NDLI: Evaluation of statistical bias correction methods for numerical weather prediction model forecasts of maximum and minimum temperatures

Content Provider	Springer Nature Link
Author	Durai, V. R. Bhradwaj, Rashmi
Copyright Year	2014
Abstract	Statistical bias correction methods for numerical weather prediction (NWP) forecasts of maximum and minimum temperatures over India in the medium-range time scale (up to 5 days) are proposed in this study. The objective of bias correction is to minimize the systematic error of the next forecast using bias from past errors. The need for bias corrections arises from the many sources of systematic errors in NWP modeling systems. NWP models have shortcomings in the physical parameterization of weather events and have the inability to handle sub-grid phenomena successfully. The statistical algorithms used for minimizing the bias of the next forecast are running-mean (RM) bias correction, best easy systematic estimator, simple linear regression and the nearest neighborhood (NN) weighted mean, as they are suitable for small samples. Bias correction is done for four global NWP model maximum and minimum temperature forecasts. The magnitude of the bias at a grid point depends upon geographical location and season. Validation of the bias correction methodology is carried out using daily observed and bias-corrected model maximum and minimum temperature forecast over India during July–September 2011. The bias-corrected NWP model forecast generally outperforms direct model output (DMO). The spatial distribution of mean absolute error and root-mean squared error for bias-corrected forecast over India indicate that both the RM and NN methods produce the best skill among other bias correction methods. The inter-comparison reveals that statistical bias correction methods improve the DMO forecast in terms of accuracy in forecast and have the potential for operational applications.
Starting Page	1229
Ending Page	1254
Page Count	26
File Format	PDF
ISSN	0921030X
Journal	Natural Hazards
Volume Number	73
Issue Number	3
e-ISSN	15730840
Language	English
Publisher	Springer Netherlands
Publisher Date	2014-03-18
Publisher Place	Dordrecht
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Global model Numerical weather prediction Statistical bias correction Maximum and minimum temperature forecast Natural Hazards Hydrogeology Geophysics/Geodesy Geotechnical Engineering & Applied Earth Sciences Civil Engineering Environmental Management
Content Type	Text
Resource Type	Article
Subject	Atmospheric Science Water Science and Technology

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