TY - JOUR

T1 - WRF Dynamical Downscaling and Bias Correction Schemes for NCEP Estimated Hydro-Meteorological Variables

AU - Srivastava, Prashant K.

AU - Islam, Tanvir

AU - Gupta, Manika

AU - Petropoulos, George

AU - Dai, Qiang

N1 - Srivastava, P. K., Islam, T., Gupta, M., Petropoulos, G., Dai, Q. (2015). WRF Dynamical Downscaling and Bias Correction Schemes for NCEP Estimated Hydro-Meteorological Variables. Water Resources Management, 29 (7), 2267-2284
Document embargo until 30/01/2016

PY - 2015/5/1

Y1 - 2015/5/1

N2 - 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.

AB - 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.

KW - Bias correction

KW - GLM

KW - Hydro-meteorological variables

KW - Weather research

KW - forecasting model

KW - WRF downscaling

KW - RVM

UR - http://hdl.handle.net/2160/30586

U2 - 10.1007/s11269-015-0940-z

DO - 10.1007/s11269-015-0940-z

M3 - Article

SN - 1573-1650

VL - 29

SP - 2267

EP - 2284

JO - Water Resources Management

JF - Water Resources Management

IS - 7

ER -