Convection-Resolving NWP with WRF. Section coordinator Ming Xue University of Oklahoma

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1 Convection-Resolving NWP with WRF Section coordinator Ming Xue University of Oklahoma

2 Convection-resolving NWP Is NWP that explicitly treats moist convective systems ranging from organized MCSs to individual convective cells; For strongly fored and organized convective systems, skillful forecasts can often be obtained as far as 36 hours using 2-4 km grid resolutions; Less organized and/or weakly forced systems require higher resolutions ( 1 km) and predictability range is much shorter; Continental-scale NWP at 1-km resolution routinely possible within a few years; Explicit prediction of convective cells important for themselves and for proper feedback to large scales

3 Research Priorities Data assimilation for convection-resolving NWP. Physics improvements for convectionresolving NWP. Model numerics and computational infrastructure. Convective-scale predictability study and probabilistic forecasting

4 Data Assimilation for Convection- Resolving NWP Need good estimate of both convective storms and their environment; Remote sensing platforms crucial; including radar, wind profilers, GPS, clear-air radar winds and refractivity, polarimetric radar parameters, and high-resolution satellite data; High-res conventional data, including mesonets; Advanced DA methods, such as 4DVAR and EnKF essential; properly designed 3DVAR and other physics-based analysis methods can still be useful. Need multi-scale DA capabilities.

5 Better understand the obs needs at this resolution detailed data impact studies and identify deficiencies; Proper handling of error characteristics and spatial representativeness of observations;

6 Physics Improvements for Convection- Resolving NWP Physics appear to be the largest source of error at cloudresolving scale; Microphysics and SGS turbulence seem to have the largest impact on resolved convective cells and QPF, especially at shorter ranges and when storms initialized well; LSM, sfc physics and PBL have largest impact on convective initiation and longer range storm evolution; Cloud-radiation interaction modulates convective system dynamics and evolution and affects surface energy balance; Need careful detailed diagnostic analyses of existing schemes in controlled settings and verifications against observations too many cheap scheme inter-comparisons not enough understanding! Need to build and evaluate consistent suites of physics packages, not in random combinations in the name of diversity!

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8 Model Numerics and Computational Infrastructure. Highly accurate numerical schemes with minimum damping, e.g., conservative and monotonic schemes, are strongly desirable for small-scale often discontinuous flows; Efficient high-order schemes preferred; Also need highly scalable pre- and post-processing software; including that for DA. Codes, including those for physics packages, need to be readable! Monotonicity v.s. lower-order enstrophy conserving.

9 Convective-scale Predictability Study and Probabilistic Forecasting Convective-scale predictability poorly understood, and varies significantly with type of convective systems; Better understanding of the error growth dynamics, in the presence of model error, is important for DA as well as for model improvement; Probability information even more important at this scale because of generally high forecast uncertainty; Ensemble-based DA and forecast systems should be developed that should include model uncertainties too; Need to support multiple well tuned physics options and perhaps dynamic cores to better capture the uncertainty range.

10 Model evalution Identify sources of errors, failures, in wholistic way need understanding of both physics and statistics physical scientists and statisticians working together to come up with most useful scores;

11 Verification of at convective-resolving scales Non-traditional verifications! Quantify and standardize things that are seen subjectively; Assess features, errors in both phase and amplitude, modes of convection, etc. Verification scores that reveal the handling of physical processes; Direction verifications against indirect observations; Close link with data assimilation systems;

12 Proposed Action Items Promote and seek community and funding agency support, through workshops, conferences, and publications, for more in-depth analysis and diagnostic studies of state of the art physics packages, and the development of more advanced physical parameterization schemes designed specifically for the convection-resolving scales. Promote the training of next-generation scientists specialized in atmospheric physics, and in advanced data assimilation, and in effectively applying statistical theories and methods to atmospheric data assimilation, verification and probabilistic prediction. Working closely with statisticians. Provide an efficient and flexible modeling and data assimilation framework that facilitates rapid experimentation.

