Abstract

The goal of this project was to analyze the accuracy of the Lufft WS600 Weather Sensor in measuring the rate of both liquid and solid precipitation. Measurement accuracy, especially in remote locations, can be difficult to obtain and quantify. Wind, blowing debris, and atmospheric particles can all have the capacity to interfere with instruments that are not being continuously compared to manual observations. Access to quality precipitation data sets are important for both hydrologic and weather forecasting, climate monitoring, and understanding the role of water cycling through ecosystems. Commercially, weather sensors are heavily relied upon by the Federal Aviation Administration (FAA) for monitoring current weather conditions at airports and airspace. The WS600 could potentially offer a possible small scale, cost effective, and reliable alternative to current weather instruments that could be utilized by both the aviation industry and scientists in need of precipitation data in remote locations.

The WS600 is a instrument that is unique because it measures precipitation (accumulation, type, and rate) by a 24 GHz vertically pointing Doppler radar; precipitation quantity and intensity are calculated from the correlation between drop size and speed. Focus was put on analyzing the liquid equivalent rate of falling precipitation through comparison with a GEONOR T-200B all-weather weighing gauge in a DFIR wind shield. Data was collected by both instruments at the National Center for Atmospheric Research’s (NCAR) instrument test site at Marshall Field in Superior, Colorado. Data was analyzed and compared for long duration snow, and rain events between 2012 and 2016. Previous research analyzed a small sample size of events and indicated increased reliability during rain events, our work expands off of these results.

Disciplines

Atmospheric Sciences | Environmental Monitoring | Fresh Water Studies | Meteorology | Water Resource Management

Mentor

Scott Landolt

Lab site

National Center for Atmospheric Research (NCAR)

Funding Acknowledgement

This material is based upon work supported by the National Science Foundation through the Robert Noyce Teacher Scholarship Program under grant# 1546150. Any opinions, finding, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. The research was made possible by the California State University STEM Teacher Researcher Program.

 

URL: http://digitalcommons.calpoly.edu/star/374

 

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