Atmospheric and Climate Sciences, 2012, 2, 532-537
http://dx.doi.org/10.4236/acs.2012.24048 Published Online October 2012 (http://www.SciRP.org/journal/acs)
Evaluation of Eta Weather Forecast Model over
Central Africa
Romeo Steve Tanessong*, Derbetini A. Vondou, P. Moudi Igri, F. Mkankam Kamga
Laboratory for Environmental Modelling and Atmospheric Physics, Department of Physics, University of Yaounde 1, Yaounde,
Cameroon
Received April 1, 2012; revised May 3, 2012; accepted May 14, 2012
ABSTRACT
The main goal of this work is to investigate the skills of Eta weather forecast model in forecasting precipitations, tem-
perature and sea level pressure. The model domain extends from 6˚W to 29˚E and 6˚S to 21˚N. The model is run with a
horizontal resolution of 48 km with 45 vertical levels and initial and boundary conditions were given by National Cen-
ters for Environmental Prediction (NCEP) 00UTC operational analysis. All the forecasts are for period of 48 hours.
They were compared to the Tropical Rainfall Measuring Mission (TRMM) derived data for precipitations and NCEP/
NCAR (National Center for Atmospheric Research) analysis for temperature and sea level pressure. The results show
that Eta model predicts fairly good 2 meters temperature and the sea level pressure. Spatial distributions of precipita-
tions are not well simulated by the model.
Keywords: Precipitations; Temperature; Sea Level Pressure; Eta Model
1. Introduction
Even since, many aspects of human’s lives were influ-
enced by the weather. Throughout the history, civiliza-
tions suffered from its direct impact on many sectors
such as transport agriculture [1], health, etc. Severe
weather events such as tornadoes, hurricanes, storms,
droughts, floods are recurring nowadays more frequently
than in the past, threatening people’s lives and unfortu-
nately, leading to the loss of thousands of them. Fur-
thermore, costs from the natural disasters caused by the
weather are enormous. The Numerical Weather Predic-
tion (NWP) was developed as one tool to try to predict
the evolution of time and especially extreme events.
Nowadays, several NWP models were developed and
continue to grow due to the increase of computing power
available and improved knowledge of the workings of
the atmosphere [2]. NWP models are in the form of
global models (GCMs) or regional models (RMs). GCMs
that represent in detail the atmospheric dynamics and
physical processes that take place, have shown great ef-
fectiveness in representing large-scale phenomena.
However, GCMs are limited when it involves taking into
account the microscale and mesoscale features [2]. The
RMs are used to try to improve some aspects of GCMs.
They generally run using a sufficiently fine mesh screen
and can better represent the conditions of the boundary
layer such as topography, vegetation, soils and coasts.
The RMs may also better represent mesoscale phenom-
ena and micro-scales. It should be noted that these im-
provements are limited by the quality of lateral boundary
conditions. The NWP generally require a very large
computational cost [3].
There are several processes in the atmosphere that can
not be directly described by the equations that describe
the atmospheric circulation such as vertical convection,
cloud physics, atmospheric radiative effects, turbulence,
condensation, evaporation, etc. The method used to in-
clude the effects of physical processes in the model is
called parameterization. The convection schemes com-
monly used are: Kain-Fritsch scheme [4], Betts-Miller-
Janjic scheme [5].
In this work, Eta model is used because of its better
representation of the topography [6]. The Eta Model was
also chosen because there are few investigations using
the Eta Model over Central Africa and because the verti-
cal coordinate system used in this model is recommended
for use over Central Africa due to the presence of steep
topography.
Eta model has been used in studies of seasonal fore-
casts over South America [7] where the forecasts were
improved with respect to the driver global model. Fen-
nessy and Shukla [8] showed that the Eta model provides
forecasts of average (daily and seasonal) rainfall close
enough observations in Northern and Northeastern Brazil.
They showed however that precipitations are not well
simulated by the model.
*Corresponding author.
C
opyright © 2012 SciRes. ACS

R. S. TANESSONG ET AL. 533
The purpose of this study is to evaluate the skills of
Eta NWP model over Central Africa in predicting pre-
cipitation, 2 meters temperature and the sea level pres-
sure.
Simulations of precipitation are compared to the
Tropical Rainfall Measuring Mission (TRMM) derived
data for precipitations and the two other parameters are
compared to the NCEP/NCAR analysis data.
In the next section, model, data and Methodology will
be described. Section 3 will present the results and dis-
cussions. Finally, Section 4 is devoted to conclusion.
2. Model, Data and Methodology
The Eta Model [9] was developed at Belgrade University
and was operationally implemented by the National
Centers for Environmental Prediction [6]. The Eta is a
hydrostatic Model, which uses the η vertical coordinate
defined by Mesinger [10] as



