Session 1.11 Depth-duration frequency for precipitation using the Oklahoma Mesonet

Monday, 20 June 2005: 11:30 AM
North & Center Ballroom (Hilton DeSoto)
Andrew Reader, Oklahoma Climatological Survey, Norman, Oklahoma

Presentation PDF (372.0 kB)

The purpose of this study is to determine the extent to which high-temporal resolution data from the Oklahoma Mesonet affect depth-duration frequency of precipitation calculations for Oklahoma. The Oklahoma Mesonet provides a network of 115 automated weather stations across the state of Oklahoma. 15, 30, 45, 60, 90-minute intervals were calculated along with 2-hour, 3-hour, 6-hour, 12-hour and 24-hour totals. Since the data has a temporal resolution of 5-minutes, totals were calculated every 5 minutes. The goal of this study was to produce a depth-duration frequency of precipitation for Oklahoma using a dense network of weather observing stations with high temporal resolution.

Numerous topics will be discussed regarding the study, including a brief description of the various QA processes. Both automated processes along with double mass analyses were used in editing the data set. The study utilizes both L-moments and various model distributions. L-moments are used because being linear functions of the data, they suffer less from the effects of sampling variability. Previous studies have found the Generalized Extreme Value Distribution (GEV) and the Generalized Logistical Distribution (GLO) to be useful models for rainfall data. Differences in the model output will be looked at, along with which is best suited for the data set.

Samples of the results of the study will be presented. Rainfall patterns will be discussed, including convective signatures in the data. The study will also look at the sensitivity of single rainfall events on depth-duration frequency patterns and how the length of record within this data set may affect these patterns. Finally, comparisons will be made to previous work done on the subject.

- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner