An approximate nearest neighbors search algorithm for low-dimensional grid locations

We propose a new algorithm for the problem of approximate nearest neighbors (ANN) search in a regularly spaced low-dimensional grid for interpolation applications. It associates every sampled point to its nearest interpolation location, and then expands its influence to neighborhood locations in the grid, until the desired number of sampled points is achieved on every grid location. Our approach makes use of knowledge on the regular grid spacing to avoid measuring the distance between sampled points and grid locations. We compared our approach with four different state-of-the-art ANN algorithms in a large set of computational experiments. In general, our approach requires low computational effort, especially for cases with high density of sampled points, while the observed error is not significantly different. At the end, a case study is shown, where the ionosphere dynamics is predicted daily using samples from a mathematical model, which runs in parallel at 56 different longitude coordinates, providing sampled points not well distributed that follow Earth’s magnetic field-lines. Our approach overcomes the comparative algorithms when the ratio between the number of sampled points and grid locations is over 2849:1.     

Moisture and heat budgets of the south American monsoon system: climatological aspects

The climatology of the moisture and heat budget equation terms for subareas within the South American Monsoon System (SAMS) region is investigated for the 1958–2014 period considering the distinct phases of the Pacific Decadal Oscillation (PDO). These budget equations are applied to the data from the National Centers for Environmental Prediction (NCEP) Reanalysis project. Sources or sinks of moisture and heat are equation residues, referred to as residue and diabatic terms, respectively. Analyses are done for the Central Amazon Basin (CAM) and Western-Central Brazil (WCB) for three distinct periods, 1958–1976, 1977–1995, and 1996–2014, that correspond to the cold, warm, and undefined PDO phases. The differences among the PDO phases for each term are discussed. The CAM region acts dominantly as a moisture sink and heat source in all months during the three phases. On the other hand, in the WCB region, the monsoon characteristics are better defined, with a moisture sink (source) and a heat source (sink) during the wet (dry) season. The main result of the present analysis is the persistence of SAMS intensification signs in both CAM and WCB areas up to the last analyzed period (1996–2014), which is consistent with intense flooding in the Amazon Basin in 2008/2009, 2012, and 2014.     

Improving the descendant relationship of cluster galaxies at z < 1

In this work, we study how to improve well-known techniques for detecting progenitors/descendants of galaxies, such as the NDpredict program, when applied to galaxies in clusters. The improvement of this particular method is based on the use of the red sequence of galaxies in those environments. Objects close to the red sequence in the color and magnitude diagram are more likely to belong to the cluster. This defines a probability scale which is then combined with the one generated by NDpredict. This procedure is optimized for the study of galaxies in clusters over different epochs. Our main result is that, for a sample composed of $120$ clusters, with masses greater than ${10}^{13.25}{M}_{\odot }$, selected from the IllustrisTNG simulations (namely, the TNG100 runs). In $99\%$ of the cases (i.e., $119$ systems), we obtain better performance with the red sequence method in comparison to the original NDpredict, and the average gain obtained is $28\%$ in the identification of descendants for this sample of cluster galaxies.