EMS ECAM 13 september 2011 GlamEps: Current and future use in operational forecasting at KNMI Adrie Huiskamp

EMS ECAM 13 september 2011 Outline GlamEps: Overview Data visualisation First user impressions Objective probabilistic guidance for weather warnings Improving the data Probabilistic user products

EMS ECAM 13 september GlamEps overview Initialisation+boundaries: ECMWF-Eps Aladin HirLam Straco HirLam KF/RK 12 pertubated runs + 1 control run per model Present 00 and 12 UTC datatime Future 06 and 18 UTC datatime Lead time +42 hrs (ECMWF +45 hrs) ‏

EMS ECAM 13 september Data visualisation Quick access Data reduction Geographic display: Adaguc Web Map Server Time series (grid point or compilation) ‏ Compilation displays

EMS ECAM 13 september WMS geographic display examples Different model grids transformed into presentation grid  method: nearest neighbour sampling Probability of precipation sum exceeding 10 mm in 24 hrs Probability of wind gust exceeding 25 m/s

EMS ECAM 13 september Grid point time series display examples Access trough clickable map Wind vector diagrams for output grid point Model source discrimination Probability distribution of wind vector

EMS ECAM 13 september Compilation diagram of postprocessed parameter

EMS ECAM 13 september Severe weather warning procedure Subjective probabilistic assessment by the forecaster probability exceeding threshold in 50x50 km area >60% : severe weather warning >90% : weather alarm Operational impact assessment (conference) ‏ Decision: YES/NO

EMS ECAM 13 september Probabilistic forecaster guidance for weather warnings DMO ensemble Wind and wind gust Heavy precipitation Postprocessed parameters Freezing rain Blizzard conditions Windchill (Heat stress) ‏ Lightning (or even dense fog..) ‏

EMS ECAM 13 september First impressions in forecasting practice 3 months of evaluation (winter 2011) ‏ Useful in assessing synoptic/mesoscale features Difficult in smaller scales Turning experience into knowledge Need for user training Emphasize on forecaster's added value

EMS ECAM 13 september Improving the forecast: need for data postprocessing Aim: consistent and reliable ensemble forecast Verification Statistical postprocessing Calibration MOS ELR Specific demands: added value in forecasting process Extreme events

EMS ECAM 13 september Probabilistic user products Nautical forecasts Wind Confidence forecast Input for nautical models Wave models Storm surge model Risk assessment & management Aeronautical forecasts Runway cross- and headwind components: airport capacity planning

EMS ECAM 13 september Thank you for your attention Any questions?