Ingredients-based Methodology and Fuzzy Logic Combined Short-Duration Heavy Rainfall Short-Range Forecasting: An Improved Scheme

Short-duration heavy rainfall (SHR), as delineated by the National Meteorological Center of the China Meteorological Administration, is characterized by hourly rainfall amounts no less than 20.0 mm. SHR is one of the most common convective weather phenomena that can cause severe damage. Short-range forecasting of SHR is an important part of operational severe weather prediction. In the present study, an improved objective SHR forecasting scheme was developed by adopting the ingredients-based methodology and using the fuzzy logic approach. The 1.0°×1.0° National Centers for Environmental Prediction (NCEP) final analysis data and the ordinary rainfall (0.1–19.9 mm h^–1) and SHR observational data from 411 stations were used in the improved scheme. The best lifted index, the total precipitable water, the 925 hPa specific humidity (Q₉₂₅), and the 925 hPa divergence (DIV₉₂₅) were selected as predictors based on objective analysis. Continuously distributed membership functions of predictors were obtained based on relative frequency analysis. The weights of predictors were also objectively determined. Experiments with a typhoon SHR case and a spring SHR case show that the main possible areas could be captured by the improved scheme. Verification of SHR forecasts within 96 hours with NCEP global forecasts 1.0°×1.0° data initiated at 08:00 Beijing Time during the warm seasons in 2015 show the results were improved from both deterministic and probabilistic perspectives. This study provides an objectively feasible choice for short-range guidance forecasts of SHR. The scheme can be applied to other convective phenomena.