The Local Analysis and Prediction The Local Analysis and Prediction System (LAPS)System (LAPS)

Local Analysis and Local Analysis and Prediction BranchPrediction Branch

NOAA Forecast Systems NOAA Forecast Systems LaboratoryLaboratory

Paul SchultzPaul Schultz

LAPS MissionLAPS MissionA system designed to:A system designed to:

Exploit all available data sourcesExploit all available data sources Create analysis grids for nowcasting and Create analysis grids for nowcasting and

“generic” model intialization “generic” model intialization Build products for specific forecast Build products for specific forecast

applicationsapplications Provide reliable forecast guidanceProvide reliable forecast guidance Use advanced display technologyUse advanced display technology

……All within a local weather office, forward site, All within a local weather office, forward site, or in fully deployed modeor in fully deployed mode

The LAPS teamThe LAPS team

John McGinley, branch chief, variational John McGinley, branch chief, variational methodsmethods

Paul Schultz, project manager, modeler, your Paul Schultz, project manager, modeler, your speaker todayspeaker today

Brent Shaw, modelerBrent Shaw, modeler Steve Albers, cloud analysis, temp/wind analysisSteve Albers, cloud analysis, temp/wind analysis Dan Birkenheuer, humidity analysisDan Birkenheuer, humidity analysis John Smart, everythingJohn Smart, everything

LAPS GUI – Global LAPS GUI – Global localizationlocalization

LAPS GUI – Grid refinementLAPS GUI – Grid refinement

Example LAPS/WRF 5km Example LAPS/WRF 5km DomainDomain

LAPS Diabatic LAPS Diabatic InitializationInitialization

CloudAnalysis

DataFusion

3DVARDynamic

Constraint

LAPSPREP

NWP SystemLAPSPOST

Surface RAOB Sat ACARS GPS Radar(Vr) Profilers Radar(Z) Sat Aircraft METAR

NWP FG

Data Ingest/Quality Control

National NWPLBC

LSM IC

NativeOutput

Forecaster

IsobaricOutput

T, , p, u, v, , RH

T,

qc qi, qr qs, qg

c

T, , p, u, v, RH

Constraints:Mass Continuityu/v Time TendenciesBackground ErrorObservation Error

Adjust for Model:Hydrometeor Concen.Saturation Condition

Cloud Analysis SchemeCloud Analysis Scheme

Uses satellite Vis and Uses satellite Vis and IRIR

Aircraft observationsAircraft observations Surface observationsSurface observations RadarRadar Interpolates cloud Interpolates cloud

obs to grid with SCMobs to grid with SCM Cloud feeds back into Cloud feeds back into

water vapor analysiswater vapor analysis

LAPS Dynamic Balance LAPS Dynamic Balance AdjustmentAdjustment

FH

FL

0ˆ T

sqq

c

Q > 0

““Hot Started” forecastsHot Started” forecasts

00Hr Fcst, Valid 28 Mar 01/00Z 01Hr Fcst, Valid 28 Mar 01/01Z

Cloud fields realistically maintained

IllustrationIllustration

         

Cloud liquid (shaded), vertical velocity (contours) and cross-section streamlines for analyses (right) and 5-min forecasts (left). The top pair shows LAPS hot-start DI with upward vertical motions where clouds are diagnosed and properly sustained cloud and vertical motions in the forecast; the bottom pair demonstrates the artificial downdraft that usually results from simply injecting cloud liquid into a model initialization without supporting updrafts or saturation. Note that cloud liquid at the top of the updraft shown in the hot-started forecast (above right) has converted to cloud ice.

Hot Start

Cloud insertion

Initialization 5 min forecast

Current LAPS ProjectsCurrent LAPS Projects

Fire Weather SupportFire Weather Support Highway Weather Support – Ensemble Highway Weather Support – Ensemble

ModelingModeling Space Center Support System - KSC and Space Center Support System - KSC and

VandenbergVandenberg Army Paradrop Project - laptop deploymentArmy Paradrop Project - laptop deployment Taiwan Central Weather BureauTaiwan Central Weather Bureau

Fire Weather Home Fire Weather Home PagePage

LAPS Ventilation IndexLAPS Ventilation Index

Front Range 600m Front Range 600m DomainDomainFeb 9, 2004Feb 9, 2004

Analyzed Surface WindsAnalyzed Surface Winds

Space Launch Operations Space Launch Operations SupportSupport

USAF Space Launch FacilitiesUSAF Space Launch Facilities Vandenberg and Cape CanaveralVandenberg and Cape Canaveral LAPS and MM5LAPS and MM5 10, 3.3, 1.1 km nests10, 3.3, 1.1 km nests Critical for launch and range Critical for launch and range

safety weather forecastingsafety weather forecasting Utilizes local towers, profilers, Utilizes local towers, profilers,

miniSODARs, etc.miniSODARs, etc. Operational “firsts”Operational “firsts”

