FORECAST
OF NORTH EAST BRAZIL SEASONAL RAINFALL FOR FEBRUARY TO MAY 2004 USING EMPIRICAL
AND DYNAMICAL METHODS USING ATMOSPHERE AND OCEAN DATA UP TO END NOVEMBER 2003
contributed
by A.W. Colman, M.K. Davey, and R.J. Graham
Met Office, Bracknell, United Kingdom
HEADLINE: The empirical and dynamical forecasts
favour above average rainfall for most of the region in the 2003 rainy season
1.
Introduction
Real-time forecasts of rainfall during the NE Brazil rainy
season (approximately March-May) have
been issued by the Met Office for each season since 1987 following research by
Ward and Folland (1991) and by Folland et al
(2001). Forecasts are issued in early February and early March using the latest available ocean
and atmosphere information.
In 1999, the forecasts were revised and extended to cover a
region from 2.5o to 10o south and from the Atlantic coast
west to 50o west. Forecasts are now for gridpoint mean rainfalls.
The grid used is the same as that of the Met Office Atmospheric General
Circulation Model (AGCM) used for seasonal prediction. This allows presentation
of both empirical and dynamical forecasts for the same region. Also in 1999,
long lead forecasts issued in early December were introduced. The forecast
period was initially January-June but was changed February-May in 2002 to be
compatible with the NE Brazil Regional Climate Outlook Forum (RCOF) product (contact www.funceme.br
for details.
This document consists of probability forecasts of terciles
(again for compatibility with RCOF products) produced using discriminant
analysis. Included with the forecast are assessments of the probability
forecasts using the Relative Operating Characteristic (ROC) skill measure, a
WMO standard assessment measure for probability forecasts.
This year, dynamical
model output from the Met Office GLObal SEAsonal (GLOSEA) coupled
ocean-atmosphere general circulation model and SST based statistical predictors
have been combined using a multi-variate discriminant equation to produce one
set of probability forecasts.
2.
Forecast System
2.1 Statistical
Predictors
The SST predictors are the same as used for previous
statistical forecasts; (see Experimental Long Lead Bulletin March issues 1993
to 2003 published by NWS, NOAA USA and currently by COLA, USA). They are an
index of Atlantic Sea Surface Temperature (SST) and an index of Pacific SST.
Current SST patterns
SST anomalies in the Pacific are weakly El-Nino like
favouring a dry season. The signal from the tropical Atlantic which comes
mainly from above average SST in the gulf of Guinea favours above average
rainfall and is slightly stronger than the Pacific signal. Statistical
predictions of SST anomaly (method described below) indicate that the El Nino
signal in the Pacific will fade substantially by February.
Predicted SST
Time coefficients of the Atlantic and SST patterns are also
calculated using predicted SST using the statistical forecast system described
in Colman and Davey (2003). SST predictions are produced from earlier SST
observations using an equally weighted average of forecasts from 3 methods,
persistence of box are SST anomaly, Global Scale Canonical Correlation Analysis
and linear regression from neighbouring boxes. Predicted SST are used in a 2
tier approach to seasonal forecasting. Firstly the SST prediction system is
used to produce forecasts of 10 degree latitude x longitude box anomalies for
February from November anomalies. Secondly, the February anomalies are used to
evaluate SST pattern time coefficients which are substituted into regression or
discriminant prediction equations to make rainfall forecasts.
2.2 GLOSEA
Dynamical Forecasts
The GLOSEA model replaced the uncoupled atmosphere GCM
forecasts as our main seasonal forecast tool in 2003. The GLOSEA forecasts are
produced monthly as an ensemble of 40 members. The members are generated from slightly
perturbed ocean initial conditions (for more about GLOSEA see http://www.metoffice.com/research/seasonal/technical.html
) . The model is run out to 6 months ahead. The model output is calibrated in
the discriminant equation using hindcasts from 43 years (1959-2001) produced as
part of the DEMETER project (see http://www.ecmwf.int/research/demeter/)
for more information about DEMETER).
For this forecast we use forecasts and hindcasts initialised
in early November which predict the rainfall for February-April. Ideally we
would use forecasts for February-May but the forecasts only go out 6 months and
The November forecast is the latest available at the time of
writing. However, May rainfall is quite highly correlated with February-April
rainfall so skill loss is minimal.
2.3 Content
of Discriminant Forecast Equation
The forecast is produced using an equation of the form
Tercile probability for gridbox g = 1/40 x SUM (f ( a x November Atlantic SST index + b
x November Pacific SST index + c x Predicted February Atlantic SST
index + d x predicted February
Pacific SST index +
e x GLOSEA
member m forecast for gridbox g
) for 40 members m=1,40)
f is an
exponential function adjusted so the sum of probabilities for the 3 terciles=1.Coefficients
a,b,c,d,e and function f are all evaluated using historical
SST data and GLOSEA hindcasts produced using the DEMETER project.
