meso- and storm-scale nwp: scientific and operational challenges for the next decade kelvin k....

Meso- and Storm-Scale NWP:Meso- and Storm-Scale NWP:Scientific and Operational Scientific and Operational Challenges for the Next Challenges for the Next

DecadeDecade

Kelvin K. DroegemeierKelvin K. DroegemeierSchool of Meteorology and School of Meteorology and

Center for Analysis and Prediction of StormsCenter for Analysis and Prediction of StormsUniversity of OklahomaUniversity of Oklahoma

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COMAP Symposium on NWPCOMAP Symposium on NWP20 May 199920 May 1999

Boulder, ColoradoBoulder, Colorado

What Are Models What Are Models Predicting?Predicting?

Global and synoptic flow patternsGlobal and synoptic flow patterns Precipitation via crude parameterizations that Precipitation via crude parameterizations that

are unable to resolve individual cloudsare unable to resolve individual clouds Topographic forcingTopographic forcing Coastal and lakeCoastal and lake

influencesinfluences Crude linkagesCrude linkages

between the landbetween the landsurface andsurface andatmosphereatmosphere

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What Are We Using?What Are We Using? Single forecastsSingle forecasts Output frequency of 3 to 12 hoursOutput frequency of 3 to 12 hours Accumulated precipitation and other Accumulated precipitation and other

traditional fieldstraditional fields Graphical Graphical overlaysoverlays of model, radar, satellite of model, radar, satellite

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GETTING THIS

FROM THIS

What Would We Like to What Would We Like to Predict?Predict?

Individual thunderstorms and squall linesIndividual thunderstorms and squall lines Lake effect snow stormsLake effect snow storms Down-slope wind stormsDown-slope wind storms Convective initiationConvective initiation Seabreeze convectionSeabreeze convection Stratocumulus decks off the coastStratocumulus decks off the coast Cold air dammingCold air damming Post-frontal rainbandsPost-frontal rainbands

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Why?Why?

Local high-impact weather causes economic Local high-impact weather causes economic losses in the US that average $300 M losses in the US that average $300 M per weekper week

Over 10% of the $7 trillion US economy is Over 10% of the $7 trillion US economy is impacted each yearimpacted each year

Commercial aviation losses are Commercial aviation losses are $1-2 B per $1-2 B per yearyear (one diverted flight costs $150K) (one diverted flight costs $150K)

Agriculture losses exceed Agriculture losses exceed $10 B/year$10 B/year Other industries (power utilities, surface Other industries (power utilities, surface

transport)transport) About About 50%50% of the loss is preventable! of the loss is preventable!

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What Do We Need?What Do We Need? Models thatModels that

– run at high spatial resolution (1-3 km)run at high spatial resolution (1-3 km)– utilize high-resolution observations (e.g., from theutilize high-resolution observations (e.g., from the

WSR-88D network)WSR-88D network)– handle terrain wellhandle terrain well– represent important physicalrepresent important physical

processes, especially microphysicsprocesses, especially microphysicsand land-surface interactionsand land-surface interactions

Probability forecasts and otherProbability forecasts and othermeasures of uncertaintymeasures of uncertainty

A single tool that A single tool that integratesintegratesmodel output and observationsmodel output and observations

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Trends in Large-Scale Trends in Large-Scale Forecast SkillForecast Skill

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Predictability: Hitting the Predictability: Hitting the WallWall

For global models, the predictability increases For global models, the predictability increases for all resolvable scales as the spatial for all resolvable scales as the spatial resolution increases (quasi 2-D dynamics) resolution increases (quasi 2-D dynamics) – The improvement is boundedThe improvement is bounded– Going beyond a few 10s of km gives little payoffGoing beyond a few 10s of km gives little payoff

The next quantum leap in NWP will come when The next quantum leap in NWP will come when we start resolving explicitly the most energetic we start resolving explicitly the most energetic weather features, e.g., individual convective weather features, e.g., individual convective storms (3-D)storms (3-D)

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60 km 30 km

30 km 10 km

10 km 2 km

Center for Analysis and Center for Analysis and Prediction of Storms Prediction of Storms

(CAPS)(CAPS) One of first 11 NSF Science and Technology One of first 11 NSF Science and Technology

Centers established in 1989Centers established in 1989

Mission: To demonstrate the practicability of Mission: To demonstrate the practicability of numerically predicting local, high-impact storm-numerically predicting local, high-impact storm-scale spring and winter weather, and to develop, scale spring and winter weather, and to develop, test, and help implement a test, and help implement a complete analysis complete analysis and forecast systemand forecast system appropriate appropriate operational, operational, commercial, and researchcommercial, and research applications applications

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The Key Scientific The Key Scientific QuestionsQuestions

Can Can value be addedvalue be added to present-day NWP and radar- to present-day NWP and radar-based nowcasting by storm-resolving models?based nowcasting by storm-resolving models?

