Toward Mining Massive and Multi-dimensional Data for Extreme Hydrometeorological and Climate Event Analyses

Authors

Ling Qiu, University of Nebraska-LincolnFollow
Francisco Munoz-Arriola, University of Nebraska-LincolnFollow
Carlos M. Carrilo, University of Nebraska-LincolnFollow

Date of this Version

5-6-2016

Document Type

Poster

Citation

Qiu, L., & Carrilo, C. M., & Munoz-Arriola, F. (2016, April). Toward Mining Massive and Multi-dimensional Data for Extreme Hydrometeorological and Climate Event Analyses. Poster presented at the UNL Spring 2016 Research Fair, Lincoln, NE.

Comments

Copyright (c) 2016 Ling Qiu, Francisco Munoz-Arriola, & Carlos M. Carrilo

Abstract

This poster presents the research on the main patterns of spatial distribution and temporal variability of precipitation and temperature in the Missouri River Basin (MRB). MRB has 117 million acres in cropland, produces of 46%, 22%, and 34% US's wheat, corn, and cattle, respectively. Also, MRB is known for intense weather and extreme climate variability. The approach is to identify different patterns of spatial distribution and temporal variability of precipitation and temperature through the use of a Principal Component Analysis. We have found that precipitation and temperature are the ideal meteorological variable to test spatiotemporal variability of extreme events using simple ( composite analysis) and sophisticated (EOF) statistical techniques. The EOF data mining technique is able to portray the dry and wet events of the MRB climatic variability of the last 50 years. Wet and dry events in the interannual scale of variability are captured as the statistically dominant mode of EOF for summer and winter.