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Arthur T. DeGaetano
Researcher at Cornell University
Publications - 95
Citations - 3765
Arthur T. DeGaetano is an academic researcher from Cornell University. The author has contributed to research in topics: Climate change & Snow. The author has an hindex of 34, co-authored 92 publications receiving 3322 citations. Previous affiliations of Arthur T. DeGaetano include Rensselaer Polytechnic Institute & Yale University.
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Using the Climate Forecast System Reanalysis as weather input data for watershed models
Daniel R. Fuka,M. Todd Walter,Charlotte MacAlister,Arthur T. DeGaetano,Tammo S. Steenhuis,Zachary M. Easton +5 more
TL;DR: In this paper, a method for using the Climate Forecast System Reanalysis (CFSR) global meteorological dataset to obtain historical weather data and demonstrates the application to modelling five watersheds representing different hydroclimate regimes.
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Trends in Twentieth-Century Temperature Extremes across the United States
TL;DR: A long-term, homogeneous set of daily maximum and minimum temperature data representing a subset of daily U.S. Historical Climatology Network stations is used to analyze trends in extreme temperature occurrence across the contiguous United States as discussed by the authors.
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Temporal, spatial and meteorological variations in hourly PM2.5 concentration extremes in New York City
TL;DR: In this paper, the authors analyzed the extreme percentiles of empirical hourly PM 2.5 concentration distributions from a unique high-density network of 20 stations within New York City and found that significant diurnal, seasonal and day-of-week variations are noted, with the highest concentrations typically found between 7:00 and 9:00 am.
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An East Coast Winter Storm Climatology
TL;DR: A climatology of East Coast winter storms (ECWS) was developed using an automated procedure as mentioned in this paper using an array of statistical analyses was used to empirically analyze the interannual variability of these cyclones.
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Time-Dependent Changes in Extreme-Precipitation Return-Period Amounts in the Continental United States
TL;DR: In this paper, partial-duration maximum precipitation series from Historical Climatology Network stations are used as a basis for assessing trends in extreme-precipitation recurrence-interval amounts.