Which storm-scale events are most Which storm-scale events are most predictablepredictable, and , and will fine-scale details enhance or reduce predictability?will fine-scale details enhance or reduce predictability?

What What physicsphysics is required, and do we understand it well is required, and do we understand it well enough for practical application?enough for practical application?

What What observationsobservations are most critical, and can data from are most critical, and can data from the national NEXRAD Doppler radar network be used to the national NEXRAD Doppler radar network be used to initialize NWP models? Can this be done in real time?initialize NWP models? Can this be done in real time?

What networking and computational What networking and computational infrastructuresinfrastructures are are needed to support high-resolution NWP?needed to support high-resolution NWP?

How can useful decision making How can useful decision making informationinformation be be generated from forecast model output?generated from forecast model output?

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Prediction TargetsPrediction Targets Somewhat problematicSomewhat problematic For 1-3 km resolution grids, location to withinFor 1-3 km resolution grids, location to within

– 200 km 6 hours in advance200 km 6 hours in advance– 100 km 4 hours in advance100 km 4 hours in advance– 50 km 2 hours in advance50 km 2 hours in advance– 10 km 1 hour in advance10 km 1 hour in advance

InitiationInitiation MovementMovement IntensityIntensity DurationDuration

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Meso-scale NWPMeso-scale NWP The prediction of the general characteristics The prediction of the general characteristics

associated with mesoscale weather associated with mesoscale weather phenomenaphenomena

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6-hour ARPS Forecast at 9 km resolutionWSR-88D CREF (02 UTC 30 Nov 1999)

Storm-scale NWPStorm-scale NWP The prediction of explicit updraft/downdrafts The prediction of explicit updraft/downdrafts

and related features (e.g., gust fronts, meso-and related features (e.g., gust fronts, meso-cyclones)cyclones)

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1-hour ARPS Forecast at 2 km resolution WSR-88D CREF (Lahoma Storm)

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Model Spatial Resolution

Bre

adth

of

Ap

plic

atio

nEconomic Impact

Neg

ativ

e C

onse

qu

ence

s of

a B

ad F

orec

ast

1980’s

1970’s

1990’s

2000-2010

Present NWS OperationsPresent NWS Operations

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CONUS RUC and Eta Models (32 & 40 km)

NCEP Central

Operations

Present NWS OperationsPresent NWS Operations

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NWS Forecast OfficesNWS Forecast Offices

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Small-Scale Weather is LOCAL!Small-Scale Weather is LOCAL!

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SevereThunderstorms

Fog Rain andSnow

Rain andSnow

IntenseTurbulence

Snow andFreezing

Rain

The Future of Operational NWPThe Future of Operational NWP

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10 km

3 km

1 km

20 km CONUS Ensembles

The Future of Operational NWP??The Future of Operational NWP??

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The Emerging Concept of a National The Emerging Concept of a National Scale “Information Power Grid”Scale “Information Power Grid”

Principal Differences Principal Differences Between Large- and Small-Between Large- and Small-

Scale NWPScale NWP Large-scaleLarge-scale: Rawinsondes observe “everything” : Rawinsondes observe “everything”

that is needed to initialize a model (T, RH, u, v)that is needed to initialize a model (T, RH, u, v) Small-scaleSmall-scale: Doppler radar observes only the : Doppler radar observes only the

radial wind and reflectivity in precipitation regions; radial wind and reflectivity in precipitation regions; clear-air PBL data available in some situations clear-air PBL data available in some situations

Large-scaleLarge-scale: Well-known balances can be applied : Well-known balances can be applied to reconcile wind and mass fields (e.g., to reconcile wind and mass fields (e.g., geostrophy, balance equation)geostrophy, balance equation)

Small-scaleSmall-scale: Only simple balances available (mass : Only simple balances available (mass continuity); otherwise, it’s the full equations!!continuity); otherwise, it’s the full equations!!

