Extreme Rainfall Events Research Articles

Decadal-scale assessment of sediment denudation rates in the Zhoukou River Basin, Taiwan: insights from improved DEMs of differencing based on spectral analysis

This study assesses sediment erosion rates in the Zhoukou River Basin in southern Taiwan over the past three decades, focusing on the impact of extreme rainfall events. While various established methods determine erosion rates over different temporal scales, we employ an independent approach for decadal-scale erosion rate calculation by utilizing open-source global and regional digital elevation models (DEMs) data for two distinct periods. We introduce a new method that applies Fourier analysis to vertically register the DEMs, significantly minimizing their vertical bias. Through spectral analysis, we identify long wavelength topography crucial for correcting vertical offsets. Erosion rates, computed through DEMs of Difference (DoD), exhibit a similar trend of significant reduction in sediment export rates from 1990–2010 to 2011–2020 due to decreased extreme rainfall events, aligning with erosion rate estimates derived from mean suspended load data at gauge stations. Over the entire period from 1990 to 2020, the calculated denudation rate was 14.19 mm/yr, whereas in the recent decade (2011–2020), it decreased to 10.46 mm/yr. Our study suggests that the improved DoD method can effectively estimate sediment transport rates by leveraging underutilized DEMs captured at distinct points in time, especially when the erosional signal dominates data noise.

Analysing the variability of non-stationary extreme rainfall events amidst climate change in East Malaysia

ABSTRACT Climate change is intensifying the occurrence of extreme rainfall events, drawing attention to the importance of understanding the return period concept within the realm of extreme weather studies. This study evaluates the stationarity of extreme rainfall series on both monthly and annual series across East Malaysia, employing the Augmented Dickey–Fuller, Phillips Perron, and Kwiatkowski–Phillips–Schmidt–Shin tests. To model these extreme rainfall series, various probability distributions were applied, followed by goodness-of-fit tests to determine their adequacy. The study identified the stationary and non-stationary return values at 25-, 50-, and 100-year return periods. Additionally, maps depicting the spatial distribution for non-stationary increment were generated. The results indicated that extreme monthly rainfall exhibited stationary characteristics, while extreme yearly rainfall displayed non-stationary characteristics. Among the tested probability distributions, the generalised extreme value distribution was found to be superior in representing the characteristics of the extreme rainfall. Furthermore, a significant finding is that the non-stationary rainfall exhibits higher return values than those of stationary rainfall across all return periods. The northeast coast of Sabah highlighted as the most affected area, with notably high return values for extreme rainfall.

Identification of Possible Incoming Runoff using Different Combinations of Extreme Rainfall Events in a Semi-arid Context: Banas River, Bisalpur Dam Catchment

ABSTRACT Event-based hydrologic models are very useful to predict peak flow and flood volume, particularly in semi-arid regions. In HEC-HMS software methods selected for loss, transformation, routing and base flow were soil conservation service (SCS) curve number (CN), SCS unit hydrograph (UH), Muskingum and recession, respectively. A total of six extreme events from the year 2011 to 2019 were selected, out of which four were used for calibration, one each for validation and application. The developed model can identify the peak discharge and flood volume satisfactorily at 2-hour intervals. During validation performance statistical viz. percent difference in runoff volume (DV%), percent difference in peak flow (DP%), Nash Sutcliffe Efficiency (NSE), percent bias (%BIAS), coefficient of determination (R2) and ratio of the root mean square error to the standard deviation (RSR) were −3.68, −25.93, 0.52, 3.52, 0.53 and 0.69, respectively. The sensitivity analysis revealed that the CN is the highest sensitive parameter followed by the storage time constant (K), which affects the peak discharge. Whereas, for flood volume, CN is the highest sensitive parameter followed by the recession constant (Rc). The relative sensitivity of CN for peak flow and flood volume were 2.11 and 1.73, respectively. Out of center maximum rainfall (CeMR) and cumulative maximum rainfall (CuMR) distribution, CeMR distribution has given higher peak discharge for all rainfall duration. The rainfall characteristics of the 2016 flood event suggest a hypothetical 9-day rainfall duration can be considered. The 9-day event with CeMR gives 70.64% higher discharge as compared to the observed peak discharge during the 2016 flood.

