Localized Heavy Rainfall Research Articles

Sensitivity of localized heavy rainfall in Northern Japan to WRF physics parameterization schemes

Sensitivity of localized heavy rainfall in Northern Japan to WRF physics parameterization schemes

Water Vapor Lidar Observation and Data Assimilation for a Moist Low-Level Jet Triggering a Mesoscale Convective System

Abstract We conducted field observations using two water vapor Raman lidars (RLs) in Kyushu, Japan, to clarify the characteristics of a moist low-level jet (MLLJ), which plays a fundamental role in the formation and maintenance of mesoscale convective systems (MCSs). The two RLs observed the inside and outside of an MLLJ, providing moisture to an MCS with local heavy precipitation on 9 July 2021. Our observations revealed that the MLLJ contained large amounts of moisture below the convective mixing layer height of 1.6 km. The large amount of moisture in the MLLJ might be intensified by low-level convergences and/or water vapor buoyancy facilitated by strong horizontal wind. We conducted four data assimilation experiments: CNTL that assimilated Japan Meteorological Agency operational observation data and three other experiments that ingested the lidar-derived vertical moisture profiles as well as the operational observation data. The experiments assimilating lidar-derived vertical moisture profiles caused intensification and southwestward extensions of the low-level convergence zone, resulting in local heavy precipitation at lower latitudes in experiments assimilating lidar-derived moisture profiles than in CNTL. All three experiments ingesting vertical moisture profiles generally produced better 9-h precipitation forecasts than CNTL, implying that the assimilation of vertical moisture profiles could be well suited for numerical weather prediction of local heavy precipitation. Moreover, the experiment assimilating both of the two RL sites’ data reproduced better forecast fields than experiments assimilating a single RL site’s data, implying that data assimilation of vertical moisture profiles at multiple RL sites enables us to improve initial conditions compared to a single RL site. Significance Statement Moist low-level jets (MLLJs) are moisture-rich airflows in the low-level atmosphere that play an important role in developing mesoscale convective systems and local heavy rainfall. To better understand the mechanisms affecting the development of local heavy rainfall events and to improve our ability to forecast them, studying the moisture structures in MLLJs is important. We succeeded in observing an MLLJ in western Japan using water vapor Raman lidars (RLs), which obtained vertical moisture profiles, and revealed details of vertical moisture structures in the MLLJ. We also performed data assimilation experiments to examine the impact of assimilating vertical moisture profiles observed by the RLs. The results showed that the assimilation of the moisture data improved the forecasting of local heavy rainfall.

Flood risk assessment using an indicator based approach combined with flood risk maps and grid data

The increasing frequency of tropical cyclones, such as typhoons and localized heavy rainfall, increases the risk of flood damage in urban areas around the world. Countries implementing flood prevention projects use qualitative risk assessments, such as flood risk assessments, to support decision-making. However, existing flood risk assessment methods are not able to select and reflect only the damage targets that are directly exposed to flood risk. In addition, existing indexing methods are limited in improving the skewness of the raw data distribution, which distorts the calculated flood risk index. This study conducted a flood risk assessment based on an indicator-based approach using flood risk maps and grid data utilizing indicators (buildings, road area, etc.) from 2016 to 2019 for 44 cities and counties in the Gyeongsang-do Province. The flood risk assessment in this study consists of four items (Hazard, Exposure, Vulnerability, and Capacity) and nine indicators. Instead of using statistical data, we used the overlapped grids with the flood risk map for Exposure and Vulnerability indicators to reflect the flood risk exposed targets in the risk assessment. The limitations of existing indexing methods, such as min–max normalization, were improved by dividing the distribution into ten quantiles and scoring each quantile interval when indexing the indicators. Finally, entropy weights and Euclidean distance were applied to calculate the annual flood risk index of 44 cities and counties. The annual flood risk indices for local governments were arranged in descending order and then, the rankings of flood damage amount in 2019 were compared with the calculated index rankings. The sum and mean of absolute errors were 386 and 9.897, and the root mean square error was calculated as 11.930, which showed an improvement over the existing method. Using the calculated indices, it is possible to create Hazard vs. Capacity graph, Exposure vs. Vulnerability graph, or annual flood risk index graph. These graphs can be utilized to understand the level of exposure to flood risk of local governments and to assess the level of response through the Capacity index that contributes to damage reduction. It is believed that the flood risk assessment method presented in this study not only accurately identify the flood risk for local governments but can also be used to support decision-making processes to strengthen the government’s disaster prevention capabilities.