0
rf t
t
s
trf t
pzsp
pp
ppp p




(1)
here p is the pressure; the subscripts t and s stand for the
top and the surface values; z is the geometric height, and
prf (z) is a reference pressure as a function of z. The η
coordinate improves the calculation of horizontal deriva-
tives near steep topographic areas. Because the surfaces
of the coordinate are approximately horizontal, this fea-
ture is particularly useful for regions with steep orogra-
phy such as Central Africa.
The prognostic variables are temperature, specific hu-
midity, horizontal wind, surface pressure, the turbulent
kinetic energy and cloud liquid water/ice. These vari-
ables are distributed on the Arakawa type E-grid.
The treatment of turbulence is based on the Mellor-
Yamada level 2.5 procedure [11]; the radiation package
was developed by the Geophysical Fluid Dynamics
Laboratory, with long wave and solar radiation param-
eterized according to Fels and Schawarztkopf [12] and
Lacis and Hansen [13], respectively.
The study area ranges from 6˚W to 29˚E longitude and
6˚S to 21˚N latitude. The model is initialized at 00 UTC
by the NCEP Global Forecasting System (GFS), which
also provides the boundary conditions of the Eta model
every 6 hours and is performed for the period ranging for
october to December 2006. Simulations are run for 48
hours at a spatial resolution of 48 km with 45 vertical
levels. The model is established with the top of the model
at 25 hPa. The convection scheme of Kain-Fritsh [4] and
Betts-Miller-Janjic scheme [5] are used. Briefly, the
Betts-Miller-Janjic scheme (BMJ) is an adjustment-type
scheme that forces soundings at each point toward a ref-
erence profile of temperature and specific humidity. The
scheme's structure favors activation in cases with sub-
stantial amounts of moisture in low and midlevels and
positive convective available potential energy (CAPE).
The Kain-Fritsh (KF) scheme removes CAPE (calculated
using the traditional, undiluted parcel-ascent method)
through vertical reorganization of mass. The scheme
consists of a convective trigger function (based on grid-
resolved vertical velocity), a mass flux formulation, and
closure assumptions. The BMJ and KF schemes are
known to differ in some features of their predicted rain-
fall, and in the response to atmospheric background con-
ditions. Gallus and Segal [14], for instance, found large
differences in the BMJ and KF bias scores. In addition,
the above convective schemes have been used widely
[13], thus furthermore providing merit to their adoption
in the present study.
For the purpose of verification, we used the Tropical
Rainfall Measuring Mission (TRMM) data as ground
thruth. In Central Africa, there is very few weather sta-
tions. TRMM is a mission Joint American National
Aeronautics and Space Administration (NASA) and the
Japanese National Space Development Agency (NASDA)
to measure precipitation in the tropics and subtropics. In
this work, version 6 of the 3B42 combined is used. Ver-
sion 6 of the 3B42 product provides three hourly estima-
tions of rainfall on a grid of 0.25˚ × 0.25˚. Nicholson et al.
[15] evaluated TRMM products over West Africa over
the period May to September. They found that TRMM-
merged rainfall products showed excellent agreement
with gauge data over West Africa on monthly-to-seaso-
nal timescales and 0.25˚ × 0.25˚ latitude/longitude spatial
scales. We also used NCEP/NCAR data. The NCEP/
NCAR data used in this work are values of every six
hours of the analysis data for October, November and
December 2006. The horizontal grid measures 2.5˚ side.
To validate the model, we proceeded as follows:
For precipitation: We calculated the 6, 12 and 24 hours
accumulated precipitation for both convective schemes
that we combined with the bias and correlation coeffi-
cient. The bias is the average gap between the fields, it is
defined as:

1
1
Bias e
n
ii
i
e
x
yxy
n


 (2)
where ne is the number of grid points, xi is the value of
the variable to the ith grid point of Eta, yi is the value of
the variable to the ith grid point of observation.
The correlation coefficient between two fields is de-
fined as:


1
2
11
e
ee
n
ii
i
nn
ii
ii
xxyy
r2
x
xyy







(3)
x
is the time average of Eta field;
y
is the time average of observation field.

R. S. TANESSONG ET AL.
534
For temperature and sea level pressure, we plotted
graphs every 3 hours and we compared with the analysis
data. For a good comparison, we took the departures be-
tween these projections and observations. As for precipi-
tation, we calculated the bias and correlation coefficient.
These quantities are defined as above.
3. Results and Discussions
3.1. 2 Meters Temperature, Reduced Mean Sea
Pressure
The Bias and correlation coefficient of some days (the
other days present the same tendency) are presented in
Table 1. The Bias and correlation coefficient were evalu-
ated based on the daily error, corresponding to the first
24 hours of each 48-hours integration. It was observed
that the Eta model presented smaller bias and greater
correlation coefficient during the study period. The hig-
her resolution and a better representation of topography
used in Eta model seem to contribute substantially to an
improvement of the 2 meters temperature.
Figures 1 and 2 display the temperature obtained from
Eta model simulation, analyses data and difference be-
tween the two fields at 00 h and 06 h. Figures obtained
from the analysis data are consistent with the decrease of
temperature in a north-south direction.
The largest differences are observed in western Cam-
eroon, part of the Adamawa Plateau and northern Sudan.
These differences were less than –6˚C. The differences
between the Eta model simulations and analysis data
could be due to errors in model parameterization. Indeed,
the choice and adjustment of parameterization schemes
has a significant impact on the quality of the forecast
[16]. In the case of Western Cameroon, characterized by
complex terrain [17], errors due to topography are also
noted. Indeed, we used as input data GFS (Global Fore-
casting System). The GFS is a global model using the
Sigma coordinate as vertical coordinate. The main draw
backs of this coordinate lies on the calculation of the
pressure gradient force in the mountainous areas thus
affecting the quality of prediction in these regions.
Table 1. Values of bias and correlation coefficient (r).
Day Variable Temperature (˚C) Pressure (hPa)
Bias –1.67 –1.18
23 October
2006 r 0.93 0.92
Bias –1.12 –1.78
24 October
2006 r 0.94 0.94
Bias –1.48 –1.25
30 October
2006 r 0.92 0.95
Bias –1.37 –1.40
31 October
2006 r 0.91 0.96
Figure 1. Temperature and sea level pressure (contours):
Eta (top), analysis data (middle) and difference (bottom) at
00 h 23-10-2006.

