Comparison of Convection-permitting and Convection-parameterizing Ensembles

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1 Comparison of Convection-permitting and Convection-parameterizing Ensembles Adam J. Clark NOAA/NSSL 18 August 2010 DTC Ensemble Testbed (DET) Workshop

2 Introduction/Motivation CAMs could lead to big improvements in forecasts of warm-season convection. Computing technology has reached a point where it is relatively easy to run CAMs Post-analysis and verification of CAM forecasts finds many aspects that provide added value relative to forecasts that parameterize convection, and this added value should make CAM ensembles especially advantageous. Purpose: Summarize advantages of CAMs and show some results comparing CAM ensembles vs. convection-parameterizing ensembles.

3 Advantages in CAMs Statistical properties CAMS can reproduce realistic convective system features and over time can better simulate convective system mode and frequency than operational models (Done et al. 2004).

4 Advantages in CAMs (cont.) Because CAMs reproduce convective features, explicit info on storm hazards can be extracted. Since convection evolves on time-scales shorter than model output frequency, code developed that computes diagnostics every model time-step and outputs max values at model output times. One example hourly max field is UH (Kain et al. 2010, 2008). Updraft Helicity

5 Advantages in CAMs (cont.) Diurnal rainfall cycle depiction (Weisman et al. 2008, Clark et al. 2007, 2009):

6 Advantages in CAMs (cont.) Resolving smaller scales in CAMs results in faster error growth. All the aforementioned advantages should result forecast PDFs more representative of range of possible solutions relative to ensemble using cumulus parameterization.

7 Traditional skill metrics When standard metrics are used to compare CAMs to coarser models sometimes it is hard to see any differences. Recent study examined differences in ETS between NAM and experimental version of WRF run by NCAR (Clark et al. 2010). Computing ETS on raw grids gave small differences. When criteria for hits was relaxed using neighborhood approach, more dramatic differences were seen that better reflected overall subjective impressions of forecasts.

8 Ensemble Comparisons Results from 3 studies summarized; all studies share same dataset. CAM ensemble (2007 SSEF system run by CAPS) Table 1 Model specifications for ENS4 (pink) and ENS4 phys (blue). Member ICs Microphysics Boundary Layer CN PH1 21Z NAMa 21Z NAMa WSM-6 Thompson MYJ MYJ N1 PH2 CN-em_pert Ferrier Ferrier MYJ MYJ P1 PH3 CN+em_pert Thompson WSM-6 MYJ YSU N2 PH4 CN-nmm_pert Thompson Thompson YSU YSU P2 PH5 CN+nmm_pert WSM-6 Ferrier YSU YSU

9 Convection-parameterizing ensemble (run in post-realtime at Iowa State University). Table 2 Model specifications for ENS20 (pink) and ENS20 phys (blue). Uncolored elements apply to both ENS20 and ENS20 phys. Member ICs/LBCs CP Microphysics Boundary Layer 1 16 em_ctl eta_ctl2 BMJ Thompson MYJ 2 17 em_p1 BMJ WSM-6 MYJ 3 18 em_n1 BMJ WSM-6 YSU 4 19 nmm_ctl BMJ Thompson YSU 5 20 nmm_p1 BMJ Ferrier YSU 6 21 nmm_n1 KF Thompson MYJ 7 22 eta_ctl KF WSM-6 MYJ 8 23 eta_n1 KF WSM-6 YSU 9 24 eta_n2 KF Thompson YSU eta_n3 KF Ferrier YSU eta_n4 Grell Thompson MYJ eta_p1 Grell WSM-6 MYJ eta_p2 Grell WSM-6 YSU eta_p3 Grell Thompson YSU eta_p4 Grell Ferrier YSU

10 Comparison of Precipitation Forecast Skill - Deterministic (ensemble mean) forecasts: Equitable Threat Score (bias correction applied) used for evaluation. Black bars denote times with significant differences. - Probabilistic forecasts: Area under the ROC curve used for evaluation.

11 Comparison of ensemble spread Configuration of 2007 SSEF system allowed for analysis of spread contributed by mixed-physics. Faster spread growth Larger mixed-physics spread contribution IC/LBC+Phys Phys Forecast hour 9 Forecast hour 33 Better statistical consistency at forecast hour Z 4-km 20-km 20-km 20-km Z 4-km 20-km 20-km 20-km Z 4-km 20-km 20-km 20-km 4-km MSLP 4-km 20-km km 4-km T km km Td2 4-km 4-km 4-km 4-km WMAG km 850T Td km 20-km PREC 4-km 4-km km MUCAPE km km WSHR 4-km 4-km km

12 Comparison of forecasts for a severe weather producing MCV

13 Tornado and damage pictures

14 Forecasts of MCV track

15 Flux-form vorticity budget diagnostics RUC analyses Member p1 of ENS4 Member 27 of ENS20

16 Conclusions Convection-allowing ensembles very promising, but many areas for improvement and research. Model initialization, spread-error relationships, post-processing, correcting for systematic errors/biases, verification approaches, forecasts of environmental fields, optimal configurations, explicit prediction of storm hazards, applications to warn-on-forecasts, etc, etc. Much of the work presented was based on 2007 SSEF ensemble; many improvements have been made since, so results may have been even better looking at more recent years.

17 References Clark, A. J., W. A. Gallus, M. Xue, and F. Kong, 2009: A comparison of precipitation forecast skill between small convection-allowing and large convection-parameterizing ensembles. Wea. Forecasting, 24, Clark, A. J., W. A. Gallus, M. Xue, and F. Kong, 2010: Growth of spread in convection-allowing and convection-parameterizing ensembles. Wea. Forecasting, 25, Clark, A. J., W. A. Gallus, M. Xue, and F. Kong, 2010: Convection-allowing and convectionparameterizing ensemble forecasts of a mesoscale convective vortex and associated severe weather environment. Wea. Forecasting, 25, Clark, A. J., W. A. Gallus, and M. L. Weisman, 2010: Neighborhood-based verification of precipitation forecasts from convection-allowing NCAR WRF model simulations and the operational NAM. Wea. Forecasting (In Press). Kain, J. S., S. R. Dembek, S. J. Weiss, J. L. Case, J. J. Levit, and R. A. Sobash, 2010: Extracting unique information from high resolution forecast models: Monitoring selected fields and phenomena every time step. Wea. Forecasting (In Press). Weisman, M. L., C. Davis, W. Wang, K. W. Manning, and J. B. Klemp, 2008: Experiences with 0-36-h explicit convective forecasts with the WRF-ARW model. Wea. Forecasting, 23,

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22 Advantages in convection-permitting models Faster error growth from resolving smaller scales Still not enough spread during first part of forecast, though. Smaller scale perturbations needed and better model initialization.