An Experimental Approach to Investigating Quasi-Saturation Using Darcy’s Law

Recent trends in abnormal weather patterns leading to sudden and localized heavy rainfall have resulted in an increased frequency of surface landslides. As a result, there is a pressing need for improved prediction and early warning systems. This research focuses on understanding soil behavior under quasi-saturation and elucidating its relationship with pore water pressure and the hydraulic head. In the present study, a mathematical model is formulated to characterize the complex dynamics of quasi-saturation based on established principles. The model demonstrates the correlation between volumetric water content and pore water pressure, considering the influence of the hydraulic gradient. Through two comprehensive model tests, empirical data are generated that highlight the intricate factors influencing quasi-saturation. The findings of this study emphasize the complex interrelationship between volumetric water content, pore water pressure, and the hydraulic head. It is worth noting that under quasi-saturation conditions, the volumetric water content may stabilize over time, indicating an equilibrium between water flow rates driven by the hydraulic gradient and pore water pressure. The present study offers new insights into soil moisture dynamics and lays the foundation for advancements in landslide prediction and mitigation strategies.

Nowcasting Multiparameter Phased-Array Weather Radar (MP-PAWR) Echoes of Localized Heavy Precipitation Using a 3D Recurrent Neural Network Trained with an Adversarial Technique

Abstract We present nowcasts of sudden heavy rains on meso-γ scales (2–20 km) using the high spatiotemporal resolution of a multiparameter phased-array weather radar (MP-PAWR) sensitive to rain droplets. The onset of typical storms is successfully predicted with 10-min lead time, i.e., the current predictability limit of rainfall caused by individual convective cores. A supervised recurrent neural network based on long short-term memory with 3D spatial convolutions (RN3D) is used to account for the horizontal and vertical changes of the convective cells with a time resolution of 30 s. The model uses radar reflectivity at horizontal polarization ZH and the differential reflectivity. The input parameters are defined in a volume of 64 × 64 × 8 km3 with the lowest level at 1.9 km and a resolution of 0.4 × 0.4 × 0.25 km3. The prediction is a 10-min sequence of ZH at the lowest grid level. The model is trained with a large number of observations of summer 2020 and an adversarial technique. RN3D is tested with different types of rapidly evolving localized heavy rainfalls of summers 2018 and 2019. The model performance is compared to that of an advection model for 3D extrapolation of PAWR echoes (A3DM). RN3D better predicts the formation and dissipation of precipitation. However, RN3D tends to underestimate heavy rainfall especially when the storm is well developed. In this phase of the storm, A3DM nowcast scores are found slightly higher. The high skill of RN3D to predict the onset of sudden localized rainfall is illustrated with an example for which RN3D outperforms the operational precipitation nowcasting system of Japan Meteorological Agency (JMA). Significance Statement Temporal extrapolation of radar observations is a means of nowcasting sudden heavy rains, i.e., forecasts with a few tens of minutes and a high spatial resolution better than 500 m. They are necessary to set up warning systems to anticipate damage to infrastructure and reduce the fatalities these storms cause. It is a difficult task due to the storm suddenness, restricted area, and nonlinear behavior that are not well captured by current operational observation and numerical systems. In this study, we use a new high-resolution weather radar with polarimetric information and a 3D recurrent neural network to improve 10-min nowcasts, the current limit of operational systems. This is a first and essential step before applying such a method for increasing the prediction lead time.

Experimental simulation of seepage field distribution for small interval tunnel under varying-head infiltration

