SPARCLE: Validation of Observing System Simulations (SPace Readiness Coherent Lidar Experiment)

NASA recently approved a mission to fly a Doppler Wind Lidar (DWL) on a U.S. Space Shuttle. SPARCLE, managed by Marshall Space Flight Center in Huntsville, AL, is targeted for launch in March 2001. This mission is viewed as a necessary demonstration of a solid state (2 micron) lidar using coherent detection before committing resources to a 3-5 year research or operational mission. While, to many, this shuttle mission is seen as the first step in a series leading to a fully operational wind observing system, to others, it is a chance to validate predictions of performance based upon theoretical models, analyses of airborne and ground-based data, and sophisticated observing system simulation experiments. This paper will be presented in two parts: first a brief overview of the SPARCLE mission and second, a summary of current performance predictions and key contributions from ground- based and airborne DWL research. The SPARCLE instrument is a 100 mJ, 6 Hz, diode-pumped 2-micron laser with a .25 m telescope using heterodyne mixing in a fiber and an InGaAs detector. A 25 cm silicon wedge scanner will be used in step-stare modes with dwells ranging from 60 seconds to .5 seconds. Pointing knowledge is achieved with a dedicated GPS/INS mounted close to the lidar. NASA's Hitchhiker program is providing the instrument enclosures (2 cans) and mission logistics support. An on-board data system is sized to record 150 Gbytes of raw signal from a two 400 MHZ A/D converters. On-board signal processing will be used to control the frequency of the Local Oscillator. SPARCLE is predicted to have a single shot backscatter sensitivity near 1x10(exp -6) m-1 sr-1, To achieve higher sensitivity, shot accumulation will be employed. Ground-based, 2 micron DWLs have been used to assess the benefits of shot accumulation (approximately SQRT for SNR). Airborne programs like MACAWS have provided good datasets for evaluating various sampling strategies and signal processing algorithms. Using these real data to calibrate our simulation models, we can describe when and how well SPARCLE is expected to preform. Outputs from these performance models will be presented.