Welcome to the SSC v3.0 webpage!

Over the past year, I have worked on developing a new version of the Spatial Synoptic Classification that could be automated much more easily than the previous version, the SSC2, which in most research is just called SSC.

First, if you're unfamiliar with the SSC, please check out here first, as this contains a full summary of what the SSC is and its uses.

Weather types

The SSC3.0 contains the same original weather types (with associated number in calendar file):

  • DM (Dry Moderate) - 10
  • DP (Dry Polar) - 20
  • DT (Dry Tropical) - 30
  • MM (Moist Moderate) - 40
  • MP (Moist Polar) - 50
  • MT (Moist Tropical) - 60
  • TR (Transition) - 70

    If you've used the SSC before, you'll notice the change that all weather types are now two digits. That is because the use of the 'plus' types is now expanded. The most common plus type, and the only one included by default before, was Moist Tropical Plus, which originally was developed for use in Heat Warning Systems, in which for many cities Moist Tropical happened far too often to be useful on its own.

    The expanded types are now all based on being one standard deviation or more away from the mean for the type based on thermal characteristics. For Dry and Moist Polar, this is one standard deviation below the mean apparent temperature; for Dry and Moist Tropical, one above. There is also an entirely new type, Supertransition, which is based on exceeding one standard deviation above Transition seed criteria. All of these are incremental, thus 61 is Moist Tropical Plus (one standard deviation), and 62 Moist Tropical Double Plus (two standard deviations). So:

  • Dry Polar: DP+ (21), DP++ (22)...
  • Dry Tripical: DT+ (31), DT++ (32)...
  • Moist Polar: MP+ (51), MP++ (52)...
  • Moist Tropical: DT+ (61), DT++ (62)...
  • Transition: TR+ (71), TR++ (72)...

    These do not have upward limits though I have not observed anything above 3 standard deviations yet.

  • Why a new SSC version?

    The SSC has been popular with a number of applications, but there have been some limitations to it, which were largely a function of the fact that it was developed as part of a dissertation and never originally intended to be expanded and updated. Namely:

  • Seed-day selection is a time-consuming process, as seed days were identified at each station based on the nearest station with a similar climate. This process is particularly difficult when trying to deal with topography or microclimates. As the seed-day selection process required a long time series (to make sure there were enough seed days for each SSC type for each year), stations with shorter data sets were particularly difficult to work with.
  • Because of the above, occasionally stations would pull in a seed day that was out of typical character, or have multiple seed days just along one side of the seed-day selection criteria, making it inappropriately different from its neighbors. This is particularly true for rarer types.
  • While the SSC has been reliable at the station level, interregional comparisons could be difficult. Namely, the original SSC was developed just for the US and Canada. When it was developed for Europe, the co-developer wanted to have a greater representation of all SSC types, and thus polar and tropical conditions were more moderate there (holding all other things consistent). This did not negatively impact any of the studies that took place, but it made broader comparisons across cities more difficult.
  • The program to identify the seed days was never automated, and thus could not be made public. It therefore could not guarantee reproducibility and tended to make the SSC more of a black box than would be ideal.
  • There was no way to take advantage of modern gridded data sets effectively.
  • Given climate change, there was no way to allow SSC types' character to change over time.

    What changes were made

    While the seed day specific criteria changes a lot from station to station, the difference between the seed day criteria and the station climatology is relatively homogeneous. That is, Dry Polar typically has a mean temperature around 1 standard deviation below the mean for much of the year. Regressions were run, and the seed day criteria for each station could be well predicted by just five variables: a station's latitude, month of the year, mean and standard deviation of the variable for the month, and annual temperature range. R-squared values with the regression approached 0.9 for the temperature variables for most SSC types.

    The development of these regression models effectively means the SSC can then be automated. All that is needed is the station's latitude and weather data, and then the SSC self-calculates based on the guiding regression equations.

    The changes do not have substantial changes on the classification across North America, where the regression was based. There are somewhat greater changes across Europe, where the calendars are now more in line with the original design in North America.

    Given the incresing focus on climate change studies, there is now a tab you can select on the left for trends. Check it out!

    What's coming up

    Both the SSC2 and this new version will continue to be updated in parallel for the foreseeable future, and there is nothing 'wrong' with the old calendars, and so either could be used.

    The SSC will now be expanded globally to all stations for which NCEI has suitable data.

    The SSC's seed days in this version are based on climate normals from 1981-2010, but this can change. Thus, future projects can examine changing character over time.

    This automated process lends itself nicely to being used with gridded data - either reanalysis or GCM - which is one of the ultimate goals.

    If you have any questions, comments, or notice any errors, please e-mail me. Thanks!

    Scott Sheridan