Rectangular cars and buses crawl along, change lanes, and queue up at stop lights. The model includes realistic features, such as cars entering an intersection after the light has turned red. But the means came with standard deviations of 76 and 91, respectively. Curiously, in trying to pinpoint the source of the large deviations, the researchers determined one hangup to be an intersection in which the simulated cars, as specified by the program, were actually stopping at a stop sign. The backups that were occurring sometimes extended to other intersections, causing a ripple of slowdowns.
Traffic models are inherently probabilistic, and that makes data gathering problematic.
The situation in atmospheric modeling is not much better. Robert Fovell, an atmospheric scientist at the University of California at Los Angeles, and UCLA statistician Richard Berk outlined some of the problems that arise with precipitation models. Rain gauges, without producing the mistakes of human car counters, still give only a rough indication of precipitation patterns, primarily because they're so widely spaced. In the LA Basin, for example, most of the rain falls on mountain slopes, but that's not where the gauges are located. Feel the Burn Wildfires are even harder to compute.