Acidification alleviates the inhibition of hyposaline stress on physiological performance of tropical seagrass Thalassia hemprichii

Since the Industrial Revolution, increasing atmospheric CO2 concentrations have had a substantial negative impact influence on coastal ecosystems because of direct effects including ocean acidification and indirect effects such as extreme rainfall events. Using a two-factor crossover indoor simulation experiment, this study examined the combined effects of acidification and hyposaline stress on Thalassia hemprichii. Seawater acidification increased the photosynthetic pigment content of T. hemprichii leaves and promoted seagrass growth rate. Hyposaline stress slowed down seagrass growth and had an impact on the osmotic potential and osmoregulatory substance content of seagrass leaves. Acidification and salinity reduction had significant interaction effects on the photosynthesis rate, photosynthetic pigment content, chlorophyll fluorescence parameters, and osmotic potential of T. hemprichii, but not on the growth rate. Overall, these findings have shown that the hyposaline stress inhibitory effect on the T. hemprichii physiological performance and growth may be reduced by acidification.

Identification of sub-urban scale extreme precipitation variability using a geospatial algorithm

Established urban hydrometeorological literature shows the enhanced rainfall occurs generally near and downwind of urban centers. This information is often too generalized for operational use. Anomaly analysis in a spatial framework similar to operational forecast zones may provide utility in prediction of extreme events like localized flash flooding. This paper introduces a spatial analysis algorithm deploying 4-km NCEP Multisensor Precipitation Estimates Stage IV data to determine specific areas, or sectors, of anomalous precipitation across three southern United States cities (Atlanta, Houston, and Raleigh). Rainfall frequency and intensity are quantified during the afternoon and early evening across a 21-year period. Hotspot analysis and sector-based comparison of anomalies show that specific areas within each city are prone to isolated extreme rainfall events more often than other areas. Isolated precipitation in central and eastern Atlanta, specifically, has a high rate of recurrence and intensity during convectively active hours. 700 hPa wind deviations from the zonal mean are mapped to domain sectors as rainfall anomalies exceed ten standard deviations. These results are an application of the provided geospatial algorithm which combines gridded raster data with subdivisions defined by polygon features to assess the clustering of variables (environmental or otherwise) within domains of interest.

Intensity-Duration-Frequency Curves for Manicaragua city, Cuba

The Intensity-Duration-Frequency (IDF) curves are a way to visualize and represent extreme hydrometeorological rainfall events. In this article, an analysis of convective rainfall events recorded at the La Piedra Meteorological Station, Villa Clara, Cuba, was conducted. To develop IDF curves, the 2006- 2019 time series was analyzed. A partial duration series was generated, including intervals from 20 minutes to 4320 minutes, subjected to an outlier detection process. The series was divided into two categories: one for durations ≤ 720 minutes and another for durations > 720 minutes. The resulting series underwent nonparametric tests to assess their independence, randomness, homogeneity, and seasonality. Subsequently, they were fitted to the Generalized Pareto probability distribution and to a parametric equation of the Montana model, and then the curves were plotted for return periods of 10, 50 and 100. The Montana model led to obtaining correlation coefficients greater than 0.90 compared to the other methods used, significantly improving the quality of the fit in both categories. This research provides information to understand and plan the management of intense climatic phenomena and adequate risk management in an area where such studies are lacking, facilitating access to crucial data essential in the design and execution of hydraulic engineering projects in the region.

Atmospheric River Rapids and Their Role in the Extreme Rainfall Event of April 2023 in the Middle East

AbstractThe mesoscale dynamics of a record‐breaking Atmospheric River (AR) that impacted the Middle East in mid‐April 2023 and caused property damage and loss of life are investigated using model, reanalysis and observational data. The high‐resolution (2.5 km) simulations revealed the presence of AR rapids, narrow and long convective structures embedded within the AR that generated heavy precipitation (>4 mm hr−1) as they moved at high speeds (>30 m s−1) from northeastern Africa into western Iran. Gravity waves triggered by the complex terrain in Saudi Arabia further intensified their effects. Given the rising frequency of ARs in this region, AR rapids may be even more impactful in a warming climate, and need to be accounted for in reanalysis and numerical models.

