New approaches to improve nearest neighbors (NN) search in ionospheric simulation system

This work is related to the Earth’s ionosphere simulation and prediction system developed in the Brazilian Space Weather program, which process a large number of data to provide total electron content (TEC) maps. We present improvements to the task of finding nearest neighbors for the interpolation method at homogeneous grid locations, which demands an important computational cost. Simulation points generated by 73 parallel executions of a physical-mathematical ionosphere model are used. The proposed approach is based on a custom code optimization and a variable reduction of the search space considering geographic information. We tested a different number of neighbors and geographic longitude ranges, using available libraries for approximate nearest neighbor (ANN), fast library for approximate nearest neighbor (FLANN) and a fast \(\varvec{K}\) Nearest Neighbors library for low-dimensional spaces (libnabo) in an ionosphere global simulation. Experiments have shown the proposed approach progressively overcomes the libraries’ best solution as the number of neighbors increases. The speedup was significantly improved for the majority of the scenarios tested. The approach was able to reduce processing time in low and mid-latitudes, while providing close-to-zero errors. On the other hand, errors are noticed at higher latitudes, especially near the poles where geomagnetic field lines converge, or the ionospheric model is unable to provide sufficient estimates.