Development And Validation Of New EFFIS Probabilistic Products Utilising the ECMWF Ensemble Prediction System

The European Forest Fire Information System (EFFIS) has been established by the Joint Research Centre (JRC) and the Directorate General for Environment (DG ENV) of the European Commission (EC) to support the services in charge of the protection of forests against fires in the EU and neighbor countries. The fire danger maps so far have been based on NWP (Numerical Weather Prediction) input provided mainly by the Meteo-France and German Weather Service (DWD) single deterministic models. The Meteo-France model ARPEGE, provides daily forecast fields used as input to estimate FWI (Fire Weather Index) fire danger fields at a resolution of 10 km (max horizon: 3days). Same wise daily numerical fields produced by the DWD model are used to estimate FWI fields at a resolution of 25 km (max horizon: 6 days). These FWI products are categorical forecasts and their quality is directly related to the skill of NWP input fields. The fact that NWP analysis fields are inaccurate and that numerical models have inadequacies, leads to forecast errors that grow with increasing forecast lead time. This might significantly affect the resulting FWI estimates especially for longer forecast horizons. Ensemble forecasting on the other hand aims at quantifying this flow-dependent forecast uncertainty by estimating the time evolution of the Probability Density Function (PDF) of forecast states. The ECMWF Ensemble Prediction System (EPS) based on a finite number of numerical integrations is a practical tool that can be used to estimate the time evolution of the PDF of forecast states by running ECMWF weather prediction model 51 times at a resolution currently of 32 km from slightly different initial conditions. Following this ensemble approach, possible future probabilistic EFFIS products have been considered. These potential new EFFIS products are in the stage of designing, testing and validating while results so far are being quite promising.

PETROLIAGKIS Thomas;  CAMIA Andrea;  SAN-MIGUEL-AYANZ Jesus; 

2015-07-31

European Association of Remote Sensing Laboratories (EARSeL)

JRC97014

http://www.earsel.org/SIG/FF/9th-workshop/,    https://publications.jrc.ec.europa.eu/repository/handle/JRC97014,