1. Comparison of Several Methods for Probabilistic Forecasting of Locally-Heavy Rainfall in the 0-3 Hour Timeframe Z. Sokol 1, D. Kitzmiller 2, S. Guan 2 1 Institute of Atmospheric Physics AS CR, Prague, Czech Republic 2 Hydrology Laboratory, Office of Hydrologic Development, NOAA National Weather Service, Silver Spring, Maryland, USA

2. Introduction Description of the current operational model used by NWS (U.S.A.) Aims of the study Alternative models tested on the selected subregion Comparison of the regression model results Conclusions

3. Current Model Predictands: probabilities that rainfall will reach or exceed 2.5, 12.5, 25.4, and 50.8 mm during the succeeding 3-h period at boxes of a 40-km grid covering the conterminous United States Predictors: –Extrapolated radar reflectivity, lightning strike rate, and cloud-top temperature by advecting the corresponding initial-time fields at the velocity of the forecasted 700-500 hPa mean wind vector –Forecasts of humidity, stability indices, moisture divergence, and precipitation from the operational Eta (NAM) model

4. Current Model Forecasting tool: –Linear regression model for each threshold amount and 8 daytimes (01-03 UTC, …, 22-00 UTC) –Separate sets of equations for warm (April- September) and cool (October-March) seasons –One model for all boxes in the conterminous United States –Regression model derived from historical data (MOS)

5. Example of Outputs 1800-2100 UTC, 4 June 2005 Radar/gauge precipitation estimates during verifying period. Categorical rain amount forecast. Probability of 25 mm (1 inch) rainfall. Probability of 50 mm (2 inches) rainfall.

6. Aims of This Study Attempt to refine existing model for U.S. –Examine regression models not previously considered –Consider effects of local and regional models, rather than single general model Consider implications for development of a model for the Czech Republic

7. Tests Selected subregion: The northeastern United States (New York, Massachusetts, Vermont, New Hampshire, Rhode Island, and Maine) during the warm season (May-September). This area has a summertime precipitation regime similar to that of the Czech Republic. Data : 4 years May-September, 1997-2000 Development of the model: –3 years – calibration data –1 year – independent data

8. Tests Categorical forecast (yes/no) for given thresholds for boxes –Mean precipitation in 40x40 km region –Maximum 4x4 km precipitation within 40x40 km region Transition from probabilistic to categorical forecast –Fixed threshold 0.5 –Optimum threshold derived on the calibration data Verification measure: Equitable thread score (ETS)

9. Types of models: REG - Linear regression REG3 - Localized linear regression models (derived for single boxes) LREG - Logistic regression RAT - Rational regression NN - Neural network (perceptron type, 1 hidden layer)

10. Predictand: 40x40 km, Precipitation  5mm (1%-3%) Model00-0304-0607-0910-1213-1516-1819-2122-00Mean REG0.200.250.210.200.170.250.270.280.229 LREG0.23 0.190.230.290.300.239 RAT0.200.220.230.220.180.250.280.300.235 NN0.220.24 0.250.150.27 0.290.238 REGG30.200.210.180. 190.140.230.210.230.199 REGALL_50.220.260.24 0.200.28 0.290.252 Model00-0304-0607-0910-1213-1516-1819-2122-00Mean REG0.040.09 0.040.020.100.070.140.073 LREG0.120.080.110.090.030.130.170.180.115 RAT0.070.10 0.110.040.13 0.170.107 NN0.060.110.100.090.030.110.100.160.096 REGG30.040.110.090.070.050.120.070.140.087 REGALL_50.060.120.080.090.060.110.070.140.090 a) Yes/No Threshold = 0.5 b) Optimum Yes/No Threshold

11. Distribution of Forecast Probabilities

12. Example of forecasts by REG and LREG predictand maximum 4x4km precipitation Probability Forecasts for 12.5 mm Probability Forecasts for 25.4 mm Verifying precipitation amount (left) and antecedent amount (right)

13. Conclusions The localized approach REGG3 did not improve the forecasts for the northeastern U.S. For the 0.5 yes/no threshold REG results are worse than results of other methods. It is valid for higher precipitation thresholds. If optimum threshold (maximizing ETS) is used then resultant ETS of all the methods are similar. REG yields smoother probability fields than other methods; LREG yields smaller areas of nonzero probabilities but higher values within those areas. In general the best results were obtained by LREG and NN methods. Our experience shows that NN method should use only a limited number (10-30) a priori selected predictors, otherwise the results are worse.