Gridding in remote sensing must re-project observations from their original coordinate system based on satellite orbit and attitude to a grid defined by Earth coordinates. Primitive methods assume that observations are located at points on Earth and typically average observations in grid cells, or interpolate geolocated observations. These approaches are inaccurate, because they do not make use of the instrument's footprint geometry, and spatial response. Observation Coverage (Obscov) gridding techniques make use of the satellite optics and geometry to more accurately describe coverage of a footprint on within each grid cell. Obscov gridding provides significant accuracy improvements exceeding 1 Kelvin Brightness Temperature over most regions on Earth for a 12 micron window channel on-board the Atmospheric Infrared Sounder (AIRS). Existing Obscov algorithms are only applicable to specific instruments and depend heavily on implicitly defined spatial response functions. We make use of raycasting and adaptive grid numerical integration to compute Obscov for the spatial response function of any instrument while processing streaming satellite observation data faster than 400 Megabits/second on a 6 machine cluster. We discuss the quality benefits of our algorithm by analyzing the results of gridded AIRS infrared sensor data with 324 operational spectral channels. Obscov gridding has improved coverage, and distorted regions such as poles no longer suffer from stippling artifacts. Secondly, Obscov gridding has improved accuracy, as our difference images show second order aliasing effects are corrected with the technique. We also address parallel processing issues to integrate AIRS Obscov gridding with SOAR, an on demand climate processing system built on a 122 processor blade server.