This paper surveys methods for representing and reasoning with imperfect information. It opens with an attempt to classify the different types of imperfection that may pervade data, and a discussion of the sources of such imperfections. The classification is then used as a framework for considering work that explicitly concerns the representation of imperfect information, and related work on how imperfect information may be used as a basis for reasoning. The work that is surveyed is drawn from both the field of databases and the field of artificial intelligence. Both of these areas have long been concerned with the problems caused by imperfect information, and this paper stresses the relationships between the approaches developed in each.

Global clusters are derived by applying the self‐organizing map technique to the Moderate Resolution Imaging Spectroradiometer cloud top pressure‐cloud optical thickness joint histograms. These cloud clusters are then used to classify Cloud Feedback Model Intercomparison Project Observation Simulator Package output from the HadGEM3 (Global Atmosphere version 7) atmosphere‐only climate model. Discrepancies in the Global Atmosphere version 7 representation of particular clusters can be established by examining the two sets of cluster's occurrence rate and radiative effect. The overall differences in the occurrence rates show major discrepancies in several of the clusters, resulting in a shift from five dominant clusters in Moderate Resolution Imaging Spectroradiometer (above 10% occurrence rate) to two dominant clusters in the model. A comparison of the geographic distributions of occurrence rate shows that the differences are strongly regional and unique to each cluster. While comparisons of the global mean longwave and shortwave cloud radiative effect (CRE) show strong agreement, examination of the CRE of individual cloud types reveals larger errors that highlight the role of compensating errors in masking model deficiencies. CRE data for each of the clusters is further partitioned into regions. This establishes that the bias associated with a cluster is highly variable globally, with no clusters showing consistent biases across all regions. Therefore, regional level phenomena likely play an important role in the creation of these errors.

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.

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