MASS-UMAP: Fast and Accurate Analog Ensemble Search in Weather Radar Archives
| Autor: | Gabriele Franch, Marta Pendesini, Giuseppe Jurman, Luca Coviello, Cesare Furlanello |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
010504 meteorology & atmospheric sciences
Nowcasting similarity search precipitation UMAP MASS PCA dimensionality reduction nowcasting analog ensemble Computer science 020209 energy Nearest neighbor search Dimensionality reduction Brute-force search Context (language use) 02 engineering and technology 01 natural sciences law.invention law Search algorithm 0202 electrical engineering electronic engineering information engineering General Earth and Planetary Sciences Weather radar Radar Algorithm 0105 earth and related environmental sciences |
| Zdroj: | Remote Sensing; Volume 11; Issue 24; Pages: 2922 |
| ISSN: | 2072-4292 |
| DOI: | 10.3390/rs11242922 |
| Popis: | The use of analog-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 show that UMAP, combined with a grid search protocol over relevant hyperparameters, can find analog sequences with lower mean square error (MSE) than principal component analysis (PCA). Moreover, we show that MASS is 20 times faster than brute force search on the UMAP embedding space. We test the architecture on 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 3 seconds on a single workstation. |
| Databáze: | OpenAIRE |
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