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Large-scaleLarge-scale: Forecasts are of sufficient : Forecasts are of sufficient duration to be produced and disseminated in duration to be produced and disseminated in reasonable time framesreasonable time frames

Small-scaleSmall-scale: Forecasts are of very short : Forecasts are of very short duration and thus are highly perishableduration and thus are highly perishable

Large-scaleLarge-scale: Observing network is mature and : Observing network is mature and errors and natural variability are understooderrors and natural variability are understood

Small-scaleSmall-scale: Key observing system (WSR-88D) : Key observing system (WSR-88D) is new; only a few links exist for providing is new; only a few links exist for providing base data in real timebase data in real time

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Large-scaleLarge-scale: Dynamics and predictability limits are fairly : Dynamics and predictability limits are fairly well understood; model physics and numerics are well understood; model physics and numerics are reasonably maturereasonably mature

Small-scaleSmall-scale: Dynamics fairly well understood, but : Dynamics fairly well understood, but predictability limits have not been established; model predictability limits have not been established; model physics still evolving; physical processes complicated physics still evolving; physical processes complicated (addition of detail a double-edged sword)(addition of detail a double-edged sword)

Large-scaleLarge-scale: Conventional data assimilation techniques : Conventional data assimilation techniques work well; large-scale features evolve slowly work well; large-scale features evolve slowly

Small-scaleSmall-scale: Conventional data assimilation techniques : Conventional data assimilation techniques not applicable; events are spatially intermittent and not applicable; events are spatially intermittent and evolve rapidly; how to remove an incorrect thunderstorm evolve rapidly; how to remove an incorrect thunderstorm and insert the correct one???and insert the correct one???

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Large-scaleLarge-scale: Computing power reasonably : Computing power reasonably sufficientsufficient

Small-scaleSmall-scale: Need 100 to 1000 times more : Need 100 to 1000 times more computing power than is now available computing power than is now available commerciallycommercially

Large-scaleLarge-scale: No lateral boundary conditions to : No lateral boundary conditions to worry about for global and hemispheric modelsworry about for global and hemispheric models

Small-scaleSmall-scale: Lateral boundaries in limited-area : Lateral boundaries in limited-area models exert a tremendous influence on the models exert a tremendous influence on the solution; compromise between high spatial solution; compromise between high spatial resolution and domain sizeresolution and domain size

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12-hr forecast @ 10 km resolution 6-hr forecast @

4 km resolution

2-hr forecast @1 km resolution

Recipe for a Storm-Scale Recipe for a Storm-Scale NWP SystemNWP System

Advanced numerical model with appropriate Advanced numerical model with appropriate physics parameterizationsphysics parameterizations

High-resolution observations (WSR-88D, High-resolution observations (WSR-88D, profilers, satellites, MDCRS)profilers, satellites, MDCRS)

Powerful computers and networksPowerful computers and networks A way to retrieve quantities that cannot be A way to retrieve quantities that cannot be

observed directlyobserved directly Strategies for converting output to useful Strategies for converting output to useful

decision making informationdecision making information

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The CAPS Advanced Regional The CAPS Advanced Regional Prediction System (ARPS)Prediction System (ARPS)

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ARPS Data Analysis System (ADAS)

ARPS Numerical Model– Multi-scale non-hydrostatic prediction model with comprehensive physics

– Plots and images – Animations – Diagnostics and statistics – Forecast evaluation

– Ingest – Quality control – Objective analysis – Archival

Single-Doppler Velocity Retrieval (SDVR)

4-D Variational

Data Assimilation

Variational Vel -ocity Adjustment

& Thermo-dynamic Retrieval

ARPS Data Assimilation System (ARPSDAS)

ARPSPLT and ARPSVIEW

Inc

om

ing

d

ata

Oklahoma MesonetWSR-88D Wideband

ASOS/AWOS

SAO

ACARS

CLASS

Mobile Mesonet

Profilers

Rawinsondes

Satellite

Lateral boundary conditions from large-scale models

Gridded first guessData Acquisition

& AnalysisData Acquisition

& Analysis

Forecast GenerationForecast Generation

Parameter Retrieval and 4DDAParameter Retrieval and 4DDA

Product Generation and Data Support System

Product Generation and Data Support System

NEXRAD Doppler Radar NEXRAD Doppler Radar DataData

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observe ...observe ...– One (radial) wind componentOne (radial) wind component– reflectivityreflectivity

need ...need ...– 3 wind components3 wind components– temperaturetemperature– humidityhumidity– pressurepressure– water substance (6-10 fields)water substance (6-10 fields)

