Probabilistic high‐impact rainfall forecasts from landfalling tropical cyclones using Warn‐on‐Forecast system

Spatial patterns of tropical cyclone tornadoes (TCTs), and their relationship to patterns of mesoscale predictors within U.S. landfalling tropical cyclones (LTCs) are investigated using multicase composites from 27 years of reanalysis data (1995–2021). For 72 cases of LTCs with wide-ranging TC intensities at landfall, daytime TCT frequency maxima are found in the northeast, right-front, and downshear-right quadrants when their composites are constructed in ground-relative, TC-heading relative, and environmental shear relative coordinates, respectively. TCT maxima are located near maxima of 10-m–700-hPa bulk wind difference (BWD), which are enhanced by the TC circulation. This proxy for bulk vertical shear in roughly the lowest 3 km is among the best predictors of maximum TCT frequency. Relative to other times, the position of maximum TCT frequency during the afternoon shifts ∼100 km outward from the LTC center toward larger MLCAPE values. Composites containing the strongest LTCs have the strongest maximum 10-m–700-hPa and 10-m–500-hPa BWDs (∼20 m s−1) with nearby maximum frequencies of TCTs. Corresponding composites containing weaker LTCs but still many TCTs, had bulk vertical shear values that were ∼20% smaller (∼16 m s−1). Additional composites of cases having similarly weak average LTC strength at landfall, but few or no TCTs, had both maximum bulk vertical shears that were an additional ∼20% lower (∼12 m s−1) and smaller MLCAPE. TCT environments occurring well inland are distinguished from others by having stronger westerly shear and a west–east-oriented baroclinic zone (i.e., north–south temperature gradient) that enhances mesoscale ascent and deep convection on the LTC’s east side.

<p>Radar rainfall nowcasting promises to result in more accurate and timely rainfall forecasts up to several hours in advance, compared to numerical weather prediction model forecasts. As flood early warning systems can benefit from this, we assessed the potential of radar rainfall nowcasting for (flash) flood forecasting with a large sample of 659 individual rainfall events for 12 catchments in the Netherlands. In this assessment, we tested four open-source nowcasting algorithms: Rainymotion Sparse (RM-S), Rainymotion DenseRotation (RM-DR), Pysteps deterministic (PS-D) and Pysteps probabilistic (PS-P) with 20 ensemble members. Eulerian Persistence (EP) forecasts and zero precipitation input (ZP) were the benchmark. The discharge forecasts had a 12-h forecast horizon and were issued for every 5-min step in the available nowcasts for the set of events. We regarded the simulations using the observed radar rainfall as reference.</p><p>We found that rainfall and discharge forecast errors increase with both increasing rainfall intensity and spatial variability. For the discharge forecasts, this relationship also depends on the initial conditions, as the forecast error increases more quickly with rainfall intensity, when the initial conditions are wet and groundwater tables are shallow. Overall, discharge forecasts using RM-DR, PS-D and PS-P outperform the other nowcasting methods.</p><p>Furthermore, we tested the potential for forecasting threshold exceedances by setting the highest discharge, per event, as threshold. Compared to benchmark ZP, a threshold exceedance is, on average, forecast 223 (EP), 196 (RM-S), 213 (RM-DR), 119 (PS-D) and 143 min (PS-P) earlier. The relatively high time profits with EP are counterbalanced by both a high false alarm ratio (FAR) and inconsistent forecasts. Contrarily, PS-D and PS-P show both lower FAR and inconsistency index values, which can lead to more trust in the simulations.</p><p>Based on this large-sample analysis, we conclude that all nowcasting methods have shown a benefit for short-term discharge forecasting compared to issuing no rainfall forecasts at all, though all have shortcomings. As forecast rainfall volumes are a crucial factor in (flash) flood forecasting, we recommend a future focus on improving this aspect in nowcasting.</p>