Impact of extreme rainfall events on soil erosion on karst Slopes: A study of hydrodynamic mechanisms

As global warming intensifies, frequent extreme rainfall events cause severe soil erosion. ​However, the impact of extreme rainfall on erosion on karst slopes and its hydrodynamic mechanisms are still poorly understood. This study aims to examine how extreme rainfall events and slope gradient affect soil erosion on karst slopes and its underlying dynamic mechanisms. Simulation experiments were performed in a steel tank that mimicked the dual hydrological structure of a karst slope, using various rainfall intensities (50, 105, 115, 125, and 145 mm·h−1) and slope gradients (5°, 15°, and 25°). The results show that in the event of extreme rainfall (105 – 145 mm·h−1), there was a substantial increase in the surface runoff coefficient, rising from 48.85 % to 89.79 %, representing an 83.81 % increase. Surface erosion was found to be the predominant factor on the slope, contributing to 98.74 % to 99.98 % of the overall sediment output, while underground sediment coefficient ranged from only 0.02 % to 1.26 % under same conditions. The unit stream power emerged as the most suitable hydrodynamic parameter for characterizing surface sediment yield (SS), with a critical threshold of unit water flow power for surface erosion identified at 0.006 m·s−1. Constructing a surface erosion model (R2 = 0.969) based on runoff velocity, rain intensity, and slope yields higher accuracy than the model (R2 = 0.966) based solely on runoff modulus, rain intensity, and slope. For the most precise predictions, it is recommended to combine runoff modulus and velocity with rainfall intensity and slope when constructing the surface erosion prediction model(R2 > 0.999). This study enhances understanding of extreme rainfall’s effect on soil erosion in karst slopes and backs the creation of prediction models for such events. Our insights offer fresh perspectives for soil and water conservation, aiding in the formulation of precise erosion control strategies for karst slopes.

Perspectives of nature-based tourism-dependent communities on climate change in the Okavango Delta, Botswana.

The intensity and frequency of climate extremes such as heat waves, droughts and extreme rainfall events are projected to rise. This will increase the severity of their impacts across socio-ecological systems. Economic sectors such as nature-based tourism become more vulnerable because of their reliance on climate and natural capital as key resources. While attempts have been made to understand how climate change may impact tourists and the industry itself, little is known about the same on tourism-dependent communities. This paper determines the extent to which tourism-dependent communities are vulnerable to climate change in the Okavango Delta, Botswana, to enhance their wider livelihood the development of strategies for improving adaptive capacity, resilience, and reduced exposure sensitivities. A household survey of 172 households was conducted in three purposively selected villages of Mababe, Sankuyo and Khwai, actively involved in community-based tourism for their socio-economic development. Information sourced related to livelihood options, peoples' resilience, local risks, and hazards. The data was analysed using descriptive statistics and thematic analysis. The results indicate that respondents have observed climatic changes over the years such as increased temperatures, decreased rainfall, and increased frequencies of extreme events. The respondents attributed changes in natural capital to these observed climatic conditions in the form of desiccation, dwindling populations of some wildlife species, decreased fish stocks and reduced vegetation cover. This renders the tourism-dependent communities vulnerable as their livelihood is threatened. The paper thus concludes that climate change adaptation is an urgent priority for local communities who are already exposed to existing climatic and non-climatic stresses.

Urbanization signature on hourly rainfall extremes of Kuala Lumpur

This study uses high-resolution satellite data to investigate the impact of urbanization on the local climate of Kuala Lumpur, Malaysia. Nonparametric statistical methods, including trend test, spatial correlation, and quantile regression, were employed to quantify interrelationships between satellite nighttime light (NTL), Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST), and Global Satellite Mapping of Precipitation (GSMaP) extreme rainfall events. The results revealed that urbanization increased Kuala Lumpur's LST by 3.8 to 4.1 °C and the 99th percentile rainfall by 10 to 43 % compared to surrounding areas. The correlation analysis showed an association (r = 0.62) between the trends in LST and rainfall extremes, with a significance level of p < 0.05. The NTL trend analysis showed a rapid expansion of the city, particularly in the west, and thus, an expansion of high LST areas and extreme rainfall amounts. Quantile regression results confirmed the influence of increased LST on rainfall extremes. The trends for hourly rainfall revealed increases mainly between 2pm and 9pm in the city, which is attributed to the intensification of convective rainfall caused by increased LST. The study highlights the importance of mitigating urban LST to reduce climatic extremes and promote sustainable urban development.

