Application of Numerical ModelVerification and Ensemble Techniques

to ImproveOperational Weather Forecasting

.

Northeast Regional Operational WorkshopAlbany, New York

5 Nov 2002

Improving Weather Forecasting: Improving Weather Forecasting:

Where Do We Go from Here?Where Do We Go from Here?

Jeffrey S. TongueJeffrey S. Tongue NOAA/NWSNOAA/NWS

Upton, NYUpton, NY

Dr. Brian A. ColleDr. Brian A. ColleInstitute for Terrestrial and Planetary SciencesInstitute for Terrestrial and Planetary Sciences

Marine Sciences Research CenterMarine Sciences Research CenterStony Brook University / SUNYStony Brook University / SUNY

Alan CopeAlan Cope NOAA/NWSNOAA/NWS

Mount Holly, NJMount Holly, NJ

Robert SheddRobert Shedd David Vallee David Vallee NOAA/NWSNOAA/NWSTaunton, MATaunton, MA

Joshua Watson, Joshua Watson, NOAA/NWS, NOAA/NWS, Bohemia, NYBohemia, NY

OverviewOverview• Where We Have Been

• What We Are Doing

• Why We Are Doing It

• Where We Are Going

Previous/Current WorkPrevious/Current Work

• Stony Brook MM5– Fall 1999

• In AWIPS (2001)– Problem ….. BANDWIDTH !!!

• Verification

• Workstation eta

Stony Brook MM5 SystemStony Brook MM5 System

http://atmos.msrc.sunysb.edu/mm5rt

• NCEP’s EDAS initializationNCEP’s EDAS initialization

• Eta Boundary ConditionsEta Boundary Conditions

• Integrated to 60 Hours (~ 4 Hours)Integrated to 60 Hours (~ 4 Hours)

• Currently Using Grell Convective ParameterizationCurrently Using Grell Convective Parameterization

• 4 km Non-Hydrostatic 12-36 hours (~6 hours)4 km Non-Hydrostatic 12-36 hours (~6 hours)

• NetCDF Files Ingested into AWIPS via LDADNetCDF Files Ingested into AWIPS via LDAD

Problem: Problem: BandwidthBandwidth- Using ADSL Connection via Host Institution:Using ADSL Connection via Host Institution:

< 10 Minutes< 10 Minutes

- Using NWS NetworkUsing NWS Network

> 2 Hours> 2 Hours

SUNY-SB Real-time MM5 Domains

MM5 In AWIPS

VerificationVerification

• 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” ?Are we Improving the “Model” ?

Verification MethodsVerification Methods Verify MM5 and Eta (through 48 h) using all available surface and rawinsonde stations for 1999-2001 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 0C/km). Average 40-m free air temperature with ground temperature to provide 2-m temperature

Accepted by WAF - Colle et al. (2002a,b)

Model Model BiasBias

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?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 29 2002:

“…. 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 2002

0.35 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?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 EnsembleNew 5 Member SBU Ensemble

Initialization PBL Cloud Convective

Eta MRF Simple Ice Grell

GFS MRF Simple Ice Grell

Eta Burk-Thompson Simple Ice Grell

Eta MRF Simple Ice Kain-Fritsch

Eta MRF 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)

http://atmos.msrc.sunysb.edu/mm5rthttp://atmos.msrc.sunysb.edu/mm5rt