2008 Briefings National Security Applications Program 6 November 2008 Yubao Liu Next Generation Operational Mesoscale NWP Technology Next Generation Operational.

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Presentation transcript:

2008 Briefings National Security Applications Program 6 November 2008 Yubao Liu Next Generation Operational Mesoscale NWP Technology Next Generation Operational Mesoscale NWP Technology

RTFDDA Regional-scale model, based on WRF / MM5 TAMDAR MESONETs GOES Wind Prof RTFDDA: Data Assimilation and Forecasts All WMO/GTS Radars Etc. ACARS Forecast Cold start t FDDA Weather observations WRF/MM5 Modified WRF/MM5: Dx/Dt =... + GW (x obs – x model ) where x = T, U, V, Q, P1, P2 … W is weight function

NCAR RTFDDA Application Areas Olympics games Joint Urban 2003, Colorado wild fire FAA aviation weather Military operations Army test ranges Homeland security Xcel Energy New York City 2005 Hurricanes AirDat TAMDAR … 20+ Special Operation Sites 12 Regular Operational RTFDDA Systems RAL: WSAP, HAP, JNT, Aviation UCAR: MMM, CGD, UOP Universities: CU, PSU, ASU, OU Collaborator s

WRF/MM5-RTFDDA: Obs-nudging Improvement areas:  Spatial and time weights  Diverse data sources  Model physics schemes Hybrids:  3DVAR  VDRAS  Grid-nudging FDDA Ensemble RTFDDA (NCAR/RAL) (Obs-nudging ensembles)  Build “proper” mesoscale ensembles - heterogeneous  Incorporate Kalman Gain to obs-nudging weights  Optimize model physics parameterization Next-Gen 4DWX 4D-EnKF System RTFDDA, E-RTFDDA and Next-Gen

DPG E-RTFDDA Operation (Debut on 10 Sep. 2007) Surface and X-sections – Mean, Spread, Exceedance Probability, Spaghetti, … Likelihood for SPD > 10m/s Mean T & Wind T Mean and SD Wind Speed T-2m Wind Rose Pin-point Surface and Profiles – Mean, Spread, Exceedance probability, spaghetti, Wind roses, Histograms …

E-RTFDDA Characteristics E-RTFDDA represents a milestone advance in RAL NWP capabilities and an important R&D frontier It is the first in the world in terms of  continuous ensemble analysis and forecasting  multi-scale, down to km grid  dynamic perturbation optimization  globally-relocatable  and a Next-Gen NWP dev-bed for integration of “cutting-edge” data assimilation technologies.

RTFDDA-3DVAR Hybrid DA WRF- RTFDDA WRF- 3DVAR WRF Hybrid Engine All U, V, T, Qv observations and data QC Upper-air U, V, T, Qv + Radar, satellite and other indirect obs Continuous 4-D analyses and forecast cycling

6 hour rain accumulation ended at 18Z 13 June 2002 StageIV (OBS) CNFCST C3DRAD HYBRID ( mm )

WRF-RTFDDA-LES 6 nested grids DX= 30/10/3.3/1.1/0.369 /0.123 km 30” terrain and land use 37 sigma levels 24h forecasts: 12Z 16 Apr – 12Z 17 Apr meter T, 9h fcsts, 4 pm D.C. Balt Phil Atlantic Ocean Chesapeake bay 3 km Cold ocean flows Warm hilly eddies Small-terrain wind streaks

      A robust NWP system built upon WRF  Lead FDDA R&D in WRF community  Application-oriented research of WRF physics parameterizations  Develop advanced data assimilation approach to take the advantages of new achievements by Community and ESSL  Customized NWP products according to specific needs of diverse decision makers and end-users Summary Questions and comments?