Utilizing NASA's Operation IceBridge Airborne SWE and Precipitation Products to Assess Surface Snow Over Alaskan Glaciers

Abstract

Accurately estimating Snow Water Equivalent (SWE) in glacierized regions is critical for glacier modeling and mass balance analysis yet remains challenging due to sparse observations and uncertainties in precipitation products. The objectives are twofold: (a) assess how well three widely used precipitation datasets—Stage IV (a radar–gauge composite product), Integrated Multi‐satellite Retrievals for GPM version 07 (IMERG V07, a satellite‐based global product), and ERA5 (a global atmospheric reanalysis)—represent end‐of‐season SWE over seven Alaskan glaciers across two winter seasons, using airborne radar SWE retrievals (ARBN) from NASA's Operation IceBridge campaign as the reference, and (b) determine whether combining precipitation with auxiliary variables via machine learning (here XGBoost) modeling can reliably predict SWE beyond airborne coverage. Results reveal systematic biases: Stage IV generally overestimates SWE, IMERG V07 underestimates it, and ERA5 aligns most closely with ARBN. Model performance depends strongly on training data representativeness, with significant discrepancies arising when training and testing SWE distributions diverge. The most effective framework integrates IMERG V07, ERA5 and key auxiliary variables–snowfall fraction, total precipitable water and 2‐m air temperature–with IMERG V07 and ERA5 found to be the dominant predictors. Nevertheless, accuracy is constrained by sparse in situ measurements and the paucity of high‐resolution data in complex terrain. These findings underscore the need for high‐quality targeted observations and integrated strategies that leverage diverse precipitation data and representative training data sets to advance SWE estimation and assessment in cryospheric environments.