NWC REU 2013
May 22 - July 30



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Using mPING Observations to Verify Surface Precipitation Type Forecasts From Numerical Models

Deanna Apps, Kim Elmore, and Heather Grams


What is already known:

  • The surface weather observing network reports of precipitation type are too geographically sparse to adequately verify details in the quality of forecasts from the numerical weather prediction.
  • A new a smartphone app called mPING allows public citizens to submit reports of the weather occurring at their location.
  • This study uses quality-controlled mPING data to examine how well the RAP, NAM, and GFS weather prediction models can forecast rare precipitation types versus more common precipitation types.

What this study adds:

  • Two events in February 2013 were chosen for analysis because snow, freezing rain, ice pellets, and rain all occurred in both events.
  • Numerical weather prediction forecasts were simplified into four main precipitation types that are reported by mPING users: freezing rain, ice pellets, rain, and snow.
  • All three numerical weather prediction models forecasted rain and snow significantly better than freezing rain or ice pellets.


The mPING app allows the public citizen to submit reports of the weather occurring at their location from anywhere on the globe. This study uses precipitation type reports made through mPING in the continental United States to verify precipitation type forecasts of operational numerical models. The models evaluated are the North American Mesoscale (NAM) model, the Global Forecast System (GFS), and the Rapid Refresh (RAP) model. Strengths and weaknesses of each model’s forecast are investigated for freezing rain, ice pellets, rain, and snow. The Heidke and Peirce skills scores are used predominantly, along with other performance measures. Overall, the models show less skill in the rare events of freezing rain and ice pellets, while overcompensating those precipitation types for rain or snow.

Full Paper [PDF]