1 Future NCEP Guidance Support for Surface Transportation Stephen Lord Director, NCEP Environmental Modeling Center 26 July 2007

2 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

3 New Ensemble Products from NCEP 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. Bright NCEP/SPC

4 SREF Likely PTYPE and Mean P03I (contours) Rain Snow ZR IP 24 h Fcst Precip Type, Amount 32 F Isotherm D. Bright NCEP/SPC

5 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 factorsTotal Compute Factor Years to Achieve at current constant funding NAM x physics72019 SREF

6 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. Pondeca J. Purser G. DiMego NOAA/GSD - RUC

7 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 UTC 7 PM 03 UTC 10 PM 06 UTC 1 AM 09 UTC 4 AM 12 UTC 7 AM 15 UTC 10 AM 18 UTC 1 PM 21 UTC 4 PM S. Kumar Jim Geiger C. Peters-Lidard J. Meng K. Mitchell Surface (skin) Temperature 50 km area Washington DC NASA LSM GFS forcing 00 UTC 1 July – 21 UTC 1 July

8 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

9 Marine Applications Multi-Grid Wave Modeling Multi-grid wave model tentative resolutions in minutes for the parallel implementation in FY2007-Q4. Deep ocean model resolution Higher coastal model resolution Highest model resolution in areas of special interest Hurricane nests moving with storm(s) like GFDL and HWRF Wave ensemble system application for ship routing

10 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

11 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

12 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

13 Concurrent execution of global and regional forecast models (Phase 2) Model Region 1 Model Region 2 Global/Regional Model Domain Analysis 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