scholarly journals Impact of Selected Options in the Weather Research and Forecasting Model on Surface Wind Hindcasts in Coastal Ghana

Energies ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3670
Author(s):  
Denis E.K. Dzebre ◽  
Muyiwa S. Adaramola
Keyword(s):  
Surface Layer ◽  
Surface Wind ◽  
Resource Assessment ◽  
Weather Research And Forecasting ◽  
Forecasting Model ◽  
Wind Speeds ◽  
Parameterization Schemes ◽  
Pbl Scheme ◽  
Level 3 ◽  
Weather Research

This paper examines the impacts of five planetary boundary layer (PBL) parameterization schemes paired with several compatible surface layer (SL) parameterization schemes in the Weather Research and Forecasting Model on wind hindcasts for resource assessment purposes in a part of Coastal Ghana. Model predictions of hourly wind speeds at 3 × 3 km2 and 9 × 9 km2 grid boxes were compared with measurements at 40 m, 50 m, and 60 m. It was found that the Mellor-Yamada Nakanishi and Niino Level 3 (MYNN3) PBL scheme generally predicted winds with a relatively better combination of error metrics, irrespective of the SL scheme it was paired with. When paired with the Eta surface layer scheme, it often produced some of the relatively fewest errors in estimated mean wind power density (WPD) and Weibull cumulative density. A change in the simulation grid size did not have a significant impact on the conclusions of the relative performance of the PBL-SL pairs that were tested. The results indicate that the MYNN3 PBL and Eta SL pair is probably best for wind speed and energy assessments for this part of coastal Ghana.

scholarly journals Evaluation of Weather Research and Forecasting Model (WRF) Simulations over Middle East

2018 ◽  
Vol 29 (2) ◽  
pp. 26
Author(s):  
Thaer Obaid Roomi
Keyword(s):  
Middle East ◽  
Initial Conditions ◽  
Wrf Model ◽  
Weather Research And Forecasting ◽  
Forecasting Model ◽  
Data Sets ◽  
Wind Speeds ◽  
Computing Skills ◽  
Predicted Values ◽  
Weather Research

The Weather Research and Forecasting model (WRF) is an atmospheric simulation system designed for both research and operational applications. This worldwide used model requires a sophisticated modeling experience and computing skills. In this study, WRF model was used to predict many atmospheric parameters based on the initial conditions extracted from NOMADS data sets. The study area is basically the region surrounded by the longitudes and latitudes: 15o-75o E and 10.5o-45o N which typically includes the Middle East region. The model was installed on Linux platform with a grid size of 10 km in the X and Y directions. A low pressure trough was tracked in its movement from west to east via the Middle East during the period from 1 to 7 January 2010 as a case study of the WRF model. MATLAB and NCAR Command Language (NCL) were used to display the model output. To evaluate the forecasted parameters and patterns, some comparisons were made between the predicted and actual weather charts. Wind speeds and directions in the prognostic and actual charts of 700 hPa were in agreement. However, the predicted values of geopotential heights in WRF are somewhat overestimate the actual ones. This may be attributed to the differences in the data sources and data analysis methods of the two data agencies, NOMADS and ECMWF.

scholarly journals Comparisons of WRF/Chem simulations in Mexico City with ground-based RAMA measurements during the MILAGRO-2006 period

2009 ◽  
Vol 9 (1) ◽  
pp. 1329-1376 ◽  
Author(s):  
Y. Zhang ◽  
M. K. Dubey ◽  
S. C. Olsen
Keyword(s):  
Relative Humidity ◽  
Land Surface ◽  
Surface Wind ◽  
Chemical Species ◽  
Correlation Coefficients ◽  
Monitoring Network ◽  
Weather Research And Forecasting ◽  
Wind Speeds ◽  
Surface Models ◽  
Pbl Scheme

Abstract. Comparison of the WRF/Chem (Weather Research and Forecasting – Chemistry) model simulations at 3-km resolution with measurements from the ground-based RAMA monitoring network during the MCMA-2006/MILAGRO field campaign is presented. The model resolves reasonably well the observed surface temperature, relative humidity and wind speed; however, large discrepancies are identified between the simulated and the observed surface wind direction for wind speeds below 2 m s−1. The simulated chemical species concentrations (CO, O3, NO, NO2 and NOy) compare favorably with the observations with the notable exception of SO2. Simulated O3 concentrations agree especially well with the observations. The model performs much better during daytime than nighttime for both chemical species and meteorological variables, although the model tends to underestimate daytime temperature and overestimate nighttime relative humidity. It is noted that the simulated nocturnal planetary boundary layer (PBL) height using the Yonsei University PBL scheme is unrealistically low. However, no combination of the available PBL schemes and land surface models (LSMs) is distinctly better than the others in reproducing the observations. The simulated meteorological fields under the O3-South, O3-North and EI Norte weather episodes exhibit similar correlation coefficients and biases for the same variable. However, the model performs best for the O3-South episode and performs poorest for the El Norte events in resolving the observed chemical species.