AWIPS IntegrationAWIPS Integration Linux cluster modelingLinux cluster modeling

Cape Canaveral 6-hour QPF on Cape Canaveral 6-hour QPF on 1-km Grid and Radar Verification1-km Grid and Radar Verification

9 Feb 049 Feb 04

FSL Support for USAF/ US Army FSL Support for USAF/ US Army Precision Air DropPrecision Air Drop

Typical Airdrop Events Treated in Typical Airdrop Events Treated in PADSPADS

PADS System Background

DESCENT TRAJECTORYFall or Glide Trajectory Model

+ 3D AtmosphericWind/Density Field

Complex 3D Atmospheric Flow

over/through Mountainous Terrain

Ballistic System or

Guided System(Corrects to Planned Descent Trajectory)

CARP

Green Light

Roll-Out

Canopy-Opening/

Deceleration

DropSonde

AssimTime

Current PADS Features

PADS Fly-Away Kit:Flight-Certified for the C-130 and the C-17

Results: Intermediate Results: Intermediate Altitude C-130 Airdrops Altitude C-130 Airdrops

(10,000-15,000 ft)(10,000-15,000 ft)CEP:Oct 02 (6 samples): 243 mNov 02 (6 samples): 177 mJan 03 (7 samples): 155 mCumulative (19 samples) : 178 m

Cumulative CEPMean(87mE,68mN)3 sigma dispersionellipse

CEP:Oct 02 (6 samples): 243 mNov 02 (6 samples): 177 mJan 03 (7 samples): 155 mCumulative (19 samples) : 178 m

Cumulative CEPMean(87mE,68mN)3 sigma dispersionellipse

Local model ensemblesLocal model ensembles

Basis: Multiple Basis: Multiple equally-skillfulequally-skillful forecasts forecasts can be combined into a single forecast can be combined into a single forecast that is better than any one of the that is better than any one of the ensemble membersensemble members

FSL’s first application: a road weather FSL’s first application: a road weather prediction projectprediction project

FWHA Road FWHA Road Maintenance Decision Maintenance Decision

Support ProjectSupport Project- - Iowa 2003, 2004Iowa 2003, 2004

RWIS tower, I-35 south of Ames

Maintenance Decision Support Maintenance Decision Support SystemSystem

Sponsored by FHWASponsored by FHWA Cooperative 5-yr project with NCAR/RAP, Cooperative 5-yr project with NCAR/RAP,

CRREL, MIT/LLCRREL, MIT/LL Help snowplow garage supervisors decide Help snowplow garage supervisors decide

when/where to send trucks, chemical when/where to send trucks, chemical treatmentstreatments

FSL: produce supplemental model runs FSL: produce supplemental model runs and transmit them to NCARand transmit them to NCAR

FSL model data

NCAR Road Weather Forecast System

CRREL Road temp/chemical module

MIT/LL rules of treatment practice

GUI in the field

MDSS MDSS modelinmodelin

g g domaindomain

Forecast point status display

Place cursor over aforecast point

Bulk statisticsBulk statisticsState variables, 12-hr forecastsState variables, 12-hr forecasts

Feb 1 – Apr 8, 2003Feb 1 – Apr 8, 2003

Temperature Temperature (K)(K)

Wind speed Wind speed (m/s)(m/s)

Dewpoint (K)Dewpoint (K)

MM5-AVNMM5-AVN 3.13.1 -0.7-0.7 2.52.5 +0.8+0.8 5.65.6 +1.5+1.5

MM5-EtaMM5-Eta 3.03.0 -0.5-0.5 2.52.5 +0.8+0.8 5.55.5 +1.6+1.6

RAMS-AVNRAMS-AVN 5.85.8 -1.1-1.1 2.62.6 +1.6+1.6 6.56.5 -0.9-0.9

RAMS-EtaRAMS-Eta 5.95.9 -1.1-1.1 2.62.6 +1.7+1.7 6.96.9 -1.0-1.0

WRF-AVNWRF-AVN 3.13.1 -0.4-0.4 2.42.4 +1.1+1.1 5.75.7 +1.4+1.4

WRF-EtaWRF-Eta 3.13.1 -0.4-0.4 2.42.4 +1.0+1.0 5.75.7 +1.3+1.3

A closer lookA closer look

9 pm model runs, verifying only Iowa stations, entire expt

MM5-EtaMM5-Eta MM5-AVNMM5-AVN WRF-AVNWRF-AVN

RAMS-EtaRAMS-Eta RAMS-AVNRAMS-AVN WRF-EtaWRF-Eta

Conclusions from 2003 Conclusions from 2003 MDSS demonstrationMDSS demonstration

Lateral bounds not useful for adding Lateral bounds not useful for adding diversity diversity for this applicationfor this application