SDVR solves the inverse problemSDVR solves the inverse problem– control theory (adjoint), simpler methodscontrol theory (adjoint), simpler methods– computationally computationally very intensivevery intensive

Single-Doppler Velocity Retrieval Single-Doppler Velocity Retrieval (SDVR)(SDVR)

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Sample SDVR ResultSample SDVR Result

Dual-DopplerDual-Doppler SDVR-RetrievedSDVR-Retrieved

Weygandt (1998)Weygandt (1998)

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Dual-DopplerDual-Doppler SDVR-RetrievedSDVR-Retrieved

Sample SDVR ResultSample SDVR Result

Weygandt (1998)Weygandt (1998)

5 April 1999 - Impact of Level II Data5 April 1999 - Impact of Level II Data

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Initial 700 mb VerticalVelocity Using NIDS

12 Z Reflectivity

Initial 700 mb VerticalVelocity Using Level II

Data and SDVR

5 April 1999 - Impact of Level II Data5 April 1999 - Impact of Level II Data

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15 Z Reflectivity

3 hr ARPS CREF Forecast (9 km) Using Level II

Data and SDVRValid 15Z

3 hr ARPS CREF Forecast (9 km) Using

NIDS DataValid 15Z

CAPS has been using Level II (base) NEXRAD CAPS has been using Level II (base) NEXRAD data in case study predictions down to 1 km data in case study predictions down to 1 km resolution and Level III data (NIDS) in its daily resolution and Level III data (NIDS) in its daily operational forecastsoperational forecasts

Although NIDS data are available in real time Although NIDS data are available in real time from all radars, they are insufficient in many from all radars, they are insufficient in many cases for storm-scale NWPcases for storm-scale NWP– Precision is degraded via value quantizationPrecision is degraded via value quantization– Only the lowest 4 tilts are transmittedOnly the lowest 4 tilts are transmitted

No national strategy yet exists for the real No national strategy yet exists for the real time collection and distribution of Level II time collection and distribution of Level II datadata

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Availability of Base DataAvailability of Base Data

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Real Time Test Bed for Acquiring WSR-Real Time Test Bed for Acquiring WSR-88D Base Data (Project CRAFT)88D Base Data (Project CRAFT)

INX

DDC

AMA

LBB

FWS

TLX KFSM

ICT

Radars Online

Approval Pending

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CRAFT Phase ICRAFT Phase I

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CRAFT Phase IICRAFT Phase II

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Regional Collection Regional Collection ConceptConcept

Must awaitopen-RPG

Kansas City

Denver

Cleveland

Atlanta

Houston

Pittsburgh

Minneapolis

ColumbusWashington

Raleigh

TrentonSalt Lake City

Wilmington

Dallas

New Orleans

Lincoln

New Haven

Detroit

Miami

Westfield

Nashville

Philadelphia

Newark

UW Pacific North West

Great Plains

MREN

Texas

OneNet

Directly Connected Participant

MAGPI

Pittsburgh (CMU)

MERIT MAX

MCNC

Abilene

GigaPoPs

CENIC

OARnet

Westnet

Albuquerque

GigaPop Connected ParticipantAny color

1999 Network - All Participants

Access NodeRouter Node

Abilene Network

Sacramento

Oakland

Eugene

Los Angeles

33 Total Access PointsServing 64 Members

Seattle

New York

Oklahoma City

Anaheim

Phoenix

Indianapolis

The CAPS VisionThe CAPS Vision Distributed data acquisition (NEXRAD radars)Distributed data acquisition (NEXRAD radars) Distributed dynamic computing - model grids respond to Distributed dynamic computing - model grids respond to

the evolving weatherthe evolving weather Requires intelligent networking, not just high bandwidthRequires intelligent networking, not just high bandwidth Distributed decision making - local weather/local Distributed decision making - local weather/local

decisionsdecisions

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CONUS Forecasts (20 km resolution)

Regionalization and Customization of NWP

Regional (5 km resolution)

