29 Jun 2026

Automated Workflows for Airborne Lidar and Photogrammetry Snow Depth Analyses

Abstract: Lidar and photogrammetry techniques provide highly accurate methods for mapping snow depth distribution. However, postprocessing point clouds for snow depth estimation is more complex compared to other earth science applications. This paper presents ice-road-copters (IRC), an open-source Python toolkit that facilitates processing and georeferencing of lidar and photogrammetry point clouds. Case studies demonstrate the tool’s utility across different sensors and platforms over a complex mountainous study area. Results show that a well-configured digital elevation model (DEM) filter effectively removes most noise and outliers from point cloud data. The Simple Morphological Filter (SMRF) generally perform well for ground segmentation but optimal results across diverse terrains may require site-specific tuning, particularly of the elevation threshold and scalar parameters in more complex landscapes. Different methods, including manual depth measurements or snow-free features, can be used to coregister DEMs, reducing vertical errors, eliminating large bias and achieving comparable accuracy to using exposed control surfaces. Derived snow depth rasters showed strong agreement with in situ probe measurements—root-mean-square error (RMSE) of less than 16 cm. Overall, IRC simplifies the transformation of raw point clouds into high-resolution DEMs and value-added snow products, facilitating efficient multitemporal analysis to support military and hydrology applications.

12 Aug 2025

Evaluating and Improving Snow in the National Water Model, Using Observations from the New York State Mesonet

Abstract: This study leverages observations from NYSM to evaluate and improve representation of snow within the NWM and its associated land surface model. Distributed NWM simulations were ran and analyzed, forced by gridded meteorological analyses, and Noah-MP point simulations, forced by NYSM observations. Distributed NWM runs, with a baseline configuration, show substantial SWE biases caused by biases in meteorological forcing used, imperfect representation of snow processes, and mismatches between land cover in the model and NYSM station locations. Noah-MP point simulations, using baseline configuration, reveal a systematic positive bias in SWE accumulation. Noah-MP point simulations, with improved precipitation phase partitioning, reveal a systematic negative bias in SWE ablation rates. Sensitivity experiments highlight uncertain parameters within Noah-MP that strongly affect ablation rates and show particularly large sensitivity to snow albedo decay time-scale parameter, which modulates snow albedo decay rates. Distributed NWM experiments, with precipitation phase partitioning and TAU0 adjusted based on Noah-MP point simulation results, show qualitatively similar sensitivities. However, the distributed experiments do not show clear improvements when compared to SWE and streamflow observations. This is likely due to some combination of sources of bias in the baseline-distributed run and biases in other parameterized processes unrelated to snow in the NWM.

2 May 2022

Summary of Ground-Based Snow Measurements for the Northeastern United States

ABSTRACT: Snow is an important resource for both communities and ecosystems of the Northeastern United States. Both flood risk management and water supply forecasts for major municipalities, including New York City, depend on the collection of snowpack information. Therefore, the purpose of this study is to summarize all of the snowpack data from ground-based networks currently available in the Northeast. The collection of snow-depth and snow water equivalent information extends back several decades, and there are over 2,200 active sites across the region. Sites are distributed across the entire range of elevations in the region. The number of locations collecting snow information has increased substantially in the last 20 years, primarily from the expansion of the CoCoRaHS (Community Collaborative Rain, Hail, and Snow) network. Our summary of regional snow measurement locations provides a foundation for future studies and analysis, including a template for other regions of the United States.