Good diversityGood diversity Models: MM5 and WRFModels: MM5 and WRF Initialization dataInitialization data

Considerable value to the client in earliest Considerable value to the client in earliest hours of forecasts (hot start)hours of forecasts (hot start)

Juggling actJuggling act

6 model runs6 model runs 4 sets per day 4 sets per day

(i.e., every 6 hrs)(i.e., every 6 hrs) 27-hr forecasts27-hr forecasts 3-hr temporal 3-hr temporal

resolutionresolution

2 model runs2 model runs 24 sets per day 24 sets per day

(i.e., every hour)(i.e., every hour) 15-hr forecasts15-hr forecasts 1-hr temporal 1-hr temporal

resolutionresolution

2003 2004

Loops of the two different Loops of the two different models initialized at the models initialized at the

same timesame time

Loops of the same model Loops of the same model (WRF) initialized an hour (WRF) initialized an hour

apartapart

4 forecasts valid at the 4 forecasts valid at the same timesame time

Bulk statisticsBulk statisticsState variables, 12-hr forecastsState variables, 12-hr forecasts

Dec 29 – Mar 19, 2004Dec 29 – Mar 19, 2004

Temperature Temperature (K)(K)

Wind speed Wind speed (m/s)(m/s)

Dewpoint (K)Dewpoint (K)

MM5MM5 3.23.2 +0.2+0.2 2.42.4 +1.6+1.6 3.73.7 +1.5+1.5

WRFWRF 3.03.0 +1.3+1.3 2.32.3 +1.3+1.3 3.73.7 +2.2+2.2

EtaEta 2.72.7 +0.5+0.5 2.72.7 -0.2-0.2 2.62.6 +1.7+1.7

Diurnal trend in temperature Diurnal trend in temperature forecast errorsforecast errors

Midnight model runs

3-h Precipitation verification3-h Precipitation verification

6-h Precipitation verification6-h Precipitation verification

Advances in numerical Advances in numerical weather prediction via weather prediction via

MDSSMDSS Practical diabatic initializationPractical diabatic initialization

Models have useful, skillful precipitation forecasts Models have useful, skillful precipitation forecasts in first few hoursin first few hours

Reduced latencyReduced latency MDSS: forecasts available ~1 h after data valid MDSS: forecasts available ~1 h after data valid

timetime NCEP: forecasts available ~3 h after data valid NCEP: forecasts available ~3 h after data valid

timetime Increased frequencyIncreased frequency

MDSS: updates every hourMDSS: updates every hour NCEP: updates every six hoursNCEP: updates every six hours

CycleCycle

MDSS model cycle

data ingest

data analysis

model initialization

model startsrunning

:20

:35

:48

:00

Ensemble applicationsEnsemble applications

•Ensembles produce probability forecasts that can be more reliable

•Probabilistic output can be input into economic cost/lost models

• Customers get a “yes-no” forecast based upon skill and spread of ensemble

Reflectivity Probabilities for Reflectivity Probabilities for AviationAviation

The forecast-area specificity decreases as forecast lead times increases. The forecast-area specificity decreases as forecast lead times increases. Example probability forecast of level 3 or greater reflectivity for various forecast lead times are Example probability forecast of level 3 or greater reflectivity for various forecast lead times are

shown. shown. The valid time is the same for all imagesThe valid time is the same for all images . The images illustrate the expected . The images illustrate the expected degradation in forecast-area specificity with time.degradation in forecast-area specificity with time.

•Probability of level 3 echo with green 10%, yellow 30% and red 60%.

0-1 hr 1-2 hr

2-3 hr3-4 hr

Slide courtesy C. Mueller, NCAR/RAP

Use of Mesoscale Model Use of Mesoscale Model Ensembles - Transport Weather Ensembles - Transport Weather

and Fire Weatherand Fire Weather

Probabability generator

Economic cost/loss modelsYes or no

Forecast for Springfield, MO:

79% chance of 1 mm

36% chance of 10 mm

100% chance T > 32F

Ensemble-Generated 1-Hr Probability of Smoke Concentration

> 60%

> 20%

Ensemble-Generated 2-Hr Probability of Smoke Concentration

> 60%

> 20%

Ensemble-Generated 3-Hr Probability of Smoke Concentration

> 60%

> 20%