AN INVESTIGATION OF DYNAMIC PARTITIONING METHODS FOR THE ANALYSIS OF RAINDROP DATA
| dc.contributor.author | Brunson, Brianna G | |
| dc.date.accessioned | 2023-05-01T13:07:18Z | |
| dc.date.available | 2023-05-01T13:07:18Z | |
| dc.date.updated | 2023-05-01T13:07:21Z | |
| dc.description.abstract | Assumptions regarding raindrop arrival statistics influence strategies for measuring and analyzing rainfall within the atmospheric science community. Because many instruments automatically aggregate rainfall data on particular time scales, it is standard to subdivide drop-by-drop arrival data into 1 or 5-minute intervals for parameter estimation. Under the assumption of homogeneous rain, there is little reason to be concerned about this aggregation. However, research suggests that rainfall has a complex spatiotemporal structure even on small scales. To explore the potential effects of assumptions made during this subdivision, we will compare the results of two dynamic partitionings of drop-by-drop rainfall arrival data. We will then discuss their possible uses and advantages over standard five-minute data aggregation. This research prompts questions about the measured variability of rainfall and how to define a rain event. | |
| dc.identifier.uri | https://repository.library.cofc.edu/handle/123456789/5437 | |
| dc.language.rfc3066 | en | |
| dc.title | AN INVESTIGATION OF DYNAMIC PARTITIONING METHODS FOR THE ANALYSIS OF RAINDROP DATA | |
| dspace.entity.type |