Crowdsourcing Research and Operational Meteorological Data through mPING (Invited Presentation)

The National Weather Service has completed upgrading the entire network of WSR-88D radars to dual polarization. Among the many expectations placed upon this radar upgrade is the ability to remotely discriminate between different winter surface precipitation types. Yet, this capability does not exist within any of the current algorithms. To that end, the NOAA National Severe Storms Laboratory has embarked upon the task of creating a purpose-made algorithm for winter surface precipitation type. But, to both develop and know how well any such algorithm performs requires high spatial and temporal resolution precipitation type data. While automated systems seem like an ideal solution in principle, identifying the myriad precipitation types that occur in winter has so far proven difficult to automate. Yet the typical human observer has few such problems. To take advantage of all of the vast number of potential human observers (a process known as “crowdsourcing”) requires a simple, readily-available tool that requires minimal extraneous manipulation by the person using it. To that end, an “app” has been developed by the University of Oklahoma, in partnership with NOAA/NSSL to allow “citizen scientists” to conveniently enter and view reports of precipitation type using “smart” devices. The app and the data it provides will be summarized along with an examination of data consistency and how the data are being used.