Application of Numerical Model Verification and Ensemble Techniques to Improve Operational Weather Forecasting. Northeast Regional Operational Workshop.

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Presentation transcript:

Application of Numerical Model Verification and Ensemble Techniques to Improve Operational Weather Forecasting. Northeast Regional Operational Workshop Albany, New York 5 Nov 2002

Improving Weather Forecasting: Where Do We Go from Here?        Jeffrey S. Tongue NOAA/NWS NOAA/NWS Upton, NY Dr. Brian A. Colle Dr. Brian A. Colle Institute for Terrestrial and Planetary Sciences Marine Sciences Research Center Stony Brook University / SUNY Alan Cope NOAA/NWS NOAA/NWS Mount Holly, NJ Robert Shedd David Vallee David ValleeNOAA/NWS Taunton, MA Joshua Watson, NOAA/NWS, Bohemia, NY

Overview Where We Have Been What We Are Doing Why We Are Doing It Where We Are Going

Previous/Current Work Stony Brook MM5 –Fall 1999 In AWIPS (2001) –Problem ….. BANDWIDTH !!! Verification Workstation eta

Stony Brook MM5 System NCEP’s EDAS initialization NCEP’s EDAS initialization Eta Boundary Conditions Eta Boundary Conditions Integrated to 60 Hours (~ 4 Hours) Integrated to 60 Hours (~ 4 Hours) Currently Using Grell Convective Parameterization Currently Using Grell Convective Parameterization 4 km Non-Hydrostatic hours (~6 hours) 4 km Non-Hydrostatic hours (~6 hours) NetCDF Files Ingested into AWIPS via LDAD NetCDF Files Ingested into AWIPS via LDAD Problem: Bandwidth - Using ADSL Connection via Host Institution: < 10 Minutes < 10 Minutes - Using NWS Network > 2 Hours > 2 Hours

SUNY-SB Real-time MM5 Domains

MM5 In AWIPS

Verification Are We Improving on the Model??? What are the Model Biases ??? - Can Forecasters Assimilate These??? - How do you convey these??? How do we Improve Performance?

Are we Improving the “Model” ?

Verification Methods Verify MM5 and Eta (through 48 h) using all available surface and rawinsonde stations for cool seasons (November through March) and 2001 warm season (May through September) QC obs data for unrealistic values and duplicate ods Bilinear interpolation from MM5/Eta grid to observation sites. For precipitation, a Cressman method was used Reduce MM5 40-m winds to 10 m using log profile and an appropriate roughness length Reduce MM5 40-m temperature to the station elevation using a standard lapse rate (6.5 0 C/km). Average 40-m free air temperature with ground temperature to provide 2-m temperature Accepted by WAF - Colle et al. (2002a,b)

ModelBias

36-km MM5 30-day average cool season precipitation

12-km MM5 30-day average cool season precipitation

(12-36 h MM5 forecasts) / observed * 100.

Where Are We Going? ` Need to make better use of high resolution mesoscale model forecasts in operations.

NATIONAL WEATHER SERVICE NEW YORK, NY: 329 AM EDT THU AUG : “…. SO LOOKING FOR QPF IN THE 2 TO 3 INCH RANGE. THIS DOES NOT POSE ANY FLOODING PROBLEMS WITH DRY CONDITIONS PREVIOUS TO EVENT AND FACT THAT PCPN WL BE OVER 36 TO 48 HOURS.” WSR-88D mosaic at 2 AM EDT L

36-km MM5 1-hr pcpn Valid: 1600 UTC 29 Aug inches

1.75 inches 12-km MM5 1-hr pcpn Valid: 1600 UTC 29 Aug 2002

1600 UTC WSR-88D 1 Hour Pcpn 1.60 inches

MM5 Mini-Ensemble Eta-MM5 (BLUE) AVN-MM5 (RED)

Where Are We going? Through use of Mesoscale Models… - Improve Operational Understanding of Physical Processes. - Improve Operational Understanding of model physical parameterizations. Along with Utilization of Local Ensembles. Goal - Make Improvement in and use of model physical parameterizations

Initial Condition Uncertainty  “Ensemble Forecasting”

Physics uncertainty  Need physics- based ensemble members

New 5 Member SBU Ensemble InitializationPBLCloudConvective EtaMRFSimple IceGrell GFSMRFSimple IceGrell Eta Burk-Thompson Simple IceGrell EtaMRFSimple IceKain-Fritsch EtaMRF Reisner2 micro Grell Short Range Ensemble – 48 Hours

Summary Operational Meteorology is Changing and Evolving on a RAPID Pace. Forecaster’s are realizing that they are being “overwhelmed” by data. We must “learn” when, where and how to improve on NWP. (Assimilate Quantitative Model Verification into the forecast process) There is significant room for improvement in model physical parameterizations. High resolution forecasts (< 10 km grid spacing) have proven to be most useful in predicting winds near steep topography and warm season diurnal flows near the coast; however, deficiencies in model physics are limiting the benefits.

Summary (Con’t) Increased use of “ensemble modeling” at moderate resolution (10-20 km grid spacing) is needed to quantify model physics and Initial Condition uncertainty (rather than model of the day)