1. Probabilistic Forecasts Based on “Reforecasts” Tom Hamill and Jeff Whitaker tom.hamill@noaa.govtom.hamill@noaa.gov and jeffrey.s.whitaker @noaa.gov

3. Improving probabilistic forecasts Better ensembles –More members –Improved initial conditions –Higher resolution –Improved forecast models Statistical corrections of the NWP forecasts (our main point: this can improve forecasts so much that it deserves more attention)

4. A tool for exploring calibration: the CDC “reforecast” data set Definition of “reforecast” : a data set of retrospective numerical forecasts using the same model to generate real- time forecasts. Model: T62L28 NCEP MRF (now “GFS”), circa 1998 ( http://www.cdc.noaa.gov/people/jeffrey.s.whitaker/refcst for details). Initial states: NCEP-NCAR reanalysis plus 7 +/- bred modes (Toth and Kalnay 1993). Duration: 15-day runs every day at 00Z from 19781101 to now. ( http://www.cdc.noaa.gov/people/jeffrey.s.whitaker/refcst/week2 ). Data: Selected fields (winds, geo ht, temp on 5 press levels, and precip, t2m, u10m, v10m, pwat, prmsl, rh700, conv. heating). NCEP/NCAR reanalysis verifying fields included ( Web form to download at http://www.cdc.noaa.gov/reforecast ). http://www.cdc.noaa.gov/reforecast Experimental PQPF: http://www.cdc.noaa.gov/reforecast/narr/

5. Application: tercile probability forecasts Climatological distribution split into 3 equally likely bins. These categories are often called Below/Near/Above Normal “terciles”. NCEP Climate Prediction Center (CPC) operational product

6. Dashed lines: tercile boundaries Red points: samples above upper tercile Blue points: samples below upper tercile Solid bars: probabilities by bin count Dotted line: a fitted model, TBD What can we do with a long data set of observed and forecast anomalies? With our reforecasts, we have 25+ years of data. Let’s use old data in a 31-day window around the date of interest to make statistical corrections.

7. Example: floods causing La Chonchita, CA landslide, 12 Jan 2005 week-2 forecast 6-10 day forecast

8. Comparison against NCEP / CPC forecasts at 155 stations, 100 days in winter 2001-2002 MOS-based Week 2 forecasts using low-res T62 model more skillful than operational NCEP/CPC 6-10 day!

9. 2004-2005 results

10. Other examples of calibration using reforecasts Example: Decile forecasts of 850 hPa temps over US

11. Analog high-resolution precipitation forecast technique (actually run with 10 to 75 analogs)

12. www.cdc.noaa.gov/reforecast/narr forecasts now downscaled to 5-km using “Mountain Mapper” technique.

13. Analog example: Day 4-6 heavy precipitation in California, 0000 UTC 29 December 1996 - 0000 UTC 1 January 1997

14. Skill as function of location Notes: (1)Less skill where it’s dry (climatological forecasts better here, tougher to beat). (2) Regions where precipitation analyses are poor are less skillful (snowy regions, poor coverage by gages & Doppler)

15. Skill as f(time of year)

16. Comparison against NCEP medium-range T126 ensemble the improvement is a little bit of increased reliability, a lot of increased resolution.

17. Regional Reforecasts based on NARR and 32-km Eta? Leverage Mesinger et al.’s Eta regional reanalysis. Run small (~5 mbr) ensemble to 3 days? 8 days? for ~25 years. Continue to run Eta in real time. Develop range of statistical products based on Eta reforecasts. Preliminary estimate: computationally very expensive. 100 K for disk storage at CDC. Need advocacy of users to make this happen.

18. Conclusions Possible to achieve near-perfect reliability, good skill by calibrating forecasts with many years of old forecasts Great results with low-res model; even better results with higher-res. model? Want your feedback on important products Continued development depends on your advocacy.

19. Hacienda Heights, CA mudslides, 22 Feb 2005 (also rain on snow event for intermountain west) 6-10 day fcst

20. 6-10 Day Week 2