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We set out to evaluate the potential of the Colombian Andes formillimeter-wave astronomical observations. Previous studies for astronomicalsite testing in this region have suggested that nighttime humidity and cloudcover conditions make most sites unsuitable for professional visible-lightobservations. Millimeter observations can be done during the day, but requirethat the precipitable water vapor column above a site stays below $\sim$10 mm.Due to a lack of direct radiometric or radiosonde measurements, we present amethod for correlating climate data from weather stations to sites with a lowprecipitable water vapor column. We use unsupervised learning techniques tolow-dimensionally embed climate data (precipitation, rain days, relativehumidity, and sunshine duration) in order to group together stations withsimilar long-term climate behavior. The data were taken over a period of 30years by 2046 weather stations across the Colombian territory. We find 6regions with unusually dry, clear-sky conditions, ranging in elevations from2200 to 3800 masl. We evaluate the suitability of each region using a qualityindex derived from a Bayesian probabilistic analysis of the station type andelevation distributions. Two of these regions show a high probability of havingan exceptionally low precipitable water vapor column. We compared our resultswith global precipitable water vapor maps and find a plausible geographicalcorrelation with regions with low water vapor columns ($\sim10$ mm) at anaccuracy of $\sim20$ km. Our methods can be applied to similar datasets takenin other countries as a first step toward astronomical site evaluation.

Low Dimensional Embedding of Climate Data for Radio Astronomical Site Testing in the Colombian Andes