Technological advancements in MicroElectroMechanical Systems and nanotechnology have inspired a concept for a revolutionary observing system called Global Environmental Micro Sensors (GEMS). The system proposes a massive, wireless network of in situ, airborne probes that can monitor all regions of the Earth's atmosphere with unprecedented spatial and temporal resolution. The probes will be designed to remain suspended in the atmosphere for hours to days and take measurements of temperature, humidity, pressure, and wind velocity that are commonly used as dependent variables in numerical weather prediction (NWP) models. While GEMS will likely complement current and even next-generation in situ sensors and ground/space-based remote sensing platforms, the system has the capability to provide a 100-fold increase in the horizontal resolution of in situ synoptic observations especially in the planetary boundary layer. GEMS could provide observing capabilities spanning an extremely broad range of time and space scales from the detailed life cycle of individual clouds through planetary-scale weather.

A set of observing system experiments (OSSEs) are currently underway to assess the impact of simulated GEMS observations on NWP model forecasts. The OSSEs are run over a regional-scale domain during a high-impact weather event utilizing simulated observations from a nature run. The model used for the nature run is the Advanced Regional Prediction System (ARPS) coupled with a Lagrangian particle model to simulate dispersion and collection of observations from an ensemble of GEMS probes. The model used for the OSSEs is the Pennsylvania State University/National Center for Atmospheric Research Fifth-generation Mesoscale Model (MM5). For the selected weather event, the MM5 is initialized 1-day prior to the time period of interest to generate a simulation with sufficient differences from the nature run. The simulated observations extracted from the ARPS nature simulation are assimilated into the MM5 forecasts during a 12-h integration window. An intermittent data assimilation technique is used to incorporate the simulated data and objective verification serves as the metric for measuring forecast improvement in subsequent OSSEs. A detailed evaluation of the forecast impact will be presented along with results from several proposed GEMS deployment scenarios.