Recommended Citation
January 1, 2016.
Abstract
In an effort to improve the precipitation detection algorithm for the Geonor All Weather Precipitation Gauge, an automated truth algorithm has been created to detect errors in the original algorithm. The original algorithm detects precipitation in real time and uses the rate of precipitation to indicate an event. The automated truth does not detect in real time, and focuses on precipitation accumulation to indicate an event. Since the automated truth is delayed, it is able to consider the data collected before and after the point it is analyzing. The automated truth is already more accurate than the original algorithm but the accuracy can be improved further. The goal of this study was to develop ways to improve the automated truth algorithm’s accuracy in order to compare it to the original algorithm to detect errors. Ultimately, this will be used to detect errors in the original algorithm for years of data. In order to improve the truth algorithm, we created a human truth output using data collected over a four month time period by four Geonor gauges located at NCAR’s Marshall Test Field in Boulder, CO. The human truth was created by two individuals who observed the Geonor accumulation data and indicated when an event occurred. Because humans are able to process and analyze images more precisely than computers, this human truth is considered the most accurate output. It was completed using a web based plotting tool to create graphs that can be further analyzed. The human truth output will be compared to the automated truth output in order to detect errors in the algorithm so that scientists will be able to correct these errors and improve the automated truth algorithm.
Disciplines
Atmospheric Sciences | Categorical Data Analysis | Design of Experiments and Sample Surveys | Longitudinal Data Analysis and Time Series | Meteorology
Mentor
Robert K. Goodrich
Lab site
National Center for Atmospheric Research (NCAR)
Funding Acknowledgement
This project has been made possible with support from Chevron (www.chevron.com) and the California State University STEM Teacher Researcher Program.
Included in
Atmospheric Sciences Commons, Categorical Data Analysis Commons, Design of Experiments and Sample Surveys Commons, Longitudinal Data Analysis and Time Series Commons, Meteorology Commons
URL: https://digitalcommons.calpoly.edu/star/416