Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Benjamin A. Toms"'
Publikováno v:
Journal of Advances in Modeling Earth Systems, Vol 12, Iss 9, Pp n/a-n/a (2020)
Abstract Neural networks have become increasingly prevalent within the geosciences, although a common limitation of their usage has been a lack of methods to interpret what the networks learn and how they make decisions. As such, neural networks have
Externí odkaz:
https://doaj.org/article/d4bc4c85ca4c4281a624d3ee8b0e0717
Autor:
Kristen L. Rasmussen, Sean W. Freeman, Gregory R. Herman, Muhammad Naufal Razin, J. Minnie Park, Christopher J. Slocum, Erik R. Nielsen, Stacey M. Hitchcock, Ryan Riesenberg, Julie Barnum, Susan C. van den Heever, Jennie Bukowski, Benjamin A. Toms, Leah D. Grant, Emily M. Riley Dellaripa, Peter J. Marinescu, Aryeh J. Drager, Adrian van den Heever, Patrick C. Kennedy, Eleanor Casas, B. Fuchs
Publikováno v:
Bulletin of the American Meteorological Society. 102:E1283-E1305
The intensity of deep convective storms is driven in part by the strength of their updrafts and cold pools. In spite of the importance of these storm features, they can be poorly represented within numerical models. This has been attributed to model
Autor:
Stephen M. Saleeby, Benjamin A. Toms, Susan C. van den Heever, Emily M. Riley Dellaripa, Eric D. Maloney
Publikováno v:
Journal of the Atmospheric Sciences. 77:647-667
While the boreal summer Madden–Julian oscillation (MJO) is commonly defined as a planetary-scale disturbance, the convective elements that constitute its cloud dipole exhibit pronounced variability in their morphology. We therefore investigate the
Autor:
Eric D. Maloney, Benjamin A. Toms, Stephen M. Saleeby, Susan C. van den Heever, Emily M. Riley Dellaripa
Publikováno v:
Journal of the Atmospheric Sciences. 77:3-30
Cloud-resolving simulations are used to evaluate the importance of topography to the diurnal cycle (DC) of precipitation (DCP) over Luzon, Philippines, and surrounding ocean during the July–August 2016 boreal summer intraseasonal oscillation (BSISO
Publikováno v:
Geoscientific Model Development, vol 14, iss 7
Geoscientific Model Development, Vol 14, Pp 4495-4508 (2021)
Geoscientific Model Development, Vol 14, Pp 4495-4508 (2021)
We test the reliability of two neural network interpretation techniques, backward optimization and layerwise relevance propagation, within geoscientific applications by applying them to a commonly studied geophysical phenomenon, the Madden-Julian Osc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9980d9bebfeaefd9999d1971a3c1fc78
https://escholarship.org/uc/item/97j0b5t5
https://escholarship.org/uc/item/97j0b5t5
Publikováno v:
Geophysical Research Letters. 48
We show that explainable neural networks can identify regions of oceanic variability that contribute predictability on decadal timescales in a fully coupled Earth system model. The neural networks ...
Autor:
Elizabeth A. Barnes, Benjamin A. Toms, David G. Anderson, James W Hurrell, Charles W. Anderson, Imme Ebert-Uphoff
Publikováno v:
Journal of Advances in Modeling Earth Systems, Vol 12, Iss 9, Pp n/a-n/a (2020)
Many problems in climate science require the identification of signals obscured by both the "noise" of internal climate variability and differences across models. Following previous work, we train an artificial neural network (ANN) to identify the ye
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e83ba727477bbba7583019aa6ee3c75f
http://arxiv.org/abs/2005.12322
http://arxiv.org/abs/2005.12322
Publikováno v:
Journal of Geophysical Research: Atmospheres. 125
Publikováno v:
Journal of Advances in Modeling Earth Systems, Vol 12, Iss 9, Pp n/a-n/a (2020)
Neural networks have become increasingly prevalent within the geosciences, although a common limitation of their usage has been a lack of methods to interpret what the networks learn and how they make decisions. As such, neural networks have often be
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::01697edbbbfc248227f9fc1e88df7e53
http://arxiv.org/abs/1912.01752
http://arxiv.org/abs/1912.01752