A Brief Guide to MDL's SREF Winter Guidance ( SWinG )

1. A Brief Guide to MDL's SREF Winter Guidance (SWinG) Version 1.0 January 2013

2. What's this all about? An innovative way to view and understand SREF output Calibrated probabilistic forecast guidance, based on NCEP's Short Range Ensemble Forecast (SREF) system--SREF Winter Guidance (SWinG) Prototype includes weather elements that focus on rain/snow/freezing rain forecast decisions Available on the web for all SREF forecast cycles and time projections at a limited number of stations

3. Why use calibrated probabilities? • Ensembles are often overconfident (underdispersed). • Too frequently the verification falls outside the spread of the ensemble members. • SWinG forecasts are calibrated. • True measure of forecast confidence. • Statistically reliable spread.

4. How do I use it? • If precipitation type is a question, and • You expect SREF to be skillful • Assess the meteograms for your stations http://www.mdl.nws.noaa.gov/~BMA-SREF/xml/meteoform_sref.php

5. SREF Winter Guidance--Full Page

6. SREF Winter Guidance—Top Half Lower confidence Higher confidence

7. SREF Winter Guidance—Bottom Half

8. How to Assess Meteograms • Time series forecasts of weather elements related to precipitation type • Black line is 50th percentile

9. How to Assess Meteograms • Grey areas show spread of the distribution (10th, 30th, 70th, and 90th percentiles) 90% 70% 30% 10%

10. How to Assess Meteograms • Red lines show the station specificclimatological boundary for rain/snow, if available

11. How to Assess Meteograms • Tri-color lines show rule of thumb values for rain/freezing/frozen Rain/freezing frozen threshold Freezing/snow line All-snow threshold

12. Up Close • Close up of 850 mb Temperature

13. Up Close • Compare spread at 0300 and 1500 UTC. Forecast is more confident at 0300.

14. Up Close • At 0300, guidance indicates ~70% chance of 850 mb temperature below key value (-1.5° C)

15. Up Close • At 1500, SWinG indicates ~20% chance of 850 mb temperature below key value (-1.5° C)

16. Which weather elements? Current • 2-m Temperature • 850 mb Temperature • 1000-500 mb Thickness • 850-700 mb Thickness • 1000-850 mb Thickness • 1000-700 mb Thickness Future • Dendritic Growth Zone Depth • Omega • Freezing Level • Positive/Negative Energy • Snow Liquid Equivalent Why these weather elements? There are better parameters for winter weather!

17. Why these weather elements? • We have a very short sample. SREF began running in this configuration 21 Aug 2012. • We are using a modified form of Bayesian Model Averaging (BMA). This technique can only forecast weather parameters that are observed daily. • Currently, SREF vertical velocities for NMM and NMMB have problems. • It's a new technique. We started with the easiest weather elements.

18. Which Stations? • On NCEP's Central Computing System, we generate SWinG for more than 3000 stations • Adapted from the BUFR station list used at NCEP • BUFR station list is source for BUFKIT application • On our web page, we generate images for ~400 stations • All upper air stations in CONUS and Alaska • Additional stations to support WFO LWX Winter Weather Pilot Project. • We can, and will, add stations to the web page • Contact us if you want us to add stations

19. How do we make SWinG? Using most recent verification... • Correct bias of each member • Weight the bias-corrected members (ARW, NMM, NMMB members) • Correct forecast spread • Compute probabilities We have named this technique Decaying Average Bayesian Model Averaging (DABMA).

20. We track and remove the bias of each member. We update this bias correction daily with the most recent verification. Latest SREF Forecast Latest SREF Forecast Previous Bias Corrections Today's Observation Previous Forecasts Update Bias Corrections Correct Bias of Each Member Correct the Bias of Each Member Bias correction for each model core ... more i.e., 1 bias correction value each for ARW, NMM, NMMB, which is applied to each of their respective members New Estimate = 0.95 x Previous Estimate + 0.05 x Today's Estimate

21. Using the most recent verification, we compute relative weights for bias-correctedARW, NMM, NMMB members Previous Weights Today's Observation Previous Bias-Corrected Forecasts Update Relative Weights Relative weights for each model core

22. Using most recent verification, correct forecast spread Today's Observation Previous Forecasts Raw Spread Corrected Compute optimal spread Optimal spread

23. We compute probabilities using a Normal Mixture Model to combine member forecasts.

24. We compute probabilities using a Normal Mixture Model to combine member forecasts. Illustration: Three members (blue) contrib-ute to final probability distribution (black) For SREF, we use all 21 members.

25. We compute probabilities using a Normal Mixture Model to combine member forecasts. Relative model weights set height of each blue curve

26. We compute probabilities using a Normal Mixture Model to combine member forecasts. Bias-corrected SREF forecasts set position of each blue curve on X-axis

27. We compute probabilities using a Normal Mixture Model to combine member forecasts. The optimal spread deter-mines the spread of each blue curve.

28. Join the conversation! We are using the NWS Innovation Web Portal (IWP) to gather feedback from forecasters. https://nws.weather.gov/innovate/group/guest/communities • You will find • Additional documentation and case studies • Forum where you can submit questions and comments • For access • Follow the URL and login with NOAA e-mail credentials • Select “Available Communities” tab • Find “SREF Winter Guidance” and “Join”