1. 1Weidinger, T., 2Costa, A. A., 3Lajos, T., 4Kiss, Á.,
Estimation of wind energy potential in the Equatorial Costal Area of Brazil based on measurements and mesoscale numerical model results
1Weidinger, T., 2Costa, A. A., 3Lajos, T., 4Kiss, Á.,
1Gyöngyösi, A. Z., 4Papp, B.
1Department of Meteorology,
4Department of Atomic Physics,
Eötvös Loránd University, Budapest
3Department of Fluid Mechanics,
Budapest University of
Technology and Economics
2Universidade Estadual do Ceará,
Campus do Itapery - Fortaleza

2. Data sets and model approaches
Contents
Problem formulation, motivation
Climate of the Coastal region of Ceará
Data sets and model approaches
Interannual variation of wind speed
Methodology of group at CEARÁ (based on RAMS)
Methodology at the Hungarian group (based on ETA)
Conclusion, remarks

3. Problem formulation, motivation
The wind power generation costs in Brazil (39-84 US$/MWh)
is already comparable to thermoelectric, nuclear and even
new hydroelectric plants.
Most focus has been put on the state of Ceará
precise and reliable wind data collection
resource assessment programs
Collaboration between the Brazil and Hungarian group
Development of methodology for wind energy potential based on mesoscale model calculation and statistical analysis:
exchange experiences,
cross checking the results.

4. Climate diagram of Fortaleza
State Ceará
Climate diagram of Fortaleza
Temperature [oC]
Precipitation [mm]
Month
Wind climate of Brazil

5. Mesoscale weather forecast models
Data sets
Synoptic measurements from 2001,
Tower measurements
Mesoscale weather forecast models
RAMS Regional Atmospheric Modeling System (Developed at Colorado State University),
ETA-model (NCEP, former NMC, Washington).

6. Variability of the wind speed
Synoptic weather station Fortalesa Airport (u [m/s])
Year
2001
2002
2003
2004
2005
2006
Sum
Average
3.9
4.1
4.4
4.8
4.7
Dispersion
1.9
2.1
2.0
Wind tower data for January 2005 and 2006
Paracuru
2005
8.4
2.3
8.55
0.07
0.15
2006
10.2
1.55
10.4
1.6
0.04
Camocim
2005
8,0
3,4
8,25
3,3
0,15
0,18
2006
9,1
9,55
0,21
0,16

7. Wind power estimation – Methodology
University of Ceará
Initial data set (GCM output, measurements)
Climatological dataset, statistics
Mesoscale Model (RAMS)
Model Output Correction
Local wind field < 90m resolution (WasP)
Interpolation,
~ 1 km x 1 km grid cells
Post processing, model output
Local surface parameters (elevation, type of vegetation, surface roughness, albedo, etc.)
Microscale modelling
Micrositing
Fine topography and land cover data set

8. Nested models for the planned Batoque wind farm:
grid resolution: 20 km, 4 km, 0,8 km and Local model
Local model

9. Wind power estimation for Batoque, 2005
[kW]
900
800
700
600
Generator
500
400
300
200
100
Wind power [kW]
Month

10. Wind power estimation for Batoque, 2005 January40
Sector: All
A:8.7 m/s
K:4.05
U:7.89 m/s
P:369 W/m2
f [%] 20
Wind rose
u [m/s] 5 10 15 20

11. ETA model calculation
Mesoscale non-hydrostatic meteorological model calculation for the NE costal region of Ceará, Brasil.
Fine grid, horizontal resolution 2 km x 2 km
Centre of model domain: S 4o, W 38o
Initial and boundary conditions from NCEP Global model for 2006.
Model integration performed automatically.

12. ETA Model domain

13. Wind profile calculation
975 hPa
Linear profil
1000 hPa
Power law profil

14. Mean wind speed at 10 m 2006. Jan. 1-10
Cross section
3.2S
3.4S
Batoque
3.6S
3.8S
4.0S

15. Mean wind speed at 1000 hPa 2006. Jan. 1-10

16. Mean wind speed at 975 hPa 2006. Jan. 1-10

17. Mean wind speed at 950 hPa 2006. Jan. 1-10

18. Cross section of the wind speed, a case study
u [m/s]
H [m]
u(975 hPa)
Uncertainty: ~0.5 m/s
u(1000 hPa)
u(10 m)
Surface

19. Measured and modeled wind, 2006. January 1 - 10,
Tower
Model
u(60)
FL1500
[kW]
9,4
954
8.1
611
u(975hPa)
9.2
905

20. BATOQE
Modeled wind, January
Wind direction
2006
Wind
[m/s]
FL1500
[kW]
60 m
8.2
623
80 m
8.5
707
hPa
9.6
997

21. Conclusion, remarks
Wind power estimations were done using mesoscale model approaches for the coastal region of Ceará, Brazil.
Interannual variation of the trade wind is not negligible for the long term wind power estimation.
Uncertainty due to choice of the grid cell is higher than 0.5 m/s for mean wind speed calculation.
Mean and daily variations of the near surface wind speed are underestimated on the coastal region using the ETA model.