As of March 3, rainfall totals measured at most of the Los Angeles weather stations was higher than the seasonal normal amount. This is especially significant as Spring is still almost a month away, and season-to-date rain totals are likely to further increase before the end of the rainy season. The interpolation graphs above illustrate the areas of Los Angeles that receive high and low precipitation totals. As can be observed, the season-to-date analysis and the normal seasonal totals are nearly identical, with the same stations measuring nearly identical amounts of rain. For instance in all six maps, it can be noticed that most rain falls in the mountains, with lower amounts falling in the high desert of Lancaster and the coastal regions, such as Santa Monica.
I find the process of interpolation to be particularly fascinating, and its wide-reaching effects are immediately noticeable. For instance, this lab project used real-world, real-time data to carry out a realistic analysis of Los Angeles rain totals. Interpolation is a method that uses data from certain geographical points, in this case rain totals. It then will use this data to fill in the gaps between points to illustrate a constant phenomenon. In this lab, I used the Inverse Distance Weighted (IDW) interpolation technique as well as the Kriging interpolation technique.
I believe that the IDW method is a more effective way of illustrating the pattern of rainfall totals in this circumstance. The IDW technique is heavily influenced by nearby points, and less influenced by data points at a greater distance. Los Angeles County is a compact region that does not cover extensive area, all of the data points are fairly close together. When comparing the IDW map and the Kriging map, the IDW output appears much more fluid and factual. The Kriging output is much more rigid and jumpy, almost as though it were unsure of its data predictions. While each technique is useful, the IDW method is superior in this particular situation.
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