Sub-regional (2 km resolution)

Local (0.5-1.0 km resolution)

GOES Satellite DataGOES Satellite Data

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ADAS Cloud Analysis SchemeADAS Cloud Analysis Scheme

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GOES Visible Image at 1745 UTC on 07 May 1995

AB


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Vertical E/W Cross-Section: METAR Only


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Vertical E/W Cross-Section: METAR + GOES IR


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Vertical E/W Cross-Section: METAR + GOES IR + WSR-88D


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PW and Vertically Integrated CondensateValid 13 UTC on 12 April 1999

GOES Visible ImageValid 13 UTC on 12 April 1999

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High-Density Surface High-Density Surface NetworksNetworks

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Commercial Aircraft Wind Commercial Aircraft Wind and Temperature and Temperature

ObservationsObservations

Daily operation of experimental forecast Daily operation of experimental forecast models is critical formodels is critical for– involving operational forecasters in R&Dinvolving operational forecasters in R&D– evaluating model performance under all conditionsevaluating model performance under all conditions– testing new forecast strategies (e.g., rapid model testing new forecast strategies (e.g., rapid model

updates, forecasts on demand, re-locatable domains)updates, forecasts on demand, re-locatable domains)– developing measures of skill and reliability based on developing measures of skill and reliability based on

a long-term data base of model outputa long-term data base of model output– learning how to integrate new forecast information learning how to integrate new forecast information

into operational decision makinginto operational decision making Over 25 groups around the US are running Over 25 groups around the US are running

models in real time in collaboration with NWS models in real time in collaboration with NWS Offices or NCEP CentersOffices or NCEP Centers

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Real Time TestingReal Time Testing

UniquenessUniqueness Daily operational forecasts with full-physics at Daily operational forecasts with full-physics at

spatial resolutions down to 3 kmspatial resolutions down to 3 km Assimilation of high-resolution observations Assimilation of high-resolution observations

consistent with the model high spatial resolutionconsistent with the model high spatial resolution– WSR-88D Level II (base) dataWSR-88D Level II (base) data– WSR-88D Level III (NIDS) dataWSR-88D Level III (NIDS) data– GOES satellite data for quantitative vapor/cloud/precipGOES satellite data for quantitative vapor/cloud/precip– MDCRS commercial aircraft T and VMDCRS commercial aircraft T and V– Surface mesonetsSurface mesonets

More than 2000 products produced each hour More than 2000 products produced each hour and posted on the web (http://hubcaps.ou.edu)and posted on the web (http://hubcaps.ou.edu)

Execution on the 256-node Origin 2000 at NCSAExecution on the 256-node Origin 2000 at NCSA

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1998 Operational 1998 Operational ConfigurationConfiguration

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9 km, 1 fcst 6 hours

27 km, 4 fcsts 12/18 hours

06Z 00Z

12Z 00Z

18Z

06Z

00Z

18Z

20Z 02Z

06Z 00Z

1 DAY

1998 Hourly Analysis 1998 Hourly Analysis DomainsDomains

D/FW Region

NE Corridor

ORD Region

Central/Eastern USs fn

ARPSView Decision Support SystemARPSView Decision Support System

Proprietary

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Forecast Status PageForecast Status Page

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Sample ARPSView Sample ARPSView ProductsProducts

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Cloud Type and LWCat FL 050

Cloud Type and LWCat FL 320

Cloud Type and LWCN/S X-Section


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Downburst Potential Surface Isotachs &Streamlines

CAPE & Helicity


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Lifted Index & CAPStrength

Sfc Moisture Convergenceand Theta-e

BRN & BRN Shear


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700 mb Winds, T,and RH

500 mb Height, Vort 700 mb Vert Velocity


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N/S X-Section ofVert Vel and Winds

N/S X-Section ofRH and Winds

Montgomery StreamFunction and Winds on

320K Isentropic Sfc


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Sounding and Hodograph Meteogram

3-4 December 19983-4 December 1998

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24 h Eta Valid 00Z 4 Dec 98

9 h RUC Valid 00Z 4 Dec 98


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KTLX 00Z 4 Dec 98

KFWS 00Z 4 Dec 98


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ARPS 4 h Forecast CREF (9 km) Valid 00Z 4 Dec 98

