Identifying precursors of daily to seasonal hydrological extremes over the USA using deep learning techniques and climate model ensembles
Focal Area(s): We focus on two areas of crosscutting interest for DOE: 1) predictability of extreme precipitation and drought in the USA and 2) the integration of climate models with new AI tools, such as convolutional neural networks (CNN) and methods to understand their output (e.g. layer-wise rel...
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ftosti:oai:osti.gov:1769719 2023-07-30T04:05:25+02:00 Identifying precursors of daily to seasonal hydrological extremes over the USA using deep learning techniques and climate model ensembles Maher, Nicola DiNezio, Pedro Capotondi, Antonietta Kay, Jennifer 2022-01-28 application/pdf http://www.osti.gov/servlets/purl/1769719 https://www.osti.gov/biblio/1769719 https://doi.org/10.2172/1769719 unknown http://www.osti.gov/servlets/purl/1769719 https://www.osti.gov/biblio/1769719 https://doi.org/10.2172/1769719 doi:10.2172/1769719 58 GEOSCIENCES 54 ENVIRONMENTAL SCIENCES 97 MATHEMATICS AND COMPUTING 2022 ftosti https://doi.org/10.2172/1769719 2023-07-11T10:01:47Z Focal Area(s): We focus on two areas of crosscutting interest for DOE: 1) predictability of extreme precipitation and drought in the USA and 2) the integration of climate models with new AI tools, such as convolutional neural networks (CNN) and methods to understand their output (e.g. layer-wise relevance propagation; LRP). This project fits into focus area 3 of this call for white paper using AI to gain insight from complex data, including explainable AI tools. Science Challenge: Predicting hydrological extremes is important due to their impacts on people, agriculture and infrastructure. This prediction is difficult due to the infrequent occurrence of extremes and their complexity. However, extreme events can be related to more predictable conditions in the ocean, such as El Nino, long-term soil moisture or large scale modes of climate variability, such as the North Atlantic Oscillation (NAO). Other/Unknown Material North Atlantic North Atlantic oscillation SciTec Connect (Office of Scientific and Technical Information - OSTI, U.S. Department of Energy) |
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SciTec Connect (Office of Scientific and Technical Information - OSTI, U.S. Department of Energy) |
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58 GEOSCIENCES 54 ENVIRONMENTAL SCIENCES 97 MATHEMATICS AND COMPUTING |
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58 GEOSCIENCES 54 ENVIRONMENTAL SCIENCES 97 MATHEMATICS AND COMPUTING Maher, Nicola DiNezio, Pedro Capotondi, Antonietta Kay, Jennifer Identifying precursors of daily to seasonal hydrological extremes over the USA using deep learning techniques and climate model ensembles |
topic_facet |
58 GEOSCIENCES 54 ENVIRONMENTAL SCIENCES 97 MATHEMATICS AND COMPUTING |
description |
Focal Area(s): We focus on two areas of crosscutting interest for DOE: 1) predictability of extreme precipitation and drought in the USA and 2) the integration of climate models with new AI tools, such as convolutional neural networks (CNN) and methods to understand their output (e.g. layer-wise relevance propagation; LRP). This project fits into focus area 3 of this call for white paper using AI to gain insight from complex data, including explainable AI tools. Science Challenge: Predicting hydrological extremes is important due to their impacts on people, agriculture and infrastructure. This prediction is difficult due to the infrequent occurrence of extremes and their complexity. However, extreme events can be related to more predictable conditions in the ocean, such as El Nino, long-term soil moisture or large scale modes of climate variability, such as the North Atlantic Oscillation (NAO). |
author |
Maher, Nicola DiNezio, Pedro Capotondi, Antonietta Kay, Jennifer |
author_facet |
Maher, Nicola DiNezio, Pedro Capotondi, Antonietta Kay, Jennifer |
author_sort |
Maher, Nicola |
title |
Identifying precursors of daily to seasonal hydrological extremes over the USA using deep learning techniques and climate model ensembles |
title_short |
Identifying precursors of daily to seasonal hydrological extremes over the USA using deep learning techniques and climate model ensembles |
title_full |
Identifying precursors of daily to seasonal hydrological extremes over the USA using deep learning techniques and climate model ensembles |
title_fullStr |
Identifying precursors of daily to seasonal hydrological extremes over the USA using deep learning techniques and climate model ensembles |
title_full_unstemmed |
Identifying precursors of daily to seasonal hydrological extremes over the USA using deep learning techniques and climate model ensembles |
title_sort |
identifying precursors of daily to seasonal hydrological extremes over the usa using deep learning techniques and climate model ensembles |
publishDate |
2022 |
url |
http://www.osti.gov/servlets/purl/1769719 https://www.osti.gov/biblio/1769719 https://doi.org/10.2172/1769719 |
genre |
North Atlantic North Atlantic oscillation |
genre_facet |
North Atlantic North Atlantic oscillation |
op_relation |
http://www.osti.gov/servlets/purl/1769719 https://www.osti.gov/biblio/1769719 https://doi.org/10.2172/1769719 doi:10.2172/1769719 |
op_doi |
https://doi.org/10.2172/1769719 |
_version_ |
1772817310763974656 |