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

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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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COMET Faculty Course on NWPCOMET Faculty Course on NWP9 June 19999 June 1999

Boulder, ColoradoBoulder, Colorado

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What Are What Are OperationalOperational Models 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 Do Forecasters Use?What Do Forecasters Use? 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

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What Do We Need to Predict?What Do We Need to 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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Pielke Jr. (1997)

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What is Needed?What is Needed? 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

Physical/theoretical understandingPhysical/theoretical understanding Tools for integrating modelTools for integrating model

output, observationsoutput, observations

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Role of the University Role of the University CommunityCommunity

Educating studentsEducating students about NWP -- a whole new about NWP -- a whole new ballgame!ballgame!– Physical processesPhysical processes– Data sets & observing platformsData sets & observing platforms– Numerical models & methodsNumerical models & methods– Data assimilation & predictabilityData assimilation & predictability

ResearchResearch in all facets of NWP in all facets of NWP Running modelsRunning models in in real time in in real time

– More than 25 universities do this today!More than 25 universities do this today!– Major change from 20 years ago!Major change from 20 years ago!– Academia is driving operational NWPAcademia is driving operational NWP

Collecting dataCollecting data– GPS, WSR-88D, otherGPS, WSR-88D, other

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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

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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 STCs were designed to attack problems of STCs were designed to attack problems of

fundamental researchfundamental research that eventually would yield that eventually would yield important benefits to societyimportant benefits to society

Mission of CAPS: To demonstrate the Mission of CAPS: To demonstrate the practicability of numerically predicting local, practicability of numerically predicting local, high-impact storm-scale spring and winter high-impact storm-scale spring and winter weather, and to develop, test, and help weather, and to develop, test, and help implement a implement a complete analysis and forecast complete analysis and forecast systemsystem appropriate appropriate operational, commercial, and operational, commercial, and researchresearch applications applications s fn

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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 The prediction of the general characteristicsgeneral 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)

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

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

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NEXRAD Radar ObservationsARPS 90 min Forecast (3 km)

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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

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Present NWS OperationsPresent NWS Operations

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CONUS RUC and Eta Models (32 & 40 km)

NCEP Central

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

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The Future of Operational NWPThe Future of Operational NWP

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10 km

3 km

1 km

20 km CONUS Ensembles

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

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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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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) and profilers, satellites, MDCRS) and appropriate appropriate ways for using themways for using them

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 informations fn

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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

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NEXRAD Doppler Radar NEXRAD Doppler Radar DataData

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We observe ...We observe ...– one (radial) wind componentone (radial) wind component– reflectivityreflectivity

We need ...We 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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real real windwind

observedobservedcomponentcomponent

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Sample SDVR ResultSample SDVR Result

Dual-DopplerDual-Doppler SDVR-RetrievedSDVR-Retrieved

Weygandt (1998)Weygandt (1998)

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5 April 1999 - Impact of Radar Data5 April 1999 - Impact of Radar Data

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Initial 700 mb VerticalVelocity Using NIDS

12 Z Reflectivity

Initial 700 mb VerticalVelocity Using Level II

Data and SDVR

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5 April 1999 - Impact of Radar Data5 April 1999 - Impact of Radar 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

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The Lahoma, OK HailstormThe Lahoma, OK Hailstorm

Conway et al. (1996)

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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 datatime collection and distribution of Level II data

An example of universities leading the way!!An example of universities leading the way!!

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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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Regional Collection Regional Collection ConceptConcept

Must awaitMust awaitopen-RPGopen-RPG

GreatGreatopportunityopportunity

forforuniversities!universities!

