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Activity Number: 199 - SPEED: Data Expo
Type: Contributed
Date/Time: Monday, July 30, 2018 : 11:35 AM to 12:20 PM
Sponsor: Section on Statistics and the Environment
Abstract #332871
Title: Exploring Spatiotemporal Patterns in Forecast Data
Author(s): Erin Howard* and Matthew Higham
Companies: Oregon State University and
Keywords: Data Expo 2018

Weather forecasts, while abundant and accessible, are rarely evaluated for their predictive accuracy. The Data Expo provides us with a unique opportunity to assess the reliability of some of these forecasts. We are interested in applying spatiotemporal visualization techniques to the meteorological data. The initial steps of our project will be exploratory in nature. We would then compare the errors involved in forecasting temperature with the errors involved in forecasting precipitation. We would like to investigate if some locations have better forecasting performance than others with regard to temperature and/or precipitation. If time permits, we would also be interested in assessing how forecasting errors might change with different location sizes and distances to the recorded airports. Understanding more about forecast uncertainty might lead to more accurate weather predictions in the future.

Authors who are presenting talks have a * after their name.

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