In complex hydrogeological environments, it is difficult to achieve a dynamic equilibrium state for the recharge, seepage, and discharge of tunnel groundwater. Especially in case of reservoir flood discharge, local heavy rainfall, extreme dry climate or other emergency situations, the groundwater level will rapidly change, and the tunnel will be under the action of hydrodynamic head height within a certain period of time. Therefore, as urban tunnels in water-rich areas develop towards large cross-section, small spacing and complex sensitive stratum, studying the spatial distribution characteristics of seepage and stress fields in small interval tunnels under variable water head infiltration conditions is an urgent problem to be solved. In this paper, using the self-developed seepage model testing equipment, tunnel seepage tests under different hydrodynamic head heights and heavy rainfall conditions are carried out on the Kexuecheng Tunnel, which is a typical small interval tunnel in complex hydrogeological environment of Chongqing, China. According to the test results, as the burial depth and water level increase, the water pressure at each monitoring point gradually increases. Benefiting from the superimposed drainage effect of adjacent hole, the average water pressure at the inner side is 8 ∼ 12% lower than that on the outer side. The grouting circle and high groundwater level will further aggravate the asymmetric distribution of hydrodynamic pressure on the lining structure. However, as the hydrodynamic head height increases, the effective stress of the stratum decreases, and the earth pressure on the tunnel decreases by 24 ∼ 40%. Under heavy rainfall conditions, the evolution of lining structure pressure can be divided into initial infiltration stage, rapid growth stage, gradually stable stage and slowly decreasing stage. Suffered by the influence of rainstorm on seepage field and stress field, the overall pressure acting on the vault, left haunch, right haunch and bottom of lining structure increase by 45.4%, 132.3%, 117.4% and 147.4%, respectively.

Impacts of an Urban Canopy Scheme and Surface Observation Data on a Heavy Rain Event through Data Assimilation

Considering urbanization effects on atmospheric states and subsequent precipitation is crucial to improve the accuracy of forecasting localized heavy rainfall around urban areas and to mitigate related disasters. For that purpose, it is effective to use a time development model that can accurately represent city-specific effects, such as urban heat island effect, in the assimilation process, and to assimilate high-frequency/high-density surface observation data that have not been used thus far. Therefore, this study incorporated a forecast model with an urban canopy scheme into an ensemble-based assimilation system and assimilated dense surface data from an Atmospheric Environmental Regional Observation System. Then, we performed analysis-forecast experiments for a heavy rain event in Tokyo metropolitan area on August 30, 2017, to examine the impact of urbanization. Our results showed that the urban scheme and surface observation improved near-surface temperature and moisture fields, thereby contributing to the formation of a clearer convergence line between the easterly and southerly winds where it was observed. Consequently, these improvements resulted in an earlier onset of rainfall and better reproduction of the heavy rainfall distribution.

Measuring Water Levels by Handheld Smartphones – A contribution to exploit crowdsourcing in the spatio temporal densification of water gauging networks

Global climate change leads to an increase in local heavy rainfall events causing nearly unpredictable flash floods worldwide. This paper introduces a novel and flexible low-cost water gauging technology, called Open Water Levels, using smartphones as low-cost measuring devices enabling the crowdsourcing of water levels on demand with accuracies of a few centimetres. This merely requires smartphone camera images of a riverbank and approximate values of the camera position and rotation measured by smartphone sensors. The images are analysed for the water line that is further projected into object space and intersected with a 3D model, e.g. from a GIS database, to derive water level information.

Measuring Water Levels by Handheld Smartphones – A contribution to exploit crowdsourcing in the spatio temporal densification of water gauging networks

Global climate change leads to an increase in local heavy rainfall events causing nearly unpredictable flash floods worldwide. This paper introduces a novel and flexible low-cost water gauging technology, called Open Water Levels, using smartphones as low-cost measuring devices enabling the crowdsourcing of water levels on demand with accuracies of a few centimetres. This merely requires smartphone camera images of a riverbank and approximate values of the camera position and rotation measured by smartphone sensors. The images are analysed for the water line that is further projected into object space and intersected with a 3D model, e.g. from a GIS database, to derive water level information.

Improvements of Procedures for Identifying and Classifying Heavy Rainfall Areas of Linear-Stationary Type

The procedures for identifying and classifying heavy rainfall areas of linear-stationary type (LS-HRAs) from the distributions of radar/raingauge-analyzed precipitation amounts (RAP) were improved to extract “senjo-kousuitai” with elongated and stagnant characteristics that causes localized heavy rainfall in Japan. The improved procedures were verified based on subjective judgments (‘certainty’, ‘doubtful’, and “suspect’) whether LS-HRAs possess the characteristics of typical senjo-kousuitai. Criteria for excluding LS-HRAs judged to be ‘suspect’, mainly associated with tropical cyclones (TCs) and non-stationary mesoscale convective systems, were introduced to optimally extract senjo-kousuitai events. The criteria were determined by the distance between LS-HRAs and TC centers (DLT), the maximum total RAP of LS-HRA (total RAP), and the properties of each RAP distribution at every hour during the LS-HRA extraction period, e.g., DLT ≤ 500 km and total RAP < 200 mm. By applying these criteria, 372 senjo-kousuitai events were extracted from 452 LS-HRAs during the warm seasons (April–November) in 2009-2020, although approximately 35% of the LS-HRAs judged to be ‘suspect’ were not excluded. The criteria associated with TCs excluded them more effectively than the other factors. The improved procedures for extracting senjo-kousuitai events are expected to be used effectively for their statistical analyses.