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The CAPS VisionThe CAPS Vision

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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)

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Daily operation of experimental forecast models Daily operation of experimental forecast models is critical foris 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 a developing measures of skill and reliability based on a

long-term data base of model outputlong-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 Centers; few are assimilating Offices or NCEP Centers; few are assimilating observationsobservations

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Real Time TestingReal Time Testing

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CAPS’ Real Time TestingCAPS’ Real Time Testing 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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ARPSView Decision Support SystemARPSView Decision Support System

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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

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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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ARPS 3km Forecast - AR TornadoesARPS 3km Forecast - AR Tornadoes

Weather Channel Radarat 2343 Z

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

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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

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9-10 May 19999-10 May 1999

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Composite Radar Valid 2347 Z on Sunday, 9 May 1999

NCEP Eta 12-hour Forecast Valid 00 Z Monday, 10 May 1999

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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

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1 June 19991 June 1999

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KFWS CREF Valid 00 Z on Tuesday, 1 June 1999

ARPS CREF Initial ConditionValid 00 Z on Tuesday, 1 June 1999

(3 km resolution with Level II data from KTLX and KFWS + NIDS)

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1 June 19991 June 1999

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ARPS CREF 1-hour ForecastValid 01 Z on Tuesday, 1 June 1999


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1 June 19991 June 1999

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1 June 19991 June 1999

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1 June 19991 June 1999

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1 June 19991 June 1999

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3 June 19993 June 1999

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KAMA CREF Valid 00 Z on 3 June 1999 ARPS 3-hour 3 km Forecast

Valid 00 Z on 3 June 1999(without NEXRAD base data)

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3 June 19993 June 1999

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3 June 19993 June 1999

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3 June 19993 June 1999

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3 June 19993 June 1999

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NEXRAD Radar Observations

5:30 pm

ARPS Prediction Model(1/2 hour forecast)

Numerical Forecasts of the May 3 Tornadic Numerical Forecasts of the May 3 Tornadic StormsStorms

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6:00 pm

ARPS Prediction Model(1 hour forecast)


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6:30 pm

ARPS Prediction Model(1 1/2 hour forecast)


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7:00 pm



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7:00 pm


12-hour Eta12-hour EtaForecastForecast

Moore, OKTornadic

Storm



Moore, OKTornadic

Storm

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7:00 pm


ARPS ARPS With and WithoutWith and Without NEXRAD Base Data NEXRAD Base Data


WITH

(3 ARPS hour forecast)

WITHOUT


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

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Forecast Verification

40 km for 3 Hour Forecast

D/FW Airport

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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

Specific character of storms (intensity, Specific character of storms (intensity, motion, initiation, decay) important for motion, initiation, decay) important for operational forecastersoperational forecasters

QPF is critical! QPF is critical! Problem: Problem: We forecast more things than we We forecast more things than we

can observe/verifycan observe/verify (how to verify 500 mb (how to verify 500 mb height fields that contain thunderstorms?)height fields that contain thunderstorms?)

Point verification is rather meaninglessPoint verification is rather meaningless

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The IssuesThe Issues

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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” Phase-shifting verificationPhase-shifting verification

– maximize spatial correlationmaximize spatial correlation– generates a shift vector generates a shift vector

Will eventually have to consider cost-benefit Will eventually have to consider cost-benefit and reliabilityand reliability

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ApproachesApproaches

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Quantitative Forecast Evaluation Quantitative Forecast Evaluation for May 3 Forecasts (3 km)for May 3 Forecasts (3 km)

Hourly analysis of echoes by county in Oklahoma Hourly analysis of echoes by county in Oklahoma (N=77)(N=77)

StatisticsStatistics– Hit RateHit Rate: The fraction of correct forecasts (best=1, : The fraction of correct forecasts (best=1,

worst=0)worst=0)– Critical Success IndexCritical Success Index (Threat Score): The hit rate after (Threat Score): The hit rate after

removing the correct forecasts of no echoes (best=1, removing the correct forecasts of no echoes (best=1, worst=0)worst=0)

– False Alarm RatioFalse Alarm Ratio: The fraction of forecasts that are : The fraction of forecasts that are incorrect (best=0, worst=1)incorrect (best=0, worst=1)

– Probability of DetectionProbability of Detection: The fraction of forecasts that are : The fraction of forecasts that are correct (best=1, worst=1)correct (best=1, worst=1)

– BiasBias: A measure of the tendency to overforecast or : A measure of the tendency to overforecast or underforecast. (bias=1 is optimal)underforecast. (bias=1 is optimal)

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Averages

HR = 0.911HR = 0.911CSI = 0.621CSI = 0.621FAR = 0.233FAR = 0.233POD = 0.798POD = 0.798BIAS = 1.110BIAS = 1.110

0

0.5

1

1.5

2

2200 2230 2300 2330 0000 0030

May 3 ARPS Forecast with WSR-88D Level II DataInitialized 22 UTC Using 3 km Spatial ResolutionExistence of Echoes for all 77 Oklahoma Counties

