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Sid-Ahmed Boukabara
,
Vladimir Krasnopolsky
,
Stephen G. Penny
,
Jebb Q. Stewart
,
Amy McGovern
,
David Hall
,
John E. Ten Hoeve
,
Jason Hickey
,
Hung-Lung Allen Huang
,
John K. Williams
,
Kayo Ide
,
Philippe Tissot
,
Sue Ellen Haupt
,
Kenneth S. Casey
,
Nikunj Oza
,
Alan J. Geer
,
Eric S. Maddy
, and
Ross N. Hoffman

misfit to observations, spatial features coherence, and interparameters correlations) to those generated by traditional physical approaches. For example, Boukabara et al. (2019a) showed that the total precipitable water vapor (TPW) retrieved from microwave brightness temperatures by AI captures all the main features of the NWP analyses. The most striking advantage of many AI approaches is efficiency. For example, while it takes about 2 h to process a full day of the Advanced Technology Microwave

Open access
Sid-Ahmed Boukabara
,
Vladimir Krasnopolsky
,
Jebb Q. Stewart
,
Eric S. Maddy
,
Narges Shahroudi
, and
Ross N. Hoffman

quality control for microwave and infrared observations: Applications in data assimilation. 23rd Conf. on Integrated Observing and Assimilation Systems for the Atmosphere, Oceans, and Land Surface , Phoenix, AZ , Amer. Meteor. Soc. , 10.4 , https://ams.confex.com/ams/2019Annual/webprogram/Paper352855.html . Karpatne , A. , W. Watkins , J. Read , and V. Kumar , 2018 : Physics-guided neural networks (PGNN): An application in lake temperature modeling. arXiv , 11 pp., http

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Imme Ebert-Uphoff
and
Kyle Hilburn

.1016/j.softx.2020.100591 Hilburn , K. A. , I. Ebert-Uphoff , and S. D. Miller , 2020 : Development and interpretation of a neural network-based synthetic radar reflectivity estimator using GOES-R satellite observations . J. Appl. Meteor. Climatol. , 60 , 3 – 21 , https://doi.org/10.1175/JAMC-D-20-0084.1 . 10.1175/JAMC-D-20-0084.1 Hornik , K. , 1991 : Approximation capabilities of multilayer feedforward networks . Neural Networks , 4 , 251 – 257 , https://doi.org/10

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