1. HMT-WPC W INTER W EATHER E XPERIMENT January 12 – February 13, 2015 College Park, MD

2. WPC W INTER W EATHER F ORECASTS Winter Weather Desk (Day 1-3) Internal deterministic guidance for WFOs 6 hr forecasts of snow, sleet, freezing rain, and SLR Public probabilistic winter precipitation forecasts (PWPF) 24 hr probabilities of exceeding numerous snow and freezing rain thresholds Surface low tracks Track forecasts for surface lows associated with significant winter weather Medium Range Desks (Day 4-7) Internal probability of winter precipitation 24 hr forecasts of QPF ≥ 0.1” (liquid equivalent) with a precipitation type of snow/sleet/freezing rain Deterministic Forecast PWPF Low Tracks Day 4-7

3. HMT-WPC W INTER W EATHER E XPERIMENT Established in 2011 to help improve WPC’s winter weather forecasts Participants from across the weather enterprise – operations, research, academia, private sector Previous areas of focus: Utility of high resolution models for winter weather Utility of microphysics-based snowfall forecasting techniques Communicating uncertainty Extending winter weather forecasts beyond 72 hours Day 2 Forecast Day 4 Forecast and Snowfall Observations Public Forecast Graphic

4. 2015 Experiment Goals: Explore the utility of alternative microphysics-based snowfall forecasting methods, including their application to ensemble forecasts Explore the utility of new ensemble datasets for winter weather forecasting Explore new datasets and techniques to improve the winter weather outlook (Day 4-7) forecast process

5. E XPERIMENT A CTIVITIES Short range forecasts (morning) 24 hr deterministic snowfall forecast, valid 00 – 00 UTC Corresponding forecast confidence graphic Participants choose either Day 1 or Day 2 Medium range forecasts (afternoon) 24 hr probabilistic forecasts, valid 12 – 12 UTC Probability of frozen precipitation ≥ 0.1” (liquid equivalent) “frozen precipitation” = snow, sleet, OR freezing rain Probability of snow ≥ 0.5” (liquid equivalent) Probability of freezing rain ≥ 0.01” (liquid equivalent) Participants choose 2 days during the Day 4-7 period Subjective forecast and model evaluations Day 1 This morning 00 UTC tonight 00 UTC tomorrow night Day 2 2” 4” 8” 12” MAX SNOWFALL 20” **1730 UTC weather briefing focused on short range forecasts**

6. E XPERIMENTAL D ATASETS NAM rime factor-modified snowfall Parallel SREF NARRE WPC PWPF ensemble

7. NAM R IME F ACTOR -M ODIFIED S NOWFALL Background Model snowfall forecasts typically depend on: Instantaneous precipitation type QPF in areas where precipitation type is snow Snow-to-liquid ratio (SLR) NAM SLR options at WPC Roebber Technique – neural network approach based on 7 predictive variables Baxter Climatology – CONUS-wide climatology that varies by season With this approach, model guidance often struggles to correctly predict snowfall amounts in precipitation type transition zones due to incorrect identification of the precipitation type Goal Use information about ice particles provided directly by the model’s microphysics scheme to better diagnose snowfall amounts in these environments Roebber, P.J., S.L. Bruening, D.M. Schultz, and J.V. Cortinas, 2003: Improving snowfall forecasting by diagnosing snow density. Wea. Forecasting, 18, 264-287. Baxter, M.A., C.E. Graves, and J.T. Moore, 2005: A climatology of snow-to-liquid ratio for the contiguous United States. Wea. Forecasting, 20, 729-744.

