  The use of analogs - similar weather patterns - for weather forecasting and
analysis is an established method in meteorology. The most challenging aspect
of using this approach in the context of operational radar applications is to
be able to perform a fast and accurate search for similar spatiotemporal
precipitation patterns in a large archive of historical records. In this
context, sequential pairwise search is too slow and computationally expensive.
Here we propose an architecture to significantly speed-up spatiotemporal analog
retrieval by combining nonlinear geometric dimensionality reduction (UMAP) with
the fastest known Euclidean search algorithm for time series (MASS) to find
radar analogs in constant time, independently of the desired temporal length to
match and the number of extracted analogs. We compare UMAP with Principal
component analysis (PCA) and show that UMAP outperforms PCA for spatial MSE
analog search with proper settings. Moreover, we show that MASS is 20 times
faster than brute force search on the UMAP embeddings space. We test the
architecture on a real dataset and show that it enables precise and fast
operational analog ensemble search through more than 2 years of radar archive
in less than 5 seconds on a single workstation.
