Providing data quality monitoring and alerting of anomalies to ensure a growing number of users have high data quality has its challenges. Multiple users access and monitor the on-line portal of trending information across multiple agencies, to include NOAA scientists, researchers, and satellite network and operations staff; NASA flight, ground and instrument cal/val teams; and academic customers, all depending on timely, accurate and relevant data monitoring, trending analysis and alerting. All users depend on a flawless and uninterrupted data and software processing, and an ability to see real-time and archived sensor health easily using all search engines. The ICVS-LTM system integrates cutting-edge science and technology to enhance its value and usefulness. For example, CrIS, ATMS, and AMSU instrument noise is now characterized very reliably with the Allan deviation methods. These accurate noise values will be of great significance for user applications, such as for numerical weather prediction data assimilation. The ICVS-LTM system also employs a scientifically sound and comprehensive methodology to detect noise, bias, autocal, and other important artifacts of sensor health. These require continuous assessment as to their ability to ensure only high quality data reaches users.
The ICVS-LTM system supports the NESDIS Ground Enterprise Architecture System (GEARS) vision by employing and sharing common standards, services and functionality. Specifically, the new and expanding ICVS-LTM capabilities, integrated software development and product applications align well with plans for the GEARS Research and Development section, designed for the development of new GEARS capabilities, GEARS-hosted applications, research activities, and collaboration between NOAA personnel and external partners, scientists and operational staff. The ICVS-LTM supports collaboration envisioned by GEARS across NOAA, NASA, EUMETSAT and other satellite space mission agencies. Moreover, STAR's exploration in developing products supporting weather forecasters in a more direct way is envisioned to be a new collaboration tool. For example, a hurricane warm core temperature and water vapor 3-dimension animation image directly derived from calibrated satellite data products is under development to help weather forecasters better estimate hurricane intensity. It is also planned to include satellite environmental data product LTM, such as land surface temperature, active fires, and other important parameters and products.