[Departement_IRSTEA]Eaux [TR1_IRSTEA]ARCEAU

This study examines the shear-relative rainfall spatial distribution of tropical cyclones (TCs) during landfall based on the 19-year (1998–2016) TRMM satellite 3B42 rainfall estimate dataset and investigates the role of upper-tropospheric troughs on the rainfall intensity and distribution after TCs make a landfall over the six basins of Atlantic (ATL), eastern and central Pacific (EPA), northwestern Pacific (NWP), northern Indian Ocean (NIO), southern Indian Ocean (SIO), and South Pacific (SPA). The results show that the wavenumber 1 perturbation can contribute ∼ 50% of the total perturbation energy of total TC rainfall. Wavenumber 1 rainfall asymmetry presents the downshear-left maxima in the deep-layer vertical wind shear between 200 and 850 hPa for all the six basins prior to making a landfall. In general, wavenumber 1 rainfall tends to decrease less if there is an interaction between TCs and upper-level troughs located at the upstream of TCs over land. The maximum TC rain rate distributions tend to be located at the downshear-left (downshear) quadrant under the high (low)-potential vorticity conditions.

Developing AI models that match or exceed the forecast skill of numerical weather prediction (NWP) systems but run much more quickly is a burgeoning area of research. Most AI‐NWP models, however, have been trained on global ECMWF Reanalysis version 5 data, which does not resolve storm‐scale evolution. We have therefore adapted Google's GraphCast framework for limited‐area, storm‐scale domains, then trained on archived forecasts from the Warn‐on‐Forecast System (WoFS), a convection‐allowing ensemble with 5‐min forecast output. We evaluate the WoFSCast predictions using object‐based verification, grid‐based verification, spatial storm structure assessments, and spectra analysis. The WoFSCast closely emulates the WoFS environment fields, matches 70%–80% of WoFS storms out to 2‐hr forecast times, and suffers only modest blurring. When verified against observed storms, WoFSCast produces contingency table statistics and fractions skill scores similar to WoFS. WoFSCast demonstrates that AI‐NWP can be extended to rapidly evolving, small‐scale phenomena like thunderstorms.

Flash floods have the potential to damage infrastructure and buildings, and pose a considerable threat to human life. The short lag times associated with flash floods (typically a few hours) present a significant challenge to existing flood warning systems. These systems currently rely on rainfall and runoff measurements, as well as hydrological models. Consequently, they are of limited applicability in ungauged catchments. This is a critical issue given that climate change is intensifying extreme rainfall and thereby increasing the potential for flash flooding.This study investigates the detection of flash floods based solely on rainfall characteristics, thus eliminating dependency on runoff measurements and hydrological infrastructure. Using high-resolution radar rainfall data and 15-minute runoff observations, 1,330 extreme rainfall-runoff events are selected across 147 German catchments with an area of up to 100 km². These events are subsequently classified as either flash or non-flash floods using a rainfall-runoff-based classification scheme as a reference, with 103 of the selected events identified as flash floods. For each event, various space-time rainfall characteristics are quantified. A random forest model for flash flood detection is then trained using only the rainfall metrics and static catchment attributes. The main aim is to assess the potential for rainfall-driven flash flood detection without relying on runoff. In addition, the most relevant rainfall characteristics associated with flash floods are identified, thereby improving our understanding of the underlying drivers.In future work, the developed detection approach will be combined with real-time rainfall nowcasting to enable earlier prediction and warning of flash floods.

Recent surface-water and flash floods have caused millions of pounds worth of damage in the UK. These events form rapidly and are difficult to predict due to their short-lived and localised nature. The interdisciplinary Flooding From Intense Rainfall (FFIR) programme investigated the feasibility of enhancing the integration of an end-to-end forecasting system for flash and surface-water floods to help increase the lead time for warnings for these events. Here we propose developments to the integration of an operational end-to-end forecasting system based on the findings of the FFIR programme. The suggested developments include methods to improve radar-derived rainfall rates and understanding of the uncertainty in the position of intense rainfall in weather forecasts; the addition of hydraulic modelling components; and novel education techniques to help lead to effective dissemination of flood warnings. We make recommendations for future advances such as research into the propagation of uncertainty throughout the forecast chain. We further propose the creation of closer bonds to the end users to allow for an improved, integrated, end-to-end forecasting system that is easily accessible for users and end users alike, and will ultimately help mitigate the impacts of flooding from intense rainfall by informed and timely action.

