Interpolation of scattered temperature data measurements onto a worldwide regular grid using radial basis functions with applications to global warming - Kansa, E. J.; Axelrod, M. C.; Kercher, J. R. (Lawrence Livermore National Laboratory, 1994)
Our current research into the response of natural ecosystems to a hypothesized climatic change requires that we have estimates of various meteorological variables on a regularly spaced grid of points on the surface of the earth. Unfortunately, the bulk of the world`s meteorological measurement stations is located at airports that tend to be concentrated on the coastlines of the world or near populated areas. We can also see that the spatial density of the station locations is extremely non-uniform with the greatest density in the USA, followed by Western Europe. Furthermore, the density of airports is rather sparse in desert regions such as the Sahara, the Arabian, Gobi, and Australian deserts; likewise the density is quite sparse in cold regions such as Antarctica Northern Canada, and interior northern Russia. The Amazon Basin in Brazil has few airports. The frequency of airports is obviously related to the population centers and the degree of industrial development of the country. We address the following problem here. Given values of meteorological variables, such as maximum monthly temperature, measured at the more than 5,500 airport stations, interpolate these values onto a regular grid of terrestrial points spaced by one degree in both latitude and longitude. This is known as the scattered data problem.