Impact of Assimilating Surface Pressure Observations from Smartphones on a Regional, High Resolution Ensemble Forecast: Observing System Simulation Experiments

Open Access
Hanson, Glen Steven
Graduate Program:
Master of Science
Document Type:
Master Thesis
Date of Defense:
March 16, 2016
Committee Members:
  • Steven J Greybush, Thesis Advisor
  • data assimilation
  • EnKF
  • smartphone
  • surface pressure
Smartphones equipped with barometers represent an untapped, and incredibly dense source of surface pressure observations that could be used to improve numerical weather prediction (NWP) forecasts. To explore their potential value, a series of observing system simulation experiments (OSSEs) were performed using WRF-ARW and the PSU WRF-EnKF Data Assimilation System at convective allowing scales to assimilate synthetic smartphone observations of a severe weather event from 20 April 2015. The experiments assessed the analysis and ensemble forecast performances for a variety of assimilation set-ups, testing the effect of observation error, horizontal radius of influence (HROI), and assimilation frequency. Additionally, neighborhood-based fractions skill scores (FSS) and relative operating characteristic (ROC) curves showed that the rapid assimilation of smartphone data can produce forecasts with more skill than forecasts that only rely on traditional surface observations (METARs). These findings can be used to guide further research using real smartphone data to supplement conventional observations or as a stand-alone observation network in otherwise data-sparse regions.