Integrating deep learning, satellite image processing, and spatial-temporal analysis for urban flood prediction

Urban flooding is escalating worldwide due to the increasing impervious surfaces from urban developments and frequency of extreme rainfall events by climate change. Traditional flood extent prediction and mapping methods based on physical-based hydrological principles often face limitations due to model complexity and computational burden. In response to these challenges, there has been a notable shift toward satellite image processing and Artificial Intelligence (AI) based approaches, such as Deep Learning (DL) models, including architectures like Convolutional Neural Networks (CNN). The objective of this research is to predict near real-time (NRT) flood extents within urban areas. This research integrated CNN (U-Net) with Sentinel-1 satellite imagery, Digital Elevation Model (DEM), hydrologic soil group (HSG), imperviousness, and rainfall data to create a flood extent prediction model. To detect flooded areas, a binary raster map was created using calibrated backscatter values derived from the VV (vertical transmit and vertical receive) polarization mode of Sentinel-1 imagery, which was highlighted as having a significant impact on backscatter behavior and prediction results. Application of the model was demonstrated in urban areas of Miami-Dade County, Florida. The results demonstrated the capability of the model to provide rapid and accurate flood extent predictions at a spatial resolution of 10 m, with an overall accuracy of 97.05 %, F-1 Score of 92.49 %, and AUC of 93 % in the study area. The U-Net model’s flood predictions were compared with historical floodplain data and then using GIS overlay analysis, resulting in a Ground Truth Index of 84.05 % that shows the accuracy of the model in identifying flooded areas. The research incorporated crucial flood-influencing data (including rainfall) to the flood extent prediction models and expanded the focus models beyond major rainfall events only to encompass a wider range of flood events. The presented NRT flood extent mapping model has a broad range of applicability, including, but not limited to, the continuous monitoring of flood events and their potential impacts on civil infrastructure assets (e.g., construction, operation, and maintenance of roads and bridges), early warning systems for timely evacuation and preparedness measures, and insurance risk assessment.

A novel voting ensemble model empowered by metaheuristic feature selection for accurate flash flood susceptibility mapping

This study addresses the challenges of flash flood susceptibility mapping in Yemen’s Qaa’Jahran Basin, characterized by complex terrain and limited hydro-meteorological data. To enhance predictive accuracy, we integrate metaheuristic feature selection with ensemble learning models. Initially, fifteen flash flood variables were retrieved using Geographic Information System (GIS) based remote sensing, setting the stage for a novel feature selection algorithm. Then, the Memo Search Algorithm (MSA), a metaheuristic approach is proposed to efficiently reduce feature space. Through comprehensive comparisons with established algorithms such as the Artificial Bee Colony (ABC) and Gray Wolf Optimizer (GWO), MSA refined the selection, identifying 'elevation’ and 'distance to streams’ as optimal factors. Statistical validations using the Friedman and Wilcoxon signed-rank tests confirmed the significant superiority of MSA over competing algorithms. Ensemble classifiers (bagging, boosting, stacking) were then applied to the reduced feature space. Comprehensive evaluation revealed the boosting ensemble with MSA outperformed traditional techniques reaching 98.75% accuracy, 0.9896 Area Under the Curve (AUC), and 98.95% the harmonic mean of the precision and recall (F1-score). Precision in identifying high-risk flash flood zones was underlined via spatial prediction, confirming the integrated framework’s ability to significantly improve forecast accuracy. The findings aid disaster management with powerful geographic mapping in data-poor regions. The proposed framework is adaptable globally for flash flood-prone areas with similar constraints. As climate change is expected to increase extreme rainfall events, communities globally will need robust data-driven methodologies for flash flood susceptibility mapping. The Key recommendations of the current study include investigating hybrid feature selection methods to better enhance predictive inputs and analyzing transferability across hydro-climatic zones.

Flash Flood Simulation for Hilly Reservoirs Considering Upstream Reservoirs—A Case Study of Moushan Reservoir

