Integration of ground-based lidar remote sensing products for model evaluation

Networks of commercial lidars are being deployed in cities to study how urban surfaces affect the planetary boundary layer (PBL). These observations are essential input for numerical models at various scales. Due to the size and complexity of urban modifications of the PBL, dense networks of sensors and models are required. Our study considers the outcomes from an intensive observation campaign within the greater Paris, France area in 2023 and 2024. The results of this campaign are used to develop, compare and integrate model simulations of the urban atmosphere (e.g., UrbanAIR, Urbisphere). 

The wind field, clouds and structures in the atmospheric boundary layer can be routinely extracted from Doppler lidar (DWL) and ceilometer lidar (ALC) observations. The co-location of such instruments allows diurnal mixed/mixing layer development to be assessed together with turbulence statistics. However, because lidar observations are inherently noisy, the derived outcomes require careful evaluation. 

We present a comprehensive dataset that has been prepared for the purpose of model evaluations. We investigate the impact of processing algorithms on the quantification and classifications of atmospheric boundary layer properties. The approach involves aligning the computations with the level-configuration of models. We will highlight prominent patterns in the observations and examine how they relate to the direction and magnitude of flow relative to the surrounding urban and rural environment, as well as discuss the limitations of comparing such observations with numerical models.