Takes a string of specimens' locality information and geocodes the info through Google Maps' API, which returns a string of text and the lat/long coordinates. The next step is fetching all the weather stations and meta data in New England and adjacent states. Following this, all the geocoded specimen lat/longs are placed into a numpy array, and matched with a separate numpy array that is comprised of the lat/longs of the weather stations. Using a cKDTree algorithm (quick nearest- neighbor lookup), each specimen is matched with its closest weather station using euclidean distance. The returns include up to 10 of the closest weather stations, up to a 50 km radius. Iterating through each specimen, doing a dictionary lookup of whether the specimen was collected with the operation dates of the station. If true, it will make a request to ACIS data services for the climate variables. If the day of collection is known, ACIS will return that day's maximum, minimum, average, and precipitation (pcpn) variables. If only the month of collection is known, it will return the monthly max, min, avg, pncp variables as well as the number of days of missing climate observations occurred that month.