What forecast users might expect: an issue of forecast performance

What forecast users might expect: an issue of forecast performance Tomas Vlasak, RadekCekal & Jan Danhelka Czech Hydrometeorological Institute Czech republic

Hydrological service in the Czech republic • Deterministic forecast (> 100 forecasting points 48hours / 1hour step) • Flash flood guidance FFG-CZ • Short-range probabilistic forecast (based on ensemble QPF) • Medium-range probabilistic forecast (based on historical analogs)

Hydrological forecast evaluation Feedback for FORECASTERS: model parameters or model structure data input human impact strategy Feedback for forecast USERS: realize forecast uncertainty estimate forecast uncertainty avoid false expectation

Evaluation methods Comprehensible not only for expert. Simplification to 1 indicator: • threshold exceeding - YES/NO • total flow volume • discharge maximum Evaluation of whole of forecasting process without prior differentiation of the source of uncertainty.

Evaluation methods – forecasts selection Target to flood forecasts Selection conditions: 1) forecasted or observed discharge exceeded flood threshold 2) last measured discharge was smaller then threshold

Categorical evaluation • portion of these category in different aspect: Threshold Basin area Season Year Forecasting office • categorical statistics Hit Rate False Alarm Ratio Frequency Bias Critical Success Index ..... HIT HIT HIT MISS FALSE ALARM FALSE ALARM

Categorical evaluation – RESULTS – different thresholds • Decrease of portion of HITS with increasing of flood extremity is small • MISS forecasts prevail to FALSE ALARM low FLOOD EXTREMITY high

Categorical evaluation – RESULTS – basin area • Forecasts for big rivers are more successful • uncertainty of QPF produce FALSE ALARMS and uncertainty of hydrological modeling MISSES

Categorical evaluation – RESULTS – lead time • Decrease of HIT rate is significant to first 24 hours of lead time. The most of forecasting points delimit basins with lag time shorter then 24h

Categorical evaluation – RESULTS - years • Without significant trend

Categorical evaluation – RESULTS – years –regional office Plzeň • change of hydrological model performance after model recalibration in 2010

Categorical evaluation – RESULTS

Categorical evaluation - RESULTS • Forecasting point on big river has more successful forecasts

Categorical evaluation - RESULTS • Influence of different forecaster‘s strategy Predominance of MISS category Predominance of FALSE ALARM category

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What forecast users might expect: an issue of forecast performance