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Future NCEP Guidance Support for Surface Transportation

Stephen LordDirector, NCEP Environmental

Modeling Center26 July 2007

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Overview• Weather for Roads, Air transportation, etc.

– National picture• New ensemble products

– Local picture• Downscaling

– Real-time Mesoscale Analysis (RTMA)– Land Information System (LIS)– Dynamical – Statistical approach

• Marine applications– Waves– Water levels

• Data availability• What’s needed to move ahead

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New Ensemble Products fromNCEP Storm Prediction Center

• NCEP Short-Range Ensemble Forecast (SREF) System• National coverage ~ 30 km grid• Probabilistic guidance with extremes

SREF Maximum (any member) 3h Accumulated Snowfall

SREF Pr[Ptype = ZR] and Mean P03I (contours)

SREF 6h Calibrated Probability of Snow/Ice Accum

Accumulation based on MADIS road surface condition

D. BrightNCEP/SPC

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SREF Likely PTYPE and Mean P03I (contours)

Rain

SnowZR

IP

24 h FcstPrecip Type, Amount

32 F Isotherm

D. BrightNCEP/SPC

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Downscaling• Future computing requirements

– National scale ~20 years to reach sufficient resolution

• Dynamical-statistical approach– Real time Mesoscale Analysis (RTMA)– Land Information System (LIS)– Bias correction and statistical processing

• Components under development

Forecast System

Current Horizontal Resolution

Current Vertical Resolution

Future Horizontal Resolution

Future Vertical Resolution

Other factors Total Compute Factor

Years to Achieve at current constant funding

NAM 12 60 2 100 2x physics 720 19

SREF 37 48 5 100 844 20

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Real-Time Mesoscale Analysis (RTMA)

RTMA Temperature Analysis (° F) (17Z 6/14/07)

RTMA 1-hour Precipitation Analysis (inches) (01z 6/14/07)

RTMA Temperature Analysis Uncertainty (° F) (17Z 6/14/07)

• 5 km National (NGDG) grid (eventually 2.5 km)• Hourly analysis

– Focus on “drawing to obs” (mesonet)– Temperature, precipitation, surface wind, dew point– Anisotropic (e.g. land-water contrast)

• Analysis uncertainty• To include cloud cover• Will cover CONUS, Alaska, Hawaii, Puerto Rico, Guam

M. PondecaJ. Purser

G. DiMegoNOAA/GSD - RUC

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Land Information System (NASA/NOAA)• Land states forced by

– Observed precipitation– Model solar, long wave radiation,

cloudiness

• Noah Land Surface Model (LSM) defines skin temperature, soil moisture, etc.

• Can be run at 1 km resolution (below)

00 UTC7 PM

03 UTC10 PM

06 UTC1 AM

09 UTC4 AM

12 UTC7 AM

15 UTC10 AM

18 UTC1 PM

21 UTC4 PM

S. Kumar Jim Geiger

C. Peters-LidardJ. Meng

K. Mitchell

Surface (skin) Temperature 50 km area Washington DC NASA LSM GFS forcing00 UTC 1 July – 21 UTC 1 July

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Dynamical Statistical Approach

• Bias correction of forecast fields with respect to model analysis (e.g. NAM)

• “Downscaling Transformation” (DT)– Produces time-dependent differences between coarse forecast

model (e.g. 12 km NAM) and RTMA (5 km)

• Downscaled (local) fcst =NAM fcst + Bias correction + DT

– On local grid

• Probabilistic products– Created from ensemble systems (SREF, GENS) through

Bayesian Model Averaging (BMA) approach– Applications for

• Road transportation• Air transportation management (NEXTGEN)• Severe weather forecasting

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Marine ApplicationsMulti-Grid Wave Modeling

Multi-grid wave model tentative resolutions in minutes for the parallel

implementation in FY2007-Q4.

Deep ocean model resolutionHigher coastal

model resolution

Highest model resolution in areas of special

interest

Hurricane nests moving with storm(s) like GFDL

and HWRFWave ensemble system application for ship routing

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NCEP Real-Time Ocean Forecast System (RTOFS)Operational December 2005, upgraded June 2007

Chesapeake Bay

• RTOFS provides– Routine estimation of the ocean

state [T, S, U, V, W, SSH]• Daily 1 week forecast

– 5 km coastal resolution– Initial and boundary conditions

for local model applications• Applications

– Downscaling support for water levels for shipping

– Water quality– Ecosystem and biogeochemical

prediction– Improved hurricane forecasts– Improved estimation of the

atmosphere state for global and regional forecasts

• Collaboration with NOAA/NOS

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Product Availability

• Three levels of information– Routinely delivered

1. Pointwise, single-valued, downscaled MLF* from all available guidance on NDGD grid

2. Description of forecast uncertainty through probability density function (mode & 10/90 %ile)

• Accompanying post-processed fields– Meteorologically consistent– Closest to MLF*

– “On-demand” (via publicly accessible server)3. Individual ensemble member forecasts available• Prototype: NOMADS

* MLF – Most Likely Forecast

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What’s Needed?

• Written requirements for surface transportation to NWS

• Operational (and research) computing resources

• Acceleration of current dynamical-statistical efforts

• Outreach and coordination with local users

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Concurrent execution of global and regional forecast models (Phase 2)

Model Region 1

Model Region 2

Global/Regional Model DomainAnalysis

Local Solution

• Real time boundary and initial conditions available hourly

– “On-demand” downscaling to local applications• Similar to current hurricane runs but run either

– Centrally at OR– Locally (B.C, I. C. retrieved from on-line data)

• No boundary or initial conditions older than 1 hour – Flexibility for “over capacity” runs (e.g. Fire Wx, Hurricane)

• Using climate fraction must be planned• No impact on remainder of services

• For NEXTGEN: A consistent solution from global to local with a single forecast system and ensembles providing estimate of uncertainty