numerical prediction of high-impact local weather: how good can it get? kelvin k. droegemeier...

Numerical Prediction of Numerical Prediction of High-Impact Local High-Impact Local

Weather: Weather: How Good Can It Get?How Good Can It Get?

Kelvin K. DroegemeierKelvin K. DroegemeierRegents’ Professor of MeteorologyRegents’ Professor of Meteorology

Vice President for ResearchVice President for ResearchUniversity of OklahomaUniversity of Oklahoma

2013 Congress2013 Congress19 April 201319 April 2013

Agriculture Agriculture $135.8B (100%)$135.8B (100%) Oil and Gas Extraction Oil and Gas Extraction $99.5B (100%)$99.5B (100%) Construction Construction $463.6B (100%)$463.6B (100%) Transportation Transportation $786.5B ( 95%)$786.5B ( 95%) Retail Trade Retail Trade $893.9B (100%)$893.9B (100%) State/Local Government State/Local Government $829.5B (100%)$829.5B (100%) OthersOthers

TotalTotal $3.86T ( 40%)$3.86T ( 40%)

40%40% of the $10T U.S. Economy of the $10T U.S. Economy is Impacted by Weather and is Impacted by Weather and

ClimateClimate

876876 deaths annually due to severe deaths annually due to severe weatherweather

7000+7000+ weather-related traffic fatalities weather-related traffic fatalities 450,000450,000 weather-related traffic injuries weather-related traffic injuries

A Great Toll in Human A Great Toll in Human LifeLife

About About 50%50% of the loss is of the loss is deemed preventable with deemed preventable with

better weather and climate better weather and climate forecasts!forecasts!

Copyright © 2003 WGN-TV

Computer ModelsComputer Models are the Primary Source are the Primary Source of Information for All Weather & Climate of Information for All Weather & Climate

PredictionsPredictions

The Prediction ProcessThe Prediction Process

Analyze ResultsAnalyze Results

Com

pare

and

Ver

ify

Com

pare

and

Ver

ify

Observe the AtmosphereObserve the Atmosphere

Identify and ApplyIdentify and ApplyPhysical LawsPhysical Laws

Create a MathematicalCreate a MathematicalModelModel

Create and Run aCreate and Run aComputer ModelComputer Model

Data Assimilation Data Assimilation

D

ata

Ass

imil

atio

n S

yste

m

RadarsRadars Radial Wind, Reflectivity

Other ObservationsOther Observations A Bit of Everything Some Places

ForecastForecastModel OutputModel Output

All Variables, But From a Forecast

3D Gridded AnalysisThat Contains allVariables, is DynamicallyConsistent, and has Minimum Global Error w/r/t theObservations

The First Numerical Weather Prediction The First Numerical Weather Prediction ExperimentExperiment

Done on ENIAC: 5 Done on ENIAC: 5 million times slower million times slower than my laptopthan my laptop

Numerically integrated Numerically integrated oneone equation at equation at oneone altitudealtitude

450 mile grid spacing450 mile grid spacing 24 hour forecast took 24 hour forecast took

24 hours24 hours

Today’s ModelsToday’s Models

Typical Forecast from Today’s Typical Forecast from Today’s Operational ModelsOperational Models

What Causes the Major What Causes the Major Problems?Problems?

A Foundational QuestionA Foundational Question

. . . explicitly predict this. . . explicitly predict thistype of weather?type of weather?

Can computer forecastCan computer forecasttechnology. . .technology. . .

Example : March 28, 2000 Fort Example : March 28, 2000 Fort Worth Tornadic StormsWorth Tornadic Storms

Tornado

Local TV Station RadarLocal TV Station Radar

NWS NWS 12-hr12-hr Computer Forecast Valid at 6 pm CDT Computer Forecast Valid at 6 pm CDT (near tornado time)(near tornado time)

No Explicit Evidence of Precipitation in North TexasNo Explicit Evidence of Precipitation in North Texas

Reality Was Quite Reality Was Quite Different!Different!

6 pm 7 pm 8 pmR

adar

Xue et al. (2003)

Fort Worth

Hourly Radar Observations(Fort Worth Shown by the Pink Star)

6 pm 7 pm 8 pmR

adar

Fcs

t Wit

h R

adar

Dat

a

2 hr 3 hr 4 hr

Fort Worth

Fort Worth

Xue et al. (2003)

How Good Are the How Good Are the Forecasts??Forecasts??

Actual Event

30 miles

D/FW Airport

A perfectly predicted storm having a position error A perfectly predicted storm having a position error of 30 miles may be a terrible forecast on the scale of 30 miles may be a terrible forecast on the scale of a single airportof a single airport

Forecast

O

F

50 km

One Forecast Verification One Forecast Verification StrategyStrategy

HIT

O

F

50 km

MISS

O

F30 km

HIT

O

F

MISS

30 km

One Forecast Verification One Forecast Verification StrategyStrategy

0.6

0.7

0.8

0.9

1

1.1

2230Z 2300Z 2330Z 0000Z

May 3 ARPS Forecast with WSR-88D Level II DataInitialized 22 UTC Using 3 km Spatial Resolution

Verification Within Circles of Radii Indicated(VIP Level 3)

POD (50 km)POD (40 km)POD (30 km)POD (20 km)POD (10 km)

Time (UTC)

Probability of DetectionProbability of Detection

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

2230Z 2300Z 2330Z 0000Z

May 3 ARPS Forecast with WSR-88D Level II DataInitialized 22 UTC Using 3 km Spatial Resolution

Verification Within Circles of Radii Indicated(VIP Level 3)

PFA (50 km)PFA (40 km)PFA (30 km)PFA (20 km)PFA (10 km)

Time (UTC)

False Alarm RateFalse Alarm Rate

Actual Event

30 miles

D/FW Airport

We need to forecast the weather PLUS We need to forecast the weather PLUS the accuracy of the forecast!!!the accuracy of the forecast!!!