With the advancement of society and the impact of various factors such as climate change, surface conditions, and human activities, there has been a significant increase in the frequency of extreme rainfall events, leading to substantial losses from flood disasters. The presence of numerous small and medium-sized water conservancy projects in the basin plays a crucial role in influencing runoff production and rainwater confluence. However, due to the lack of extensive historical hydrological data for simulation purposes, it is challenging to accurately predict floods in the basin. Therefore, there is a growing emphasis on flood simulation and forecasting that takes into account the influence of upstream water projects. Moushan Reservoir basin is located in a hilly area of an arid and semi-arid region in the north of China. Flooding has the characteristics of sudden strong, short confluence time, steep rise, and steep fall, especially floods caused by extreme weather events, which have a high frequency and a wide range of hazards, and has become one of the most threatening natural disasters to human life and property safety. There are many small and medium-sized reservoirs in this basin, which have a significant influence on the accuracy of flood prediction. Therefore, taking Moushan Reservoir as an example, this paper puts forward a flash flood simulation method for reservoirs in hilly areas, considering upstream reservoirs, which can better solve the problem of flood simulation accuracy. Using the virtual aggregation method, the 3 medium-sized reservoirs and 93 small upstream reservoirs are summarized into 7 aggregated reservoirs. Then, we construct the hydrological model combining two method sets with different runoff generation and confluence mechanisms. Finally, after model calibration and verification, the results of different methods are analyzed in terms of peak discharge error, runoff depth error, difference in peak time, and certainty coefficient. The results indicate that the flooding processes simulated by the proposed model are in line with the observed ones. The errors of flood peak and runoff depth are in the ranges of 2.3% to 15% and 0.1% to 19.6%, respectively, meeting the requirements of Class B accuracy of the “Water Forecast Code”. Method set 1 demonstrates a better simulation of floods with an average flood peak error of 5.63%. All these findings illustrate that the developed model, utilizing aggregate reservoirs and dynamic parameters to reflect regulation and storage functions, can effectively capture the impact of small water conservancy projects on confluence. This approach addresses challenges in simulating floods caused by small and medium-sized reservoirs, facilitating basin-wide flood prediction.

Climate change, seasonality and household water security in rural Gambia: A qualitative exploration of the complex relationship between weather and water

Climate change could pose a threat to water security for many communities, particularly in settings where rainfall patterns are becoming more varied and there is higher frequency of extreme events, such as heavy rainfall and droughts. Understanding how rainfall affects water security—including water access, water quality and water use behaviours—can inform investment in more climate-resilient infrastructure and safeguard against future health risks. This study aims to explore how households in rural Gambia experienced water security in relation to seasonal rainfall patterns and extreme weather events. Data collection focused on two communities (Kiang West and Basse) with differing access to water infrastructure, within which some villages had greater access to groundwater sources, such as solar-powered boreholes, and others primarily used uncovered wells. 46 participants were interviewed in Spring 2022 using multiple qualitative methods, including in-depth interviews and transect walks. We found that people’s experience of water security and rainfall (including seasonal rainfall, drought and heavy rainfall) was complex and varied according to the primary household water source. Both dry and rainy season posed challenges to household water security in terms of quality and quantity. Households with access to more resilient infrastructure, such as solar-powered boreholes, discussed a shift in the relationship between weather and water security, where they were less vulnerable to water shortages during dry conditions compared to those using wells. However, these sources did not fully resolve water security issues, as they experienced water shortages during cloudy conditions. Extreme weather events, such as heavy rainfall, heightened perceived water issues, as these events sometimes damaged water infrastructure and contaminated water sources. Seasonal workloads, that were higher in the rainy season, also jeopardised water security, as this limited time for water collection. Increased investment in infrastructure, maintenance, water-treatment and behavioural change is required to mitigate the risks.

A new conceptual model for understanding and predicting life-threatening rainfall extremes

The motivation of our study is to provide forecasters and users complementary guidance and tools to identify and predict atmospheric conditions that could lead to life-threatening flash floods. Using hourly and sub-hourly rainfall datasets, proximity radiosondes, ERA5 reanalysis of extreme rainfall events in the UK during 2000–2020, and case studies in 2021, we observe a three-layered atmospheric structure, consisting of Moist Absolute Unstable Layers (MAULs) embedded in a conditional unstable layer sandwiched between a stable upper layer and a near-stable low layer. Based on our analysis, we propose a conceptual model to describe the atmospheric properties of a ‘rainfall extreme’ environment, with a particular focus on the thermodynamics associated with sub-hourly rainfall production processes. We then set this model within a wider framework to describe the precursor synoptic and mesoscale environments necessary for sub-hourly rainfall extremes in the mid-latitudes. We show that evolution of the Omega block and Rex Vortex couplet provides the optimal environmental conditions for sub-hourly rainfall extremes. These results provide the potential to develop a ‘4-stage’ warning system to assist in the identification and forecasting of life threatening short-duration extreme rainfall intensities and flash floods.

