Probability Forecasting of Short-Term Short-Duration Heavy Rainfall Combining Ingredients-Based Methodology and Fuzzy Logic Approach

Abstract

Highly convection-related short-duration heavy rainfall (SDHR), defined as rainfall greater than 20 mm h−1 of a whole hour, causes severe damage every year in China. An objective forecasting method is developed to provide guidance products for the short-term probability of SDHR. Representative parameters of environmental moisture content, instability, and dynamical forcing are selected as predictors based on the ingredients-based methodology. The predictors are selected by comparing their ability to discriminate between SDHR and both no rainfall and ordinary rainfall with hourly rainfall records and the NCEP reanalysis dataset during the warm seasons of 2002 and 2009. A fuzzy logic approach is obtained for the calculation of SDHR probability. Intervals of intensities are obtained based on specific percentiles and various weight settings examined. The probabilistic SDHR forecasts during the 2015 warm seasons with the NCEP GFS dataset are obtained, and forecasts are evaluated by using an operational used spatial verification method. Results show that the reference operational SDHR forecasts are surpassed by the 00–12 h period objective SDHR forecasts measured with the maximum critical success index (CSI), and even the average CSI (CSIave) for the top groups is better than the reference. The guidance SDHR products are skillful within 60 h. Although the weights vary significantly, the short-term patterns of the SDHR probability are mainly determined by the environmental conditions. The objective forecasting method is ingredients-based but is combined with fuzzy logic algorithms. The new approach provides a feasible exploration of the convective weather phenomenon.

The features and probability forecasting of short–duration heavy rainfall in the Beijing-Tianjin-Hebei region caused by North China cold vortices

“…It is found that specific humidity at 850 hPa with higher weights could lead to a slight increase of the highest TS. Besides, the indicators representing atmospheric environmental conditions are important for probability forecasting (Tian et al, 2022). In this study, we make an attempt to forecast the probability of SDHR occurrence induced by NCCV based on limited environmental parameters, more work should be carried out to improve the forecast capability for SDHR caused by NCCV in the future.…”

“…In order to verify the indicators of the above environmental parameters for SDHR, we quantitatively select some representative parameters to make probability forecasting for SDHR caused by NCCV (Tian et al, 2022). The overlapping size of the relative frequencies of SDHR and ordinary precipitation (S1) and SDHR and non-precipitation (S2) are calculated for the above parameters (Table 1).…”

“…Based on the fuzzy logic algorithm and ingredient method (Tian et al, 2022), the probability for SDHR occurrence at each grid point is obtained by weighted summation on the basis of the single physical quantity potential of short-term heavy precipitation weather. The calculation formula of probability P is as follows:…”

“…where n represents the number of predictors, n = 4 in this study; w i represents the weight of the ith factor, f i the membership function of the ith factor. Based on the method of Tian et al (2022), this study determines the membership function based on the 20th, 40th, 60th, and 80th percentile values of physical quantities of SDHR induced by NCCV for 2009-2019. Equal weight is assigned in this study.…”

“…Moreover, short-term predictions for SDHR can be made by analyzing the environmental parameters based on the ingredients-based methodology, which focus on the physical processes involved (Doswell et al, 1996;Lei et al, 2012;Dong et al, 2021). Tian et al (2022) explore a new approach for the probability forecasting of SDHR based on ingredients-based methodology and fuzzy logic approach. However, the results have certain limitations due to the small number of samples in previous studies.…”