POP-Climo Training National Weather Service Western Region

1. POP-ClimoTrainingNational Weather Service Western Region July 2006 Developed by Western Region POP-Climo Team under direction from the WR Digital Service Management Team

2. Section 1: Goals of Training Improve the accuracy and usefulness of WR NDFD POP grids through the application of probabilistic forecasting verification feedback climatological POPs.

3. Section 2: Our Current Situation, Is It Cause For Concern? National NDFD verification stats indicate that a significant dry bias exists in Western Region by day 5.

4. Section 2: Our Current Situation, Is It Cause For Concern? A human low-POP bias is contributing to lower scores for the NDFD grids, especially for longer forecast ranges. Our partners and customers are not well served when a recognizable and correctable bias is not removed from one of our forecast systems Through the correct use of climatological PoPs and MOS, this low-PoP bias can be significantly reduced or even removed.

5. Section 3: What Is A POP? 12 hour NDFD POP (NWS Directive 10-506 Digital Data Products/ Services Specification) 12-hour Probability of Precipitation (PoP12) is the likelihood, expressed as a percent, of a measurable precipitation event (1/100th of an inch) at a grid point during the valid period. The 12-hour periods in NDFD begin and end at 0000 and 1200 UTC.

6. Section 3: What Is A POP? Climatological POP Represents a long term average POP, which includes both wet and dry periods or upper level ridges and trough patterns. Conceptually, it can be compared to a long term temperature average.

7. Section 5: The Transition From Deterministic To Probabilistic Forecasting As lead time increases, probabilities: tend to be less extreme the use of the very low and especially the very high values is less frequent Eventually (5-10 days) the range has shrunk to the single value of the climate frequency or climate POP.

8. Section 5: The Transition From Deterministic To Probabilistic Forecasting One effective way to estimate the uncertainty for a given forecast, and the forecast period past which it is no longer reasonable to make forecasts deterministically, is to use ensembles. Ensemble forecasting yields information about the magnitude and nature of the uncertainty in a forecast (Wilks, 1995). For more information about the best way to use ensemble forecasts, visit the following excellent training sites: http://www.hpc.ncep.noaa.gov/ensembletraining http://meted.ucar.edu/nwp/pcu1/ensemble

9. Section 6: What Is A Good POP? Forecasters should strive for POPs that are accurate and unbiased So far as the scientific problem of weather forecasting is concerned, the forecaster’s duty ends with providing accurate and unbiased estimates of the probabilities of different weather situations. (Brier, 1944, Hughes, 1980) POPs should reflect both uncertainty and knowledge about future weather so customers can make appropriate decisions based on risk. (Wilks, 1995) POP grids should reflect weather conditions, e.g., elevation differences should be much more distinct during convective or upslope precipitation events and less distinct during stratiform periods. Instead of trending towards 0% during a period of forecast uncertainty in the longer term forecasts, trend towards climatology (unless climatology is represented by zero).  This does not mean simply populate directly with climatology, but as a rough starting point.  Using collaboration with surrounding offices, trend above climo if the models are hinting at a long wave trough, or below climo if the models are hinting at a ridge.

10. Section 7: Final Recommendations Use Climatological Pops as a “first-guess field” when there is uncertainty, especially in the longer range forecast period. The range of usable POP values narrows with forecast time, eventually shrinking to the climatological POP at some time in the future. Use references to Climatological PoP grids in 12Planet chats to make collaboration more efficient. MOS and Climatological POPs are not a perfect forecast. However, significant deviations in the longer term should be limited to known model bias periods and other known local confidence factors. Ensemble forecasts provide a means to help determine confidence. Know your skill, verify your forecasts, adjust accordingly.