasts were improved by an average 
of around 8 %, while nearly 80% of the VICBAR forecasts 
showed some improvement with the inclusion of the water 
vapor winds into the initial analysis. 
5. REFERENCES 
DeMauia, M., S.D. Abenron and K.V. Ooyama, 1992: A 
msted spectral model for hurricane track fonmdng. 
Mon. Wea. Rev., l20,1628-1643. 
Franklin, J.L. and M. DeMaaia, 1992: The impact of 
Omego dropwindsonde observations on bamtmpic 
hurricane track forcusts. Mon. Wea. Rev., 120,381- 
391. 
Hayden, C.M. and T. Stewart, 1987: An update on cloud 
and water vapor tracm for providing wind estimoteS. 
Reprints, 6th amp. Meteor. Obs. and Inrtr. ., New 
Orleans, Amer. Meteor. Soc., 70-75. 
Stewart, T.R., C.M. Hayden, and W.L. Smith, 1985: A 
note on water vapor wind tracking using VAS data on 
McIDAS. Bull. A , .  Metwr. Sot., 66, 1111-1115. 
Velden, C.S., 1990: The implct of satellitederived winds 
on hurricane analysis and track fwecrsring. Reprints, 
5th &I$ SlateUite Metwr., London, England, Amer. 
Meteor. Soc.,215-219. 
Velden, C.S., C.M. Hayden, W.P. Menzel, J.L. Franklin, 
and J.S. Lynch, 1992: 'Ihc impact of satellite-derived 
winds on numerical hurricane track forecasting. Wea. 
and Forecasting, 7, 107-118. 
Weldon, R.B., and S J .  Holmes, 1991: Water vapor 
imagery: Interpretation and applications to weather 
analysis and forecrsting. NOM Tech. Report NESDIS 
57,5200 Auth Rd, Wash., D.C., 213 pp. 


Tracking motions from satellite water vapor imagery: Quantitative applications to hurricane track forecasting

Abstract

Similar works

Full text

Available Versions

NASA Technical Reports Server