Probabilistic Forecasting Course

Friday, 27 October 2017

Paviljoen 3
Boussinesqweg 1, Delft, The Netherlands
Date 27 October 2017
Time 09:00 - 17:30
Course
Event is closed for registration
Registry fee € 565 excluding 21% VAT
Chair Jan Verkade
 
Sign in to join

Description

Many hydrologic forecasting agencies are moving towards probabilistic forecasting for the purpose of estimating predictive hydrologic uncertainties. These estimates are often based on ensemble techniques and/or statistical post-processing. This course gives an introduction to these techniques as well as discusses related topics such as verification of probabilistic forecasts and effectively using probability forecasts in operational practice.

 

Probabilistic forecasting

Forecasting may reduce but cannot eliminate inherent uncertainties about the future. One approach to managing this is to estimate remaining uncertainties in a probabilistic fashion and thus arrive at a probabilistic forecast. This estimation can be done using ensemble techniques, statistical post-processing or a combination of these.

The quality of resulting forecasts can be assessed through a process called forecast verification. Verification of probabilistic forecasts uses different techniques than verification of deterministic forecasting. Within the framework of a forecast – decision – response system, effective forecasting requires that attention is given to additional issues such as forecast visualization, communication, decision-making and training.

The present course comprises an introduction to these topics. It is aimed at both practitioners and scientists.

 

 

Programme

  • Introduction to uncertainty, risk and probability
  • Techniques for estimating predictive hydrological uncertainty: ensembles and post-processing
  • Verification: how good is my (probabilistic) forecast?
  • Forecasting applications: (i) storm surge forecasting for the North Sea coast; (ii) fluvial forecasting in Rhine, Meuse and the EFAS system
  • Serious game: making forecast sensitive decisions
  • Using probabilistic forecasts in operational practice.