1 An overview of the use of reforecasts for improving probabilistic weather forecasts Tom Hamill NOAA / ESRL, Physical Sciences Div. NOAA Earth System Research Laboratory

2 What do we want from ensemble forecasts? O O BAD (“unreliable”) GOOD (“reliable”) O BEST “sharp” and “reliable”

33 Dealing with ensemble errors: problems we’d like to correct through “calibration”

44 bias (drizzle over-forecast) ensemble members too similar to each other. Probabilities too smooth; downscaling needed.

5 Calibration and reforecasting Problems with probabilities directly estimated from raw ensemble: so then what? Would like f(O|F), that is, the probability distribution of the expected observed state given the forecast (much like your thought process as a forecaster). today’s ensemble mean forecast

6 Calibration and reforecasting Problems with probabilities from raw ensemble: so then what? Would like f(O|F), that is, the probability distribution of the expected observed state given the forecast. (much like your thought process as a forecaster). today’s ensemble mean forecast lots of other forecasts that are like today’s forecast

7 Calibration and reforecasting Problems with probabilities from raw ensemble: so then what? Would like f(O|F), that is, the probability distribution of the expected observed state given the forecast. (much like your thought process as a forecaster). today’s ensemble mean forecast lots of other forecasts that are like today’s forecast form ensemble from observed weather on days of those past forecasts

8 The concept of reforecasting Approach: use FIXED model and data set of many past forecasts from this model. Correct current forecast using knowledge about the forecast errors of this model for several decades in the past (MOS on steroids) “Calibration” should implicitly: –adjust for model bias –adjust for any spread deficiency –downscale (coarse prediction grid --> predictable local detail in observations).

9 NOAA’s reforecast data set “Reforecast” definition: a data set of retrospective numerical forecasts using the same model as is used to generate real-time forecasts. Model: T62L28 NCEP GFS, circa 1998 Initial States: NCEP-NCAR Reanalysis II plus 7 +/- bred modes. Duration: 15 days runs every day at 00Z from to now. ( Data: Selected fields (winds, hgt, temp on 5 press levels, precip, t2m, u10m, v10m, pwat, prmsl, rh700, heating). NCEP/NCAR reanalysis verifying fields included (Web form to download at Real-time probabilistic precipitation forecasts:

10 Can’t we calibrate with only a past few forecasts? Consider training with a short sample in a climatologically dry region. How could you calibrate this latest forecast? you’d like enough training data to have some similar events at a similar time of year to this one.

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

12 Analog high-resolution precipitation forecast calibration technique (actually run with 10 to 75 analogs) Approximate O | F

13 Example: probability of greater than 25 mm/day (downscaled to 5 km) Downscaling using PRISM / Mountain Mapper technology (C. Daly. Oregon St., NOAA RFC’s, OHD)

14 Verified over 25 years of forecasts; skill scores use conventional method of calculation which may overestimate skill (Hamill and Juras 2006).

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

16 Effect of training sample size colors of dots indicate which size analog ensemble provided the largest amount of skill.

17 Real-time products

18 Conclusions Large improvement in probabilistic forecast skill and reliability by calibrating using large, stable data set of NWP forecasts / obs. Precipitation products are out there for you to use ( The NWS expects to produce more reforecasts and calibrated products in the coming years. We’re working with Zoltan Toth’s group at NCEP on this.

19 References Hamill, T. M., J. S. Whitaker, and X. Wei, 2003: Ensemble re-forecasting: improving medium-range forecast skill using retrospective forecasts. Mon. Wea. Rev., 132, Hamill, T. M., J. S. Whitaker, and S. L. Mullen, 2005: Reforecasts, an important dataset for improving weather predictions. Bull. Amer. Meteor. Soc., 87, Whitaker, J. S, F. Vitart, and X. Wei, 2006: Improving week two forecasts with multi-model re-forecast ensembles. Mon. Wea. Rev., 134, Hamill, T. M., and J. S. Whitaker, 2006: Probabilistic quantitative precipitation forecasts based on reforecast analogs: theory and application. Mon. Wea. Rev., 134, http :// http :// Wilks, D. S., and T. M. Hamill, 2006: Comparison of ensemble-MOS methods using GFS reforecasts. Mon. Wea. Rev., 135, Hagedorn, R, T. M. Hamill, and J. S. Whitaker, 2008: Probabilistic forecast calibration using ECMWF and GFS ensemble forecasts. Part I: 2-meter temperature. Mon. Wea. Rev., 136, Hamill, T. M., R. Hagedorn, and J. S. Whitaker, 2008: Probabilistic forecast calibration using ECMWF and GFS ensemble forecasts. Part II: precipitation. Mon. Wea. Rev., 136,