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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?