Weather Model Development for Aviation Stan Benjamin and Steve Weygandt: Assimilation and Modeling Branch, Chief/Deputy NOAA Earth System Research Laboratory, Global Systems Division, Boulder, CO Stan/Steve: Lead/Expert Model Development and Enhancement Product Development Team, AWRP/FAA

12h NOAA HRRR model forecastValid 03z

NOAA/ESRL/GSD 30 Oct 2013 1Aviation Model Development

Observed radar03z June 30 2012

An Important Pinpoint Prediction Challenge: The 29 June 2012 Mid-Atlantic Derecho

A fast-moving damaging wind event…

700 mile long swath of damage, 5 million without

power, 22 fatalities

2 PM 4 PM 6 PM 8 PM 10 PM MID11 AM

StartHRRRrun

Computer weather modeling:What is the potential?

HRRR 2012 derecho loop

Observed radarHRRR forecast initialized 15z (11am Eastern Time)

29 June 2012 – Mid-Atlantic/DC thunderstorm/derecho event

Computer weather modeling:How is it done?

4

Weather computer

model:Solve physics

equations

at many points repeatedly to mimic time-

evolution of 3-D of temperature, wind, moisture,

clouds, etc.

1800 points

1060 p

oin

ts

Model Terrain

1800 x 1060 pointsx 50 levels

= 95,000,000 3-d points every 20 seconds

50- levels

Model Equations

NOAA Next-Generation Model Development

RAP

HRRR

RAP - Rapid Refresh – NOAA “situational awareness” model

for high impact weather– New 18-hour forecast each hour– NOAA operational – 1 May 2012– Hourly use by National Weather

Service, Storm Prediction Center, FAA, private sector

HRRR – High-ResolutionRapid Refresh

- Next-generation storm/energy/aviation guidance

- New 15-h forecast each hour- Real-time experimental on ESRL

supercomputer- Open ftp access

RAP and HRRR data assimilation

6

RAP

Data Assimilation cycle

Observations

Hourly cycling model

HRRR

EnKF-Hybrid +Radar andCloud anx Radar and

Cloud anx+ 3DVAR

Operational Prediction Process

Observations

ObjectiveAnalysis(adjust background)

ModelPrediction

AnalysisUpdate Cycle

HumanForecaster

Statistical post-processing

(downscaling, probability)

Data Assimilation

HRRR (and RAP) Future MilestonesHRRR MilestonesRapid updating – Why do it?

Betterforecasts

6 AM time9 AM noon 3 PM 6 PM

12-h fcst

Truth

12-h update to previous forecast

More frequentmodel updateswith newer obs

Smaller adjustments

9-h fcst

6-h fcst

3-h fcst

3-h update to previous forecast

Next forecast

Benefits of Rapid Cycling NWPRapid update cycling with latest observations

improves short-range forecasts (including upper-level winds)

RUC jet-level (35 kft) wind forecast errors

3-h fcstwind errors

6-h fcstwind errors

12-h fcst wind errors

LAX

ORD

LAX

ORD

LAX

ORD

NOAA/ESRL/GSD 12 July 2012 9Aviation Model DevelopmentNOAA/ESRL/GSD 30 Oct 2013 9Aviation Model Development

RAP error reduction to 1-h forecast

1h 3 6 1218h

Rapid RefreshWind forecastaccuracy vs.

forecast length

The Rapid Refresh is able to use recent obs to improve forecast skill down to 1-h projection

1 Jan - 7 Mar 2012- Verification against weather balloon data

NCEP Production Suite Review 4-5 December 2012Rapid Refresh / HRRR 10NOAA/ESRL/GSD 30 Oct 2013 10Aviation Model Development

Rapid RefreshHourly Update Cycle

1-hrfcst

1-hrfcst

1-hrfcst

11 12 13Time (UTC)

AnalysisFields

3DVAR

Obs

3DVAR

Obs

Back-groundFields

Partial cycle atmospheric fields – introduce GFS information 2x/dayCycle hydrometeorsFully cycle all land-sfc fields(soil temp, moisture, snow)

Hourly Observations RAP 2013 N. Amer

Rawinsonde (T,V,RH) 120

Profiler – NOAA Network (V) 21

Profiler – 915 MHz (V, Tv) 25

Radar – VAD (V) 125

Radar reflectivity - CONUS 1km

Lightning (proxy reflectivity) NLDN, GLD360

Aircraft (V,T) 2-15K

Aircraft - WVSS (RH) 0-800

Surface/METAR (T,Td,V,ps,cloud, vis, wx) 2200- 2500

Buoys/ships (V, ps) 200-400

Mesonet (T, Td, V, ps) flagged

GOES AMVs (V) 2000- 4000

AMSU/HIRS/MHS radiances Used

GOES cloud-top press/temp 13km

GPS – Precipitable water 260

WindSat scatterometer 2-10K

Nacelle/Tower/Sodar 20/100/10

Observations assimilated in hourly updated models (Rapid Refresh)- All used to initialize 3km HRRR

Radar reflectivity

12

HRRR (and RAP) Future MilestonesHRRR MilestonesHigh Resolution – Why do we need it?

