EVALUATION OF ETA MODEL WEATHER FORECASTS OVER
NORTH/NORTHEAST BRAZIL FROM FEBRUARY UNTIL MAY 2003
Clemente Augusto Souza Tanajura1
Edilene Moraes Branco1,2
José Walter Cárdenas1
Ricardo do Amaral Branco1,2
Abstract
This work focuses on the validation of the Eta model weather forecasts over the north and
northeast regions of Brazil from February until May 2003. The model runs operationally at the
Laboratório Nacional de Computação Científica (LNCC/MCT). It is nested in the forecasts of the
National Centers for Environmental Prediction (NCEP/NOAA) global circulation model (GCM)
T126L28. The Eta model is configured with 40 km of horizontal resolution and 38 vertical levels.
The domain covers a tropical sector from the East Pacific until western Africa ranging from 20°S
to 11°N. The forecasts were compared with observational data, NCEP analyses and GCM
forecasts. The results indicate that, in general, the Eta model was not able to provide better skills
than the GCM. Also, a strong precipitation spin-up was obverved during the first 6h-forecasts over
the Amazon and regions of the tropical Atlantic.
Resumo
Este trabalho é sobre a validação das previsões de tempo do modelo Eta sobre as regiões norte e
nordeste do Brasil de fevereiro a maio 2003. O modelo roda operacionalmente no Laboratório
Nacional de Computação Científica (LNCC/MCT) aninhado nas previsões do modelo de
circulação geral do National Centers for Environmental Prediction (NCEP/NOAA) com resolução
T126L28. O modelo Eta foi configurado com 40 km de resolução horizontal e 38 níveis verticais.
Seu domínio cobre um setor tropical do Pacífico leste até a África ocidental de 20°S a 11°N. As
previsões foram comparadas com dados de observação, análises e previsões do MCG do NCEP. O
presente estudo indica que, em geral, o modelo de Eta não produziu resultados melhores que o
1
Laboratório Nacional de Computação Científica (LNCC/MCT)
Av. Getúlio Vargas 333, Petrópolis –RJ, CEP:25.651-075. Tel (24) 22336079. Fax (24) 22315595.
E-mail: cast@lncc.br
2
Dept. de Meteorologia, Universidade Federal do Rio de Janeiro (UFRJ)
Av. Brigadeiro Trompowisky, s/n, Cidade Universitária, Ilha do Fundão, Rio de Janeiro – RJ, CEP:21.949-900
MCG. Também foi observada uma forte precipitação associada ao spin-up durante as primeiras 6h
de previsão do modelo sobre a Amazônia e regiões do Atlântico tropical.
Palavras-chave: modelagem regional da atmosfera, modelo Eta; validação; previsibilidade de
tempo; norte e nordeste do Brasil.
INTRODUCTION
The general circulation models (GCMs) and the regional models (RMs) of the atmosphere
are today the basis of the weather forecast and climate around the world. The RMs are used in an
attempt to improve the GCM solution. They are normally run with higher resolution than the
GCM, and they can better represent the surface boundary conditions, such as topography, coastal
lines, soil and vegetation cover. Also they may better represent atmospheric phenomena in the
meso- and micro-scales, and, therefore, improve predictability. A RM that has been used for
operational weather forecasts and climate studies over South America and other continents is the
Eta model (Fennessy and Shukla 2000; Chou et al. 2002; Tanajura et al. 2003).
The main goal of the present work is to study the quality of the weather forecasts produced
by the Eta model (Mesinger et al. 1988) over the North and the Northeast regions of Brazil during
the Northeast rainy season. The period chosen was from February 1, to May 31, 2003. The
forecasts were produced operationally at the Brazilian National Laboratory for Scientific
Computation (LNCC/MCT), in collaboration with the Federal University of Alagoas (UFAL). The
forescasts were compared with the operational National Centers for Environmental Prediction
(NCEP/NOAA) and in situ observational data.