This paper was initially motivated by a flash flood index (FFI) study performed by Bhaskar et al. and others that explained flash floods using a runoff perspective by extracting a rising curve gradient, the flood magnitude ratio, and flood response time variables from runoff hydrograph. However, the runoff data are not sufficient to estimate a robust flood magnitude ratio in Korea. In addition, the existing model is not able to consider high rainfall intensity, which is an important flash flood characteristic in Korea. Flood magnitude ratio variables, thus, were replaced by rainfall intensity variables. Then, the results were compared with the existing FFI. The spatial distribution of the flood event in the Han River basin that occurred in July 2006 showed that a modified FFI, which reflects rainfall characteristics, outperformed in representing damaged (flash flood occurrence) areas in the past. Furthermore, the FFI for three different time windows (early, middle, and late periods) for the Han River basin during the localised intensive rainfall in July 2006 were analysed. Analysis of spatiotemporal distribution showed that sudden intensity was pronounced in the Seoul area during the first 10 days of July. During the second 10 days of July, a probable high‐intensity flash flood appeared in the Gangwon‐do area. Meanwhile, an onsite survey of the Gangwon‐do area showed that the actual occurrences of a high‐intensity flash flood were largely in agreement with the predictions made by the model.

As tropical cyclones make landfall along coastlines all over the world, havoc is wreaked on families, businesses, economies, etc. A generally overlooked topic is one of the inherent dangers in tropical cyclone landfalls. These can produce tornadoes in the spiral rain bands coming ashore by encountering increasingly higher frictional convergence. The second component will be assessing which other parameters could be analyzed on a synoptic timescale to evaluate how we can potentially improve longer term predictions of tropical cyclone (TC)-induced tornadogenesis. The analysis showed that the combination of high low-level moisture, vertical temperature gradients, and enhanced vertical wind shear is the key factor that links landfalling tropical cyclones to associated tornadogenesis prior to, during, and/or after the time of landfall. An integral part of the process preceding TC-induced tornadogenesis is the enhanced vertical temperature gradient that develops as the storm maintains warm-core characteristics aloft but develops cold-core characteristics closer to the surface. The examination of forecaster perspectives showed that over the past few years there is strong evidence of forecaster improvement based upon greater cognizance of forecast variables linked to TC-induced tornadoes.

Results from object‐based verification of rainfall forecasts for landfalling tropical cyclones (TCs) over China during the period 2012–2015 are presented. The sample consists of 25 landfall events and 133 operational numerical forecasts from the TC version of the Australian Community Climate and Earth System Simulator. Mean equitable threat scores, probabilities of detection and false alarm ratios for the 30 mm isohyet for the unadjusted forecasts at 0–6 hr (essentially the initialization) are (0.23, 0.55, 0.65), while the performance measures of 24 hr forecast accumulations are the best for the 0–24 hr forecast (0.37, 0.67, 0.40) and then worsen to (0.16, 0.38, 0.66) for the 48–72 hr forecast. Forecast ability also decreases with the increase in rainfall amount. The contiguous rain area (CRA) verification method is used to diagnose the source of systematic errors from the displacement, rotation, volume and pattern of the forecasted rain fields. Results show that the errors are mostly from rainfall patterns, followed by displacement errors, particularly for very heavy rain. After application of the displacement and rotation adjustments of the CRA method, averaged errors improve by about 15%. Results suggest that rainfall prediction will continue to improve with improved track prediction, but more work is needed on model initialization and the prediction of TC structure. The study has uncertainty related to the limited sample size, which could cause large variability, particularly for heavy rainfall at 6 and 72 hr. However, the results still represent a useful benchmark for future verification of landfalling TCs.

