Performance Analysis of an Efficient Algorithm for Feature Extraction from Large Scale Meteorological Data Stores

In recent years, Numerical Weather Prediction (NWP) has undergone a major shift with the rapid move towards kilometer-scale global weather forecasts and the emergence of AI-based forecasting models. Together, these trends will contribute to a significant increase in the daily data volume generated by NWP models. Ensuring efficient and timely access to this growing data requires innovative data extraction techniques. As an alternative to traditional data extraction algorithms, the European Centre for Medium-Range Weather Forecasts (ECMWF) has introduced the Polytope feature extraction algorithm. This algorithm is designed to reduce data transfer between systems to a bare minimum by allowing the extraction of non-orthogonal shapes of data. In this paper, we evaluate Polytope's suitability as a replacement for current extraction mechanisms in operational weather forecasting. We first adapt the Polytope algorithm to operate on ECMWF's FDB (Fields DataBase) meteorological data stores, before evaluating this integrated system's performance and scalability on real-time operational data. Our analysis shows that the low overhead of running the Polytope algorithm, which is in the order of a few seconds at most, is far outweighed by the benefits of significantly reducing the size of the extracted data by up to several orders of magnitude compared to traditional bounding box methods. Our ensuing discussion focuses on quantifying the strengths and limitations of each individual part of the system to identify potential bottlenecks and areas for future improvement.