Error Correction Modelling of Wind Speed Through Hydro-Meteorological Parameters and Mesoscale Model: A Hybrid Approach

Ishak, Asnor Muizan; Remesan, Renji; Srivastava, Prashant K.; Islam, Tanvir; Han, Dawei

doi:10.1007/s11269-012-0130-1

Cited by 38 publications

(16 citation statements)

References 26 publications

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“…It is developed as a collaborative effort among several universities and federal research agencies in the USA. It is the next generation of PSU/NCAR fifthgeneration mesoscale model, known as MM5 (Chen and Dudhia 2001;Barker et al 2004;Ishak et al 2013). The WRF has significant improvement over the MM5 and includes recent physics parameterizations.…”

Section: Model Descriptionmentioning

confidence: 99%

Tracking a tropical cyclone through WRF–ARW simulation and sensitivity of model physics

et al. 2014

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The Weather Research and Forecasting (WRF) model's Advanced Research WRF (ARW) dynamic solver is one of the most popular regional numerical weather prediction models being used by operational and research personnel. In this study, we simulate a tropical cyclone to reproduce the track direction and strength of the storm that formed at low latitudes in the West Pacific Ocean. The cyclone is known as ''Haiyan'' and assessed as category-5 equivalent super typhoon status due to its strong sustained winds and gusts, making it the strongest tropical cyclone in the region. We study the sensitivity of three different model physics options: the microphysics schemes, the planetary boundary layer schemes, and the impact of cumulus parameterization schemes. The realism of the cyclone simulation for different physics options is assessed through the comparison between the model outputs and the best track data, which are taken from the Japan

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Section: Model Descriptionmentioning

confidence: 99%

Tracking a tropical cyclone through WRF–ARW simulation and sensitivity of model physics

et al. 2014

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“…After its successful application, an advanced version of the model i.e., the Weather Research Forecast (WRF) model was released to the research community (Skamarock et al 2005). After its release, and due to the high versatility of the WRF model, a number of studies have utilized this advanced model in research related to wide range of atmospheric phenomena, producing meteorological variables, hydrological modeling, weather research, climate monitoring and change, flood forecasting and water resource management amongst others (Borge et al 2008;Chen et al 2011;Ishak et al 2013;Islam et al 2013;Price et al 2012;Srivastava et al 2013bSrivastava et al , 2014a. Aside from its use as a stand-alone NWP model, the WRF model can also be used with global reanalysis datasets, such as those available from the National Centre for Environmental Prediction (NCEP) and the European Center for Medium range Weather Forecasting (ECMWF).…”

Section: Introductionmentioning

confidence: 99%

WRF Dynamical Downscaling and Bias Correction Schemes for NCEP Estimated Hydro-Meteorological Variables

Srivastava

Islam

Gupta

et al. 2015

Water Resour Manage

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Rainfall and Reference Evapotranspiration (ETo) are the most fundamental and significant variables in hydrological modelling. However, these variables are generally not available over ungauged catchments. ETo estimation usually needs measurements of weather variables such as wind speed, air temperature, solar radiation and dew point. After the development of reanalysis global datasets such as the National Centre for Environmental Prediction (NCEP) and high performance modelling framework Weather Research and Forecasting (WRF) model, it is now possible to estimate the rainfall and ETo for any coordinates. In this study, the WRF modelling system was employed to downscale the global NCEP reanalysis datasets over the Brue catchment, England, U.K. After downscaling, two statistical bias correction schemes were used, the first was based on sophisticated computing algorithms i.e., Relevance Vector Machine (RVM), while the second was based on the more simple Generalized Linear Model (GLM). The statistical performance indices for bias correction such as %Bias, index of agreement (d), Root Mean Square Error (RMSE), and Correlation (r) indicated that the RVM model, on the whole, displayed a more accomplished bias correction of the variability of rainfall and ETo in comparison to the GLM. The study provides important information on the performance of WRF derived hydro-meteorological variables using NCEP global reanalysis datasets and statistical bias correction schemes which can be used in numerous hydro-meteorological applications

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“…The modelled wind speed is generally greater than the measured during all the seasons under consideration. Previous studies by (Ishak et al ., 2010;Ishak et al ., 2013) also indicate that wind speed is the most difficult variable to downscale using the MM5 model with the results showing significant over estimation. On the basis of the NSE statistics, dew point and temperature have the highest values followed by least RMSE.…”

Section: Performance Of Hydro-meteorological Variablesmentioning

confidence: 99%

Comparative assessment of evapotranspiration derived from NCEP and ECMWF global datasets through Weather Research and Forecasting model

Srivastava

Han

Rico‐Ramirez

et al. 2013

Atmospheric Science Letters

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In many hydro-meteorological applications, it is not always possible to get access to in situ weather measurements, especially for the ungauged catchments. This study explores the performances of downscaled weather data for Reference Evapotranspiration (ET o ) retrieval using the global European Centre for Medium Range Weather Forecasts (ECMWF) ERA interim and National Centers for Environmental Prediction (NCEP) reanalysis data, simulated through Weather Research and Forecasting (WRF) mesoscale model. The range of the Nash-Sutcliffe efficiency calculated for the ECMWF pooled datasets derived ET o varies from 0.31 to 0.87, while for NCEP it is found to be 0.11 to 0.38. Bias and Root Mean Square Error (RMSE) are also indicating a very high discrepancy in the NCEP ET o (Bias = −0.05; RMSE = 0.11) as compared to ECMWF (Bias = 0.00; RMSE = 0.06). The overall findings reveal that ECMWF downscaled products have a much better performance than the NCEP's counterparts.

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Error Correction Modelling of Wind Speed Through Hydro-Meteorological Parameters and Mesoscale Model: A Hybrid Approach

Cited by 38 publications

References 26 publications

Tracking a tropical cyclone through WRF–ARW simulation and sensitivity of model physics

Tracking a tropical cyclone through WRF–ARW simulation and sensitivity of model physics

WRF Dynamical Downscaling and Bias Correction Schemes for NCEP Estimated Hydro-Meteorological Variables

Comparative assessment of evapotranspiration derived from NCEP and ECMWF global datasets through Weather Research and Forecasting model

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