Zobrazeno 1 - 10 of 37 pro vyhledávání: '"Peter B Gibson"'
1
Akademický článek
On the Extrapolation of Generative Adversarial Networks for Downscaling Precipitation Extremes in Warmer Climates
Autor: Neelesh Rampal, Peter B. Gibson, Steven Sherwood, Gab Abramowitz
Publikováno v: Geophysical Research Letters, Vol 51, Iss 23, Pp n/a-n/a (2024)
Abstract While deep‐learning downscaling algorithms can generate fine‐scale climate projections cost‐effectively, it is unclear how effectively they extrapolate to unobserved climates. We assess the extrapolation capabilities of a deterministic
Externí odkaz: https://doaj.org/article/108e438a842f41b5b5ec21281eaf6a33
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2
Akademický článek
High-resolution downscaling with interpretable deep learning: Rainfall extremes over New Zealand
Autor: Neelesh Rampal, Peter B. Gibson, Abha Sood, Stephen Stuart, Nicolas C. Fauchereau, Chris Brandolino, Ben Noll, Tristan Meyers
Publikováno v: Weather and Climate Extremes, Vol 38, Iss , Pp 100525- (2022)
The gap in resolution between existing global climate model output and that sought by decision-makers drives an ongoing need for climate downscaling. Here we test the extent to which developments in deep learning can out-perform existing statistical
Externí odkaz: https://doaj.org/article/7cdf657bd73142f7bd8230dbf4abdc09
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3
Akademický článek
Training machine learning models on climate model output yields skillful interpretable seasonal precipitation forecasts
Autor: Peter B. Gibson, William E. Chapman, Alphan Altinok, Luca Delle Monache, Michael J. DeFlorio, Duane E. Waliser
Publikováno v: Communications Earth & Environment, Vol 2, Iss 1, Pp 1-13 (2021)
Seasonal forecasting skill in machine learning methods that are trained on large climate model ensembles can compete with, or out-compete, existing dynamical models, while retaining physical interpretability.
Externí odkaz: https://doaj.org/article/91735fc8166e455f88e2a5b2a1307447
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5
Why Seasonal Prediction of California Winter Precipitation Is Challenging
Autor: Xianan Jiang, Duane E. Waliser, Peter B. Gibson, Gang Chen, Weina Guan
Publikováno v: Bulletin of the American Meteorological Society. 103:E2688-E2700
Despite an urgent demand for reliable seasonal prediction of precipitation in California (CA) due to the recent recurrent and severe drought conditions, our predictive skill for CA winter precipitation remains limited. October hindcasts by the couple
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::5ceb67ddf8d1805898fead6bc89484c1
https://doi.org/10.1175/bams-d-21-0252.1
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6
The Lifecycle of New Zealand Atmospheric Rivers and Relationship with the Madden-Julian Oscillation
Autor: Hamish D Prince, Peter B Gibson, Nicolas J. Cullen
New Zealand atmospheric river (AR) lifecycles are analyzed to examine the synoptic conditions that produce extreme precipitation and regular flooding. An AR lifecycle tracking algorithm, novel to the region, is utilized to identify the genesis locati
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::37ca055c6f3dd0b5a4b56407fcdaf45f
https://doi.org/10.22541/essoar.168500261.14419790/v1
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7
Large-Scale Environments of Successive Atmospheric River Events Leading to Compound Precipitation Extremes in California
Autor: Meredith A. Fish, James M. Done, Daniel L. Swain, Anna M. Wilson, Allison C. Michaelis, Peter B. Gibson, F. Martin Ralph
Publikováno v: Journal of Climate. 35:1515-1536
Successive atmospheric river (AR) events—known as AR families—can result in prolonged and elevated hydrological impacts relative to single ARs due to the lack of recovery time between periods of precipitation. Despite the outsized societal impact
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::e9f4ee779458c5fe5cc3d31a23da4977
https://doi.org/10.1175/jcli-d-21-0168.1
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9
Training machine learning models on climate model output yields skillful interpretable seasonal precipitation forecasts
Autor: Alphan Altinok, Luca Delle Monache, Michael J. DeFlorio, Peter B. Gibson, W. Chapman, Duane E. Waliser
Publikováno v: Communications Earth & Environment, Vol 2, Iss 1, Pp 1-13 (2021)
A barrier to utilizing machine learning in seasonal forecasting applications is the limited sample size of observational data for model training. To circumvent this issue, here we explore the feasibility of training various machine learning approache
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::885c2dcf5b481300ef4e1c48efe03895
https://doaj.org/article/91735fc8166e455f88e2a5b2a1307447
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10
A Climatology of Atmospheric Rivers in New Zealand
Autor: Hamish D. Prince, Peter B. Gibson, Nicolas J. Cullen, Daniel G. Kingston, Jono Conway
Publikováno v: Journal of Climate. 34:4383-4402
The occurrence of extreme precipitation events in New Zealand regularly results in devastating impacts to the local society and environment. An automated atmospheric river (AR) detection technique (ARDT) is applied to construct a climatology (1979–
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::e51cc814073122d46a6899c674f97b56
https://doi.org/10.1175/jcli-d-20-0664.1
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