1. Using Ensemble Model Output Statistics to Improve 12-Hour Probability of Precipitation Forecasts John P. Gagan NWS Springfield, MO Chad Entremont NWS Jackson, MS

2. TROUBLE GOOD Wet PoP Dry PoP

3. The Problem Dry Bias –Improvement noted with No Precip –Forecaster not as “wet” as GFS MOS when there is Precip National problem –Almost all areas exhibit same tendencies –Issues in both cold and warm seasons

4. The Problem (con’t) PoP definition –The probability of occurrence of measurable precipitation (0.01 inch) at a given point for each 12-hour period through Day 7. The Interpretation –In spite of a straightforward definition, it’s as unique as the individual asked “PoPs ‘look’ too high today” “It’s not going to rain that much, so I’m lowering PoPs” “I never go likely beyond 48 hours”

5. A Generic Forecast ‘In Words’ “Models increasing PWs to 200% of normal” “High Θ e air being pumped into region by 50kt LLJ” “Area in right entrance region of ULJ” “Large area of rain and embedded thunderstorms will move over the area today” And so on…

6. A Generic Forecast ‘By Numbers’ MOS PoP Forecasts –MAV – 90% –MET – 85% –Ensemble MOS – 80% Forecaster’s PoP –70% area wide

7. What Happened? Numerous reasons why it WILL rain Yet, the forecast is drier than MOS Why? –Mistrust/misunderstanding of MOS? –Lack of understanding of the 12-hr PoP? –Reasons vary by individual CONFIDENCE –The main issue - CONFIDENCE

8. A Solution Ensemble MOS –Started April 2001 –Currently a 16-member suite Operational MEX Control Run 14 Perturbations –Run 1-time per day (00z issuance) A bulletin is created showing the Max/Min/Avg of MOS output

9. A Solution (con’t) Use the Ensemble Average PoP as a means to improve PoP forecasts –DO NOT use the ensemble average value as an EXPLICIT forecast –Use the ensemble average value as CONFIDENCE factor The higher the ensemble average, the more confidence in precip occurrence

10. Data Manipulation This investigation is for the COLD SEASON ONLY! –October to April Data collected from Oct 2003 – Apr 2006 Investigated 6 sites –SGF CWFA – KSGF, KVIH –JAN CWFA – KGWO, KTVR, KJAN, KMEI

11. Data Manipulation (con’t) ~ 4000 data points collected –Stratified by rain/no rain Periods 1-10 studied (Days 1-5) Graphs produced to highlight rainfall frequency for a given value of the ensemble average PoP confidenceEnsemble Average PoP is NOT used as a PoP but a confidence factor

12. 7 / 317243 / 4496 1127 / 1501 891 / 1046 371 / 390 Cases Rain / All Cases

13. 5 / 252366 / 4372 966 / 1297 613 / 715 24 / 25 Cases Rain / All Cases

14. 776 / 1084 7 / 195099 / 4149 397 / 471 Cases Rain / All Cases

15. 19 / 1217109 / 3770 891 / 1046 211 / 263 Cases Rain / All Cases

16. 55 / 1823143 / 3253 559 / 992 221 / 344 Cases Rain / All Cases

17. Using Ensemble PoP in Real Time Using the ensemble average alone does well However, using this in tandem with the full suite of models/guidance is best –SREF – (Probabilities from SPC web page) –Other global models –Mesoscale models confidenceThe more datasets that say “YES” should increase confidence and result in a better quality PoP

18. Observations and Further Study Watch the day-to-day trend of the ensemble average PoP confidence –If the value increases for a particular period, confidence increases –Should be able to hone in on 1-3 12-hour periods (“windows of opportunity”) Watch for MEX PoP values LESS THAN the ensemble average –Observation has shown that it does not rain as often

19. Does It Work? A quick look at verification from KJAN

20. Dry Bias Prominent

21. Dry Bias Eliminated

22. Questions, Comments? If you are interested in this study, we’d like to hear your opinions –John.Gagan@noaa.govJohn.Gagan@noaa.gov –Chad.Entremont@noaa.govChad.Entremont@noaa.gov