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

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GlamEps overviewInitialisation+boundaries: ECMWF-EpsAladinHirLam StracoHirLam KF/RK

12 pertubated runs + 1 control run per modelPresent 00 and 12 UTC datatimeFuture 06 and 18 UTC datatimeLead time +42 hrs (ECMWF +45 hrs)

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Data visualisationQuick accessData reductionGeographic display: Adaguc Web Map ServerTime series (grid point or compilation)Compilation displays

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WMS geographic display examplesDifferent model grids transformed into presentation gridmethod: nearest neighbour sampling

Probability of precipation sum exceeding 10 mm in 24 hrsProbability of wind gust exceeding 25 m/s

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Grid point time series display examplesAccess trough clickable map

Wind vector diagrams for output grid point•Model source discrimination

Probability distribution of wind vector

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Compilation diagram of postprocessed parameterPrecipitation phase

0%

20%

40%

60%

80%

100%

0 3 6 9 12 15 18 21 24time

snow

sleet

freezing rain

rain or icepelletsrain, sleet orice pelletsrain or sleet

rain

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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

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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..)

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First impressions in forecasting practice

3 months of evaluation (winter 2011)Useful in assessing synoptic/mesoscale featuresDifficult in smaller scales

Turning experience into knowledgeNeed for user trainingEmphasize on forecaster's added value

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Improving the forecast: need for data postprocessing

Aim: consistent and reliable ensemble forecastVerificationStatistical postprocessing

CalibrationMOSELR

Specific demands: added value in forecasting processExtreme events

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Probabilistic user productsNautical forecasts

WindConfidence forecast

Input for nautical modelsWave modelsStorm surge model

Risk assessment & managementAeronautical forecasts

Runway cross- and headwind components: airport capacity planning

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Thank you for your attention

Any questions?