The Impact of Simulated GEMS Observations on Regional Weather Forecasts

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 wireless network of in situ, buoyant 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) has been conducted to assess the improved skill of NWP model forecasts from the assimilation of simulated GEMS observations. The OSSEs are run over a regional-scale domain during two different time periods, similar to an operational cycle. 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). Experiments conducted thus far show a significant impact from the addition of simulated GEMS observations over a conventional data suite (rawinsonde, surface, and aircraft). A detailed evaluation of the OSSE methodology, calibration, forecast impact, and several model sensitivity tests will be presented.