Numerical Prediction of an Antarctic Severe Wind Event with the Weather Research and Forecasting (WRF) Model

2007 ◽  
Vol 135 (9) ◽  
pp. 3134-3157 ◽  
Author(s):  
Jordan G. Powers
Keyword(s):  
Mesoscale Model ◽  
Initial Conditions ◽  
Statistical Significance ◽  
Resolution Enhancement ◽  
Wrf Model ◽  
Weather Research And Forecasting ◽  
Polar Regions ◽  
Weather Event ◽  
Pressure System ◽  
Weather Research

Abstract This study initiates the application of the maturing Weather Research and Forecasting (WRF) model to the polar regions in the context of the real-time Antarctic Mesoscale Prediction System (AMPS). The behavior of the Advanced Research WRF (ARW) in a high-latitude setting and its ability to capture a significant Antarctic weather event are investigated. Also, in a suite of sensitivity tests, the impacts of the assimilation of Moderate Resolution Imaging Spectroradiometer (MODIS) atmospheric motion vectors on ARW Antarctic forecasts are explored. The simulation results are analyzed and the statistical significance of error differences is assessed. It is found that with the proper consideration of MODIS data the ARW can accurately simulate a major Antarctic event, the May 2004 McMurdo windstorm. The ARW simulations illuminate an episode of high-momentum flow responding to the complex orography of the vital Ross Island region. While the model captures the synoptic setting and basic trajectory of the cyclone driving the event, there are differences on the mesoscale in the evolution of the low pressure system that significantly affect the forecast results. In general, both the ARW and AMPS’s fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) tend to underforecast the wind magnitudes, reflecting their stalling and filling of the system near Ross Island. It is seen, however, that both targeted data assimilation and grid resolution enhancement can yield improvement in the forecast of the key parameter of wind speed. It is found that the assimilation of MODIS observations can significantly improve the forecast for a high-impact Antarctic weather event. However, the application to the retrievals of a filter accounting for instrument channel, observation height, and surface type is necessary. The results indicate benefits to initial conditions and high-resolution, polar, mesoscale forecasts from the careful assimilation of nontraditional satellite observations over Antarctica and the Southern Ocean.

scholarly journals Assessing the Potential of a Long-Term Climate Forecast for Cuba Using the WRF Model

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Lourdes Álvarez-Escudero ◽  
Yandy G. Mayor ◽  
Israel Borrajero-Montejo ◽  
Arnoldo Bezanilla-Morlot
Keyword(s):  
Mesoscale Model ◽  
Initial Conditions ◽  
Wrf Model ◽  
Reanalysis Data ◽  
Weather Research And Forecasting ◽  
Tropical Storms ◽  
Precipitation Regime ◽  
Wide Debate ◽  
One Year ◽  
Cumulative Precipitation

Seasonal climatic prediction studies are a matter of wide debate all over the world. Cuba, a mainly agricultural nation, should greatly benefit from the knowledge, which is available months in advance of the precipitation regime and allows for the proper management of water resources. In this work, a series of six experiments were made with a mesoscale model WRF (Weather Research and Forecasting Model) that produced a 15-month forecast for each month of cumulative precipitation starting at two dates, and for three non-consecutive years with different meteorological characteristics: one dry year (2004), one year that started dry and turned rainy (2005), and one year where several tropical storms occurred (2008). ERA-Interim reanalysis data were used for the initial and border conditions and experiments started 1 month before the beginning of the rainy and the dry seasons, respectively. In a general sense, the experience of using WRF indicated that it was a valid resource for seasonal forecast, since the results obtained were in the same range as those reported by the literature for similar cases. Several limitations were revealed by the results: the forecasts underestimated the monthly cumulative precipitation figures, tropical storms entering through the borders sometimes followed courses different from the real courses inside the working domain, storms that developed inside the domain were not reproduced by WRF, and differences in initial conditions led to significantly different forecasts for the corresponding time steps (nonlinearity). Changing the model parameterizations and initial conditions of the ensemble forecast experiments was recommended.

scholarly journals Evaluation of the wind farm parameterization in the Weather Research and Forecasting model (version 3.8.1) with meteorological and turbine power data

