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Z. Sokol 1 , D. Kitzmiller 2 , S. Guan 2

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 

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Z. Sokol 1 , D. Kitzmiller 2 , S. Guan 2

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  1. Comparison of Several Methods for Probabilistic Forecasting of Locally-Heavy Rainfall in the 0-3 Hour Timeframe Z. Sokol1, D. Kitzmiller2, S. Guan2 1Institute of Atmospheric Physics AS CR, Prague, Czech Republic  2Hydrology 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 temperatureby 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 Outputs1800-2100  UTC, 4 June 2005 Probability of 25 mm (1 inch) rainfall. Probability of 50 mm (2 inches) rainfall. Radar/gauge precipitation estimates during verifying period. Categorical rain amount forecast.

  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. Model Model 00-03 00-03 04-06 04-06 07-09 07-09 10-12 10-12 13-15 13-15 16-18 16-18 19-21 19-21 22-00 22-00 Mean Mean REG REG 0.04 0.20 0.25 0.09 0.09 0.21 0.20 0.04 0.17 0.02 0.25 0.10 0.27 0.07 0.14 0.28 0.229 0.073 LREG LREG 0.23 0.12 0.08 0.23 0.11 0.23 0.23 0.09 0.19 0.03 0.13 0.23 0.17 0.29 0.18 0.30 0.115 0.239 RAT RAT 0.07 0.20 0.10 0.22 0.10 0.23 0.11 0.22 0.18 0.04 0.13 0.25 0.28 0.13 0.17 0.30 0.107 0.235 NN NN 0.22 0.06 0.24 0.11 0.10 0.24 0.25 0.09 0.15 0.03 0.11 0.27 0.27 0.10 0.16 0.29 0.096 0.238 REGG3 REGG3 0.04 0.20 0.11 0.21 0.09 0.18 0.07 0. 19 0.05 0.14 0.12 0.23 0.21 0.07 0.23 0.14 0.087 0.199 REGALL_5 REGALL_5 0.06 0.22 0.26 0.12 0.08 0.24 0.09 0.24 0.20 0.06 0.11 0.28 0.07 0.28 0.29 0.14 0.090 0.252 Predictand: 40x40 km, Precipitation  5mm (1%-3%) a) Yes/No Threshold = 0.5 b) Optimum Yes/No Threshold

  11. Distribution of Forecast Probabilities

  12. Example of forecasts by REG and LREGpredictand 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.

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