Applying Deep Learning to Hail Detection: A Case Study

Pullman, Melinda; Gurung, Iksha; Maskey, Manil; Ramachandran, Rahul; Christopher, Sundar A.

doi:10.1109/tgrs.2019.2931944

Cited by 11 publications

(6 citation statements)

References 14 publications

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“…Convolutional neural networks were used by Duan et al (2021) to propose a data-driven model that reconstructs radar reflectivity using deep learning and RR using Himawari-8 radiation data. Deep learning is used by Pullman et al (2019) to identify infrared brightness temperature and other hail-related parameters for hail detection. In a study published in 2021, Adikari et al (2021) compared the predictive abilities of wavelet decomposition function, convolutional neural network, short-term memory network, and adaptive neuro-fuzzy inference system in flood and drought.…”

Section: Using Artificial Intelligence In Weather Forecastingmentioning

confidence: 99%

Artificial intelligence-based solutions for climate change: a review

Chen

Zhang

et al. 2023

Environ Chem Lett

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Climate change is a major threat already causing system damage to urban and natural systems, and inducing global economic losses of over $500 billion. These issues may be partly solved by artificial intelligence because artificial intelligence integrates internet resources to make prompt suggestions based on accurate climate change predictions. Here we review recent research and applications of artificial intelligence in mitigating the adverse effects of climate change, with a focus on energy efficiency, carbon sequestration and storage, weather and renewable energy forecasting, grid management, building design, transportation, precision agriculture, industrial processes, reducing deforestation, and resilient cities. We found that enhancing energy efficiency can significantly contribute to reducing the impact of climate change. Smart manufacturing can reduce energy consumption, waste, and carbon emissions by 30–50% and, in particular, can reduce energy consumption in buildings by 30–50%. About 70% of the global natural gas industry utilizes artificial intelligence technologies to enhance the accuracy and reliability of weather forecasts. Combining smart grids with artificial intelligence can optimize the efficiency of power systems, thereby reducing electricity bills by 10–20%. Intelligent transportation systems can reduce carbon dioxide emissions by approximately 60%. Moreover, the management of natural resources and the design of resilient cities through the application of artificial intelligence can further promote sustainability.

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Section: Using Artificial Intelligence In Weather Forecastingmentioning

confidence: 99%

Artificial intelligence-based solutions for climate change: a review

Chen

Zhang

et al. 2023

Environ Chem Lett

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“…Gagne et al hypothesizes that the ability for DL to outperform the traditional ML methods stems from the ability of DL methods, even shallow CNNs, to identify complex spatial patterns in the input data. Pullman et al (2019) also explored whether CNNs could detect hail using geostationary satellite and reanalysis data, finding good performance in comparison with storm reports, as measured using the critical success index (CSI . 0.4) using their limited dataset.…”

Section: B Hailmentioning

confidence: 99%

A Review of Machine Learning for Convective Weather

McGovern

Chase

Flora

et al. 2023

Artificial Intelligence for the Earth Systems

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We present an overviewof recentwork on using artificial intelligence/machine learning techniques for forecasting convective weather and its associated hazards, including tornadoes, hail, wind, and lightning. These high-impact phenomena globally cause both massive property damage and loss of life yet they are quite challenging to forecast. Given the recent explosion in developing machine learning techniques across the weather spectrum and the fact that the skillful prediction of convective weather has immediate societal benefits, we present a thorough review of the current state of the art in artificial intelligence and machine learning techniques for convective hazards. Our review includes both traditional approaches, including support vector machines and decision trees as well as deep learning approaches. We highlight the challenges in developing machine learning approaches to forecast these phenomena across a variety of spatial and temporal scales. We end with a discussion of promising areas of future work for ML for convective weather, including a discussion of the need to create trustworthy AI forecasts that can be used for forecasters in real-time and the need for active cross-sector collaboration on testbeds to validate machine learning methods in operational situations.

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“…Finally, Some recent works have applied DL approaches to problems of hail prediction. In Pullman et al (2019), a DL network has been applied to a problem of hailstorm detection. The GOES satellite imagery and MERRA-2 reanalysis data are used as predictive variables in this case.…”

Section: Hailstormsmentioning

confidence: 99%

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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Applying Deep Learning to Hail Detection: A Case Study

Cited by 11 publications

References 14 publications

Artificial intelligence-based solutions for climate change: a review

Artificial intelligence-based solutions for climate change: a review

A Review of Machine Learning for Convective Weather

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

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