Ultra-fast analog ensemble using kd-tree

Analog ensemble (AnEn) is a popular probabilistic weather forecasting method based on similarity search. In that, forecasters are tasked to search for the top-m nearest neighbors (e.g., in terms of Euclidean distance) to a length-k query, from a set of historical data points in k-dimensional space. This is a straightforward yet time-consuming procedure, and few methods seem to be significantly better than a brute-force computation of all distances. To that end, I recommend using a kd-tree to perform AnEn, which appears to be one of (if not) the fastest approaches.