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, 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
, 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
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
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
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
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
.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
.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
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
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
-infrared frequencies, whereas thick vegetation impedes microwave frequencies. Some ways of gap-filling satellite-based SCF include the more common spatiotemporal neighborhood persistence-based simple heuristic approaches, and modeling/data assimilation techniques. The temporal persistence techniques rely on the most recent clear sky observation ( Hall et al. 2010 ), while spatial techniques use the information from nearby clear sky pixels. These approaches work best over areas with seasonal snow packs and are
-infrared frequencies, whereas thick vegetation impedes microwave frequencies. Some ways of gap-filling satellite-based SCF include the more common spatiotemporal neighborhood persistence-based simple heuristic approaches, and modeling/data assimilation techniques. The temporal persistence techniques rely on the most recent clear sky observation ( Hall et al. 2010 ), while spatial techniques use the information from nearby clear sky pixels. These approaches work best over areas with seasonal snow packs and are
.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
.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
