Observing System Simulation Experiments to evaluate the impact of remotely sensed data on hurricane prediction

Observing System Simulation Experiments (OSSEs) are an important tool for evaluating the potential impact of proposed new observing systems, as well as for evaluating trade-offs in observing system design, and in developing and assessing improved methodology for assimilating new observations. Detailed OSSEs have been conducted at NASA/ GSFC and NOAA/AOML in collaboration with Simpson Weather Associates and operational data assimilation centers over the last three decades. These OSSEs determined correctly the quantitative potential for several proposed satellite observing systems to improve weather analysis and prediction prior to their launch, evaluated trade-offs in orbits, coverage and accuracy for space-based wind lidars, and were used in the development of the methodology that led to the first beneficial impacts of satellite surface winds on numerical weather prediction. New, more realistic OSSEs related to hurricane track and intensity prediction are being conducted at the present time as a collaboration between NOAA, NASA, Simpson Weather Associates, the University of Miami, and the Joint Center for Satellite Data Assimilation. The objectives of these OSSEs are to determine (1) the potential impact of unmanned aerial systems, (2) the relative impact of alternative concepts for space-based lidar winds, and (3) the relative impact of alternative concepts for polar and geostationary hyperspectral sounders. For these experiments, the Weather Research and Forecasting (WRF ARW) mesoscale model at 1- and 3-km resolutions was embedded in the T511 global nature run that had previously been generated by the European Centre for Medium Range Weather Forecasting. The first nature runs to be generated covered a 13-day period and included tropical cyclone formation, movement, and rapid intensification. In this paper, we will summarize early applications of global OSSEs to hurricane track forecasting and new experiments, using both global and regional models, that are aimed at both track and intensity forecasting.