Investigating the Use of OPIR Data in Support of Cloud Retrieval Applications

ABSTRACT:Overhead Persistent Infrared (OPIR) sensors are a key part of a developing mission area supported by the Air Force and the Intelligence Community to provide worldwide, persistent surveillance of missile launches and other operations. These sensors operate in the SWIR and MWIR spectral regions. While this spectral range has been valuable for decades to the meteorological community, it is always used in combination with visible, longwave IR, or thermal IR observations to characterize clouds. The meteorological community may benefit from the OPIR technology as it offers the potential to cover more of the globe and its higher temporal resolution may allow for better characterization of some environmental phenomena. In addition to these operational specifications, the addition of spectral information could augment the current suite of meteorological satellites. With a combined modeling and algorithm development effort, we propose to investigate the SWIR/MWIR region with the goal of identifying an optimal standalone OPIR channel set that maximizes the information content in support of cloud property retrievals. The spectral characteristics of the channels chosen for the proposed algorithm will become the starting point for the definition of the initial collection requirements for the OPIR sensor and the platform it is deployed on.BENEFIT:The US Government Accounting Office (GAO), in their latest High Risk Report titled Mitigating Gaps in Weather Satellite Data, says that the continuity in US weather-satellite data is at risk. The programs intended to replace aging satellite systems have had to reduce functionality and slip planned launch dates due, in part, to cost increases, missed milestones, technical problems, and management challenges. With concerns surrounding our weather and climatological observations, new technologies have an opportunity to make an impact. It has been shown many times through Observational System Simulation Experiment (OSSE) studies that improved cloud and water vapor retrievals dramatically improve the skill scores of weather forecasts. We anticipate, through the development of an accurate cloud retrieval algorithm applicable to OPIR data, the generation of improved inputs to forecasting models such as, CDFS-II. The real-time and persistent nature of the OPIR data stream will provide improved cloud products for forecasting models through OPIRs improved temporal resolution and wider global coverage. Commercial applications will come in the form of providing improved cloud observations to the meteorological community who use this environmental intelligence data in their forecasting models.