METHODOLOGY
The Eta model implemented in LNCC produces daily 72-hour prognostics in 6-hour
intervals. In the present study, the domain of the RM covered a tropical region from the East
Pacific until the African west coast between 18°S and 12°N. The model horizontal resolution was
40 km and the number of vertical layers was 38. The investigated period was from February 1 to
May 31, 2003. The forecasts of the GCM and the RM are evaluated independently for the periods
of 0-24h, 24-48h, and 48-72h. They were compared with observed in situ data of surface winds,
temperature, reduced mean sea level pressure, from the ground station data maintained by the
Brazilian National Institute of Meteorology (INMET). The forecast wer also compared with the
operational NCEP GCM T126L28 analyses and with estimates of precipitation by satellite data.
Root mean square errors (RMSE) and the indices Thread Score (TS) and BIAS were
calculated for the objective validation. These scores are given by:
TS 
H
(F  O  H )
BIAS 
F
O
where
F is the number of forecast points above a threshold;
O is the number of observed points above a threshold;
H is the number of correctly forecasted points above a threshold.
If TS is equal to 1, it means a perfect forecast was produced.
Three different regions were investigated. One was the Northeast region of Brazil (35-48oW,
1-18oS). The other was the Amazon (48-75W, 18S-6N). The last one was the whole model domain.
For the calculation of the errors in relation to the analysis, an interpolation from the analysis grid
to the Eta model grid was done.
RESULTS AND DISCUSSION
Fig.1a shows the four-month averaged estimated precipitation by the GOES Precipitation
Index (GPI). Figs.1b and 1c show the averaged precipitation forecasted by the Eta model the
NCEP GCM during the first 24 hours of each 72 h forecast. It can be observed that both the RM
and the GCM captured the major observed precipitation pattern, in the Inter-Tropical Convergence
Zone (ITCZ), over Africa and South America. However, the magnitude of the Eta model forecast
was much higher than observations and the GCM.
For the forecast interval of 24-48h (not shown) there is a substantial reduction of the Eta
model precipitation, mainly in the regions where the precipitation was maximum. The GCM field
remained approximately constant and closer to the GPI data.
Fig.1d and Fig. 1e show similar figures to 1b and 1c, but for the forecast interval of 48-72h.
A reduction of the RM precipitation was observed in comparison to the 24-48h period. The RM
pattern and magnitude became much closer to the GPI data in relation to the 0-24h period.
The precipitation reduction of the Eta clearly indicates the presence of a strong spin-up due
to differences in the physics of the Eta model in relation to the GCM initial condition. The latter
was prepared by a data assimilation scheme that uses the GCM. No precipitation spin-up was
observed for the GCM.
The spin-up can be also characterized by a precipitation time series constructed by the
average of all the precipitation forecasts in the interval of 0-6h, 6-12h...,and 66-72h. The areal
mean precipitation calculated in this time series over the whole model domain is shown in Fig. 2.
The strong precipitation decrease during the first 12 h suggests that the precipitation forecasts in
the first few forecast hours should be discarded. However, it should be emphasized that the spin-up
was not observed with the operational forecasts performed at LNCC over two regions in the
Southeast Brazil. It seems the spin-up occurs only in the equatorial region.
Table 1 presents the RMSE for the RM and GCM with respect to NCEP analysis for the
whole RM domain. The errors were calculated for the three time windows of 0-24h, 24-48h, 4872h. As expected, the errors increase along with the forecast time.from 0 to 72 h for both the RM
and the GCM.
Table 1. RMSE for the RM and GCM with respect to NCEP analysis for the whole RM domain.
The errors produced by RM are larger than the errors produced by the GCM for all the
investigated variables. It can be also noted that the error growth rate of the RM is much smaller
than the GCM. The Eta model growth defined by the error in the 48-72h period divided by the
error in the 0-24h period is in average 1.2 for the RM, and 2.1 for the GCM.
The behaviour of the error or the Northeast region (35-48oW, 1-18oS) was similar to the error
in Table 1 taken over the whole domain.
Table 2 is similar to Table 1, but for the Amazon region (48-75oW, 18oS-6oN). The Eta
model forecast errors on the 2m temperature, reduced mean sea level pressure, temperature in 200
hPa decreases from the 0-24h to the 24-48h period. After this, the RM error remained
approximately constant. The error decrease from the first to the second 24-h period is probably
associated with the spin-up.
Table 2. The RMSE for the RM and GCM with respect to NCEP analysis for the Amazon region.
