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GlamEps: Current and future use in operational forecasting at KNMI

GlamEps: Current and future use in operational forecasting at KNMI. Adrie Huiskamp. Outline. GlamEps: Overview Data visualisation First user impressions Objective probabilistic guidance for weather warnings Improving the data Probabilistic user products. GlamEps overview.

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GlamEps: Current and future use in operational forecasting at KNMI

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  1. GlamEps: Current and future use in operational forecasting at KNMI Adrie Huiskamp

  2. Outline GlamEps: Overview Data visualisation First user impressions Objective probabilistic guidance for weather warnings Improving the data Probabilistic user products

  3. 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)‏

  4. Data visualisation • Quick access • Data reduction • Geographic display: Adaguc Web Map Server • Time series (grid point or compilation)‏ • Compilation displays

  5. 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

  6. 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

  7. Compilation diagram of postprocessed parameter

  8. 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

  9. 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..)‏

  10. 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

  11. 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

  12. 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

  13. Thank you for your attention • Any questions?

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