HRCSIFARPODBias

Time (UTC)

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Averages


-0.5

0

0.5

1

1.5

2

2200 2300 0000 0100 0200

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

CREF Echoes of 50 dBz +/- 10 dBz by County

HRCSIFARPODBias

Time (UTC)

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Averages


-0.2

0

0.2

0.4

0.6

0.8

1

1.2

2200 2300 0000 0100 0200

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

CREF Echoes of 50 dBz +/- 5 dBz by County

HRCSIFARPODBias

Time (UTC)

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t = 2 hourst = 2 hourst = 1 hourt = 1 hour

TruthTruth

ForecastForecast

Zhang (1999)Zhang (1999)

TheTheImportanceImportance

ofofPhase ErrorsPhase Errors

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StandardStandardThreat ScoreThreat Score

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Phase-ShiftedPhase-ShiftedThreat ScoreThreat Score

Average Phase Average Phase Shift Error (km)Shift Error (km)

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Getting the larger-scale features correct is the easy part -- Getting the larger-scale features correct is the easy part -- getting the reflectivity correct is tough!getting the reflectivity correct is tough!– But does it matter?But does it matter?– These models are not reflectivity generators!These models are not reflectivity generators!

Solution sensitivity (surface characteristics, soil moisture)Solution sensitivity (surface characteristics, soil moisture) Initial conditions are Initial conditions are thethe critical aspect -- much work needed critical aspect -- much work needed

in data assimilation and parameter retrievalin data assimilation and parameter retrieval Model physics seem adequate (QPF needs work, though)Model physics seem adequate (QPF needs work, though) How good is good enough?How good is good enough? Fine resolution gives more detail but also greater Fine resolution gives more detail but also greater

uncertainty and sensitivity (e.g., caps, outflow boundaries)uncertainty and sensitivity (e.g., caps, outflow boundaries) Forecasters easily overwhelmed by zillions of new productsForecasters easily overwhelmed by zillions of new products More experience needed with ensemble forecastingMore experience needed with ensemble forecasting

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Lessons LearnedLessons Learned

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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

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Ensemble ForecastingEnsemble Forecasting AdvantagesAdvantages

– Ensemble mean is generally superior Ensemble mean is generally superior – 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

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 in real time to Expose operational forecasters in real time to both types of outputboth types of output

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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 Observed Precipitation

25-Member Ensemble POP > 0.1 inch/hour

Oops!!

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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

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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

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Some Key Scientific IssuesSome Key Scientific Issues Predictability of storm-scale flows and application of ensemble Predictability of storm-scale flows and application of ensemble

strategies and forecast verification techniques at 1-3 km resolutionstrategies and forecast verification techniques at 1-3 km resolution Data impact/sensitivity, especially land-atmosphere interactionsData impact/sensitivity, especially land-atmosphere interactions Advanced data assimilation techniques (3DVAR, 4DVAR): Advanced data assimilation techniques (3DVAR, 4DVAR): most most

everything boils down to the initial conditionseverything boils down to the initial conditions!! Feedback of cloud-scale NWP to global and regional climateFeedback of cloud-scale NWP to global and regional climate Use of cloud-scale forecasts in hydrologic modelsUse of cloud-scale forecasts in hydrologic models Application of new remote sensing technologies (e.g., GPS, Application of new remote sensing technologies (e.g., GPS,

phased-array radars, polarization-diversity radars, MDCRS)phased-array radars, polarization-diversity radars, MDCRS) Linkages between high-impact local weather and local Linkages between high-impact local weather and local

ecosystems, biodiversity, and healthecosystems, biodiversity, and health Intelligent distributed computing and networking: learning how to Intelligent distributed computing and networking: learning how to

create and deliver the informationcreate and deliver the information Economic and societal impacts and mitigation: learning how to Economic and societal impacts and mitigation: learning how to

use the informationuse the information

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

Documents

operational models

stormscale nwp

models thatrun

wallfor global models

stormscale events

highimpact stormscale

facets of nwprunning

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