Current interferometer and grating high spectral-resolution sounders aboard polar orbiting satellites comprise several thousands of spectrally resolved channels. The data from these together provide a wealth of information for the global mapping of air and surface temperature, moisture, greenhouse gases, and cloud properties. This class of ultra-spectral sounder (and companion hyper-spectral imager technology, typically comprising hundreds of spectral channels but at a higher spatial resolution) is slated for deployment aboard platforms in geosynchronous orbits, thereby gaining the further advantage of rapid measurement revisit times. Such observations will furnish the time-critical information required for decision-making during severe weather events and when natural disasters, such as fires and floods, strike. However, this increased capability comes at the cost of collecting, managing and exploiting several orders of magnitude more data (compared to present day systems), collected around the clock, from this new class of environmental satellite.

To take full advantage of these new satellite assets, and to be able to provide the real-time information for time-critical, decision-making needed to save lives, protect property, and mitigate risks and loss, we must get ready for their utilization. This means full leverage of high-performance and energy-efficient computing technology now becoming available to us. High-speed, low-cost and low-energy-consumption Graphics Processing Units (GPUs) are emerging as the backbone of the next generation of low-cost, small footprint, super-computing engines. Currently the top-performing super computers use GPU technology to achieve both cost and energy advantages over CPU configurations of equivalent processing power. The combined features of general-purpose GPU supercomputing — i.e. high parallelism, high memory bandwidth (102 GB per GPU), low cost (a few thousands of dollars), and compact size (1U) — are what make a GPU-based personal desktop supercomputer an appealing alternative to a massively parallel system made up of commodity CPUs (e.g. Beowulf clusters).

This paper starts with a review of our successful implementation of a GPU-based high-performance IASI radiative transfer model running on NVIDIA GPUs via CUDA (Compute Unified Device Architecture), NVIDIA's parallel computing architecture for massively multi-threaded parallel computation. The paper continues with a review of the progress made so far in developing a GPU-based high-performance implementation of the Weather Research and Forecasting (WRF) model and a review of the status of a similar implementation of the ultra spectral sounder component of Community Radiative Transfer Model (CRTM). We conclude with an emphasis on the design and demonstration of a GPU-based high-performance computing infrastructure to be developed for the next generation of satellite sensor systems, such as hyper-spectral imagers and ultra-spectral sounders, these to be flown aboard both polar-orbiting and geostationary platforms. This is our first step toward the development of a GPU-based high-performance computing infrastructure to support sustained processing throughput of future geosynchronous earth remote sensing systems such as GOES-R, GMW, MTG, FY-4, et al. for real-time product generation, numerical weather prediction and data assimilation.