scholarly journals Performance of the WRF model with different physical parameterizations in the precipitation simulation of the state of Puebla

2020 ◽  
Author(s):  
Indalecio Mendoza Uribe ◽  
Diosey Ramón Lugo Morín
Keyword(s):  
Wrf Model ◽  
The State ◽  
Precipitation Simulation ◽  
Physical Parameterizations

scholarly journals Numerical rainfall simulation with different spatial and temporal evenness by using a WRF multiphysics ensemble

2017 ◽  
Vol 17 (4) ◽  
pp. 563-579 ◽  
Author(s):  
Jiyang Tian ◽  
Jia Liu ◽  
Denghua Yan ◽  
Chuanzhe Li ◽  
Fuliang Yu
Keyword(s):  
Relative Error ◽  
Wrf Model ◽  
Temporal Distribution ◽  
Northern China ◽  
Rainfall Simulation ◽  
Average Relative Error ◽  
Storm Events ◽  
Cumulative Rainfall ◽  
Physical Parameterizations

Abstract. The Weather Research and Forecasting (WRF) model is used in this study to simulate six storm events in two semi-humid catchments of northern China. The six storm events are classified into four types based on the rainfall evenness in the spatial and temporal dimensions. Two microphysics, two planetary boundary layers (PBL) and three cumulus parameterizations are combined to develop an ensemble containing 16 members for rainfall generation. The WRF model performs the best for type 1 events with relatively even distributions of rainfall in both space and time. The average relative error (ARE) for the cumulative rainfall amount is 15.82 %. For the spatial rainfall simulation, the lowest root mean square error (RMSE) is found with event II (0.4007), which has the most even spatial distribution, and for the temporal simulation the lowest RMSE is found with event I (1.0218), which has the most even temporal distribution. The most difficult to reproduce are found to be the very convective storms with uneven spatiotemporal distributions (type 4 event), and the average relative error for the cumulative rainfall amounts is up to 66.37 %. The RMSE results of event III, with the most uneven spatial and temporal distribution, are 0.9688 for the spatial simulation and 2.5327 for the temporal simulation, which are much higher than the other storms. The general performance of the current WRF physical parameterizations is discussed. The Betts–Miller–Janjic (BMJ) scheme is found to be unsuitable for rainfall simulation in the study sites. For type 1, 2 and 4 storms, member 4 performs the best. For type 3 storms, members 5 and 7 are the better choice. More guidance is provided for choosing among the physical parameterizations for accurate rainfall simulations of different storm types in the study area.

scholarly journals Análise da Sensibilidade das Parametrizações no Modelo WRF para o Estado do Rio Grande do Sul Durante o Inverno de 2014 (Analysis Sensitivity of the Physics Parameterization WRF Model for the Rio Grande do Sul State on the winter of 2014)

2016 ◽  
Vol 9 (2) ◽  
pp. 368
Author(s):  
Ricardo Antonio Mollmann Junior ◽  
Rita De Cassia Marques Alves ◽  
Gabriel Bonow Muchow ◽  
Bruno Dias Rodrigues ◽  
Rosiberto Salustiano da Silva Junior ◽  
...  
Keyword(s):  
Wrf Model ◽  
Rio Grande ◽  
Atmospheric Modeling ◽  
The State ◽  
Precipitation Event ◽  
Winter Period ◽  
Rain Event ◽  
Rio Grande Do Sul ◽  
Physical Parameterization

