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

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Presentation on theme: "A Brief Guide to MDL's SREF Winter Guidance (SWinG) Version 1.0 January 2013."— Presentation transcript:

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

3 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

4 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.

5 How do I use it?  If precipitation type is a question, and  You expect SREF to be skillful  Assess the meteograms for your stations SREF/xml/meteoform_sref.php

6 SREF Winter Guidance--Full Page

7 SREF Winter Guidance—Top Half Higher confidence Lower confidence

8 SREF Winter Guidance—Bottom Half

9 How to Assess Meteograms  Time series forecasts of weather elements related to precipitation type  Black line is 50th percentile

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

11 How to Assess Meteograms  Red lines show the station specific climatological boundary for rain/snow, if available

12 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

13 Up Close  Close up of 850 mb Temperature

14 Up Close  Compare spread at 0300 and 1500 UTC. Forecast is more confident at 0300.

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

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

17 Which weather elements? Current  2-m Temperature  850 mb Temperature  mb Thickness  mb Thickness  mb Thickness  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!

18 Why these weather elements?  We have a very short sample. SREF began running in this configuration 21 Aug  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.

19 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

20 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).

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

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

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

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

25 Illustration: Three members (blue) contrib- ute to final probability distribution (black) For SREF, we use all 21 members.

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

27 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

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

29 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 credentials  Select “Available Communities” tab  Find “SREF Winter Guidance” and “Join”


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