Meteorological Phenomena Identification Near the Ground (mPING)
To know how well precpipitation type algorithms perform and whether new algorithms are an improvement, observations of winter precipitation type are needed. Unfortunately, the automated observing systems cannot discriminate between some of the more important types. Thus, human observers are needed. Yet, to deploy dedicated human observers is impractical because the knowledge needed to identify the various precipitation types is common among the public. To most efficiently gather such observations requires the public to be engaged as citizen scientists using a very simple, convenient, nonintrusive method. To achieve this an application program inteface (API) that can be contained in a simple 'app' was developed. This API is the citizen science tool that makes the Meteorological Phenomena Identification Near the Ground (mPING) possible. The apps using the mPING API run on 'smart' phones or, more generically, web-enabled devices with GPS location capabilities. Using mPING, anyone with a smartphone can pass observations to researchers and National Wether Servince peronnel at no additional cost to their phone service or to the research project. Deployed in mid-December 2012, mPING has proven to be not only very popular, but also capable of providing consistent, accurate observational data.
Project URL: https://mping.ou.edu
Geographic Scope: Any location on the globe - there is no fixed location of activity.
Project Status: Active - recruiting volunteers
Participation Tasks: Classification or tagging, Data analysis, Data entry, Geolocation, Observation, Problem solving,
Start Date: 12/19/2012
Project Contact: firstname.lastname@example.org
Federal Government Sponsor:
Other Federal Government Sponsor:
Fields of Science: Climate and weather, Computers and technology, Disaster response, Education, Nature and outdoors, Science Policy, Social Science, Transportation
Intended Outcomes: To determine how well precpipitation type algorithms perform and whether new algorithms are an improvement.