8. R IME F ACTOR M ODIFICATION Rime Factor (RF) – amount of growth of ice particles by riming and liquid water accretion Instantaneous model output Percentage of Frozen Precipitation (POFP) – percent of precipitation reaching the ground that is frozen Instantaneous model output Rime Factor1> 1 to ~2~2 to ~4~4 to ~8~8 to ~40> 40 Hydrometeor Type Unrimed snow Lightly rimed snow Moderately rimed snow Heavily rimed snow GraupelSleet Snowflake images courtesy of - http://www.its.caltech.edu/~atomic/snowcrystals/

9. R IME F ACTOR M ODIFICATION Rime factor used to modify the original model SLR Percentage of frozen precipitation used to modify the model QPF Rime FactorSLR Modification 1 < RF < 2 (fluffy snow) 2 < RF < 5 (rimed snow) 5 < RF < 20 (graupel) RF > 20 (sleet) Snowfall = (QPF)(POFP)(SLR RF ) Increase rime factorDecrease SLR Decrease snowfall Increase % frozen Increase available QPFIncrease snowfall

10. R IME F ACTOR M ODIFICATION - E XAMPLE 24 hr snowfall forecast ending 00Z 27 November 2014: Washington D.C. ~4-6” New York City ~6-8” Boston ~3-6” 24 hr NAM snowfall forecast (Roebber SLR) BUT…. Rime Factor valid 17 UTCRime Factor valid 19 UTCPOFP valid 17 UTCPOFP valid 19 UTC High rime factor values in major cities indicate more heavily rimed particles (lower snowfall amounts) Relatively low percentage of frozen precipitation (50% or less)

11. Taking rime factor and POFP into account…. 24 hr NAM snowfall forecast (RF-modified Roebber SLR) 24 hr snowfall forecast ending 00Z 27 November 2014: Washington D.C. <1” New York City ~2-4” Boston ~3-6” WPC snowfall analysis

12. E XPERIMENTAL D ATASETS NAM rime factor-modified snowfall Parallel SREF NARRE WPC PWPF ensemble

13. P ARALLEL SREF Scheduled for implementation in Spring 2015* Membership increased from 21 members to 26 members Model cores reduced from 3 to 2 Initial conditions and microphysics varied across model cores in effort to increase diversity SLR based on 2 m temperature (same as operational) Baxter climatology Operational SREF Parallel SREF Number of Members 2126 Model Cores WRF-NMM WRF-ARW NMMB WRF-ARW NMMB * Full ensemble configuration available at end of presentation SREF 24 hr mean snow valid 00Z 12/25 SREFP (SREF SLR) 24 hr mean snow valid 00Z 12/25 SREFP (Baxter SLR) 24 hr mean snow valid 00Z 12/25

14. P ARALLEL SREF R IME F ACTOR -M ODIFIED S NOWFALL Rime factor modification can also be applied to the 16 members of the SREF that use Ferrier microphysics **RF modification uses snow water accumulation from the model microphysics to create snow** 24 hr mean snow valid 00Z 12/2524 hr RF mean snow valid 00Z 12/25

15. E XPERIMENTAL D ATASETS NAM rime factor-modified snowfall Parallel SREF NARRE WPC PWPF ensemble

16. N ORTH A MERICAN R APID R EFRESH E NSEMBLE (NARRE) Preliminary version of ensemble under development at ESRL Current ensemble configuration: 8 members, 13 km Run at 00 UTC and 12 UTC out 48 hours Planned ensemble configuration: Increase to 10+ members Hourly 18-24 hour forecast Base for SREF Member Initial Conditions Microphysics Ctl RAPGFSThompson RAP1GEP01Thompson RAP2GEP02Ferrier RAP3GEP03Ferrier Ctl NMMBGFSFerrier NMMB1GEP01Ferrier NMMB2GEP02Ferrier NMMB3GEP03Ferrier 24 hr mean snow valid 00Z 12/25 24 hr member snow valid 00Z 12/25

17. E XPERIMENTAL D ATASETS NAM rime factor-modified snowfall Parallel SREF NARRE WPC PWPF ensemble

18. WPC PWPF E NSEMBLE Used to derive public probabilistic forecasts from internal deterministic forecasts SLR is an average of: Roebber Technique applied to the NAM Roebber Technique applied to the GFS Baxter climatology 11:1 WPC Deterministic Forecast WPC “most likely” deterministic value Probability Snowfall

