Dynamic snow surface aerodynamic roughness lengths (z0) characterized by snow depths using LIDAR

A shallow, seasonal snowpack is rarely homogeneous in depth, layer characteristics, or surface structure throughout an entire winter. Aerodynamic roughness length (z0) is typically considered a static parameter within hydrologic and atmospheric models. However, observations have shown that z0 is a dynamic variable, necessitating accurate spatial and temporal measurements of z0. Terrestrial LiDAR data were collected at nine different study sites in northwest Colorado from the 2019-2020 winter season to observe variability of the snowpack surface. Geometric z0 and snow depth (ds) observation over 112 site visits illustrated a change in z0 as a function of ds. Values of z0 decrease during initial snow accumulation, as the snow conforms to the underlying terrain. Once the snowpack is sufficiently deep, which depends on the height of the ground surface roughness features, the surface becomes more uniform. As melt begins, z0 increases, when the snow surface becomes more irregular. The correla..., , # Dynamic snow surface aerodynamic roughness lengths (z0) characterized by snow depths using LIDAR

Dataset DOI: [10.5061/dryad.80gb5mm35](10.5061/dryad.80gb5mm35)

## Description of the data and file structure

Data are point clouds of the Area of Interest (AOI) throughout the 2019-2020 winter season of 9 field sites. The accompanied excel sheet describes the site visit information and the associated label to each point cloud.

## Code/software

Point clouds were built and modified in Cloud Compare ([CloudCompare - Open Source project) . ](https://www.cloudcompare.org/)However, any software that allows the use of point clouds should work with the data. Data are in .dat, but will need to be converted to .txt to run with Cloud Compare. The .dat files are used in the interpolation software (https://www.goldensoftware.com/products/surfer/), which were then converted to the attached .dat files for use in the MATLAB code described in Neville et al., 2025 (https://www.mdpi.com/2072-4292/17/12...,