Modeling ice storm climatology

Ranjini Swaminathan, Mohan Sridharan, Gillian Dobbie, Katharine Hayhoe

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Extreme weather events such as ice storms cause significant damage to life and property. Accurately forecasting ice storms sufficiently in advance to offset their impacts is very challenging because they are driven by atmospheric processes that are complex and not completely defined. Furthermore, such forecasting has to consider the influence of a changing climate on relevant atmospheric variables, but it is difficult to generalise existing expertise in the absence of observed data, making the underlying computational challenge all the more formidable. This paper describes a novel computational framework to model ice storm climatology. The framework is based on an objective identification of ice storm events by key variables derived from vertical profiles of temperature, humidity, and geopotential height (a measure of pressure). Historical ice storm records are used to identify days with synoptic-scale upper air and surface conditions consistent with an ice storm. Sophisticated classification algorithms and feature selection algorithms provide a computational representation of the behavior of the relevant physical climate variables during ice storms. We evaluate the proposed framework using reanalysis data of climate variables and historical ice storm records corresponding to the north eastern USA, demonstrating the effectiveness of the climatology models and providing insights into the relationships between the relevant climate variables.

Original languageEnglish
Title of host publicationAI 2015
Subtitle of host publicationAdvances in Artificial Intelligence - 28th Australasian Joint Conference, Proceedings
EditorsJochen Renz, Bernhard Pfahringer
PublisherSpringer-Verlag
Pages539-553
Number of pages15
ISBN (Print)9783319263496
DOIs
StatePublished - 2015
Event28th Australasian Joint Conference on Artificial Intelligence, AI 2015 - Canberra, Australia
Duration: Nov 30 2015Dec 4 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9457
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference28th Australasian Joint Conference on Artificial Intelligence, AI 2015
CountryAustralia
CityCanberra
Period11/30/1512/4/15

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