Forecast

As a Forecaster As a Forecaster Worried About Worried About This Reality… This Reality…

7 pm

As a Forecaster As a Forecaster Worried About Worried About This Reality… This Reality…

How Much How Much Trust Would Trust Would You Place in You Place in This Model This Model Forecast? Forecast?

3 hr

7 pm

Initial State Uncertainty

Truth

Single Forecast

Traditional Forecasting

Methodology

t critical

Deterministic Forecast

Probabilistic Forecast

Ensemble Forecasting

Initial State Uncertainty

Mean

Truth

Actual RadarActual Radar

Forecast #1Forecast #1 Forecast #2Forecast #2

Forecast #3Forecast #3 Forecast #5Forecast #5Forecast #4Forecast #4

Actual RadarActual Radar

Probability of Intense PrecipitationProbability of Intense Precipitation

Model Forecast Radar Observations

MUCH MORE Computing Power is MUCH MORE Computing Power is Required!!Required!!

Each set of forecasts (ensemble and individual)Each set of forecasts (ensemble and individual)– produces 6 TB of output PER DAYproduces 6 TB of output PER DAY– Requires 9000 cores (750 nodes) of the Kraken Cray XT5 at Oak RidgeRequires 9000 cores (750 nodes) of the Kraken Cray XT5 at Oak Ridge– Takes 6.5 hours to runTakes 6.5 hours to run

Provisioning of data in real timeProvisioning of data in real time Management in a repository – retention time?Management in a repository – retention time? Experiment reproducibility!!Experiment reproducibility!! Creating products that will benefit the public (smart device location-Creating products that will benefit the public (smart device location-

based warnings)based warnings)

ChallengesChallenges

A Fundamental Research A Fundamental Research QuestionQuestion

Can we better understand the atmosphere, Can we better understand the atmosphere, educate more effectively about it, and forecast educate more effectively about it, and forecast more accurately if we more accurately if we adaptadapt our technologies and our technologies and approaches to the weather approaches to the weather as it occursas it occurs??

People, even animals adapt/respond: Why don’t People, even animals adapt/respond: Why don’t our resources???our resources???

The VisionThe VisionRevolutionize the ability of scientists, students, Revolutionize the ability of scientists, students,

and operational practitioners to observe, and operational practitioners to observe, analyze, predict, understand, and respond to analyze, predict, understand, and respond to intense local weather by interacting with it intense local weather by interacting with it

dynamically and adaptivelydynamically and adaptively in real time in real time

The Value of Adaptation: Forecaster-The Value of Adaptation: Forecaster-Initiated PredictionsInitiated Predictions

Brewster et al. (2008)

Observed Composite Reflectivity

20 hr Pre-ScheduledWRF-ARF

5 hr LEAD Dynamic WRF-ARF With RadarData Assimilation

The Million Dollar The Million Dollar Question: Will Question: Will

Computer Models Ever Computer Models Ever Be Able to Be Able to PredictPredict

Tornadoes?Tornadoes?

24 May 2011 Tornado Outbreak: 24 May 2011 Tornado Outbreak: Warning on a Numerical ForecastWarning on a Numerical Forecast

NWS OUN Graphic

24 May 2011 Tornado Outbreak: 24 May 2011 Tornado Outbreak: Warning on a Numerical ForecastWarning on a Numerical Forecast

Are All the Data Making Are All the Data Making a Difference?a Difference?

44

45

Are All the Data Making Are All the Data Making a Difference?a Difference?

Numerical Simulation24 hours CPU = 1 hour real20 TB of outputStill trying to understand

Mother NatureReal time!Still trying to understand

Data Don’t Guarantee Data Don’t Guarantee Understanding!Understanding!

Be careful what you wish for! A one-hour model-based “tornado Be careful what you wish for! A one-hour model-based “tornado warning” would be a game changerwarning” would be a game changer

Social and behavioral science elements are criticalSocial and behavioral science elements are critical– Why did 550 people die in the US last year from tornadoes?Why did 550 people die in the US last year from tornadoes?

Our ability to effectively warn the public and understand its Our ability to effectively warn the public and understand its response is relatively cruderesponse is relatively crude

This is an area ripe for additional research – and it is ESSENTIAL This is an area ripe for additional research – and it is ESSENTIAL for making progressfor making progress

ChallengesChallenges

TODAY: Centralized TODAY: Centralized Prediction, Distributed DataPrediction, Distributed Data

TOMORROW: Distributed & Cloud-TOMORROW: Distributed & Cloud-Based Models Run Locally, On Based Models Run Locally, On

DemandDemand

10 km

3 km

3 km

3 km3 km

10 km

20 km CONUS Ensembles