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NCAR Microphysical parameterization work (FAA Model Development and Enhancement PDT supported)

NCAR Microphysical parameterization work (FAA Model Development and Enhancement PDT supported). Goals: Improve forecasts of water phase at surface and aloft to support aircraft icing forecasts.

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NCAR Microphysical parameterization work (FAA Model Development and Enhancement PDT supported)

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  1. NCAR Microphysical parameterization work(FAA Model Development and Enhancement PDT supported) • Goals: • Improve forecasts of water phase at surface and aloft to support aircraft icing forecasts. • Incorporate recent microphysical observations from field projects (WISP, AIRS, SLDRP, ICE-L, PLOWS, etc.). • Transition to operations (RUC and WRF-Rapid Refresh). • Sponsor: FAA’s Aviation Wx Research Program • Participants: • NCAR: Greg Thompson, Roy Rasmussen, Trude Eidhammer • Collaborators: Istvan Geresdi (U. of Pecs, Hungary), Ulrich Blahak (Germany), Hugh Morrison (NCAR), John Brown (NOAA/ESRL)

  2. Model/ Horizontal Number Assimilation Implemented MicrophysicsSystem Resolution levels Frequency at NCEP RUC1 60 km 25 3 hr Sept. 1994 RUC2 40 km 40 1 hr April 1998 Reisner 1998 RUC20 20 km 50 1 hr April 2002 Thompson 2004 RUC13 13 km 1 hr 2005 Thompson 2004 RR 13 km 1 hr mid- 2010 Thompson 2008 HRRR 3 km mid-2011 Aerosols in Thompson scheme

  3. Aerosols and Clouds • Observations showed that aerosol chemical and hygroscopic features are modified by clouds. • Cloud-processed aerosol particles affect the radiation. • Cloud-processed aerosol particles impact secondary cloud/precipitation development. • Numerical representation of the cloud processing of aerosol particles is difficult. Those aerosol particles forming cloud droplets are called Cloud Condentation Nuclei (CCN)

  4. Integration of Aerosols into WRF Microphysics Schemes Crawl: Constant cloud droplet number that influences precipitation. Ice nucleation only dependent on temperature or saturation. Walk: Creating ice and droplet number based on simple, but realistic aerosols Run: Full integration with WRF-Chem and multiple aerosol species

  5. Current work • Adding aerosols into the microphysics scheme: dust/ice nuclei • incorporate dust/mineral category and influence ice nucleation • ICE_L case studies • Trude Eidhammer leading this effort

  6. Ice nucleation parameterizations in models

  7. Relationship between aerosol concentration and ice nuclei (IN) concentration DeMott et al. (2008)

  8. Ice nucleation parameterizations in models Ice nucleation in Thomspson microphysics scheme Homogeneous freezing of deliquesced aerosols (can be > 1000L-1) No heterogeneous freezing above -12 °C DeMott et al 2010. IN dependent on aerosols > 0.5 μm. Typical aerosol > 0.5 μm background concentration values

  9. Aerosols in Thompson microphysics scheme:Ice nucleation from Dust particles GOCART 1.5o (lon) x 1.0o (lat) global monthly avg data, 20 sigma levels μg m-3

  10. Case studies • Ongoing testing for large number of cases in all seasons and weather regimes: • weakly-forced mesoscale events • orographic clouds, upslope and wave clouds • large & deep synoptic storms • convective events including squall lines and supercells • all cases taken from intensive field projects with specialized data resources for verification • Initial success • overall more liquid water should verify better against icing pilot reports • deep/cold glaciated storms contain less liquid • shallow & relatively warm clouds contain more liquid

  11. Bob Rauber, Brian Jewett, Greg McFarquharMelissa Peterson, David Plummer, Andrew Rosenow, and Joseph Wegman

  12. 2302 UTC KILX

  13. WSR-88D Cross section along flight path at 2215 UTC from IND

  14. Wyoming Cloud Radar reflectivity Zavg= 3.7 km, Tavg= -23.7ºC Supercooled water present at aircraft’s position David Plummer

  15. Zavg= 3.7 km, Tavg= -23.7ºC David Plummer

  16. S N 100 km 8 6 Height AGL (km) KARX KDVN 4 2 KARX KARX KDVN 02:50 03:00 03:10 03:20 03:30 03:40 Time (UTC) KDVN KARX

  17. Some PBL schemes available in WRF-ARW: QNSE MYJ • 2.5 level closure; similar to MYJ in neutral-unstable conditions, but in stable conditions, QNSE scheme is activated. • Turbulent eddies and waves are treated as one entity in the stable regime. • Similar physics as MYJ, but enhanced for stable nocturnal boundary layer. • Local mixing. • 2.5 level closure. • Commonly used (Rapid Refresh, NAM, etc.). • Typical biases include: • - Shallow daytime PBL . • - Cold and moist in daytime. YSU MYNN • First-order bulk scheme. • Includes a countergradient term to parameterize nonlocal mixing. • Explicit entrainment which is proportional to surface buoyancy fluxes. • Stronger vertical mixing may alleviate the bias found in the MYJ. • 2.5 and 3.0 level closure. • The master length scale is a function of 3 independent length scale (turbulent, surface layer, and stable layer). • Revised stability functions • Condensation Module. • Similar physics as MYJ, but tuned to LES simulations (aggressive vertical mixing). (from Joe Olson, NOAA)

  18. Vertical x-section through low-level jet Central Plains 15-h fcst valid 09Z 19 Aug 2007 (wind speed, PBL top) MYJ QNSE Temperature • Comparison • of PBL schemes • QNSE  strongest, widest LLJ • MYNN  strongest vertical mixing (highest jet top) • YSU  weakest LLJ, vertically diffuse MYNN YSU Wind Speed (from Joe Olson, NOAA)

  19. PBL Parameterizations • Impacts the cloud stability and therefore the vertical motion and microphysics

  20. Parent (BMJ) Nest (BMJ_DEV) Parent (BMJ) Nest (BMJ_DEV) Modified Convection in Nests (2) 0-60 h Cu QPF Deep Cu Cloud Top Pressure(hPa)

  21. Future Challenges • Parameterization of fine scale processes currently poorly represented • Embedded convection and associated ice nucleation • Shallow convection and stratocumulus clouds • Impacts of fine scale moisture variations on cloud initiation • Local sources of aerosols (lacking measurements) • Need to consider the feedbacks of various physical parameterizations on each other • Land surface/PBL/Convective parameterization linkages • Microphysics and radiation

  22. Priorities and Plans for FY11-12 • Priorities: • Continue case study analysis and transition to operations. • Use explicit supercooled water predictions to improve FAA icing algorithms (FIP). • Incorporate more aerosol chemical species and interactions plus explicit droplet nucleation and ice nucleation. • Continue to improve bulk schemes using guidance from bin/detailed scheme(s).

  23. Questions?

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