Low dimensional embedding of climate data for radio astronomical site testing in the Colombian Andes

Abstract

We set out to evaluate the potential of the Colombian Andes for millimeter-wave astronomical observations. Previous studies for astronomical site testing in this region have suggested that nighttime humidity and cloud cover conditions make most sites unsuitable for professional visible-light observations. Millimeter observations can be done during the day, but require that the precipitable water vapor column above a site stays below similar to 10 mm. Due to a lack of direct radiometric or radiosonde measurements, we present a method for correlating climate data from weather stations to sites with a low precipitable water vapor column. We use unsupervised learning techniques to low dimensionally embed climate data (precipitation, rain days, relative humidity, and sunshine duration) in order to group together stations with similar long-term climate behavior. The data were taken over a period of 30 years by 2046 weather stations across the Colombian territory. We find six regions with unusually dry, clear-sky conditions, ranging in elevations from 2200 to 3800 masl. We evaluate the suitability of each region using a quality index derived from a Bayesian probabilistic analysis of the station type and elevation distributions. Two of these regions show a high probability of having an exceptionally low precipitable water vapor column. We compared our results with global precipitable water vapor maps and find a plausible geographical correlation with regions with low water vapor columns (similar to 10 mm) at an accuracy of similar to 20 km. Our methods can be applied to similar data sets taken in other countries as a first step toward astronomical site evaluation.