The RMSE with respect to the INMET network database was also calculated for the
Northeast and Amazon regions. These results are shown in Tables 3 and 4. Again, the Eta model,
in general, produced larger errors than the GCM. However, the errors of the mean sea level
pressure of the RM were smaller than the errors of the GCM in the Amazon. The errors in the
Amazon were smaller than the errors in the Northeast for both the RM and the GCM, except for
the GCM reduced mean sea level pressure. The large error growth of the GCM in time shown in
Tables 1 and 2 was not observed in Tables 3 and 4, in which the GCM was evaluated with respect
to observational data.
The TS and the BIAS calculated for the 12-36 h forecast window over the Amazon region is
shown in Fig. 3. The Eta model produces a slightly better TS than the GCM. For heavy
precipitation the models coincide. However, the BIAS score of the Eta model is much higher than
the GCM, which oscillates around 1. This is also a signature of the spin-up.
For the 36-60h forecast window in Fig.4, the Eta model and GCM TS coincide for all
precipitation thresholds. The BIAS produced by the models are almost the same for light and
moderate rain rates. However, a substantial improvement in the Eta model was verified in relation
to the previous period. The Eta model also produced a better BIAS than the GCM for the
thresholds larger than 25.4 mm d-1.
Table 3. The RMSE with respect to the INMET network database for the Northeast region.
Table 4. The RMSE with respect to the INMET network database for the Amazon region.
CONCLUSIONS
This work indicated that the Eta model was not able to clearly improve the operational
weather forecasts produced by the NCEP GCM T126L28 in the 72-h window. The Eta model
presented a strong spin-up during the first few hours of forecast in the tropics. The spin-up affected
other variables and influenced the error with respect to the analyses and the observational data.
However, there was a substantial improvement on the precipitation distribution after the spin-up
period.
In general, the errors of both the Eta model and the GCM with respect to the INMET data
were smaller in the Amazon than in the northeast region. In the Amazon, the Eta model mean sea
level pressure produced smaller errors than the GCM. Also, the Eta model presented a slightly
better TS than the GCM in the 12-36 h window, as well as a smaller bias than the GCM for heavy
rains away from the spin-up period.
The present work suggests that the Eta model has an enormous potential to provide better
precipitation forecasts at all time windows in the 72-h period if the spin-up problem is overcome.
The spin-up can be substantially reduced if, for instance, the Eta model is initialized with an
analysis prepared in conjunction with the Eta model. This is demands a regional operational data
assimilation system.
Fig.1a. February-May 2003 averaged estimated precipitation (mm d-1) by the GOES Precipitation
index.
Fig.1b. February-May 2003 averaged Eta model precipitation (mm d-1) forecasted for the interval
of 0-24h.
Fig.1c. February-May 2003 averaged NCEP GCM precipitation (mm d-1) forecasted for the
interval of 0-24h.
Fig.1d. February-May 2003 averaged Eta model precipitation (mm d-1) forecasted for the interval
of 48-72h.
Fig.1e. February-May 2003 averaged NCEP GCM precipitation (mm d-1) forecasted for the
interval 48-72h.
Fig.2. Areal mean precipitation (mm d-1) over the whole model domain for each forecast interval.
Fig. 3. TS and the BIAS calculated for the 12-36 h forecast window over the Amazon region. The
precipitation thresholds are in mm d-1. The numbers in blue and gray are the number of ground
stations used in the validation.
Fig. 4. TS and the BIAS calculated for the 36-60 h forecast window over the Amazon region. The
precipitation thresholds are in mm d-1. The numbers in blue and gray are the number of ground
stations used in the validation.
ACKNOWLEDGMENTS
The Authors would like thank Drs. Thomas Black and Matthew Pyle from NCEP/NOAA for
providing the Eta model and Mr Brian Doty from COLA/IGES for the software GrADS. This work
was partially supported by PRONEX-LNCC Modelagem Computacional em Engenharia e
Ciências Aplicadas, FAPERJ E-26/171.199/2003 and the Brazilian Conselho Nacional de
Desenvolvimento Científico e Tecnológico (CNPq) grants PCI 382461/02-9 and PIBIC
100866/2003-4.
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