Doppler Wind Lidar Compass - Development of a Comparative Assessment Tool for Existing and Potential Space-Based Wind Lidar Technologies

3D Winds continues to be the biggest unmet observation need in numerical weather forecasting, leading NOAA NESDIS Office of System Architecture and Advanced Planning (OSAAP) Joint Venture Partnership Program in 2022 to fund several studies looking at potential space-based systems and architectures that might address the 3D Winds observation gap. These studies are looking at various technologies and techniques, including imagers and sounders for atmospheric motion vector (AMV) winds using various AMV retrieval methods, as well as Doppler wind lidar (DWL, e.g., like that demonstrated on ESA’s Aeolus mission, 2018-2023)

Under this program, BAE Systems, Space and Mission Systems (SMS) performed a comparative assessment study of various Doppler wind lidar (DWL) technologies that have plans for space-based operation. Doppler wind lidars are already being built using a number of different wavelengths and technological approaches, enabling wind measurements in different parts of the atmosphere (e.g., PBL aerosols, clear upper troposphere, cloudy conditions) with varying performance capability and cost metrics.

In addition to reviewing the technology readiness, mission complexity, challenges, and cost risks for a range of proposed DWL systems, we also modeled measurement performance the systems, demonstrating expected coverage for each approach on scales from 1 hour to 1 year. We developed extensive lidar radiometric performance modeling and uncertainty quantification tools for the DWL systems using 7km grid scale atmospheric optical properties (i.e., aerosol backscatter and extinction versus altitude) provided by NASA GSFC’s Global Modeling and Assimilation Office. Using these tools, we simulated lidar wind observations for two orbits and multiple lines of sight. The simulated data is being used to help anchor simulated water-vapor AMVs and show how future 3D Winds observation systems may be optimized, demonstrating the value in combining active and passive observation systems.