Modeling Ice Storm Climatology

Ranjini Swaminathan, Mohan Sridharan, Gillian Dobbie, Katharine Hayhoe

Research output: Chapter in Book/Report/Conference proceedingChapter


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: Advances in Artificial Intelligence
Subtitle of host publication28th Australasian Joint Conference, Canberra, ACT, Australia, November 30-December 4, 2015, Proceedings
EditorsBernhard Pfahringer, Jochen Renz
ISBN (Electronic)978-3-319-26350-2
ISBN (Print)978-3-319-26349-6
Publication statusPublished - 22 Nov 2015
Event28th Australasian Joint Conference on Artificial Intelligence (AusAI) - Canberra, Australia
Duration: 30 Nov 20154 Dec 2015

Publication series

NameLecture Notes in Artificial Intelligence
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference28th Australasian Joint Conference on Artificial Intelligence (AusAI)


  • root mean square error
  • feature selection
  • climate variable
  • classification performance
  • geopotential height


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