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Anthony Wimmers, Christopher Velden, and Joshua H. Cossuth

, DL performs best with at least tens of thousands of training samples, and model performance scales logarithmically with the training sample size ( Sun et al. 2017 ). Thus we have sought out the largest available dataset of TC observations in the 37- and 89-GHz bands. This is available in the Microwave Imagery from NRL TC (MINT) collection, which covers global conical scanner observations from 1987 to 2012. As described in Cossuth et al. (2013) , the dataset includes brightness temperatures from

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Kyle A. Hilburn, Imme Ebert-Uphoff, and Steven D. Miller

1. Introduction Geostationary Operational Environmental Satellite (GOES) imagery is a key element of U.S. operational weather forecasting, supporting the need for high-resolution, rapidly refreshing imagery for situational awareness ( Line et al. 2016 ). While used extensively by human forecasters, its usage in data assimilation (DA) for numerical weather prediction (NWP) models is limited. Instead DA makes greater usage of microwave and infrared sounder data on low-Earth-orbiting satellites

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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

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

.1080/17517575.2018.1493145 Iskenderian , H. , and Coauthors , 2019 : Global synthetic weather radar capability in support of the U.S. Air Force. 19th Conference on Aviation, Range, and Aerospace Meteorology , Phoenix, AZ , Amer. Meteor. Soc. , 7.1 , https://ams.confex.com/ams/2019Annual/webprogram/Paper355542.html . Jones , E. , E. Maddy , K. Garrett , and S.-A. Boukabara , 2019 : The MIIDAPS algorithm for retrieval and quality control for microwave and infrared observations: Applications in data assimilation

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Yaling Liu, Dongdong Chen, Soukayna Mouatadid, Xiaoliang Lu, Min Chen, Yu Cheng, Zhenghui Xie, Binghao Jia, Huan Wu, and Pierre Gentine

.agwat.2008.09.022 . 10.1016/j.agwat.2008.09.022 Kolassa , J. , P. Gentine , C. Prigent , F. Aires , and S. Alemohammad , 2017 : Soil moisture retrieval from AMSR-E and ASCAT microwave observation synergy. Part 2: Product evaluation . Remote Sens. Environ. , 195 , 202 – 217 , https://doi.org/10.1016/j.rse.2017.04.020 . 10.1016/j.rse.2017.04.020 Kolassa , J. , and Coauthors , 2018 : Estimating surface soil moisture from SMAP observations using a Neural Network technique . Remote

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Ryan Lagerquist, Amy McGovern, Cameron R. Homeyer, David John Gagne II, and Travis Smith

last decade ( Brooks and Correia 2018 ). During this time, the amount of data available to forecasters has exploded—including dual-polarization radar observations, high-resolution satellite observations, and forecasts from convection-allowing models (CAM). However, none of these datasets explicitly resolves tornadoes, so they must still be translated into useful information by forecasters, which can lead to cognitive overload ( Wilson et al. 2017 ). This problem can be alleviated by explicit

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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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