Application of ECMWF ensemble prediction system on an extreme heavy rainfall cause by a remote tropical cyclone

In this study, the mechanism and predictability of a remote typhoon heavy rainfall in eastern Zhejiang Province are investigated by ensemble anomaly forecasting, with ECMWF reanalysis data, ECMWF ensemble prediction data, and automatic weather station precipitation data. Results show that: (1) Under the intrusion of cold air from the north side of the typhoon inverse which existed for a long time, it caused frontal formation in the coastal area of Zhejiang, and induced a low pressure circulation at the top of the inverse trough, was the main reason for the heavy rainfall in the northeast of Zhejiang. (2) The ensemble forecast with 36-hour prediction valid shows that precipitable water vapor, south wind component on 850 hPa, water vapor flux on 925 hPa and divergence of 200 hPa all exceed 3~4 standard deviations of climate average, and the probability of standard deviation exceeding 3 reaches 70%~90%. These factors indicate that great dynamic condition and abundant water vapor exist along the coast of Zhejiang. Strong anomalous signals predict high probability of an extreme heavy rainfall event. (3) With the extension of the forecast lead time, the anomalous probability of each physical quantity is significantly reduced. Therefore, the threshold of anomalous probability should be reduced for the early warning of extreme weather. (4) The EFIs (Extreme Forecast Index) with different forecast lead time have good prediction results for 95% and 99% percentile precipitation events, and it can provide extreme precipitation signals 3~4 days earlier than deterministic forecasts. The information for extreme rainfall events provided by ensemble prediction EFI index is more reliable and stable than deterministic forecast, and has higher reference value in decision-making services. (5) Ensemble anomaly forecasting method quantitatively measures the abnormal probability of synoptic scale pattern and physical quantities. EFI shows advantage in longer-time scale forecast. The combination of these two forecast methods could provide more comprehensive signal for extreme weather event.