1. Sensitivity of PBL Parameterization on Ensemble Forecast of Convection Initiation Bryan Burlingame M.S. Graduate Research Assistant University of Wisconsin-Milwaukee burling6@uwm.edu http://derecho.math.uwm.edu/~bmburlin/ Clark Evans - UWM Paul Roebber - UWM Ryan Torn – SUNY Albany Glen Romine - UCAR

2. Overview  Goal  What is CI, and how we define it?  Model configuration and tools  Preliminary results/findings

3. Convection Initiation (CI)

4. WRF Configuration

5. PBL Schemes  Five PBL Schemes used:  Non-Local  ACM2 (Asymmetric Convective Model 2)  YSU (Yonsei University)  Local  MYJ (Mellor-Yamada-Janjic)  QNSE (Quasi-Normal Scale Elimination)  MYNN2.5 (Mellor-Yamada-Nakanishi-Niino level 2.5)

6. 3 Cases  May 19-20, 2013  Deep trough  SW flow into Plains  Initiation along boundaries  May 31-June 1, 2013  500mb cutoff low  Westerly winds into the plains  June 8-9, 2013  Ridge in Pacific NW  NW flow into the Central Plains.  Minimal initiation

7. Forecast Verification  Verified against observed CI  Domain – 2000 J/kg CAPE field  18z RAP (13km) 00 hour analysis  5 Time and Space bins  40 km/1 hour  80 km/1.5 hour  120 km/2 hour  160 km/2.5 hour  200 km/3 hour (Van Klooster and Paul J. Roebber 2009) – Figure 1

8. Forecast Verification (Roebber 2009)  Brier Skill Score  Performance Diagram  POD vs SR (1-FAR)  Bias (Blue)  Critical Success Index (Black)

9. Performance Diagram 40 km/1 hour 80 km/1.5 hour

10. Performance Diagram CAPE < 2000 J/kg 40 km/1 hour 80 km/1.5 hour

11. CI Overproduction (19-20 May 2013 Example) Observed QNSE MYJ ACM2 YSU MYNN2.5

12. Conclusions  Forecasts overproduce initiation events  Overproduce in areas of less instability  PBL schemes too energetic??  In area of high probability of convective occurrence:  All forecasts verified well within 80km / 1 hour

13. References  Adam J. Clark, Michael C. Coniglio, Brice E. Coffer, Greg Thompson, Ming Xue, and Fanyou Kong, 2015: Sensitivity of 24-h Forecast Dryline Position and Structure to Boundary Layer Parameterizations in Convection-Allowing WRF Model Simulations. Wea. Forecasting, 30, 613–638.  Ariel E. Cohen, Steven M. Cavallo, Michael C. Coniglio, and Harold E. Brooks, 2015: A Review of Planetary Boundary Layer Parameterization Schemes and Their Sensitivity in Simulating Southeastern U.S. Cold Season Severe Weather Environments. Wea. Forecasting, 30, 591–612.  Michael C. Coniglio, James Correia Jr., Patrick T. Marsh, and Fanyou Kong, 2013: Verification of Convection-Allowing WRF Model Forecasts of the Planetary Boundary Layer Using Sounding Observations. Wea. Forecasting, 28, 842–862.  Gremillion M.S. and R.E. Orville 1999: Thunderstorm characteristics of cloud-to-ground at the Kennedy 146 Space Center, Florida: A study of lightning initiation signatures as indicated by the WSR-88D. 147 Wea. Forecasting, 14, 640-649.  V. Lakshmanan, K. Hondl, and R. Rabin, “ An efficient, general-purpose technique for identifying storm cells in geospatial images,'' J. Ocean. Atmos. Tech., vol. 26,, no. 3, pp. 523-37, 2009An efficient, general-purpose technique for identifying storm cells in geospatial images  V. Lakshmanan and T. Smith, “ An objective method of evaluating and devising storm tracking algorithms,'' Wea. and Forecasting, pp. 721-729, vol. 29 no. 3, 2010. An objective method of evaluating and devising storm tracking algorithms  Paul J. Roebber, 2009: Visualizing Multiple Measures of Forecast Quality. Wea. Forecasting, 24, 601–608  Sara L. Van Klooster and Paul J. Roebber, 2009: Surface-Based Convective Potential in the Contiguous United States in a Business-as-Usual Future Climate. J. Climate, 22, 3317–3330