Multi-temporal downscaling of daily to sub-daily streamflow for flash flood watersheds at ungauged stations using a hybrid framework

An approach for improving the capability of a coupled meteorological and hydrological model for rainfall and flood forecasts

Flash floods are one of the most frequent natural disasters in Fujian Province, China, and they seriously threaten the safety of infrastructure, natural ecosystems, and human life. Thus, recognition of possible flash flood locations and exploitation of more precise flash flood susceptibility maps are crucial to appropriate flash flood management in Fujian. Based on this objective, in this study, we developed a new method of flash flood susceptibility assessment. First, we utilized double standards, including the Pearson correlation coefficient (PCC) and Geodetector to screen the assessment indicator. Second, in order to consider the weight of each classification of indicator and the weights of the indicators simultaneously, we used the ensemble model of the certainty factor (CF) and logistic regression (LR) to establish a frame for the flash flood susceptibility assessment. Ultimately, we used this ensemble model (CF-LR), the standalone CF model, and the standalone LR model to prepare flash flood susceptibility maps for Fujian Province and compared their prediction performance. The results revealed the following. (1) Land use, topographic relief, and 24 h precipitation (H24_100) within a 100-year return period were the three main factors causing flash floods in Fujian Province. (2) The area under the curve (AUC) results showed that the CF-LR model had the best precision in terms of both the success rate (0.860) and the prediction rate (0.882). (3) The assessment results of all three models showed that between 22.27% and 29.35% of the study area have high and very high susceptibility levels, and these areas are mainly located in the east, south, and southeast coastal areas, and the north and west low mountain areas. The results of this study provide a scientific basis and support for flash flood prevention in Fujian Province. The proposed susceptibility assessment framework may also be helpful for other natural disaster susceptibility analyses.

One of the costliest natural hazards around the globe is flash floods, resulting from localized intense convective precipitation over short periods of time. Since intense convective rainfall (especially over the continents) is well correlated with lightning activity in these storms, a European Union FP6 FLASH project was realized from 2006 to 2010, focusing on using lightning observations to better understand and predict convective storms that result in flash floods. As part of the project, 23 case studies of flash floods in the Mediterranean region were examined. For the analysis of these storms, lightning data were used together with rainfall estimates in order to understand the storms’ development and electrification processes. In addition, these case studies were simulated using mesoscale meteorological models to better understand the local and synoptic conditions leading to such intense and damaging storms. As part of this project, tools for short-term predictions (nowcasts) of intense convection across the Mediterranean and Europe, and long-term forecasts (a few days) of the likelihood of intense convection, were developed and employed. The project also focused on educational outreach through a special Web site http://flashproject.org supplying real-time lightning observations, real-time experimental nowcasts, medium-range weather forecasts and educational materials. While flash floods and intense thunderstorms cannot be prevented, long-range regional lightning networks can supply valuable data, in real time, for warning the public, end-users and stakeholders of imminent intense rainfall and possible flash floods.

Identifying areas susceptible to flash flood hazards is essential to mitigating their negative impacts, particularly in arid regions. For example, in southeastern Sinai, the Egyptian government seeks to develop its coastal areas along the Gulf of Aqaba to maximize its national economy while preserving sustainable development standards. The current study aims to map and predict flash flood prone areas utilizing a spatial analytic hierarchy process (AHP) that integrates GIS capabilities, remote sensing datasets, the NASA Giovanni web tool application, and principal component analysis (PCA). Nineteen flash flood triggering parameters were initially considered for developing the susceptibility model by conducting a detailed literature review and using our experiences in the flash food studies. Next, the PCA algorithm was utilized to reduce the subjective nature of the researchers’ judgments in selecting flash flood triggering factors. By reducing the dimensionality of the data, we eliminated ten explanatory variables, and only nine relatively less correlated factors were retained, which prevented the creation of an ill-structured model. Finally, the AHP method was utilized to determine the relative weights of the nine spatial factors based on their significance in triggering flash floods. The resulting weights were as follows: rainfall (RF = 0.310), slope (S = 0.221), drainage density (DD = 0.158), geology (G = 0.107), height above nearest drainage network (HAND = 0.074), landforms (LF = 0.051), Melton ruggedness number (MRN = 0.035), plan curvature (PnC = 0.022), and stream power index (SPI = 0.022). The current research proved that AHP, among the most dependable methods for multi-criteria decision-making (MCDM), can effectively classify the degree of flash flood risk in ungauged arid areas. The study found that 59.2% of the area assessed was at very low and low risk of a flash flood, 21% was at very high and high risk, and 19.8% was at moderate risk. Using the area under the receiver operating characteristic curve (AUC ROC) as a statistical evaluation metric, the GIS-based AHP model developed demonstrated excellent predictive accuracy, achieving a score of 91.6%.