How can LAMEPSHow can LAMEPS** help you to make help you to make a better forecast for extreme weathera better forecast for extreme weather

Henrik Feddersen, DMIHenrik Feddersen, DMIhf@dmi.dkhf@dmi.dk

*LAMEPS = Limited-Area Model Ensemble Prediction System

OutlineOutline

MotivationMotivation Ensemble methodologyEnsemble methodology Rainfall case studiesRainfall case studies Probability upscalingProbability upscaling Verification, summer 2011Verification, summer 2011 Wind case studyWind case study SummarySummary

MotivationMotivation

Assess forecast uncertaintyAssess forecast uncertainty Assess risk of high-impact weatherAssess risk of high-impact weather

Heavy rain (>24mm/6h)Heavy rain (>24mm/6h) Cloudburst (>15mm/30min)Cloudburst (>15mm/30min) Heavy snowfall (>15mm/6h)Heavy snowfall (>15mm/6h) Snowstorm (>10mm/6h and >10m/s)Snowstorm (>10mm/6h and >10m/s) Storm (mean and gust) (>24m/s)Storm (mean and gust) (>24m/s) Hurricane (mean and gust) (>32m/s)Hurricane (mean and gust) (>32m/s)

Ensemble methodologyEnsemble methodology

Sample uncertainty in Forecast initial conditions Model formulation Lateral boundary conditions

Initial conditionsInitial conditionsPerturbation of analysisPerturbation of analysis

ObservationsObservations AnalysisAnalysisObservationsObservations ForecastForecast

Perturbed analysesPerturbed analyses Ensemble membersEnsemble members

Initial conditionsInitial conditionsPerturbation of observationsPerturbation of observations

ObservationsObservations AnalysisAnalysisObservationsObservations ForecastForecast

Ensemble data assimilationEnsemble data assimilation Ensemble membersEnsemble membersPerturbed Perturbed observationsobservations

Model uncertaintyModel uncertaintyMulti-model ensembleMulti-model ensemble

NWP Model A

Model B

Model C

Model uncertaintyModel uncertaintyStochastic physicsStochastic physics

Initialization Dynamics

Postprocessing

Physics

NWP model

Model uncertaintyModel uncertaintyMulti-scheme ensembleMulti-scheme ensemble

Turbulence Surface 1

Surface 2

RadiationConvection 1

Convection 2

NWP model physics

Model uncertaintyModel uncertaintyMulti-parameter ensembleMulti-parameter ensemble

Turbulence Surface

Radiation

NWP model physics

Convection

Limited-area ensembles vs Limited-area ensembles vs global ensemblesglobal ensembles

Global ensembles Uncertainty in synoptic development in the medium-

range Limited-area ensembles

Uncertainty in mesoscale development in the short-range

DMI-HIRLAM Ensemble DMI-HIRLAM Ensemble Prediction SystemPrediction System

Resolution = 0.05Resolution = 0.05° horizontal / 40 vertical levels° horizontal / 40 vertical levels

Members = 25Members = 25

Forecast length = 54hForecast length = 54h

Forecast frequency = 4 times per dayForecast frequency = 4 times per day

Initial and lateral boundary conditions = 5Initial and lateral boundary conditions = 5

Scaled Lagged Average Forecast (SLAF) error perturbationsScaled Lagged Average Forecast (SLAF) error perturbations

Cloud schemes = 2Cloud schemes = 2

STRACO and KF/RKSTRACO and KF/RK

Stochastic physics = yes/noStochastic physics = yes/no

Surface schemes = 2Surface schemes = 2

ISBA and ISBA/NewsnowISBA and ISBA/Newsnow

Independent of ECMWF's ensemble prediction systemIndependent of ECMWF's ensemble prediction system

Short-range ensemble spreadShort-range ensemble spread

Short-range ensemble spreadShort-range ensemble spread

Case study, 2 July 2011Case study, 2 July 2011Precipitation stamp mapPrecipitation stamp map

Case study, 2 July 2011Case study, 2 July 2011Probability mapProbability map

Probability = 10-20%: Only 3-4 members Probability = 10-20%: Only 3-4 members predict the event!?predict the event!?

Case study, 2 July 2011Case study, 2 July 201150 50 mm/6h contoursmm/6h contours

More than 4 members predict the event!More than 4 members predict the event!

Different members Different members in different coloursin different colours

Probability upscalingProbability upscaling

Conventional probabilityConventional probability— In every grid point: Fraction of members that predict In every grid point: Fraction of members that predict

the eventthe event Upscaled probabilityUpscaled probability

— In every grid point: Fraction of members that predict In every grid point: Fraction of members that predict the event the event in a neighbourhood in a neighbourhood of the grid pointof the grid point

— Probability that the event will happen Probability that the event will happen somewheresomewhere near grid pointnear grid point

Probability upscaling exampleProbability upscaling example

Prob = 1/25

Prob = 3/25

Prob = 8/25

Upscaled probabilitiesUpscaled probabilities

Max probability > 40%Max probability > 40%

Upscaling diameter = 15 grid cells ~ 80 kmUpscaling diameter = 15 grid cells ~ 80 km

Verification of 2 July 2011 caseVerification of 2 July 2011 case

Note the agreement between locations of Note the agreement between locations of max probability and max observed rainfall!max probability and max observed rainfall!

