Free space optical communications are impacted by atmospheric effects including clouds and aerosols. Clouds can partially or fully obscure lines of site requiring a reduction in data rate or a link handover. These impacts can be mitigated by identifying a geographically diverse set of Optical Ground Stations (OGS) that optimize Cloud Free Line of Sight (CFLOS). The Lasercom Network Optimization Tool identifies the smallest number of ground stations that achieves the required CFLOS availability. During mission operations, negative impacts are further mitigated through accurate atmospheric characterization and predictions, enabling consistent and secure communication from space to ground. The Laser communications Atmospheric Monitoring and Prediction System (LAMPS) is a critical component of operational OGSs, providing real-time situational awareness and informing prediction systems that provide advance warning of communication outages. LAMPS consists of three instruments including a laser ceilometer, an infrared whole sky cloud imager and an automated weather station. Measurements from these instruments serve as inputs to a set of neural networks which are trained to learn and predict the state of the atmospheric channel. LAMPS’ deep learning models provide cloud predictions for three time periods: days-ahead, hours-ahead, and minutes-ahead. These time scales optimize operational planning, link handovers, OGS maintenance, and inter-operability and cross support. LAMPS, which follows the best practices in the Consultative Consortium on Space Data Systems Magenta book, has been deployed to two sites. This talk will give an overview of LAMPS and provide recent observations from the Laser Communications Relay Demonstration.
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