Visualizing gridded datasets with large number of missing values
Suzana Djurcilov, Alex Pang · 2003
Much of the research in scientific visualization has focused on complete sets of gridded data. The paper presents our experience dealing with gridded data sets with a large number of missing or invalid data, and some of our experiments in addressing the shortcomings of standard off-the-shelf visualization algorithms. In particular, we discuss the options in modifying known algorithms to adjust to the specifics of sparse datasets, and provide a new technique to smooth out the side-effects of the operations. We apply our findings to data acquired from NEXRAD (NEXt generation RADars) weather radars, which usually have no more than 3 to 4 percent of all possible cell points filled.