Making the black box more transparent: Understanding the physical implications of machine learning

Amy McGovern; Ryan Lagerquist; David John Gagne; G. Eli Jergensen; Kimberly L. Elmore; Cameron R. Homeyer; Travis Smith

doi:10.1175/BAMS-D-18-0195.1

Making the black box more transparent: Understanding the physical implications of machine learning

Amy McGovern
, Ryan Lagerquist
, David John Gagne
, G. Eli Jergensen
, Kimberly L. Elmore
, Cameron R. Homeyer
, Travis Smith

Machine Integration and Learning for Earth Systems

University of Oklahoma
National Oceanic and Atmospheric Administration

Research output: Contribution to journal › Article › peer-review

403 Scopus citations

Abstract

Machine learning model interpretation and visualization focusing on meteorological domains are introduced and analyzed.

Original language	English
Pages (from-to)	2175-2199
Number of pages	25
Journal	Bulletin of the American Meteorological Society
Volume	100
Issue number	11
DOIs	https://doi.org/10.1175/BAMS-D-18-0195.1
State	Published - 2019

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10.1175/BAMS-D-18-0195.1

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title = "Making the black box more transparent: Understanding the physical implications of machine learning",

abstract = "Machine learning model interpretation and visualization focusing on meteorological domains are introduced and analyzed.",

author = "Amy McGovern and Ryan Lagerquist and Gagne, \{David John\} and Jergensen, \{G. Eli\} and Elmore, \{Kimberly L.\} and Homeyer, \{Cameron R.\} and Travis Smith",

note = "Publisher Copyright: {\textcopyright} 2019 American Meteorological Society.",

year = "2019",

doi = "10.1175/BAMS-D-18-0195.1",

language = "English",

volume = "100",

pages = "2175--2199",

journal = "Bulletin of the American Meteorological Society",

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T1 - Making the black box more transparent

T2 - Understanding the physical implications of machine learning

AU - McGovern, Amy

AU - Lagerquist, Ryan

AU - Gagne, David John

AU - Jergensen, G. Eli

AU - Elmore, Kimberly L.

AU - Homeyer, Cameron R.

AU - Smith, Travis

PY - 2019

Y1 - 2019

N2 - Machine learning model interpretation and visualization focusing on meteorological domains are introduced and analyzed.

AB - Machine learning model interpretation and visualization focusing on meteorological domains are introduced and analyzed.

UR - https://www.scopus.com/pages/publications/85076246426

U2 - 10.1175/BAMS-D-18-0195.1

DO - 10.1175/BAMS-D-18-0195.1

M3 - Article

AN - SCOPUS:85076246426

SN - 0003-0007

VL - 100

SP - 2175

EP - 2199

JO - Bulletin of the American Meteorological Society

JF - Bulletin of the American Meteorological Society

IS - 11

ER -

Making the black box more transparent: Understanding the physical implications of machine learning

Abstract

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