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

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.

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).

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). Advantages in CAMs (cont.) Updraft Helicity

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

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.

Ensemble Comparisons Results from 3 studies summarized; all studies share same dataset. CAM ensemble (2007 SSEF system run by CAPS) MemberICsMicrophysicsBoundary Layer CNPH121Z NAMa WSM-6ThompsonMYJ N1PH2CN-em_pertFerrier MYJ P1PH3CN+em_pertThompsonWSM-6MYJYSU N2PH4CN-nmm_pertThompson YSU P2PH5CN+nmm_pertWSM-6FerrierYSU Table 1 Model specifications for ENS4 (pink) and ENS4 phys (blue).

Convection-parameterizing ensemble (run in post-realtime at Iowa State University). MemberICs/LBCsCPMicrophysicsBoundary Layer 116em_ctleta_ctl2BMJThompsonMYJ 217em_p1BMJWSM-6MYJ 318em_n1BMJWSM-6YSU 419nmm_ctlBMJThompsonYSU 520nmm_p1BMJFerrierYSU 621nmm_n1KFThompsonMYJ 722eta_ctlKFWSM-6MYJ 823eta_n1KFWSM-6YSU 924eta_n2KFThompsonYSU 1025eta_n3KFFerrierYSU 1126eta_n4GrellThompsonMYJ 1227eta_p1GrellWSM-6MYJ 1328eta_p2GrellWSM-6YSU 1429eta_p3GrellThompsonYSU 1530eta_p4GrellFerrierYSU Table 2 Model specifications for ENS20 (pink) and ENS20 phys (blue). Uncolored elements apply to both ENS20 and ENS20 phys.

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.

Comparison of ensemble spread Configuration of 2007 SSEF system allowed for analysis of spread contributed by mixed-physics.

Comparison of forecasts for a severe weather producing MCV

Tornado and damage pictures

Forecasts of MCV track

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

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.

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 convection- parameterizing 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,

Questions?

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