2017 ◽  
Vol 10 (11) ◽  
pp. 4229-4244 ◽  
Author(s):  
Joseph C. Y. Lee ◽  
Julie K. Lundquist
Keyword(s):  
Wind Speed ◽  
Wind Farm ◽  
Statistical Significance ◽  
Wrf Model ◽  
Grid Cell ◽  
Power Production ◽  
Weather Research And Forecasting ◽  
Model Version ◽  
The Impact ◽  
Weather Research

Abstract. Forecasts of wind-power production are necessary to facilitate the integration of wind energy into power grids, and these forecasts should incorporate the impact of wind-turbine wakes. This paper focuses on a case study of four diurnal cycles with significant power production, and assesses the skill of the wind farm parameterization (WFP) distributed with the Weather Research and Forecasting (WRF) model version 3.8.1, as well as its sensitivity to model configuration. After validating the simulated ambient flow with observations, we quantify the value of the WFP as it accounts for wake impacts on power production of downwind turbines. We also illustrate with statistical significance that a vertical grid with approximately 12 m vertical resolution is necessary for reproducing the observed power production. Further, the WFP overestimates wake effects and hence underestimates downwind power production during high wind speed, highly stable, and low turbulence conditions. We also find the WFP performance is independent of the number of wind turbines per model grid cell and the upwind–downwind position of turbines. Rather, the ability of the WFP to predict power production is most dependent on the skill of the WRF model in simulating the ambient wind speed.

scholarly journals Evaluation of the wind farm parameterization in the Weather Research and Forecasting model (version 3.8.1) with meteorological and turbine power data

2017 ◽  
Author(s):  
Joseph C. Y. Lee ◽  
Julie K. Lundquist
Keyword(s):  
Wind Speed ◽  
Wind Farm ◽  
Statistical Significance ◽  
Atmospheric Stability ◽  
Wrf Model ◽  
Power Production ◽  
Weather Research And Forecasting ◽  
Model Version ◽  
The Impact ◽  
Weather Research

Abstract. Forecasts of wind power production are necessary to facilitate the integration of wind energy into power grids, and these forecasts should incorporate the impact of wind turbine wakes. This paper focuses on a case study of four diurnal cycles with significant power production, and assesses the skill of the wind farm parameterization (WFP) distributed with the Weather Research and Forecasting (WRF) model version 3.8.1, as well as its sensitivity to model configuration. After validating the simulated ambient flow with observations, we quantify the value of the WFP as it accounts for wake impacts on power production of downwind turbines. We also illustrate that a vertical grid with nominally 12-m vertical resolution is necessary for reproducing the observed power production, with statistical significance. Further, the WFP overestimates wake effects and hence underestimates downwind power production during high wind speed and low turbulence conditions. We also find the WFP performance is independent of atmospheric stability, the number of wind turbines per model grid cell, and the upwind-downwind position of turbines. Rather, the ability of the WFP to predict power production is most dependent on the skill of the WRF model in simulating the ambient wind speed.

Fire weather simulation skill by the Weather Research and Forecasting (WRF) model over south-east Australia from 1985 to 2009

2013 ◽  
Vol 22 (6) ◽  
pp. 739 ◽  
Author(s):  
Hamish Clarke ◽  
Jason P. Evans ◽  
Andrew J. Pitman
Keyword(s):  
Initial Conditions ◽  
Full Range ◽  
Wrf Model ◽  
Weather Research And Forecasting ◽  
Fire Weather ◽  
Grid Spacing ◽  
Skill Scores ◽  
Forest Fire Danger ◽  
Environmental Prediction ◽  
Weather Research

The fire weather of south-east Australia from 1985 to 2009 has been simulated using the Weather Research and Forecasting (WRF) model. The US National Oceanic and Atmospheric Administration Centers for Environmental Prediction and National Center for Atmospheric Research reanalysis supplied the lateral boundary conditions and initial conditions. The model simulated climate and the reanalysis were evaluated against station-based observations of the McArthur Forest Fire Danger Index (FFDI) using probability density function skill scores, annual cumulative FFDI and days per year with FFDI above 50. WRF simulated the main features of the FFDI distribution and its spatial variation, with an overall positive bias. Errors in average FFDI were caused mostly by errors in the ability of WRF to simulate relative humidity. In contrast, errors in extreme FFDI values were driven mainly by WRF errors in wind speed simulation. However, in both cases the quality of the observed data is difficult to ascertain. WRF run with 50-km grid spacing did not consistently improve upon the reanalysis statistics. Decreasing the grid spacing to 10km led to fire weather that was generally closer to observations than the reanalysis across the full range of evaluation metrics used here. This suggests it is a very useful tool for modelling fire weather over the entire landscape of south-east Australia.

