Influence of the WRF Model and Atmospheric Reanalysis on the Offshore Wind Resource Potential and Cost Estimation: A Case Study for Rio De Janeiro State

2021 ◽  
Author(s):  
Luiz Filipe de Assis Tavares ◽  
Milad Shadman ◽  
Luiz Paulo de Freitas Assad ◽  
Segen F. Estefen
Keyword(s):  
Cost Estimation ◽  
Rio De Janeiro ◽  
Wrf Model ◽  
Offshore Wind ◽  
Resource Potential ◽  
Wind Resource ◽  
Janeiro State

The impact of SST on the wind and air temperature simulations: a case study for the coastal region of the Rio de Janeiro state

2018 ◽  
Vol 131 (4) ◽  
pp. 1083-1097 ◽  
Author(s):  
Ian Cunha D`Amato Viana Dragaud ◽  
Mauricio Soares da Silva ◽  
Luiz Paulo de Freitas Assad ◽  
Márcio Cataldi ◽  
Luiz Landau ◽  
...  
Keyword(s):  
Air Temperature ◽  
Rio De Janeiro ◽  
Coastal Region ◽  
The Impact ◽  
Janeiro State

scholarly journals Flash Flood and Extreme Rainfall Forecast through One-Way Coupling of WRF-SMAP Models: Natural Hazards in Rio de Janeiro State

Atmosphere ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 834
Author(s):  
Priscila da Cunha Luz Barcellos ◽  
Marcio Cataldi
Keyword(s):  
Natural Hazards ◽  
Land Surface ◽  
Rio De Janeiro ◽  
Flash Flood ◽  
Weather Prediction ◽  
Wrf Model ◽  
Temporal Distribution ◽  
Atmospheric Conditions ◽  
Streamflow Forecast ◽  
Janeiro State

Flash floods and extreme rains are destructive phenomena and difficult to forecast. In 2011, the mountainous region of Rio de Janeiro state suffered one of the largest natural hazards in Brazil, affecting more than 300,000 people, leaving more than 900 dead. This article simulates this natural hazard through Quantitative Precipitation Forecasting (QPF) and streamflow forecast ensemble, using 18 combinations of parameterizations between cumulus, microphysics, surface layer, planetary boundary layer, land surface and lateral contour conditions of the Weather Research and Forecasting (WRF) Model, coupling to the Soil Moisture Accounting Procedure (SMAP) hydrological model, seeking to find the best set of parametrizations for the forecasting of extreme events in the region. The results showed rainfall and streamflow forecast were underestimated by the models, reaching an error of 57.4% to QPF and 24.6% error to streamflow, and part of these errors are related to the lack of skill of the atmospheric model in predicting the intensity and the spatial-temporal distribution of rainfall. These results bring to light the limitations of numerical weather prediction, possibly due to the lack of initiatives involving the adaptation of empirical constants, intrinsic in the parametrization models, to the specific atmospheric conditions of each region of the country.

scholarly journals Verification of the Reliability of Offshore Wind Resource Prediction Using an Atmosphere–Ocean Coupled Model

Energies ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 254
Author(s):  
Minhyeop Kang ◽  
Kyungnam Ko ◽  
Minyeong Kim
Keyword(s):  
Wind Speed ◽  
Energy Production ◽  
Prediction Models ◽  
Wrf Model ◽  
Coupled Model ◽  
Sea Surface ◽  
Offshore Wind ◽  
Wind Effect ◽  
Observation Data ◽  
Wind Resource

An atmosphere–ocean coupled model is proposed as an optimal numerical prediction method for the offshore wind resource. Meteorological prediction models are mainly used for wind speed prediction, with active studies using atmospheric models. Seawater mixing occurring at sea due to solar radiation and wind intensity can significantly change the sea surface temperature (SST), an important variable for predicting wind resources and energy production, considering its wind effect, within a short time. This study used the weather research forecasting and ocean mixed layer (WRF-OML) model, an atmosphere–ocean coupled model, to reflect time-dependent SST and sea surface fluxes. Results are compared with those of the WRF model, another atmospheric model, and verified through comparison with observation data of a meteorological mast (met-mast) at sea. At a height of 94 m, the wind speed predicted had a bias and root mean square error of 1.09 m/s and 2.88 m/s for the WRF model, and −0.07 m/s and 2.45 m/s for the WRF-OML model, respectively. Thus, the WRF-OML model has a higher reliability. In comparing to the met-mast observation data, the annual energy production (AEP) estimation based on the predicted wind speed showed an overestimation of 15.3% and underestimation of 5.9% from the WRF and WRF-OML models, respectively.

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