СРАВНИТЕЛЬНАЯ ОЦЕНКА АНОМАЛЬНО ЗАСУШЛИВЫХ ПОГОДНЫХ УСЛОВИЙ УЛЬЯНОВСКОЙ ОБЛАСТИ В ХХI ВЕКЕ НА ПРИМЕРЕ 2010 и 2021 ГГ.

The article presents comparative characteristics of agrometeorological monitoring of drought in 2021 with an intensely dry year in 2010 in order to optimize measures to reduce the negative impact and improve knowledge about climate mechanisms that control the periods of their onset and termination. The results of observations of the agrometeorological post Timiryazevsky from 2000 to 2021, on the territory of the Ulyanovsk region, revealed that the average annual air temperature in the twenty-first century increased by 1.4 ° C and, as a result of the increase in temperature, the number of dry years increased. For twenty-two years of the current century, for fourteen years, differential droughts of varying intensity were observed. However, the persistent droughts of 2010 and 2021 proved to be the most devastating, depending on the duration and severity of the temperature impact, and caused significant damage to both early and late crops. Despite local heavy rainfall in April-August 2021, an abnormally high temperature regime was observed, which characterized a stable drought ahead of 2010. The high air temperatures of the growing season in 2021 (23.6°С) exceeded the temperature anomaly in 2010 (15.8°С) by 7.8°С, precipitation in 2021 for the same period (-36.3 mm) fell by 102 .2 mm more than in 2010 (-138.5 mm). Since precipitation fell locally and was of a stormy nature, they could not make up for the excess heat and the yield turned out to be lower than in 2010 by 0.04 t/ha. This comparative assessment of the most anomalously drought year 2010 in the history of meteorological observations in the region and their analysis call for more attention to preventive measures: to move from reactive approaches to proactive actions that allow to eliminate the root causes of drought and socio-environmental vulnerability, as well as avoiding and minimizing risks that require much less cost than response and response.

Surface Wind Convergence During Localized Heavy Rainfall in Tokyo in Summer: Evaluation and Utilization of the Amount of Divergence Using High-Density Observation Data

Surface Wind Convergence During Localized Heavy Rainfall in Tokyo in Summer: Evaluation and Utilization of the Amount of Divergence Using High-Density Observation Data

Relationship between the Upper-level Winds and the Horizontal Movement of Localized Heavy Rainfall in the Afternoon of Summer Days around Tokyo

Relationship between the Upper-level Winds and the Horizontal Movement of Localized Heavy Rainfall in the Afternoon of Summer Days around Tokyo

A New Application Method of Radar/Raingauge-Analyzed Precipitation Amounts for Long-term Statistical Analyses of Localized Heavy Rainfall Areas

We propose a new application method in which radar/raingauge-analyzed precipitation amounts (RAP) produced by the Japan Meteorological Agency are spatially converted into 5km-resolution data, in addition to a three-hourly accumulation procedure, in order to statistically analyze localized heavy rainfall areas (HRAs) for a long period. A long-term trend and homogeneity in the appearance frequency of RAP with 5km-resolution converted by several methods, including the conventional method, are statistically evaluated in comparison with rain-gauge observations. The results indicate that the following application method is the most suitable to represent long-term variations in the appearance frequency of HRAs; (1) the converted value of RAP from 1 km to 5 km resolution is set to the 90th percentile value in 30 segments with 1km-resolution included in a grid with 5km-resolution, and (2) this spatial conversion is conducted after accumulating original RAP with 1km-resolution for three hours. Statistical analyses were performed for the appearance frequency of HRAs extracted from 5km-resolution RAP that were produced by the new application method, which indicates that the number of HRAs of the linear-stationary type could be increased without compromising the characteristics of HRAs extracted by the conventional method.

