A New Way to Define Anomalous Years

Recently we used networks to examine year-to-year relationships in hurricane activity. This requires mapping the time series of hurricane counts onto a network. In this way the network is physically related to the variation of hurricanes from one year to the next. This idea is relatively new and was introduced by Lacasa et al. [2008]. By doing this we address the following two questions: How can the occurrence of hurricane landfalls over time be examined from the perspective of network analysis? And, what advantages are gained from this perspective? The intellectual merit of the work is an advance in our understanding of historical coastal hurricane activity and the broader impact is a new method for identifying anomalies from time series data. The paper will appear in a forthcoming issue of Geophysical Research Letters. It is coauthored with Thomas Jagger and Emily Fogarty.

The picture shows the visibility network based on the time series of U.S. hurricane counts over the period 1851--2008. The colors indicate the node degree (number of links); 2 or less (red), 3--5 (orange), 6--10 (yellow), 11--20 (green), 21--30 (blue), and more than 30 (dark blue). The network suggests a novel way to think about anomalies in a time series. Years are anomalous not in a statistical sense of violating a Poisson assumption, but in the sense that the temporal ordering of the counts identifies a year that is unique in that it has a large count but is surrounded in time by years with low counts. Thus we contend that node degree is a useful indicator of an anomalous year. That is, a year that stands above most of the other years, but particularly above its "neighboring" years represents more of an anomaly in physical terms than does a year that is simply well-above the average. Node degree captures information about the frequency of hurricanes for a given year and information about the relationship of that frequency to the frequencies over the given year's recent history and near future. With this definition 1985 stands out as the most anomalous of the hurricane years with 1933, 1886, and 1964 also unusual.

Lacasa, L., B. Luque, F. Ballesteros, J. Luque, and J.C. Nuno (2008), From time series to complex networks: The visibility graph. Proc. Nat. Acad. Sci., USA, 105, 4972--4875.

A hurricane network

Relationships of hurricanes affecting the United States can be examined using the methods of network analysis. Network analysis has been used in a variety of fields to examine relational data, but has yet to be used in the study of hurricane climatology. A single hurricane can affect more than one coastal region. This can happen when the regions are small relative to the hurricane size, when the hurricane comes onshore near regional boundaries, and when the hurricane makes multiple landfalls. Thus we suggest a network that links coastal locations (termed nodes) with particular hurricanes (termed links). The topology of the network can then be examined using local and global metrics. Certain regions of the coast (like Louisiana) may have high occurrence rates, but not high values of connectivity. Regions with the highest values of connectivity should include Florida and North Carolina. Virginia which has a relatively low occurrence rate is well-positioned in the network having a relatively high value of "betweenness". Conditional networks can be constructed based on below and above average values of important climate variables. Significant differences in the connectivity of the network are likely for different phases of ENSO.