R. S. TANESSONG ET AL. 535
Figure 2. Temperature and sea level pressure (contour
lines): Eta (top), Analysis data (middle) and difference (bot-
tom) at 06 h 23-10-2006.
The unavoidable source of error in NWP models
comes from initial data. Even a perfect model (i.e. perfect
parameterization, mesh sufficiently fine, no errors due to
numerical methods adopted) could not produce a perfect
forecast, as errors in initial conditions will then grew
louder in the forecast and it will diverge from reality. The
determination of the atmospheric state at the beginning
of the forecast is itself a major scientific challenge.
3.2. Precipitations
Figure 3 presents 6 hours accumulated rainfall. KF, BMJ
schemes and TRMM show zero precipitation fields in the
north of 12˚N. Precipitations simulated by the KF and
BMJ schemes are oriented along east-west direction. The
maximum simulated by the KF scheme is 20 mm and is
located toward the center of Ghana; the BMJ scheme
maximum is 40 mm and is located in the same region.
TRMM maximum is 80 mm around 6˚S - 18˚E. Neither
of the two schemes was detected this maximum. KF and
BMJ schemes have very dense precipitation fields com-
pared to TRMM. In general, there’s an important differ-
ence between KF, BMJ scheme and TRMM. The 24-
hour accumulated precipitation simulated by the Eta
model (figures not shown) exhibit maxima of the same
order of magnitude as the observations. It should be
noted that 6-hour accumulated precipitation, is more dif-
ficult to forecast than the 24-hour accumulated precipita-
tion.
Precipitation results from several processes, which
makes modeling difficult. For Eta like most numerical
weather models, rainfall is separated into two groups:
convective precipitation (mainly related to an upward
vertical motion of air mass and its condensation by adia-
batic expansion) and stratiform precipitation (related to
horizontal movement of air particles and their saturation
when moisture convergence is sufficient). Exceptionally
heavy events may be associated with organized meso-
scale convective systems (MCSs) embedded in large
scale synoptic systems, but the majority of rainfall epi-
sodes are linked to isolated convective cells not excess-
ing a few hundred meters in extension.
After this analysis, we can note first of all a sizeable
gap on the accumulated rainfall; neither convective sche-
mes used is well suited to simulate rainfall. The spatial
distribution of precipitation is errenous.
4. Conclusions
The 2-meter temperature and the sea level pressure have
been compared to NCEP/NCAR analysis data. The 6, 12
and 24 hours accumulated rainfall were also compared to
the Tropical Rainfall Measuring Mission (TRMM). The
results show that temperature and the sea level pressure
are quite well simulated by the Eta model. The strong

R. S. TANESSONG ET AL.
536
Figure 3. 6 hours accumulated precipitation: 00 h - 06 h of
23-10-2006.
correlation coefficients (Table 1) justify this assertion.
The precipitations are not well simulated by the model.
There is a poor spatial distribution of precipitation
around the equator. The discordances observed may be
due to errors of parameterization in the model. Indeed,
the choice and adjustment of parameterization schemes
has a significant impact on the quality of prediction [16].
These errors may also come from initial data. Even a
perfect model (i.e. perfect parameterization, mesh suffi-
ciently fine, no errors due to numerical methods adopted)
could not produce a perfect forecast for errors in initial
conditions will then grew louder in the forecast and that
it diverge from reality.
In our future work, we intend to change the model
parameterization (e.g. frequency of advection). If the
forecasts are improved, we will do simulations with
higher resolution. This will allow us to better appreciate
the performance of the Eta NWP model to simulate rain-
fall locally. We also aim to perform those simulations
with Weather Research and Forecasting Model (WRF)
using probabilistic forecasts and data assimilation.
5. Acknowledgements
We thank the Maroochy Shire project working group, led
by Damian McGarry, who provided the wide range of
data and analysis for the analysis. We also thank Dr.
Heinz Schandl of CSIRO for suggestions to improve the
paper.
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