Potential Seasonal Predictability of the Risk of Local Rainfall Extremes Estimated Using High‐Resolution Large Ensemble Simulations

AbstractSignificant progress has been made in seasonal climate predictions based on general circulation models (GCMs). However, seasonal prediction of local heavy rain remains challenging due to limited resolution. A small signal‐to‐noise ratio also prevents meaningful predictions. In this study, 100‐member large‐ensemble sets of GCM simulations and high‐resolution downscaled products with a regional climate model were used to identify the unknown source of seasonal predictability for the risk of local heavy rainfall around Japan and Taiwan. The detected predictable signals depended on the location and the season. Highly predictable signals were found for events caused by typhoons and the stationary front. Further analyses of the large‐scale backgrounds simulated by the parent‐GCM revealed that each source of predictability comes from various flavors of El Niño/Southern Oscillation and anomalies in the Indian Ocean. The approach of this study potentially provides an opportunity to identify unknown predictable regional signals all over the world.

Variations of water vapor transport and water vapor-hydrometeor-precipitation conversions during a heavy rainfall event in the Three-River-Headwater region of the Tibetan Plateau

A summertime torrential heavy rainfall event which was occurred in the eastern Three-River-Headwater region (TRHR) of the Tibet Plateau (TP) was simulated by the Weather Research and Forecasting (WRF) model. Variations of three-dimensional water vapor transport and water vapor-hydrometeor-precipitation conversions were analyzed in complex terrains. The results show that: before and in the beginning of the precipitation, the surface evaporation (ES) in the forenoon is the most important water vapor source of hydrometeor formation (Micss), and is much larger than water vapor flux convergence (Qvac). During the developmental stage in the afternoon, Qvac surpasses ES, and contributes the most hydrometeor mainly through condensation (Qcon) for the sporadic precipitation on the mountains, which is related to the convergence of the vertically integrated water vapor flux (IWVF) belts from the northern lower altitude under the topography-forced and thermal-driven uplift with the southerly water vapor flux. During the mature stage at night, the northerly and southerly water vapor fluxes merge and transport eastward downhill, converge further with another easterly stronger IWVF belt, leading to the local heavy rainfall on the lower topography slope. The hydrometeor generated by Qcon and deposition (Qdep) is the primary source of heavy precipitation, while the weak precipitation is prone to directly come from the hydrometeor convergence (Qhc) and local hydrometeor consumption (Qhl). During the dissipative stage before the dawn, the persistence of precipitation is significantly through the consumption of local accumulated water vapor (Qvl) and hydrometeor (Qhl). This study is helpful to provide scientific reference for the rational development and utilization of local cloud water resources and the local disastrous weather prevention and reduction.

Forecasting of Debris Flow Using Machine Learning-Based Adjusted Rainfall Information and RAMMS Model

In recent years, climate change and extreme weather conditions have caused natural disasters of various sizes and forms across the world. The increase in the resulting flood damage and secondary damage has also inflicted massive social and economic harm. Korea is no exception, where debris flows created by typhoons and localized heavy rainfalls have caused human injuries and property damage in the Wumyeonsan Mountain in Seoul, Majeoksan Mountain in Chuncheon, Sinnam in Samcheok, Gokseong in Jeollanam-do, and Anseong in Gyeonggi-do. Disaster damage needs to be minimized by preparing for typhoons and heavy rainfalls that cause debris flow. To that end, we need accurate prediction of rainfall and flooding through simulations based on debris flow models. Most of the previous literature analyzed debris flows using rainfall events in the past before debris flow occurrence, rather than analyzing and predicting based on rainfall predictions. The main body of this study assesses the applicability of hydrological quantitative precipitation forecast (HQPF) generated through a machine learning method named the Random Forest (RF) method to debris flow analysis models. To that end, this study uses scatter plots to compare and analyze the precipitation observation data collected from the areas hit by debris flows in the past, and the quantitative precipitation forecast (QPF) and HQPF data from the Korea Meteorological Administration (KMA). Based on the verified HQPF data, runoff was calculated using the spatial runoff assessment tool (S-RAT) model, and the soil amount was calculated to simulate the debris flow damage with a two-dimensional rapid mass movements (RAMMS) model. The debris flow simulation based on the said data indicated varying degrees of flow depth, impact force, speed, and damage area depending on the precipitation. The correction of the HQPF was verified by measuring and comparing the spatial location accuracy by analyzing the Lee Sallee shape index (LSSI) of the damage areas. The findings confirm the correction of the HQPF based on machine learning and indicate its applicability to debris flow models.