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 EVAPOTRANSPIRAÇÃO DE REFERÊNCIA ESTIMADA PELO MÉTODO DE PENMAN-MONTEITH FAO-56 EM FUNÇÃO DAS SIMULAÇÕES DO MODELO ATMOSFÉRICO DE MESOESCALA WRF - WEATHER RESEARCH AND FORECASTING

Irriga ◽  
2015 ◽  
Vol 20 (4) ◽  
pp. 762-775
Author(s):  
José Leonaldo De Souza ◽  
Gustavo Bastos Lyra ◽  
Valesca Rodrigues Fernandes ◽  
Rosiberto Salustiano Silva-Junior ◽  
Guilherme Bastos Lyra ◽  
...  
Keyword(s):  
Mesoscale Model ◽  
Meteorological Data ◽  
Extreme Temperature ◽  
Wrf Model ◽  
Simulated Data ◽  
Absolute Error ◽  
Atmospheric Model ◽  
Atmospheric Modeling ◽  
Weather Research And Forecasting ◽  
Weather Research

EVAPOTRANSPIRAÇÃO DE REFERÊNCIA ESTIMADA PELO MÉTODO DE PENMAN-MONTEITH FAO-56 EM FUNÇÃO DAS SIMULAÇÕES DO MODELO ATMOSFÉRICO DE MESOESCALA WRF - WEATHER RESEARCH AND FORECASTING  JOSÉ LEONALDO DE SOUZA1; GUSTAVO BASTOS LYRA2; VALESCA RODRIGUES FERNADES1; ROSEBERTO SALUSTIANO DA SILVA JUNIOR1; GUILHERME BASTOS LYRA3; VINICIUS BANDA SPERLING1; RICARDO ARAUJO FERREIRA JUNIOR3 E IÊDO TEODORO3 1Instituto de Ciências Atmosférica (ICAT), Universidade Federal de Alagoas (UFAL), Campus A.C. Simões, Av. Lourival Melo Mota, s/n,  Tabuleiro dos Martins, CEP:57072-900, Maceió - AL, [email protected]/[email protected]/[email protected]/ [email protected] de Florestas, Dep. de Ciências Ambientais, Universidade Federal Rural do Rio de Janeiro, Seropédica - RJ, [email protected] de Ciências Agrarias (CECA), Universidade Federal de Alagoas (UFAL), Rio Largo - AL, [email protected]/[email protected]/[email protected]        1 RESUMO O objetivo do trabalho foi avaliar a estimativa da evapotranspiração de referência (ETo) pelo método de Penman-Monteith parametrizado no boletim FAO-56 (PM-FAO56) utilizando dados meteorológicos observados e os simulados pelo modelo atmosférico Weather Research and Forecasting (WRF). Na estimativa de ETo utilizaram-se dados meteorológicos observados (extremos da temperatura e umidade do ar, radiação solar e velocidade do vento) e simulados pelo WRF no período seco (janeiro a março e de outubro a dezembro de 2008) da região de Rio Largo - AL (9°28’02’’ S, 35º49’44’’ W e 127 m). As estimativas foram avaliadas pelo coeficiente de determinação (r2) entre ETo obtida com os dados observados e simulados, pelo índice de concordância de Willmott (dr) e pelo erro médio absoluto (MAE). O método PM-FAO56 apresentou maior sensibilidade ao saldo de radiação, em relação aos seus termos aerodinâmicos. As estimativas de ETo apresentaram baixa precisão (r2 = 0,41) e acurácia moderada (dr = 0,77 e MAE = 0,79 mm d-1). É necessário melhorar as simulações dos componentes de radiação do WRF para melhor estimar ETo pelo método de PM-FAO56 na região de Rio Largo, AL. Palavras Chave: Dados Meteorológicos, Modelagem Atmosférica, Penman-Monteith  DE SOUZA, J. L.; LYRA, G. B.; FERNADES,V. R.; SILVA-JUNIOR, R. S.; LYRA, G. B.; SPERLING, V. B.; FERREIRA JUNIOR, R. A.; TEODORO, I.REFERENCE EVAPOTRANSPIRATION BY PENMAN-MONTEITH METHOD  FAO56 USING THE ATMOSPHERIC MESOSCALE MODEL WRF- WEATHER RESEARCH AND FORECASTING    2 ABSTRACT The objective of this study was to assess the Reference evapotranspiration (ETo) by the Penman-Monteith method, described in FAO paper No 56 (PM-FAO56) using observed meteorological data and those simulated by the atmospheric model Weather Research and Forecasting (WRF).  For ETo estimate,  meteorological data were collected   (extreme temperature and air humidity, solar radiation and wind speed)   and  data were  simulated  by the WRF in the dry period (January to March and October to December 2008) in Rio Largo region, AL (9°28’02’’ S, 35º49’44’’ W and 127 m). The estimates were evaluated using the determination coefficient (r2) between ETo from observed and simulated data, by the Willmott concordance index (dr) and mean absolute error (MAE). The PM-FAO56 method showed higher sensitivity to net radiation in relation to the aerodynamic terms.  Estimates of ETo were of low precision (r2 = 0.41) and moderate accuracy (dr = 0.77 and MAE = 0.79 mm d-1). Simulations of the radiation components of the WRF model   have to be improved in order to better estimate ETo by the PM-FAO56 method for  the Rio Largo region,  AL. Keywords: Meteorological data, atmospheric modeling, Penman-Monteith.  

