Optimization of Enhanced Observing Payload System through characterization of calibration and historical data sampling

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Wednesday, 5 February 2014
Hall C3 (The Georgia World Congress Center )
Takeo Tabuchi, Northrop Grumman, Azusa, CA; and S. S. Choi and J. Castillo

Handout (326.2 kB)

The cancellation of the NOAA NPOESS Weather Satellites due to the dissolution of the joint civilian and military venture has become a turning point for global weather analysis. As Payload Meteorology is becoming more essential for modern weather forecasting, the government and weather agencies are asking contractors to build cost effective payloads while optimizing the weather data from the instruments at the data centers. For years there have been several different Payload Instruments used in modeling different adverse weather conditions. Up until now, the focus has always been in optimizing the ground processing software to perform these tasks, which is quickly reaching the limit for the present ground stations. With limited resources and the need to make every dollar count, launch intervals between payloads are being stretched out to extend the service life of our weather satellites. As the intervals between launches are extended, this opens up an opportunity to make necessary improvements to the payloads for increased performance.

Northrop Grumman Electronic Systems (NGES) in Azusa is a leading systems integrator for Microwave payloads that supports many government programs like DWSS, NPOESS, JPSS, METOP, and others. NGES has developed imager and sounder payloads like AMSU, ATMS, and SSMIS which have been flying operationally for decades, providing successful data for weather forecasting over different frequency channels and data fusion for improved weather characterization.

In our presentation we would like to share, our conceptual instrument's capability (based on the SSMIS payload) for improved detection and forecasting through a highly evolved Noise injection calibration method. We will provide an overview of our instrument. Finally, we will show how low-cost improvements may be made to the design to significantly improve the weather forecasting capabilities of the sensor.