A Comparison Between Global Satellite Mapping of Precipitation Data and High-Resolution Radar Data – A Case Study of Localized Torrential Rainfall over Japan

This paper presents a case study comparing the latest algorithm version of Global Satellite Mapping of Precipitation (GSMaP) data with C-band and X-band Multi-Parameter (MP) radar as high-resolution rainfall data in terms of localized heavy rainfall events. The study also obliged us to clarify the spatial and temporal resolution of GSMaP data using high-accuracy ground-based radar, and evaluate the performance and reporting frequency of GSMaP satellites. The GSMaP_Gauge_RNL data with less than 70 mm/day of daily rainfall was similar to the data of both radars, but the GSMaP_Gauge_RNL data with over 70 mm/day of daily rainfall was not, and the calibration by rain-gauge data was poor. Furthermore, both direct/indirect observations by the Global Precipitation Measurement/Microwave Imager (GPM/GMI) and the frequency thereof (once or twice) significantly affected the difference between GPM/GMI data and C-band radar data when the daily rainfall was less than 70 mm/day and the hourly rainfall was less than 20 mm/h. Therefore, it is difficult for GSMaP_Gauge to accurately estimate localized heavy rainfall with high-density particle precipitation.

Characteristics of Summer Hourly Extreme Precipitation Events and Its Local Environmental Influencing Factors in Beijing under Urbanization Background

Studies on urban extreme precipitation and its influencing factors are significant for prevention and reduction of meteorological disasters; however, few studies focus on hourly extreme precipitation (HEP) events due to the limitation of observation. By using the summer hourly precipitation data in Beijing from 1980 to 2020, the spatial distribution and temporal variation of HEP as well as its local environmental influencing factors are investigated. It is found that both summer precipitation amount and frequency of HEP are affected by topography, with high values in windward slope area. The summer precipitation amount of HEP is 160–200 mm, accounting for 42–47% of the annual summer precipitation amount, while the frequency proportion of HEP is only 5.5–6.0%. Although the summer precipitation amount and frequency in Beijing both decrease in the past 41 years, those for HEP present an opposite trend mainly due to the increasing HEP since 2003 and this is a phenomenon worthy of attention. A similar bimodal pattern in diurnal variation is found for the summer precipitation amount and frequency of HEP, with two peaks in 19–22 LT and 01–05 LT, respectively, indicating that HEPs are more concentrated in the evening and early morning especially in urban area. Moreover, the urbanization process of Beijing is consistent with the change trend of HEP, implying that the stronger the urban heat island intensity (UHI), the higher the probability of HEP. Furthermore, the convergence lines of terrain are also conducive to local heavy rainfall, and lower tropospheric stability (LTS) and convective available potential energy (CAPE) as well as aerosols may also affect HEP in Beijing.

Synoptic structure of a sub-daily extreme precipitation and flood event in Thohoyandou, north-eastern South Africa

An extreme sub-daily precipitation event produced about 300 mm of rainfall in less than 4 h overnight from 13–14 February 2019 resulting in high floods in Thohoyandou, a small town northeast of South Africa. We employed station, radar, satellite and reanalysis datasets to investigate the rainfall, circulation and thermodynamic fields and understand the meteorological structure of the extreme event via a multiscale analysis. The large-scale synoptic environment was characterized by a mid-tropospheric tropical-temperate trough and attendant cloud band coupled to a surface high ridging over the southeast coast of the country. We found that whilst heavy rainfall (>50 mm/24 h) was widespread ahead of the upper trough, extreme amounts (∼100 mm/h) were localized due to a cloudburst. A small perturbation to the favorable large scale mid-tropospheric environment also contributed to localized heavy rainfall. The south-north pressure gradient was steepened by a surface low over southern Mozambique resulting in enhanced moisture fluxes deriving from the southwest Indian Ocean. The interaction of prevailing surface winds and a low-level jet with the steep topography of the adjacent Soutpansberg Mountain Range enhanced low-level convergence and lifting in the area. We also show that the highest rainfalls were uphill of the location of flooding which was contained in a poorly drained valley. Whereas the Unified Model forecasts appeared accurate for the large-scale pattern of heavy rainfall in the area, the rainfall peak was generally underestimated, whilst the timing of extreme rainfall was delayed in the 18Z simulation, which is used by forecasters operationally. Our findings contribute to understanding the occurrence of extreme weather events over northeastern South Africa and also how models treat them, towards natural disaster risk reduction.