Paper Info
Title
Deriving Data-Driven Insights from Climate Extreme Indices for the Continental US
Abstract
Daily climate data observations from more than 3000 climate measurement sites in the continental U.S. were mined and analyzed to derive insights and trends from climate extreme indices. Daily climate data observations were aggregated by climate divisions and analyzed to derive a new climate extremes indices data set (Threshold Exceedence Frequency, TEF). Each climate division was statistically assessed for the following elements: maximum and minimum temperature, precipitation and snowfall. The climate data time series were divided into 2 time intervals (1946-1980 and 1981-2015) and the occurrence frequencies of various climate extreme indices was statistically examined. Results revealed interesting insights such as an increasing frequency of occurrence of night-time temperatures in South-east US and decreasing frequency of winter temperature and snowfall extremes in northern US. The study also produced a new web-based visualization system to analyze the results of the study. The visualization system included interactive choropleth maps and charts to depict spatiotemporal changes in various climate thresholds over time.
Year
DOI
Venue
2017
10.1109/ICDMW.2017.46
2017 IEEE International Conference on Data Mining Workshops (ICDMW)
Keywords
Field
DocType
climate extremes,spatiotemporal data mining,time-series analysis
Data mining,Data visualization,Data-driven,Computer science,Choropleth map,Visualization,Frequency conversion,Climatology,Snow,Precipitation
Conference
ISSN
ISBN
Citations 
2375-9232
978-1-5386-3801-9
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Xinbo Huang143.18
David Sathiaraj221.07
Lei Wang3244.76
Barry Keim400.34