13 Comments Need to evaluate over extended periods Issue of the suppression of convection by cumulus scheme Need for cumulus scheme at intermediate (~few km) resolutions; B.C. effect? Solution: large domain, twoway nesting?

14 GSI Analyses of Radar Data and Impact on forecasts of WRF-ARW and WRF-NMM Ming Hu, Shun Liu and Ming Xue CAPS, University of Oklahoma

15 May 23, 2005 Test Case

16 May 23, 2005 Case Impact of GSI + ARPS cloud analysis on WRF-ARW forecast 6-hour forecast starting at 0600 UTC 9-km resolution grid Working on merging and improving RUC and ARPS cloud analysis for more general applications (e.g., dx~10 km) within GSI framework, and using additional satellite and sfc cloud obs in NCEP data stream

17 Observation GSI+Cloud Analysis GSI Interpolated NAM 0600 UTC t=0.0h

18 Observation GSI+Cloud Analysis GSI Interpolated NAM 0700 UTC t=1.0h

19 Observation GSI+Cloud Analysis GSI Interpolated NAM 0800 UTC t=2.0h

20 Observation GSI+Cloud Analysis GSI Interpolated NAM 0900 UTC t=3.0h

21 Observation GSI+Cloud Analysis GSI Interpolated NAM 1000 UTC t=4.0h

22 Observation GSI+Cloud Analysis GSI Interpolated NAM 1100 UTC t=5.0h

23 Observation GSI+Cloud Analysis GSI Interpolated NAM 1200 UTC t=6.0h

24 GSI-analyzed winds and increments with superobbed radial velocity data NAM Background GSI analysis 1x filter scale, 0.5 superobbing GSI analysis 1/8 x filter scale, 0.1 superobbing full winds increments

25 Composite reflectivity at 11UTC from 6 radars and 6-h WRF-NMM forecast valid at 12 UTC Observed reflectivity at 1100 UTC Predicted reflectivity at 1200 UTC NAM Background GSI analysis 1x filter scale, 0.5 superobbing GSI analysis 1/8 x filter scale, 0.1 superobbing

26 Forecast Examples: May 8 th, 2003 OKC tornado OKC tornado UTC 30 km long path KTLX F4 Tornado # UTC UTC

27 Prediction using 100 m resolution grid sfc winds pert. pressure obs. tornado track (over 22 minutes)

28 Assimilation and Prediction of May 29-30, 2004 North OKC Tornado case using EnKF X KVNX 1 km Analysis and Prediction Grid F1 F3 F0 F2 F2 OKC X KTLX 1h EnKF Assimilation 40 members 1 h Forecast 00 UTC 01 UTC 02 UTC

29 Animation of 1-h Forecast Initialized with EnKF Reflectivity at 1.2 Elevation Forecast Observation 0100 UTC 0200 UTC

30 Wakimoto et al.(2006 MWR). Surface analysis + satellite images Dryline Convective Initiation Study of Xue and Martin (2006a,b MWR) May 24, 2002 IHOP Case

31 t=3h, 2100 UTC sfc. winds, qv, and composite reflectivity

32 t=4h, 2200 UTC

33 t=5h, 2300 UTC

34 2000 UTC 2015 UTC 2030 UTC 2045 UTC t=2h t=2h 15min t=2h 30min t=2h 45min C C B B B A A A C B A

35 June 12, 2002 IHOP CI Case 2140 UTC Courtesy of Wilson and Roberts (2006)

36 June 12, 2002 IHOP CI Case 21:20Z Wed 12 Jun 2002 T= s (3:20:00) FIRST LEVEL ABOVE GROUND (SURFACE) (km) (km) Sat. obs 2120 UTC Ref (dbz, shaded) qv (g/kg, contour) U-V (m/s, vector) MIN=0.000 MAX=56.1 MIN=2.491 MAX=21.41 inc=1.000 Umin= Umax=15.08 Vmin= Vmax=15.23 ARPS 3km Starting from 18 UTC IC 6 h assimilation at 1 h intervals