KFWS 00Z 4 Dec 98


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ARPS 12 h Accumulated Precipitation(27 km) Valid 12Z 4 Dec 98

Observed 24-hourAccumulated Precip(Valid 12Z 4 Dec 98)


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ARPS 6 h Accumulated Precipitation(9 km) Valid 02Z 4 Dec 98

Observed 24-hourAccumulated Precip(Valid 12Z 4 Dec 98)

23 December 199823 December 1998

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NORTH TEXAS FORECAST DISCUSSIONNATIONAL WEATHER SERVICE FORT WORTH TX935 PM CST TUE DEC 22 1998

. . . HOW ABOUT THE WINTER STORM WARNING?: MY CONFIDENCE IN IT ISLOW...BUT NOT LOW ENOUGH TO CANCEL IT...GIVEN THAT MOST OF THEPRECIP HAS YET TO DEVELOP (CEILINGS ARE BEGINNING TO DECREASE ATDFW...WHICH IS A FAVORABLE TREND FOR PRECIP). THE ADVISORY LOOKSOK...AS MOST OF THE PRECIP WILL BE FREEZING RAIN/SLEET...AND LIGHTPRECIP COULD CAUSE WIDESPREAD ROAD PROBLEMS. THUS...WE WILL KEEPTHE ADVYS AS IS...AND KEEP THE WARNING...POSSIBLY EVEN EXPANDING ITTO INCLUDE THE EXTREME SOUTHEAST COUNTIES THAT ADJOIN THE WINTERSTORM WARNING AREA THAT HOUSTON HAS GOING.

ANOTHER COMMENT: MESOSCALE MODELS ARE NOT HELPING MUCH INTHIS TOUGH SITUATION...AS THEY SHOW RATHER DISPARATE SOLUTIONS.THE RUC II SAYS "NON-EVENT", THE ARPS SHOWS PRECIP STREAKINGDIRECTLY ACROSS DFW...AND THE SYNOPTIC ETA SAYS NORTHEASTTEXAS!


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3 h RUC Valid 12Z 23 Dec 98


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12Z Surface Obs

REUTERS. At least 10 people died in road accidents in TexasWednesday as storms brought widespread ice and wreakedhavoc on routes to Dallas from San Antonio, 250 milessouthwest, officials said. Two people died when 59 carscrashed in two pile-ups on an Austin highway as snow andrain combined with freezing temperatures, said Austin PoliceDepartment spokeswoman Tracy Karol. Hundreds of flightswere canceled at Dallas-Fort Worth International Airport(DFW). "We're having definite problems at DFW today. Ourbest guess is that...we'll be operating approximately 50 percentof our flights,'' said American Airlines spokesman Tim Smith.The U.S. carrier usually has 525 flights a day leaving theairport, its main hub, and a similar number of arrivals, headded. Extensive de-icing of planes had slowed schedules,while road conditions prevented employees reaching work.


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ARPS 6 h Forecast Explicit (left) and Conditional (right) Precipitation Type (27 km) Valid 12Z 23 Dec 98

1999 Special Operational Period1999 Special Operational Period

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5-Member, 30 km Ensemble

9 km

3 km

WSR-88D Base Data Being Ingested WSR-88D Base Data Pending

6 January 19996 January 1999

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GOES Visible Image1745Z, 6 Jan 99

ARPS 12 h Forecast Visibility (27 km) Valid 18Z, 6 Jan 99

6 February 1999 - Bust!6 February 1999 - Bust!

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Fort Worth Radar at 00Z Sunday, 7 Feb 1999

ARPS 4-hour Forecast Reflectivity (9 km grid) Valid 00Z Sunday,

7 Feb 1999

1 May 19991 May 1999

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Radar Valid 1930 Z Saturday, 1 May 1999

NWS RUC Model Forecast Valid 21 Z Saturday, 1 May 1999

1 May 19991 May 1999

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Radar (1930 Z Saturday, 1 May 1999)

ARPS 9 km CREF Forecast Valid 20 Z Saturday, 1 May 1999

ARPS 32 km Forecast - AR TornadoesARPS 32 km Forecast - AR Tornadoes

Radar(Tornadoes

in Arkansas)

ARPS 12-hour, 32 km Resolution

Forecast CREF Valid at 00Z on 1/22/99

Proprietary

Radar

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ARPS 9km Forecast - AR TornadoesARPS 9km Forecast - AR Tornadoes