The discriminant equation effectively calibrates the the
model output and corrects for any bias observed over the 1959-2001 training
period. (See Afifi and Azen, 1979 for
more about discriminant analysis)
3.
Northern Brazil Regions
Our forecast area consists of 11 rectangular regions each
covering 3.75o longitude x 2.5o latitude which are
displayed in figure 2. Grid point rainfalls at the corner points are averaged
to calculate values for the region. This smoothing reduces local errors and
improves skill scores. The rainfall data used to make and assess the forecasts
were supplied to us by the Climate Research Unit at the University of East
Anglia, by Prof. Hastenrath at the University of Wisconsin and by INPE in
Brazil. GPCP (Global Precipitation
Climatology Project) data were used to
fill data gaps for recent years.
Observational data for at least 2 of the grid points at opposite corners
are required for a region to be considered for the forecast. The regions were chosen
where the correlation between
independent empirical forecasts (using SST information up to January) and
observed values exceeded 0.5 over the period 1948-1997.
Region number 8 centred at 39.375oW, 6.25oS
is the closest to the regions used previously for issued forecasts and is
referred to as the Standard NE Brazil region (SNEBR).
The rainfall for each region is categorised into 3
equiprobable categories called terciles
over the same period referred to as
Dry, Average and Wet. The terciles are evaluated over 1961-1990 for
compatability with RCOF products.
4. Forecasts and Forecast
Skill
Forecast probabilities from the combined GLOSEA/Statistical
prediction model are displayed in graphical format in figure 1. The wet
category is favoured most in most regions and with the exception of region 6, the
highest probabilities for the WET category are near the coast. The forecasts are consistent with GLOSEA only
forecasts from November which are
displayed on the Met Office website at http://www.metoffice.com/weather/seasonal/index.html.
The ROC skill of forecasts for the 3 terciles and 11 regions
is displayed in figure 3. ROC (Graham et. al. 2000) scores are now a widely used measure for probability forecasts and are a WMO
standard measure. The expected ROC score from random chance is 0.5. If the areas is greater than 0.5 there
is evidence of skill. Monte Carlo tests
have been used to locate the 5% significance levels for tercile probability ROC
scores. The 5 % significance level is about 0.6 and significant skills are
marked by * on fig 3. Skill is mainly
confined to the outer categories (DRY and WET).
5.
Overall Summary and ”BEST ESTIMATE” TERCILE category forecast
All regions except
region 2,7 and 10 but including SNEBR: The WET tercile
category is most probable in these regions hence our best estimate for these
regions is the WET tercile category
with moderate confidence.
Regions 2,7: The AVERAGE tercile
category is most probable in these regions. However the ROC skill of forecasts
for the AVERAGE category is less than chance for these regions, hence our best
estimate for these regions is the AVERAGE
tercile category but with low confidence.
Region 10: The DRY
tercile category is most probable in these regions but ROC skill is less than
chance for all 3 terciles Our best
estimate for this region is the DRY tercile
category but with low confidence.
REFERENCES
Afifi, A.A.
and Azen, S.P. 1979: Statistical Analysis – a computer oriented Approach.
Academic Press, New York.
Colman, A.W.,
Davey M.K. 2003: Statistical Prediction of Global Sea-Surface Temperature
Anomalies. Int. J.Climatology, 23 1677-1697.
Folland,
C.K., Colman, A.W., Rowell, D.P &
Davey M.K. 2001: Predictability of northeast Brazil rainfall and real-time
forecast skill, 1987-98. J.Climate, 14 1937-1958.
Graham,R.J.,
Evans, A.D.L, Mylne,K.R., Harrison, M.S.J. and Robertson, K.B. 2000: Assessment of seasonal predictability using
atmospheric general circulation models. Q.J.Royal.Met.Soc.,126 2211-2240.
Potts,
J.M., Folland, C.K., Jolliffe, I. and Sexton, D. 1996: Revised “LEPS” scores
for assessing climate model simulations and long-range forecasts. J. Climate, 9, 34-53.
Ward, M.N. and Folland, C.K. 1991: Prediction of seasonal
rainfall in the North Nordeste of Brazil using eigenvectors of sea surface
temperature. Int. J. Climatology.,11,711-743.
FIGURES
FIGURE 1: Combined GLOSEA/statistical discriminant Probability forecasts of terciles for February-May
2004.
FIGURE 2: Forecast regions
FIGURE 3: ROC Skill of combined forecasts (1959-2001)
FIGURE 1
FIGURE 3