Observed

Alternative verificationAlternative verification

Location of ensemble member maximaLocation of ensemble member maxima

Where is max precip most likely?Where is max precip most likely?

Where density of ensemble members is highest!Where density of ensemble members is highest!

Upscaling method will show just that!Upscaling method will show just that!

Heavy rainfall examplesHeavy rainfall examples

Some things to consider...Some things to consider...

At what probability threshold should you take At what probability threshold should you take action?action?

How does forecast skill depend on forecast How does forecast skill depend on forecast range?range?

How many false alarms can you expect?How many false alarms can you expect?

Verification, JJA 2011Verification, JJA 2011Relative operating characteristicRelative operating characteristic

Hit ra

te =

events correctly foreacast / events occurred

False alarm rate = events falsely foreacast / events non-occurred

Perfect forecastPerfect forecast

(FAR,HR) if forecast, when prob > 1/25(FAR,HR) if forecast, when prob > 1/25

(FAR,HR) if forecast, when prob > 2/25(FAR,HR) if forecast, when prob > 2/25

Relative operating characteristicRelative operating characteristicUpscaling vs No upscalingUpscaling vs No upscaling

NB. False alarms are acceptable, if they are NB. False alarms are acceptable, if they are accompanied by nearby hits for the same forecast!accompanied by nearby hits for the same forecast!

Relative operating characteristicRelative operating characteristicForecast skill as a function of lead timeForecast skill as a function of lead time

Relative operating characteristicRelative operating characteristicEnsemble vs DeterministicEnsemble vs Deterministic

Threat scoreThreat score

TS= hitshitsmissesfalse alarms

If probability If probability threshold = 50%...threshold = 50%...

False alarmFalse alarm

HitHitMissMiss

Simplified threat scoreSimplified threat score

HIT = 1 if at least one hitHIT = 1 if at least one hit FA = 1 if at least one false alarm and no hitsFA = 1 if at least one false alarm and no hits MISS = 1 if at least one missMISS = 1 if at least one miss

Count for each forecast...Count for each forecast...

Purpose: Find optimal probability threshold Purpose: Find optimal probability threshold for which an event should be forecastfor which an event should be forecast

Simplified threat scoreSimplified threat score

The threat score is maximized if warnings are The threat score is maximized if warnings are issued when the forecast probability issued when the forecast probability ≥ 20%≥ 20%

Simplified threat scoreSimplified threat score

The threat score is maximized if warnings are only The threat score is maximized if warnings are only issued when the max forecast probability issued when the max forecast probability >> 45% 45%

Issue a warning only if the max forecast probability exceeds Issue a warning only if the max forecast probability exceeds a certain threshold (will reduce false alarms)a certain threshold (will reduce false alarms)

Guidelines to forecastersGuidelines to forecasters

20-50% probability: Pay attention20-50% probability: Pay attention > 50% probability: Take action> 50% probability: Take action

Why not use ECMWF's ensemble Why not use ECMWF's ensemble prediction system?prediction system?

LAM-EPS 50mm contoursLAM-EPS 50mm contours ECMWF-EPS 50mm contoursECMWF-EPS 50mm contours

Why not use ECMWF's ensemble Why not use ECMWF's ensemble prediction system?prediction system?

LAM-EPS 50mm contoursLAM-EPS 50mm contours ECMWF-EPS 15mm contoursECMWF-EPS 15mm contours

50mm/10km50mm/10km22 vs 15mm/1000km vs 15mm/1000km22

Wind case, 8 Feb 2011Wind case, 8 Feb 2011Deterministic model vs ensemble meanDeterministic model vs ensemble mean

24h forecast24h forecast

Wind case, 8 Feb 2011Wind case, 8 Feb 2011Deterministic model vs ensemble meanDeterministic model vs ensemble mean

12h forecast12h forecast

Wind case, 8 Feb 2011Wind case, 8 Feb 2011Deterministic modelDeterministic model

““Truth”Truth”

SummarySummary

Limited-area, high-resolution, short-range ensemble forecasts Limited-area, high-resolution, short-range ensemble forecasts can provide guidance for extreme weather for can provide guidance for extreme weather for localizedlocalized events events

Particularly useful for forecasting the Particularly useful for forecasting the locationlocation of extreme of extreme events, e.g. convective rainfall events (using the upscaling events, e.g. convective rainfall events (using the upscaling method)method)

Guidelines must be provided for the usage of probabilistic Guidelines must be provided for the usage of probabilistic forecasts of extreme events, e.g.forecasts of extreme events, e.g.

20-50% probability: pay attention20-50% probability: pay attention > 50% probability: take action> 50% probability: take action

Upscaled probability forecasts have been used frequently by Upscaled probability forecasts have been used frequently by DMI forecasters for guidance this summerDMI forecasters for guidance this summer

Focus on rainfall events, but also potential for wind eventsFocus on rainfall events, but also potential for wind events