Using Optimal Estimation for Mixed Observation Analysis in Mesoscale Networks

Forecasters and scientists continue to express the need for more routine atmospheric boundary layer (ABL) observations to achieve meaningful improvements to forecasts and understanding of multi-scale processes. Currently, a unified network of ABL profilers does not exist within the United States. A few states, universities, and government labs have begun to invest in profilers, but they are largely disjointed and are made up of different types of thermodynamic and kinematic profilers with varying degrees of propriety depending on the manufacturer of the instruments. This also introduces difficulty when interpreting and ingesting the data since the uncertainties vary wildly.

This has been a classical issue with targeted field campaign datasets as well. Many different instruments will be used simultaneously to study an atmospheric phenomenon, but they will have various levels of uncertainty based on the retrieval technique, instrument calibration, etc. New and re-tooled optimal estimation retrievals are making it possible to blend observations and their uncertainties to make heterogeneous networks of instrumentation homogeneous in output. This will be critical for wide adoption of current and future boundary layer profiling network usage.

This study uses thermodynamic and kinematic boundary-layer profiler data collected from the Propagation, Evolution, and Rotation in Linear Storms (PERiLS) field experiment to examine what a future boundary layer profiling network may be able to observe to improve forecasts and situational awareness in high-impact weather conditions. The Troposphere Remotely Observed Profiling via Optimal Estimation (TROPoe) and Wind via Optimal Estimation (WINDoe) algorithms are used to combine observations from Doppler wind lidar, weather-sensing uncrewed aerial vehicles, microwave radiometer, infrared spectrometers, and radiosondes