Indian West Coast's Extreme Rainfall: Sub-daily scale variability

Unlike daily precipitation, the intensity variation of extreme rainfall events (EREs) on a sub-daily scale is governed by fast response dynamics through the variability of the vertical structure of thermodynamics. Understanding the causative mechanisms and controlling factors responsible for the rapid intensification of rainfall on the sub-daily scale over the West Coast (WC) of India is still inadequate. In this study, we investigated the sub-daily scale variability of extreme rainfall events and possible regulating factors over the WC region, India. Our results suggest that, on average, the rainfall event spell lasts for ∼4–6 days, having an intense rain rate distribution for a few hours (∼3–5 h). We observed that the nonlinear complex interaction among various regulating factors in a conducive environment responsively influences the extreme rain rate distribution on sub-daily scale over the WC region. Sub-daily scale rainfall durations of 1–3, 3–5, and 5–15 h are crucial factors pertinent to EREs. Offshore trough-driven large-scale moisture loaded winds along the west coast, when endured with strong updrafts, enhance the ice growth mechanism and lead to high surface rain intensity. The mid-troposphere moistening during the peak stage of the EREs amplifies the hydrometeor growth mechanisms as revealed by cloud specific liquid/ice water content distribution and cloud radar reflectivity profiles. A slopy and narrow radar reflectivity pattern above zero degrees isotherm suggests a contrast cloud vertical structure during the intense phase of the EREs. Though the intense nature of precipitation is caused by warm and mixed-phased clouds, the mixed-phase clouds result in precipitation events of a relatively longer duration.

Application of Machine Learning Algorithms in Predicting Extreme Rainfall Events in Rwanda

Application of Machine Learning Algorithms in Predicting Extreme Rainfall Events in Rwanda

Climate-resilient urban drainage planning: An approach using a GIS-based SCS-CN model

ABSTRACT Inexorable urbanization continues apace across the world and urban flooding in megacities is now frequently evidenced due to extreme rainfall events due to climate change (CC). Climate-resilient urban drainage planning is critical towards making sustainable cities or any new urbanization. This paper presents an approach through an insightful assessment of climate resilient urban drainage applying GIS-based Soil Conservation Service-Curve Number (SCS-CN) model of a new urban growth of megacity Dhaka, Bangladesh. A precise DEM (Digital Elevation Model) of the study area has been used for catchment delineation using ArcSWAT. Localized climate anomalies of rainfall of around 11% have been identified during monsoon from statistical downscaling and included in the cumulative rainfall event of 5 days. The effect of historical and CC-induced rainfall have been used to identify and map the peak discharges of sub-catchments considering the return period of 5-day cumulative rainfall for 10, 25, and 100 years of the urban catchment for both existing and future land-use scenarios accounting for the change in CN. The varying results of the peak discharges of the sub-catchments for resilient drainage planning is not only dependent on the increase in rainfall but also the combined response of the land-use and soil profile.

Improving Forecasts of the “21⋅7” Henan Extreme Rainfall Event Using a Radar Assimilation Scheme that Considers Hydrometeor Background Error Covariance

Abstract On 20–21 July 2021, a record-breaking rainfall event occurred in Henan Province, China, and a maximum hourly accumulated precipitation of 201.9 mm was recorded at Zhengzhou Meteorological Station. To improve the prediction of such extreme rainfall and to better understand the impacts of the radar reflectivity assimilation on forecasting, we assimilated radar reflectivity data using the hydrometeor background error covariance (HBEC) that includes vertical and multivariate correlations and then diagnosed the dynamic, thermal, and microphysical forecasts of this event. The results show that the radar reflectivity assimilation based on the HBEC properly transferred the observed radar reflectivity to the analysis of hydrometeors and other model states, and clearly improved the heavy rainfall forecast. The diagnosis of the dynamic and thermal forecasts indicated that the reflectivity assimilation based on the HBEC improved the convective environments of the precipitation systems, with stronger cold pools near the surface and deeper and wetter updrafts near Zhengzhou station, when compared with the experiment that did not assimilate radar reflectivity and the experiment that assimilated radar reflectivity without using the HBEC. The diagnosis of the microphysical forecasts further shows that assimilating reflectivity data using HBEC contributed to higher conversion rates of water vapor and cloud water to graupel and higher conversion rates of graupel and cloud water to rainwater, when compared with the other experiments. These improvements of both convective environments and microphysical processes within the convections ultimately enhanced the forecasts of this extreme rainfall event.

Correction to: Patterns and drivers of heavy and extreme hourly rainfall events over Metro Manila, Philippines

Correction to: Patterns and drivers of heavy and extreme hourly rainfall events over Metro Manila, Philippines