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Other methods may increase or decrease ensemble spread to improve forecast reliability [e.g., Gneiting et al., 2005; Hamill and Colucci, 1997; Eckel and Walters, 1998] but are not specifically designed to adjust the ensemble mean which is required to match the predictable variances in the forecasts and observations.
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Could they improve forecasting?
How might this research be used to improve forecasts?
Improved forecasts that are downscaled to a finer geographic scale can help to limit uncertainty.
A system dynamic model with 36 cohorts was used to provide maximum flexibility and improved forecasting.
These are: 1) Improve the fidelity of the climate models at the heart of the forecast systems; 2) develop better measures to define and verify forecast reliability and skill; 3) design improved ensemble prediction systems; and 4) refine product outputs to have greater utility for decision-making.
Results obtained indicate that BANN-hydrologic forecasting models with confidence bounds can improve their reliability for flood forecasts.
The use of the e-kmf® model, instead of the common climatological reference, may improve the long-term forecasting reliability by 35% as computed in the data analysis for the year 2010.
All this information could be converted into a set of scalar time-series feeding a predictor model, together with the other time series already used, in order to improve the reliability of the forecast.
In this study, we propose a Bayesian EPP approach based on copula functions (COP-EPP) to improve the reliability of the precipitation ensemble forecast.
It is therefore necessary to improve the reliability and accuracy of the flood forecasting model.
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