1. Nesting

4. Eta Model

5. Eta Coordinate And Step Mountains MSL ground  = 1 Ptop  = 0

7. Horizontal resolution of 12 km 12-km terrain

8. Drawbacks of the Eta Coordinate The failure to generate downslope wind storms in regions of complex terrain Weak boundary layer winds over elevated terrain when compared to observations The displacement of precipitation maxima too far toward the bottom of steeply sloping terrain as opposed to the observed location near the top half of the terrain slope The reduction in the number of vertical layers used to define the model atmosphere above elevated topography particularly within the boundary layer

10. WRF Model Family A Tale of Two Dynamical Cores

11. Why WRF? An attempt to create a national mesoscale prediction system to be used by both operational and research communities. A new, state-of-the-art model that has good conservation characteristics (e.g., conservation of mass) and good numerics (so not too much numerical diffusion) A model that could parallelize well on many processors and easy to modify. Plug-compatible physics to foster improvements in model physics. Designed for grid spacings of 1-10 km

12. Two WRF Cores ARW (Advanced Research WRF) developed at NCAR Non-hydrostatic Numerical Model (NMM) Core developed at NCEP Both work under the WRF IO Infrastructure NMM ARW

13. The NCAR ARW Core Model: (See: www.wrf-model.org)  Terrain following vertical coordinate  two-way nesting, any ratio  Conserves mass, entropy and scalars using up to 6 th order spatial differencing equ for fluxes. Very good numerics, less implicit smoothing in numerics.  NCAR physics package ( converted from MM5 and Eta ), NOAH unified land-surface model, NCEP physics adapted too

14. The NCEP Nonhydrostatic Mesoscale Model: NMM (Janjic et al. 2001)  Hybrid sigma  pressure vertical coord.  3:1 nesting ratio  Conserves kinetic energy, enstrophy and momentum using 2 nd order differencing equation  Modified Eta physics, Noah unified land-surface model, NCAR physics adapted too  Parallelized within WRF infrastructure

15. Hybrid and Eta Coordinates ground MSL ground Pressure domain Sigma domain  = 0  = 1  = 1 Ptop  = 0

16. WRF Modeling System Obs Data, Analyses Post Processors, Verification WRF Software Infrastructure Dynamic Cores Mass Core NMM Core … Standard Physics Interface Physics Packages Static Initialization 3DVAR Data Assimilation

17. WRF Hierarchical Software Architecture Top-level “Driver” layer –Isolates computer architecture concerns –Manages execution over multiple nested domains –Provides top level control over parallelism patch-decomposition inter-processor communication shared-memory parallelism –Controls Input/Output “Mediation” Layer –Specific calls to parallel mechanisms Low-Level “Model” layer –Performs actual model computations –Tile-callable –Scientists insulated from parallelism –General, fully reusable Mediation Layer wrf initial_configalloc_and_configureinit_domainintegrate solve_interface solve Model Layer Driver Layer prep filter big_step decouple advance uv recouple scalars physics advance w

18. The National Weather Service dropped Eta in 2006 as the NAM (North American Mesoscale) run and replaced it with WRF NMM. The Air Force uses WRF ARW. Most universities use WRF ARW

19. WRF-NMM Same domain as Eta Sixty levels like Eta Essentially same physics as ETA Much better in terrain…doesn’t share the eta’s problems. Clearly inferior synoptic initialization and synoptic forecast than GFS

20. NMM WRF NAM NMM upgrades December 2008, include GDAS (GFS analysis) as initial first guess. use of global analysis (GDAS) for first guess at t-12 hour (the start of the analysis cycle) improves the evolution of synoptic scale features in the new NAM-WRF. This is found consistently throughout the 84-hour forecast. Improved physics higher resolution snow analysis and changes to snow impact on surface energy budget, increased absorptivity of model clouds

21. NMM Generally inferior to GFS

25. Rapid Update Cycle-RUC

26. RUC A major issue is how to assimilate and use the rapidly increasing array of offtime or continuous observations (not a 00 and 12 UTC world anymore! Want very good analyses and very good short- term forecasts (1-3-6 hr) The RUC ingests and assimilates data hourly, and then makes short-term forecasts Uses the MAPS mesoscale model…which uses a hybrid sigma/isentropic vertical coordinate Resolution: 13 km and 50 levels

27. 13km RUC Improvements expected from 13km RUC - Improved near-surface forecasts - Improved precipitation forecasts - Better cloud/icing depiction - Improved frontal/turbulence forecasts Terrain elevation - 100 m interval NCEP computer upgrade allows RUC13 to run in same time as current RUC20

28. Observations used in RUC Data Type ~Number Freq. -------------------------------------------------- Rawinsonde 80 /12h NOAA profilers 30 / 1h VAD winds 110-130 / 1h Aircraft (V,temp) 1400-4500 / 1h Surface/METAR 1500-1700 / 1h Buoy/ship 100-150 / 1h GOES precip water 1500-3000 / 1h GOES cloud winds 1000-2500 / 1h GOES cloud-top pres 10 km res / 1h SSM/I precip water 1000-4000 / 6h -------------------------------------------------- GPS precip water ~300 / 1h Mesonet ~5000 / 1h METAR-cloud-vis-wx ~1500 / 1h -------------------------------------------------- NCEP RUC20 operational RUC13 (at NCEP June 2005) Cloud analysis variables

29. RUC History – NCEP (NMC) implementations 1994 - First operational implementation of RUC - 60km resolution, 3-h cycle 1998 – 40km resolution, 1-h cycle, - cloud physics, land-sfc model 2002 – 20km resolution - addition of GOES cloud data in assimilation 2003 – Change to 3dVAR analysis from previous OI (April) 2004 – Vertical advection, land use (April) PBL-depth for surface assimilation (September) 2005 – 13km resolution, new obs, new model physics (June) 2007 – WRF-based Rapid Refresh w/ GSI to replace RUC

30. More detailed coastline with 13km resolution 13km RUC20km RUC Soil moisture – 22z - 21 Feb 2005 Dark blue = water

31. WRF RUC A new version of RUC has been developed, but not yet operational that uses the WRF model instead of the MAPS model.