WINDoe: A Flexible, Multi-instrument Optimal Estimation Retrieval for Wind Profiles

Current observational networks significantly undersample kinematic profiles of the lower atmosphere, resulting in what is known as the boundary layer data gap. Reducing this data gap requires networks that include observational platforms such as Doppler lidars, radar wind profilers, and uncrewed aircraft systems (UAS). These wind profiling instruments have different measurement principles, resolutions, and error characteristics. As such, it is likely that future observation networks will consist of multiple types of these instruments depending on the environment that needs sampled. This type of heterogeneous network would benefit from a retrieval that can process the observations from the different platforms in a similar manner, maximizes the use of the information content from the observations in the retrieved wind profiles, and has well defined error estimates for the retrieved profiles. To address this potential need, a flexible, multi-instrument optimal estimation retrieval for wind profiles called WINDoe (Winds via Optimal Estimation) has been developed. WINDoe was designed to be a complement to the thermodynamic retrieval, TROPoe, and therefore, the structure of WINDoe closely follows that of TROPoe. The optimal estimation approach used in WINDoe results in deeper and more accurate wind profiles from existing wind profiling instrumentation than traditional wind profiling techniques. WINDoe also enables seamless, multi-instrument retrievals that capitalize on the synergistic information available from different wind profiling platforms. This presentation will describe the WINDoe retrieval and show example cases to highlight the potential benefit of the retrieval to the atmospheric observing community.