Understanding ozone variability in spatial responses to emissions and meteorology in China using interpretable machine learning. [PDF]
Zhang X, Zhang WC, Wu W, Liu HB.
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A novel approach to predict the arctic stratospheric ozone from stratospheric polar vortex dynamics using explainable machine learning. [PDF]
Kumar A, Mandal J, Mehrdad S, Jacobi C.
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Ensemble and temporal feature-based framework for rainfall classification in Bangladesh. [PDF]
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Uncovering Benzene Pollution Patterns Using an Interpretable, Setting-Aware Artificial Intelligence Approach. [PDF]
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Species Richness of Freshwater Fish Trophic Guilds Increases With Tropical River Discharge and Decreases With Variability. [PDF]
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Mountain flood forecasting in small watershed based on loop multi-step machine learning regression model. [PDF]
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Recent cloud trends and extremes reaffirm moderate climate sensitivity
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Field Training in Radar Meteorology
Bulletin of the American Meteorological Society, 1993Abstract The NSF Division of Mesoscale Meteorology and the University of Nevada—Reno (UNR) provided support for a two-week field course at the CSU—CHILL radar during 12—24 May 1991. Ten atmospheric science graduate students and two faculty from the Desert Research Institute (University of Nevada) spent the two weeks at the Greeley, Colorado, site of ...
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The Effect of Training Set Selection in Meteorological Data Mining
2010 14th Panhellenic Conference on Informatics, 2010We use meteorological data from the European Centre for Medium-Range Weather Forecasts (ECMWF) and the Meteorological Station of Mikra (Thessaloniki, Greece) as input to five data mining algorithms with the aim to build classification models for the prediction of the occurrence of precipitation in the station.
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