Atlas of Extratropical Storm Tracks, 1961-1998

[Source: NASA/GISS]

Extratropical storms directly effect the environment and economy throughout the mid-latitudes, greatly impacting the lives of billions of people on a daily basis. This on-line atlas provides fundamental information about extratropical storm systems, including maps of storm frequency and intensity as well as plots of individual storms paths. In addition to storm intensity being presented as time average the "most severe" storms are also plotted. Maps are available as monthly and seasonal means for the years 1961 through 1999. Together, the images presented in this atlas describe the state of the mid-latitude storm tracks during the last half of the 20th century.

There are many methods used to define the position of the extratropical storm tracks, probably the most common being to plot the variance of the 500-mb geopotential height field. That technique, however, does not yield information on the transient position of individual storms. Here we have employed a different technique in which we track the position of each low pressure center within a series of gridded sea level pressure (SLP) fields. The technique was originally developed to examine the storm tracks produced by atmospheric general circulation models (GCMs), but it is directly applicable to other gridded SLP datasets, such as those derived in weather forecasts or reanalysis projects. The SLP fields used in this atlas are derived from the 12-hourly (0Z and 12Z) 500 HPa and 1000 HPa geopotential heights generated by the NCEP Reanalysis project.

It is important to note that neither the SLP data nor our storm products represent raw meteorological observations. Rather, they are a data assimilation product, based on both observations and computer modeling techniques. The advantage is that we are able to produce storm data sets that are global in extent. However, users should be aware that the input data, and thus the results, are not of equal quality at all locations (e.g. over remote ocean regions) due to the nonuniform distribution of observations used in the data assimilation process.