An automated satellite cloud classification scheme using self-organizing maps: Alternative ISCCP weather states

This study explores the application of the self-organizing map (SOM) methodology to cloud classification. In particular, the SOM is applied to the joint frequency distribution of the cloud top pressure and optical depth from the International Satellite Cloud Climatology Project (ISCCP) D1 data set. We demonstrate that this scheme produces clusters which have geographical and seasonal patterns similar to those produced in previous studies using the k-means clustering technique but potentially provides complementary information. For example, this study identifies a wider range of clusters representative of low cloud cover states with distinct geographic patterns. We also demonstrate that two rather similar clusters, which might be considered the same cloud regime in other classifications, are distinct based on the seasonal variation of their geographic distributions and their cloud radiative effect in the shortwave. Examination of the transitions between regimes at particular geographic positions between one day and the next also shows that the SOM produces an objective organization of the various cloud regimes that can aid in their interpretation. This is also supported by examination of the SOM's Sammon map and correlations between neighboring nodes geographic distributions. Ancillary ERA-Interim reanalysis output also allows us to demonstrate that the clusters, identified based on the joint histograms, are related to an ordered continuum of vertical velocity profiles and two-dimensional vertical velocity versus lower tropospheric stability histograms which have a clear structure within the SOM. The different nodes can also be separated by their longwave and shortwave cloud radiative effect at the top of the atmosphere.