Improving the heavy rainfall forecasting using a weighted deep learning model

Chen, Yutong; Huang, Gang; Wang, Ya; Tao, Wanyin; Tian, Qun; Yang, Kai; Zheng, Jiangshan; He, Hubin

doi:10.3389/fenvs.2023.1116672

Cited by 7 publications

(2 citation statements)

References 32 publications

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“…Nowcasts are unable to provide precise predictions for longer lead times, and often have low skill in predicting medium-to-heavy rainfall events accurately (Ravuri et al, 2021). For this reason, NWP models are blended with nowcasts to provide longer lead times 6-72 h (Chen et al, 2023, Kendon et al, 2023.…”

Section: Advances In Nowcasting and Numerical Weather Prediction And ...mentioning

confidence: 99%

Forecasting bathing water quality in the UK: A critical review

Krupska,

Speight,

Robinson

et al. 2024

WIREs Water

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Climate change is altering rainfall patterns resulting in increasing variability and intensity of rainfall events worldwide. Increases to short duration, intense rainfall (i.e., convective rainfall), will lead to increases in sewage overflow and run‐off from agricultural land. Such events generate spikes in micro‐organisms from feces and manure, especially Escherichia coli and intestinal enterococci, that temporarily end up in bathing waters posing serious health risks to bathers. Forecasting of bathing water quality associated with convective rainfall presents a distinctive forecasting challenge due to high uncertainties associated with predicting the timing, location, and impact of such events. In this article, we review examples of bathing water quality forecasting practices, with a focus on the United Kingdom where convective rainfall in the summer bathing water season is a particular concern, and question whether the current approach is robust in a changing climate. We discuss potential upgrades in bathing water forecasting and identify the main challenges that must be addressed before an improved framework for bathing water forecasting can be achieved. Although developments in meteorological and hydrological short‐range forecasting capabilities are promising, convective rainfall forecasting has significant predictability limits. We suggest taking full advantage of short‐range forecasts to provide sub‐daily bathing water forecasts, focusing on targeted bathing water monitoring regimes to improve model accuracy with the ultimate goal of providing improved information and guidance for beach users.This article is categorized under: Science of Water > Water and Environmental Change Human Water > Water Governance Science of Water > Water Quality

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Section: Advances In Nowcasting and Numerical Weather Prediction And ...mentioning

confidence: 99%

Forecasting bathing water quality in the UK: A critical review

Krupska,

Speight,

Robinson

et al. 2024

WIREs Water

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“…The advantages of employing multiple probability distributions to describe daily rainfall may be better understood by looking at changes in precipitation patterns that occur in different locations of the globe, which can give a helpful insight into those benefits. Extensive research on this subject has been conducted by a community of scientists from all over the world in locations such as Bangladesh (Alam et al [ 1 ]), Ethiopia (Liyew and Melese [ 2 ]), Costa Rica (Altman et al [ 3 ]), Nagpur (India) (Mohanty et al [ 4 ]), China (Chen et al [ 5 ]), Pakistan (Yonus et al [ 6 ]), Europe (Wouters et al [ 7 ]), Colombia (Correa et al [ 8 ]), Saudi Arabia (Hasanean and Almazroui [ 9 ]), Brazil (Beskow et al [ 10 ]), Pakistan (Amin et al [ 11 ]), Egypt (Gado et al [ 12 ]), and many others.…”

Section: Introductionmentioning

confidence: 99%

A new alpha logarithmic-generated class to model precipitation data with theory and inference

Al Mutairi

2023

Heliyon

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Analysis, characterization, prediction, and attribution of extreme atmospheric events with machine learning and deep learning techniques: a review

Salcedo-Sanz,

Pérez-Aracil,

Ascenso

et al. 2023

Theor Appl Climatol

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Atmospheric extreme events cause severe damage to human societies and ecosystems. The frequency and intensity of extremes and other associated events are continuously increasing due to climate change and global warming. The accurate prediction, characterization, and attribution of atmospheric extreme events is, therefore, a key research field in which many groups are currently working by applying different methodologies and computational tools. Machine learning and deep learning methods have arisen in the last years as powerful techniques to tackle many of the problems related to atmospheric extreme events. This paper reviews machine learning and deep learning approaches applied to the analysis, characterization, prediction, and attribution of the most important atmospheric extremes. A summary of the most used machine learning and deep learning techniques in this area, and a comprehensive critical review of literature related to ML in EEs, are provided. The critical literature review has been extended to extreme events related to rainfall and floods, heatwaves and extreme temperatures, droughts, severe weather events and fog, and low-visibility episodes. A case study focused on the analysis of extreme atmospheric temperature prediction with ML and DL techniques is also presented in the paper. Conclusions, perspectives, and outlooks on the field are finally drawn.

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Improving the heavy rainfall forecasting using a weighted deep learning model

Cited by 7 publications

References 32 publications

Forecasting bathing water quality in the UK: A critical review

Forecasting bathing water quality in the UK: A critical review

A new alpha logarithmic-generated class to model precipitation data with theory and inference

Analysis, characterization, prediction, and attribution of extreme atmospheric events with machine learning and deep learning techniques: a review

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