Radar(Tornadoes

in Arkansas)

ARPS 6-hour, 9 kmForecast CREF Valid

at 00Z on 1/22/99

Proprietary

Radar

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Weather Channel Radarat 2343 Z

ARPS 6-hour, 3 kmForecast CREF Valid at 00Z

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ARPS 6-hour, 3 km (E/W x-section)Forecast Reflectivity and Cld/Ice Valid at 00Z

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3 May 1999 Oklahoma 3 May 1999 Oklahoma TornadoesTornadoes

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KTLX CREF 00 Z on Tuesday, 4 May 1999(7 pm CDT on 3 May)

ARPS 9 km CREF Forecast Valid 00 Z Tuesday, 4 May 1999

3 May 1999 Oklahoma 3 May 1999 Oklahoma TornadoesTornadoes

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KTLX CREF 06 Z on Tuesday, 4 May 1999(1 am CDT on 4 May)

ARPS 4-hour 9 km CREF Forecast Valid 06 Z Tuesday, 4 May 1999

9-10 May 19999-10 May 1999

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Composite Radar Valid 0344 Z on Monday, 10 May 1999

ARPS 4-hour, 3 km CREF Forecast Valid 04 Z Monday, 10 May 1999

How Good are the Forecasts?How Good are the Forecasts?

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Forecast Verification

40 km for 6 Hour Forecast

D/FW Airport

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

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Traditional skill measures (e.g., threat score Traditional skill measures (e.g., threat score or “overlap” agreement) not appropriate for or “overlap” agreement) not appropriate for intermittent storm-scale phenomenaintermittent storm-scale phenomena

SPC concern is the specific character of SPC concern is the specific character of storms (intensity, motion, initiation, decay); storms (intensity, motion, initiation, decay); precipitation is less of a concernprecipitation is less of a concern

We forecast more things than we can We forecast more things than we can observe/verify (how to verify 500 mb height observe/verify (how to verify 500 mb height fields that contain thunderstorms?)fields that contain thunderstorms?)

Point verification is rather meaninglessPoint verification is rather meaningless

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The IssuesThe Issues

Phase-shifting verificationPhase-shifting verification– maximize spatial correlationmaximize spatial correlation– generates a shift vectorgenerates a shift vector

Qualitative (by hand) verification Qualitative (by hand) verification – location, speed, timing, duration, intensity, location, speed, timing, duration, intensity,

orientation, modeorientation, mode– ““With 4 hours of lead time, the location of storms With 4 hours of lead time, the location of storms

was within 30 km of observed 80% of the time”was within 30 km of observed 80% of the time”– ““The model predicted storms 10% of the time when The model predicted storms 10% of the time when

none were observed”none were observed” Seeking to create a unified approachSeeking to create a unified approach Will eventually have to consider cost-benefit Will eventually have to consider cost-benefit

and reliabilityand reliabilitys fn

ApproachesApproaches

Storm-scale models are not reflectivity generators, yet Storm-scale models are not reflectivity generators, yet reflectivity is what we’re used to seeing!reflectivity is what we’re used to seeing!– Must be careful not to focus on the final outcomeMust be careful not to focus on the final outcome– Forecasters not used to seeing storms on a 500 mb map!Forecasters not used to seeing storms on a 500 mb map!– Even when reflectivity is incorrect, many other features Even when reflectivity is incorrect, many other features

may be accuratemay be accurate Fine resolution Fine resolution

– means thinking across many more scales of motionmeans thinking across many more scales of motion– gives more detail but also greater uncertainty and gives more detail but also greater uncertainty and

sensitivity (e.g., caps, outflow boundaries)sensitivity (e.g., caps, outflow boundaries) Forecasters easily overwhelmed by zillions of new Forecasters easily overwhelmed by zillions of new

productsproducts– must determine what’s really needed and usefulmust determine what’s really needed and useful

More experience needed with ensemble outputMore experience needed with ensemble output

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Lessons LearnedLessons Learned

Ensemble ForecastingEnsemble Forecasting The NeedThe Need

– Small errors in numerical weather forecasts Small errors in numerical weather forecasts can grow quickly and render the solution can grow quickly and render the solution indistinguishable from a randomly chosen indistinguishable from a randomly chosen forecast at some later timeforecast at some later time

