Synoptic weather-typing - the classification of ambient weather conditions into categories - is a useful tool for numerous climate impact applications. On this website, the classifications produced by one such scheme, the Spatial Synoptic Classification (SSC), are presented.
The SSC is a hybrid classification scheme, based on both manual and automated processes. Initially, weather type (click link on the left) identification was made manually for each of the weather types, based on climatological knowledge. As the character associated with these weather types changes from season to season, typical days in each type- "seed days"- were picked for each station for different times of the year. Algorithms then develop hypothetical seed days for each of the 365 days of the year.
Once this process is complete, actual conditions on each day were compared to the seed days, and the day ends up being classified as the one it most closely resembles. Hence, when the process is complete, a weather type 'calendar' is available, whereby each day in a station's period of record is classified into one of the weather types.
The SSC is based solely on surface based observations at an individual station. Four-times daily observations of temperature, dew point, wind, pressure, and cloud cover are incorporated into the model. It does not take upper-level conditions into account, and does not concern itself with the origin of the air above a station, though there are obvious correlations. Hence, the SSC is most properly called a weather type classification and not an air mass classification system.
Within the SSC scheme, weather-type characteristics change from station to station and day to day. Thus, a Moist Tropical weather type is hotter and more humid in the southeastern US, nearer its source region, than in the northeastern US, after it has modified somewhat. Similarly, MT is warmer at all locations in July than in January. You can look up an air mass climatology for a particular station on the left.
Larry Kalkstein and Scott Greene are two principal developers of the original SSC, created in the mid-1990s for all stations east of the Rockies within the US. Calendars were originally available for only winter and summer. Kalkstein et al. (1996) contains a detailed discussion of this original system.
The SSC was then later redeveloped to be able to classify days year-round; an expansion was also done geographically to include more than 300 stations across the US and Canada. The SSC2 is written up in significant detail in Sheridan (2002).
The SSC became a hybrid classification scheme, based on both manual and automated processes. Initially, weather type identification was made manually for each of the weather types, based on climatological knowledge. As the character associated with these weather types changes from season to season, typical days in each type- "seed days"- were picked for each station for different times of the year. Algorithms then develop hypothetical seed days for each of the 365 days of the year.
Once this process is complete, actual conditions on each day were compared to the seed days, and the day ends up being classified as the one it most closely resembles. Hence, when the process is complete, a weather type 'calendar' is available, whereby each day in a station's period of record is classified into one of the weather types.
Later expansions of the SSC took the SSC global. Donna Bower, as part of her dissertation, worked with Glenn McGregor and Scott Sheridan to expand the SSC to Western Europe. Whereas the US and Canada classifications all began from one origin, the European classifications began from multiple origins in station 'clusters'. More detail on this methodology can be found in Bower al. (2007).
As the original version of the SSC was never widely available, most papers used SSC and SSC2 interchangeably as terms for what was data from the SSC2.
Data from the SSC2 is no longer downloadable from this site, but is still available and kept current for use. If you have an ongoing project that requires the older version, please e-mail me.
This website presents data from the latest version 3.0 of the Spatial Synoptic Classification, which was developed in late 2019. The concept of the SSC is the same as previous versions, but with a few different methods that allow a much more flexible and automated classification process.
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 the previous version was developed as part of my dissertation and never originally conceptualized to be expanded, updated, and used as widely as it has been. 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, and stations with climates dissimilar to locations around them, were particularly difficult to work with.
-Because of the above, occasionally stations identify a seed day that was out of typical character, or have multiple seed days just along one side of the seed-day selection criteria (e.g., within a 5-degree range, it would identify only days in the coldest 2 degrees), 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 in character there (holding all other things consistent). This did not negatively impact any of the studies that took place, but it made broader comparisons across regions 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.
While the specific seed-day 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 on all of the original stations categorized, 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 differences between the SSC2 and SSC3.0 across Europe, where the calendars are now more in line with the original concept in North America.
If you have any questions, comments, or notice any errors, please e-mail me. Thanks!
On this website, SSC "calendars" are available for nearly 400 stations across the US, Canada, and parts of Europe. The European calendars are limited to 1974-2000, but the US and Canadian calendars generally cover a station's total period of record. A total of over 8,000,000 days have been classified across all of these stations. The SSC is also being continually updated - you can see yesterday's (preliminary) classifications and today's and tomorrow's forecast classifications as well. The "official" SSC calendar for a year will be added to this site within a couple of months of the calendar year's end.
These data are free to be used in research, as long as proper citation is given. The SSC is not free to use for any commercial purpose, and the classification program is not publicly available.