scholarly journals Development and Testing of Polar Weather Research and Forecasting (WRF) Model. Part I: Greenland Ice Sheet Meteorology*

2008 ◽  
Vol 136 (6) ◽  
pp. 1971-1989 ◽  
Author(s):  
Keith M. Hines ◽  
David H. Bromwich
Keyword(s):  
Boundary Layer ◽  
Land Surface ◽  
Mesoscale Model ◽  
Ice Sheet ◽  
Wrf Model ◽  
Greenland Ice Sheet ◽  
Forecast Skill ◽  
Weather Research And Forecasting ◽  
Surface Model ◽  
Weather Research

Abstract A polar-optimized version of the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) was developed to fill climate and synoptic needs of the polar science community and to achieve an improved regional performance. To continue the goal of enhanced polar mesoscale modeling, polar optimization should now be applied toward the state-of-the-art Weather Research and Forecasting (WRF) Model. Evaluations and optimizations are especially needed for the boundary layer parameterization, cloud physics, snow surface physics, and sea ice treatment. Testing and development work for Polar WRF begins with simulations for ice sheet surface conditions using a Greenland-area domain with 24-km resolution. The winter month December 2002 and the summer month June 2001 are simulated with WRF, version 2.1.1, in a series of 48-h integrations initialized daily at 0000 UTC. The results motivated several improvements to Polar WRF, especially to the Noah land surface model (LSM) and the snowpack treatment. Different physics packages for WRF are evaluated with December 2002 simulations that show variable forecast skill when verified with the automatic weather station observations. The WRF simulation with the combination of the modified Noah LSM, the Mellor–Yamada–Janjić boundary layer parameterization, and the WRF single-moment microphysics produced results that reach or exceed the success standards of a Polar MM5 simulation for December 2002. For summer simulations of June 2001, WRF simulates an improved surface energy balance, and shows forecast skill nearly equal to that of Polar MM5.

Predicting lightning activity in Greece with the Weather Research and Forecasting (WRF) model

2015 ◽  
Vol 156 ◽  
pp. 1-13 ◽  
Author(s):  
Theodore M. Giannaros ◽  
Vassiliki Kotroni ◽  
Konstantinos Lagouvardos
Keyword(s):  
Wrf Model ◽  
Lightning Activity ◽  
Weather Research And Forecasting ◽  
Weather Research
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