scholarly journals Implementation of a roughness sublayer parameterization in the Weather Research and Forecasting model (WRF version 3.7.1) and its evaluation for regional climate simulations

2020 ◽  
Vol 13 (2) ◽  
pp. 521-536
Author(s):  
Junhong Lee ◽  
Jinkyu Hong ◽  
Yign Noh ◽  
Pedro A. Jiménez
Keyword(s):  
Air Temperature ◽  
Surface Wind ◽  
Winter Season ◽  
Atmospheric Modeling ◽  
Roughness Sublayer ◽  
Rainfall Simulation ◽  
Weather Research And Forecasting ◽  
Forecasting Model ◽  
Climate Simulations ◽  
Weather Research

Abstract. The roughness sublayer (RSL) is one compartment of the surface layer (SL) where turbulence deviates from Monin–Obukhov similarity theory. As the computing power increases, model grid sizes approach the gray zone of turbulence in the energy-containing range and the lowest model layer is located within the RSL. From this perspective, the RSL has an important implication in atmospheric modeling research. However, it has not been explicitly simulated in atmospheric mesoscale models. This study incorporates the RSL model proposed by Harman and Finnigan (2007, 2008) into the Jiménez et al. (2012) SL scheme. A high-resolution simulation performed with the Weather Research and Forecasting model (WRF) illustrates the impacts of the RSL parameterization on the wind, air temperature, and rainfall simulation in the atmospheric boundary layer. As the roughness parameters vary with the atmospheric stability and vegetative phenology in the RSL model, our RSL implementation reproduces the observed surface wind, particularly over tall canopies in the winter season by reducing the root mean square error (RMSE) from 3.1 to 1.8 m s−1. Moreover, the improvement is relevant to air temperature (from 2.74 to 2.67 K of RMSE) and precipitation (from 140 to 135 mm per month of RMSE). Our findings suggest that the RSL must be properly considered both for better weather and climate simulations and for the application of wind energy and atmospheric dispersion.

scholarly journals Implementation of a roughness sublayer parameterization in the Weather Research and Forecasting model (WRF version 3.7.1) and its validation for regional climate simulations

2019 ◽  
Author(s):  
Junhong Lee ◽  
Jinkyu Hong ◽  
Yign Noh ◽  
Pedro Jiménez
Keyword(s):  
Air Temperature ◽  
Surface Wind ◽  
Atmospheric Stability ◽  
Winter Season ◽  
Roughness Sublayer ◽  
Rainfall Simulation ◽  
Climate Simulation ◽  
Weather Research And Forecasting ◽  
Forecasting Model ◽  
Weather Research

Abstract. The roughness sublayer (RSL) is one compartment of the surface layer (SL) where turbulence deviates from Monin–Obukhov similarity theory. As the computing power increases, model grid sizes approach to the gray zone of turbulence in the energy containing range and the lowest model layer is located within the RLS. In this perspective, the RSL has an important implication in atmospheric modelling research. However, it has not been explicitly simulated in atmospheric mesoscale models. This study incorporates the RSL model proposed by Harman and Finnigan (2007, 2008) into the Jiménez et al. (2012) SL scheme. A high-resolution simulation performed with the Weather Research and Forecasting model (WRF) illustrates the impacts of the RSL parameterization on the wind, air temperature, and rainfall simulation in the atmospheric boundary layer. As the roughness parameters vary with the atmospheric stability and vegetative phenology in the RSL model, our RSL implementation reproduces the observed surface wind, particularly over tall canopies in the winter season by reducing the root mean square error (RMSE) from 3.1 to 1.8 m s−1. Moreover, the improvement is relevant to air temperature (from 2.74 to 2.67 K of RMSE) and precipitation (from 140 to 135 mm month−1 of RMSE), although its impact is not as substantial as that to wind speed. Our findings suggest that the RSL must be properly considered both for better weather and climate simulation and for the application of wind energy and atmospheric dispersion.

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