19. WPC PWPF E NSEMBLE Membership prior to March 2014: 21 SREF members ECENS mean GEFS mean 5 random GEFS members Deterministic NAM, GFS, CMC, ECMWF Current membership: 21 SREF members 5 GEFS members (selected randomly) 25 ECENS members (selected randomly) GEFS mean ECENS mean Deterministic NAM, GFS, CMC, ECMWF 32 members 57 members

20. Develop Guidance: Disaggregate WPC Day 4-5 & Day 6-7 QPF into 6 hour QPF Use ensemble means to determine percentages of 48 QPF attributed to each 6 hour time period Use GEFS, ECENS & CMCE to generate CDF to extract QPF probabilities Apply basic p-type algorithm to each ensemble member to construct ensemble p-type probabilities Combine QPF probabilities and ensemble probability of p-type Day 4-7 Winter Weather PQPF Prob of WPC QPF >= 0.10”Ensemble Prob of Fzn PrecipProb of Frozen Precip > 0.10”

21. Develop Guidance: Disaggregate WPC Day 4-5 & Day 6-7 QPF into 6 hour QPF Use ensemble means to determine percentages of 48 QPF attributed to each 6 hour time period Use GEFS, ECENS & CMCE to generate CDF to extract QPF probabilities Apply basic p-type algorithm to each ensemble member to construct ensemble p-type probabilities Combine QPF probabilities and ensemble probability of p-type Operations: Probability of frozen QPF >.1” (liquid equivalent)  ZR, IP, SN Produced daily; available on internal WPC webpage 2015 WWE: Probability of snow > 5” .5” QPF; SN & IP Probability of freezing rain >.01” .01” QPF; ZR Day 4-7 Winter Weather PQPF Prob of frozen QPF >= 0.1” Prob of snow >= 5” Prob of freezing rain >=.01”

22. Q UESTIONS ?? Contact a member of the 2015 HMT-WPC Winter Weather Experiment team: Faye Barthold (faye.barthold@noaa.gov)faye.barthold@noaa.gov Tom Workoff (thomas.workoff@noaa.gov)thomas.workoff@noaa.gov Mike Bodner (mike.bodner@noaa.gov)mike.bodner@noaa.gov Mark Klein (mark.klein@noaa.gov)mark.klein@noaa.gov Tony Fracasso (anthony.fracasso@noaa.gov)anthony.fracasso@noaa.gov Dave Novak (david.novak@noaa.gov)david.novak@noaa.gov

23. P ARALLEL SREF C ONFIGURATION Member Initial Conditions PhysicsConvection NMMB_CTL NDASFerrier_hiresBMJ old shallow NMMB_N1 NDASWSM6SAS NMMB_P1 NDASFerrier_hiresBMJ new shallow NMMB_N2 NDASFerrierSAS NMMB_P2 NDASWSM6BMJ old shallow NMMB_N3 GFSFerrier_hiresSAS NMMB_P3 GFSWSM6BMJ new shallow NMMB_N4 GFSWSM6SAS NMMB_P4 GFSFerrier_hiresBMJ old shallow NMMB_N5 RAPWSM6SAS NMMB_P5 RAPFerrier_hiresBMJ new shallow NMMB_N6 RAPFerrier_hiresSAS NMMB_P6 RAPWSM6BMJ old shallow ARW_CTL RAPWSM6KF ARW_N1 RAPFerrierBMJ ARW_P1 RAPThompsonGrell ARW_N2 RAPFerrierKF ARW_P2 RAPThompsonBMJ ARW_N3 GFSWSM6Grell ARW_P3 GFSThompsonKF ARW_N4 GFSWSM6BMJ ARW_P4 GFSFerrierKF ARW_N5 NDASFerrierGrell ARW_P5 NDASWSM6KF ARW_N6 NDASThompsonBMJ ARW_P6 NDASThompsonGrell