– Errors are unavoidable: observations, Errors are unavoidable: observations, models, understandingmodels, understanding

– We desire to predict forecast uncertainty as We desire to predict forecast uncertainty as well as the weatherwell as the weather

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Ensemble ForecastingEnsemble Forecasting StrategyStrategy

– In addition to a control forecast, create a In addition to a control forecast, create a number of other forecasts whose initial number of other forecasts whose initial conditions are equally plausible but differ conditions are equally plausible but differ slightly from those of the controlslightly from those of the control

– Ensemble averaging acts as a non-linear Ensemble averaging acts as a non-linear filter to smooth out the unpredictable filter to smooth out the unpredictable components of the flowcomponents of the flow

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Initial State Uncertainty

Truth

Single Forecast

Traditional Forecasting

Methodology

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t critical

Deterministic Forecast

Probabilistic Forecast

Ensemble Forecasting

Initial State Uncertainty

Mean

Truth

Ensemble ForecastingEnsemble Forecasting AdvantagesAdvantages

– Ensemble mean is generally superior to Ensemble mean is generally superior to control forecast control forecast

– Ensembles provideEnsembles provide a measure of expected skill or confidencea measure of expected skill or confidence a quantitative basis for probabilistic forecastinga quantitative basis for probabilistic forecasting a rational framework for forecast verificationa rational framework for forecast verification information for targeted observationsinformation for targeted observations

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Ensemble ForecastingEnsemble Forecasting Limitations/ChallengesLimitations/Challenges

– Not clear how to optimally specify the initial Not clear how to optimally specify the initial conditions (singular vectors, breeding, conditions (singular vectors, breeding, perturbed observations)perturbed observations)

– Requires more computer resourcesRequires more computer resources

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Collaborative effort among CAPS, NCAR, AFWA, Collaborative effort among CAPS, NCAR, AFWA, NCEP and NSSLNCEP and NSSL

Performed during May, 1998 Performed during May, 1998 Goal: Examine the value of coarse-resolution, Goal: Examine the value of coarse-resolution,

multi-model ensemble forecasts versus single multi-model ensemble forecasts versus single high-resolution deterministic forecastshigh-resolution deterministic forecasts

Expose operational forecasters to both types of Expose operational forecasters to both types of outputoutput

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Storm and Mesoscale Storm and Mesoscale Ensemble Ensemble

Experiment (SAMEX)Experiment (SAMEX)

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SAMEX DomainsSAMEX Domains

NSSL (32 km)

NCAR (30 km)

NCAR (10 km)

CAPS (32 km)

CAPS (9 km), NCEP (10 km)

CAPS (3 km)

AFWA (9 km)

AFWA (3 km)

CAPS (32 km), NCEP (32 km)

AFWA (27 km)

Ensemble Product Domain

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3-hour Accumulated Precipitation

25-Member Ensemble POP > 0.1 inches/hour

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Explicit 9 km PredictionExplicit 9 km Prediction

3-hour Accumulated Precipitation 9 km, 15-hour ARPS Forecast Reflectivity

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500 mb Errors 500 mb Errors

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20-30 km Resolution Ensemble Domain

Pacific Northwest

California Coast

Central and Southern

Great Plains

Inter-Mountain

Florida Coast

Great Lakes

Southeast US

Storm-scale NWP is a significant scientific and Storm-scale NWP is a significant scientific and technological challengetechnological challenge

Predictability appears plausible at storm scalesPredictability appears plausible at storm scales More work needed inMore work needed in

– data assimilation, especially from satellite, GPS, WSR-88Ddata assimilation, especially from satellite, GPS, WSR-88D– physics parameterizations (especially cloud microphysics, physics parameterizations (especially cloud microphysics,

radiation, and land-atmosphere exchanges)radiation, and land-atmosphere exchanges)– fundamental predictability and sensitivityfundamental predictability and sensitivity

Transition to operations will be a major challengeTransition to operations will be a major challenge– centralized versus distributed?centralized versus distributed?– verification techniquesverification techniques– creation of useful productscreation of useful products– forecaster interpretation and utilizationforecaster interpretation and utilization

NWS FO involvement in R&D will be criticalNWS FO involvement in R&D will be critical

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SummarySummary

meso- and storm-scale nwp: scientific and operational challenges for the next decade kelvin k....

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highresolution nwp

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