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

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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 first authors would like to thank the Commonwealth Scholarship Commission, British Council, United Kingdom and Ministry of Human Resource Development, Government of India for providing the necessary support and funding for this research. The authors are also thankful to Research Data Archive (RDA) which is maintained by the Computational and Information Systems Laboratory (CISL) at the National Center for Atmospheric Research (NCAR). The authors would like to acknowledge the British Atmospheric Data Centre, United Kingdom for providing the ground observation datasets. The authors also acknowledge the Advanced Computing Research Centre at University of Bristol for providing the access to supercomputer facility (The Blue Crystal). Dr. Petropoulos’s contribution was supported by the European Commission Marie Curie Re-Integration Grant “TRANSFORM-EO” and the High Performance Computing Facilities of Wales “PREMIER-EO” projects. Authors would also like to thank Gareth Ireland for the language proof reading of the manuscript. Authors are also grateful to the anonymous reviewers for their useful criticism which helped improving the manuscript. The views expressed here are those of the authors solely and do not constitute a statement of policy, decision, or position on behalf of NOAA/NASA or the authors’ affiliated institutions.

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Correspondence to Prashant K. Srivastava.

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Srivastava, P.K., Islam, T., Gupta, M. et al. WRF Dynamical Downscaling and Bias Correction Schemes for NCEP Estimated Hydro-Meteorological Variables. Water Resour Manage 29, 2267–2284 (2015). https://doi.org/10.1007/s11269-015-0940-z

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  • Hydro-meteorological variables
  • Weather research and forecasting model
  • WRF downscaling
  • RVM, GLM, Bias correction