Application of the Warn‐on‐Forecast system for flash‐flood‐producing heavy convective rainfall events

Abstract

The vision of the National Oceanic and Atmospheric Administration's (NOAA) Warn‐on‐Forecast (WoF) research and development project is to provide very short‐term probabilistic model guidance products that will aid the ability of National Weather Service forecasters to issue probabilistic warnings of severe convective hazards with higher accuracy and longer lead times. The experimental Warn‐on‐Forecast System (WoFS), which is under development, is a frequently cycled, regional, convective‐scale, on‐demand, ensemble data assimilation and prediction system. The system assimilates thunderstorm observations to forecast the life cycle and associated hazards of individual convective storms. Most of the initial research effort, since the beginning of the WoF project in 2009, has been focused on tornadic events, which constitute one of the most violent and difficult to predict weather threats. However, the system emerging from this project has significant potential to help with prediction of other hazards as well: in particular, flash‐flood‐producing, convectively driven intense rainfall events. This study focuses on the application of WoFS in 0–6‐hr probabilistic rainfall forecasts of several flash‐flood‐producing heavy rainfall events during the spring and summer of 2015 and 2016. Results indicate that WoFS successfully initializes the convective storms in the ensemble. Visual inspections and probabilistic verification metrics show that WoFS predicts the intense rainfall that often leads to flash flooding at the correct location with higher accuracy in areal coverage and amount during the 0–3 hr forecast period than during the later 3–6 hr period. Overall results indicate that the frequently updated 0–6‐hr ensemble forecast from the WoFS has the potential to highlight areas where intense rainfall can result in flash flooding and increase near‐term situational awareness of flash flood threats.

A New Hybrid Firefly–PSO Optimized Random Subspace Tree Intelligence for Torrential Rainfall-Induced Flash Flood Susceptible Mapping

“…The TWI is considered the most critical parameter measuring topographic controls of basic hydrological processes [50]. The TWI map was created using the altitude map by applying Equation (5) [51,52].…”

“…The swarm population was 30, whereas the total number of iterations was 1000. The searching space was as follows: m-ss ∈ [10-500], p-ffi ∈ [1][2][3][4][5][6][7][8][9][10][11], and d-max ∈ . It should be noted that the default maximum depth of the tree was computed using an integer value.…”

“…Flash floods that occurr in tropical and semi-tropical areas, caused by extraordinary rainfall, are one of the most dangerous natural phenomena due to the significant socio-economic damage and loss of human lives, particularly in the frequent cyclone regions in Southeast Asia [1,2]. Floods are often classified into different types, i.e., city flooding, river flooding, coastal flooding, and flash flood [3], of which flash floods are more vulnerable and severe because of their speed in short timescales [4,5]. Prior studies suggest that most of the areas are exposed to offensive and destructive flooding, resulting in an increase in the huge damages, casualties, and financial losses during flooding events.…”

The heavy precipitation event of 14–15 October 2018 in the Aude catchment: A meteorological study based on operational numerical weather prediction systems and standard and personal observations

“…Nowcasting systems provide forecasts with lead times of less than a few hours that can be useful to face the unpredictable nature of very intense and localized rainfall events (Stensrud et al, 2009;Sun et al, 2014;Stensrud et al, 2013;Yussouf and Knopfmeier, 2019). A few hours of anticipation time could afford great benefits to flood warning and intervention of emergency services.…”

Enhanced surface water flood forecasts: User‐led development and testing

“…There are methods to address this issue by using probabilistic forecasting and by post‐processing raw forecast information. For example, using neighbourhood processing methods to search for storms within a defined radius and communicate the degree of probability of occurrence within a given area (Golding, Roberts, Leonicini, Mylne, & Swinbank, 2016; Olsson et al, 2017; Yussouf & Knofmeier, 2019).…”