RAP

HRRR

Thunderstorm

~3km horizontal resolution needed to “resolve” thunderstorms

HRRR (and RAP) Future MilestonesHRRR MilestonesHigh Resolution – Why do we need it?

RAP

HRRR

Thunderstorm

~3km horizontal resolution needed to “resolve” thunderstorms

~3km horizontal resolution needed to “resolve” thunderstorms

…but 4x resolution costs 64x computer power

13-km 6hr forecast HRRR 6hr forecast

13-kmResolution

ParameterizedConvection

3-kmResolution

ExplicitConvection

5 PM EDTobserved

07 June 2012NO

STORM STRUCTURE

NO ESTIMATE OFSTORM

PERMEABILITY

ACCURATESTORM

STRUCTURE

ACCURATE ESTIMATEOF STORM

PERMABILITY

HRRR (and RAP) Future MilestonesHRRR Milestones3-km HRRR – what it gets you...

Radar Obs06:00z

18 May 201305z + 1 hour

Radar data assimilation: Getting storms in the right places

1-hr fcstradar DA(13-km and 3-

km)

1-hr fcstNO radar DA

• Run model backwards in time (reversible processes only)• Run model forward in time (heating from radar observations) • Digital filter after backward and forward step

Forward integration,full physics with obs-based latent heating

-20 min -10 min Initial +10 min + 20 min

RAP / HRRR model forecast

Backwards integration, no physics

Initial fields with improved balance, storm-scale circulation

17

Radar data assimilation: How it works for RAP and HRRR

NO backward step or filter

for HRRR

00z init00z 12 Aug

2011

Convergence Cross-Section

RAPHRRR

RADAR

RAPHRRR

no radar

Rapid convective spin-up with radar data

Radar data assimilation: How it works for RAP

Reflectivity

+1 hr fcst01z 12 Aug

2011

Convergence Cross-Section

RAPHRRR

RADAR

RAPHRRR

no radar

Rapid convective spin-up with radar data

Radar data assimilation: How it works for RAP

Reflectivity

Cloud and Hydrometeor Analysis

Hydrometeor designation from radar

Adjust cycled explicit cloud fields using METAR and

satellite data

YES HM

29th Conf on EIPT (IIPS) 08 January 2013High-Resolution Rapid Refresh 20NOAA/ESRL/GSD 30 Oct 2013 20Aviation Model Development

Observations

Data Assimilation Cycle

Rapid cyclingNWP

Data Assimilation and Rapid Cycling Numerical Weather Prediction (model)

Air transportation (NextGen)

Detailed, precise short-range weather guidance needed for:

Required for improved weather guidance for:• Turbulence• Ceiling/visibility• Convective weather• Icing• Terminal/enroute weatherSafety and efficiency

Aviation hazard forecasts – all based on RAP and HRRR models (out to 15-18h)

Hourly updated 13km Rapid Refresh model forecasts

(development supported by FAA/MDE, NOAA)

Refreshing from latest observations every hour gives better accuracy

23

Subset of full domain

An example of computations needed1800x1059x50 grid points = 95 E6 grid pointsx 50,000 floating pt ops per grid point = 4.75 E12 FPA / time stepx 2160 time steps / 12h forecast = 10 E15 FPA / 12h forecast

10,000,000,000,000,000 calculations for one12h HRRR CONUS forecast

Weather computer model: Solving physics equations on many points repeatedly to provide 3-D forecast forecast of temperature, wind, moisture, clouds, etc.

1800 points

1060 points

Model Version Initialized Forecast Length Run Time # CPUs Disk Space

RAP WRFv3.3.1+ Hourly 18 hrs ~30 min 200 230 GB (per run)

HRRR WRFv3.3.1+ Hourly 15 hrs ~50 min 1128 800 GB (per run)

Model Run at: Domain Grid Points

Grid Spacing

Vertical Levels

Height Lowest Level

Pressure Top Initialized

RAP GSD,NCO

North America

758 x 567 13 km 50 8 m 10 mb Hourly

(cycled)

HRRR GSD CONUS 1799 x 1059 3 km 50 8 m 20 mb Hourly

(no-cycle)

RAP and HRRR Resources

CW Overview Meeting 12 June 2012High-Resolution Rapid Refresh 24

NOAAHigh-Performance Computer System

Number of Filesystems

Total Reserved Disk Space CPU Type Total Reserved

CPUsPerformance

Increase

Jet (current) 4 150 TB Intel Nehalem 1736 -

Zeus (new) 2 230 TB Intel Westmere 2000-4000 30%

NOAA/ESRL/GSD 12 July 2012 24Aviation Model DevelopmentNOAA/ESRL/GSD 30 Oct 2013 24Aviation Model Development