Impact of Assimilating Smartphone Pressure Observations on a Regional, High Resolution Ensemble Forecast: Observing System Simulation Experiments with an EnKF

Glen Hanson* and Steven Greybush
Penn State Department of Meteorology

Smartphones equipped with barometers represent an incredibly dense source of surface pressure observations that could be used to improve numerical weather prediction (NWP) forecasts. A series of observing system simulation experiments (OSSEs) were performed using WRF-ARW at convective allowing scales and the PSU WRF-EnKF Data Assimilation system to evaluate the assimilation of smartphone observations for a severe weather event. The experiments assessed the analyses 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. Configuring the EnKF with a 500 km HROI led to the most consistent reduction in RMSD for surface variables (PSFC, T2, U10, V10, and Q2) between the forecast ensemble mean using the synthetic smartphone observations and the truth simulation. Additionally, a neighborhood approach was used to construct fraction skill scores (FSS) and relative operating characteristic (ROC) curves which showed that the rapid assimilation of smartphone data can produce forecasts with more skill than forecasts that only rely on the traditional source of surface data (METARs). These findings can be used to guide further research that uses real smartphone data to supplement conventional observations or as a stand-alone observation network in otherwise data-sparse regions.

*Preference: Poster