Recommended Citation
January 1, 2019.
Abstract
An algorithm (Alg. 2) was created to calculate precipitation rates and to predict weather events using raw data from a weather event simulator for the duration of two months. The Alg. 2’s results from the simulation were compared to the results of the algorithm that currently produces the data for the Marshall Field Site Webplots (Alg. 1) using a Pierce Skill Score(PSS) as a performance comparison. The two algorithms were also compared using the precipitation accumulation data, acquired from the Tall Double Fence Intercomparison Reference(DFIR) shielded GEONOR gauge in the Marshall field site, 1 April – 30 April 2017. The precipitation rate results from the two algorithms were then compared visually for this data set. The data acquired from these two comparisons showed that the adjusted algorithm had a Pierce Skill Score for predicting weather events that was 8.99% higher than the Alg. 1, it was quicker at identifying weather events, and had a reliable and accurate precipitation rate detection, but had a higher false alarm rate.
Mentor
Scott Landolt, Kent Goodrich
Lab site
National Center for Atmospheric Research (NCAR)
Funding Acknowledgement
The 2018 STEM Teacher and Researcher Program and this project have been made possible through support from Chevron (www.chevron.com), the National Marine Sanctuary Foundation (www.marinesanctuary.org), the National Science Foundation through the Robert Noyce Program under Grant #1836335 and 1340110, the California State University Office of the Chancellor, and California Polytechnic State University in partnership with National Center for Atmospheric Research. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the funders.
URL: https://digitalcommons.calpoly.edu/star/499