O objetivo do presente do estudo foi observar a sensibilidade das parametrizações do modelo WRF ao quantificar as variáveis em superfície: pressão atmosférica, temperatura do ar, umidade relativa e precipitação durante o Inverno de 2014 no Estado do Rio Grande do Sul (RS). Os resultados foram demonstrados a partir de análise dos índices estatísticos, bias e Raiz do Erro Quadrático Médio (REQM), quando calculados para comparações entre os dados extraídos de 6 experimentos de simulações do modelo WRF com dados de estações de monitoramento do Instituto Nacional de Meteorologia (INMET) no RS. Os experimentos foram configurados com diferentes parametrização físicas, para assim poder verificar qual combinação apresenta melhor desempenho na representação das condições de Inverno do RS. A partir do reconhecimento das diferentes interpretações físicas que cada conjunto de parametrização pode representar, foi apresentado um estudo de caso afim de diagnosticar as precipitações ocorridas no Estado, principalmente no município de Irai-RS. As análises partiu de um acompanhamento de evento de chuvas ocorrido entre os dias 25 e 30 de junho de 2014, utilizando-se de cartas dos campos meteorológicos de Linhas de Corrente em 850hPa e Precipitação. Percebeu-se que tanto temperatura quanto pressão, o bias e o REQM obtiveram diferenças não significativas entre os experimentos. A UR, no cálculo do bias mostrou uma grande diferença entre os experimentos, devido a forma de seu cálculo considerar apenas o erros sistemáticos, podendo haver cancelamento de erros entre subestimativas e superestimativas. A REQM para a mesma variável, mostrou que os experimentos não se diferenciaram em valores significativos, obtendo apenas nos experimentos 3 e 5, menor valor de erro em comparação aos outros experimentos (~2%). Ao tecer considerações sobre a precipitação, o bias diagnosticou subestimativas nos experimentos para as chuvas durante o inverno de 2014, entretanto no cálculo da REQM os experimentos não tiveram assentimento entre si, exceto o 4 e o 6, onde os valores dos erros totais ficaram inferiores à 2mm. Para o estudo de caso, onde foi acompanhado as chuvas ocorridas durante a passagem de um fenômeno Ciclone Extratropical, em todos os experimentos mostrou a caracterização do evento de precipitação. Com isso, ao diagnosticar a quantidade de precipitação durante o evento ocorrido sobre a estação meteorológica de Irai-RS com os dados do modelo, somado as análises estatísticas, o experimento 6 dentre as combinações de parametrizações apresentadas neste estudo, obteve o melhor desempenho para caracterizar o estado atmosférico durante o período de inverno no RS.   ABSTRACT The objective of this study is to observe the sensitivity of parameterizations of the WRF model to quantify the variables in surface: atmospheric pressure, air temperature, relative humidity and precipitation during the winter of 2014 in the State of Rio Grande do Sul (RS).  The results were demonstrated from analysis of statistical indices, bias and Mean Squared Error root (RMSE) calculated for comparisons between the data extracted from 6 experiments of the WRF model simulations with data from the National Institute of Meteorology monitoring stations (INMET) in RS. The experiments were configuring with different physical parameterization, so that it may examine what combination performs better in the representation of the RS winter conditions. From the recognition of different physical interpretations that each set of parameterization can represent, a case study was made in order to diagnose the precipitations that occurred in the State, mainly in the municipality of Irai. The analysis came from a monitoring rain event occurred between 25 and 30 June 2014, using meteorological fields of 850hPa stream lines and rainfall. However, realizes that both temperature as pressure, the bias and the RMSE obtained no significant differences between experiments. UR, in the calculation of bias showed a big difference between the experiments, because of the manner of calculation only considers the systematic errors, which may cause cancellation of errors between underestimation and overestimation. The RMSE for the same variable showed no differences in significant amounts in the experiments, only in experiments 3 and 5, smallest error value when compared to the other experiments (~ 2%). To develop some considerations on the precipitation, the bias diagnosed underestimates the experiments for the rains during the winter of 2014; however, in the calculation of RMSE the experiments had not consent to each other, except 4 and 6, where the values of total errors were lower to 2mm. For the case study, which was accompanied rainfall occurred during the passage of an extratropical cyclone, in all experiments showed the characterization of the precipitation event. Thus, to diagnose the amount of precipitation during the event occurring on the Irai weather station with model data, combined with statistical analysis, the experiment 6 from the parameterization of combinations shown in this study had the best performance to characterize the atmospheric state during the winter period in the RS. Keywords: Weather numerical forecast, WRF, physical parameterization, atmospheric modeling.   

scholarly journals WRF-Simulated Low-Level Jets over Iowa: Characterization and Sensitivity Studies

2020 ◽  
Author(s):  
Jeanie A. Aird ◽  
Rebecca J. Barthelmie ◽  
Tristan J. Shepherd ◽  
Sara C. Pryor
Keyword(s):  
Wind Speed ◽  
Wrf Model ◽  
Stable Stratification ◽  
Detection Algorithm ◽  
The State ◽  
Sensitivity Analyses ◽  
Vertical Resolution ◽  
Low Level ◽  
Sensitivity Studies ◽  
Low Level Jets

Abstract. Output from high resolution simulations with the Weather Research and Forecasting (WRF) model are analyzed to characterize local low level jets (LLJ) over Iowa. Analyses using a detection algorithm wherein the wind speed above and below the jet maximum must be below 80 % of the jet wind speed within a vertical window of approximately 20 m–530 m a.g.l. indicate the presence of a LLJ in at least one of the 14700 4 km by 4 km grid cells over Iowa on 98 % of nights. Nocturnal LLJ are most frequently associated with stable stratification and low TKE and hence are more frequent during the winter months. The spatiotemporal mean LLJ maximum (jet core) wind speed is 9.55 ms−1 and the mean height is 182 m. Locations of high LLJ frequency and duration across the state are seasonally varying with a mean duration of 3.5 hours. LLJ are most frequent in the topographically complex northwest of the state in winter, and in the flatter northeast of the state in spring. Sensitivity of LLJ characteristics to the: i) LLJ definition and ii) vertical resolution at which the WRF output is sampled are examined. LLJ definitions commonly used in LLJ literature are considered in the first sensitivity analysis. These sensitivity analyses indicate that LLJ characteristics are highly variable with LLJ definition. Further, when the model output is down-sampled to lower vertical resolution, the maximum LLJ wind speed and mean height decrease, but spatial distributions of regions of high frequency and duration are conserved.

WRF model for precipitation simulation and its application in real-time flood forecasting in the Jinshajiang River Basin, China

2017 ◽  
Vol 130 (6) ◽  
pp. 635-647 ◽  
Author(s):  
Jianzhong Zhou ◽  
Hairong Zhang ◽  
Jianyun Zhang ◽  
Xiaofan Zeng ◽  
Lei Ye ◽  
...  
Keyword(s):  
Real Time ◽  
River Basin ◽  
Wrf Model ◽  
Flood Forecasting ◽  
Precipitation Simulation

scholarly journals Impact of Different Reanalysis Data and Parameterization Schemes on WRF Dynamic Downscaling in the Ili Region

Water ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 1729 ◽  
Author(s):  
Yulin Zhou ◽  
Zhenxia Mu
Keyword(s):  
Correlation Coefficient ◽  
Wrf Model ◽  
Final Analysis ◽  
Reanalysis Data ◽  
Cold Bias ◽  
Physical Parameters ◽  
Geophysical Fluid Dynamics Laboratory ◽  
Parameterization Schemes ◽  
Precipitation Simulation ◽  
Pbl Scheme

Different reanalysis data and physical parameterization schemes for the Weather Research and Forecasting (WRF) model are considered in this paper to evaluate their performance in meteorological simulations in the Ili Region. A 72-hour experiment was performed with two domains at the resolution of 27 km with one-way nesting of 9 km. (1) Final Analysis (FNL) and Global Forecast System (GFS) reanalysis data (hereafter, WRF-FNL experiment and WRF-GFS experiment, respectively) were used in the WRF model. For the simulation of accumulated precipitation, both the WRF-FNL (mean bias of 0.79 mm) and WRF-GFS (mean bias of 0.31 mm) simulations can display the main features of the general temporal pattern and geographical distribution of the observed precipitation. For the simulation of the 2-m temperature, the simulation of the WRF-GFS experiment (mean warm bias of 1.81 °C and correlation coefficient of 0.83) was generally better than that of the WRF-FNL experiment (mean cold bias of 1.79 °C and correlation coefficient of 0.27). (2) Thirty-six physical combination schemes were proposed, each with a unique set of physical parameters. Member 33 (with the smallest mean-metric of 0.53) performed best for the precipitation simulation, and member 29 (with the smallest mean-metric of 0.64) performed best for the 2-m temperature simulation. However, member 29 and 33 cannot be distinguished from the other members according to their parameterizations. For this domain, ensemble members that contain the Mellor–Yamada–Janjic (MYJ) boundary layer (PBL) scheme and the Grell–Devenyi (GD) cumulus (CU) scheme are recommended for the precipitation simulation. The Geophysical Fluid Dynamics Laboratory (GFDL) radiation (RA) scheme and the MYJ PBL scheme are recommended for the 2-m temperature simulation.

scholarly journals WRF-simulated low-level jets over Iowa: characterization and sensitivity studies

2021 ◽  
Vol 6 (4) ◽  
pp. 1015-1030
Author(s):  
Jeanie A. Aird ◽  
Rebecca J. Barthelmie ◽  
Tristan J. Shepherd ◽  
Sara C. Pryor
Keyword(s):  
Wind Speed ◽  
Polynomial Interpolation ◽  
Wrf Model ◽  
Detection Algorithm ◽  
The State ◽  
Sensitivity Analyses ◽  
Vertical Resolution ◽  
Low Level ◽  
Low Level Jets ◽  
The Mean

Abstract. Output from 6 months of high-resolution simulations with the Weather Research and Forecasting (WRF) model are analyzed to characterize local low-level jets (LLJs) over Iowa for winter and spring in the contemporary climate. Low-level jets affect rotor plane aerodynamic loading, turbine structural loading and turbine performance, and thus accurate characterization and identification are pertinent. Analyses using a detection algorithm wherein the wind speed above and below the jet maximum must be below 80 % of the jet wind speed within a vertical window of approximately 20–530 m a.g.l. (above ground level) indicate the presence of an LLJ in at least one of the 14 700 4 km×4 km grid cells over Iowa on 98 % of nights. Nocturnal LLJs are most frequently associated with stable stratification and low turbulent kinetic energy (TKE) and hence are more frequent during the winter months. The spatiotemporal mean LLJ maximum (jet core) wind speed is 9.55 m s−1, and the mean height is 182 m. Locations of high LLJ frequency and duration across the state are seasonally varying, with a mean duration of 3.5 h. The highest frequency occurs in the topographically complex northwest of the state in winter and in the flatter northeast of the state in spring. Sensitivity of LLJ characteristics to the (i) LLJ definition and (ii) vertical resolution at which the WRF output is sampled is examined. LLJ definitions commonly used in the literature are considered in the first sensitivity analysis. These sensitivity analyses indicate that LLJ characteristics are highly variable with definition. Use of different definitions identifies both different frequencies of LLJs and different LLJ events. Further, when the model output is down-sampled to lower vertical resolution, the mean jet core wind speed height decreases, but spatial distributions of regions of high frequency and duration are conserved. Implementation of a polynomial interpolation to extrapolate down-sampled output to full-resolution results in reduced sensitivity of LLJ characteristics to down-sampling.

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