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{"Abstract":["The accelerated melting of ice sheets in Greenland and Antarctica, driven by climate warming, is significantly contributing to global sea level rise. To better understand this phenomenon, airborne radars have been deployed to create echogram images that map snow accumulation patterns in these regions. Utilizing advanced radar systems developed by the Center for Remote Sensing and Integrated Systems (CReSIS), around 1.5 petabytes of climate data have been collected. However, extracting ice-related information, such as accumulation rates, remains limited due to the largely manual and time-consuming process of tracking internal layers in radar echograms. This highlights the need for automated solutions.\n\nMachine learning and deep learning algorithms are well-suited for this task, given their near human performance on optical images. The overlap between classical radar signal processing and machine learning techniques suggests that combining concepts from both fields could lead to optimized soluti 

Abstract. During the concluding phase of the NASA OperationIceBridge (OIB), we successfully completed two airborne measurementcampaigns (in 2018 and 2021, respectively) using a compact S and C band radarinstalled on a Single Otter aircraft and collected data over Alaskanmountains, ice fields, and glaciers. This paper reports seasonal snow depthsderived from radar data. We found large variations in seasonalradar-inferred depths with multi-modal distributions assuming a constantrelative permittivity for snow equal to 1.89. About 34 % of the snowdepths observed in 2018 were between 3.2 and 4.2 m, and close to 30 % of thesnow depths observed in 2021 were between 2.5 and 3.5 m. We observed snowstrata in ice facies, combined percolation and wet-snow facies, and dry-snow facies fromradar data and identified the transition areas from wet-snow facies to icefacies for multiple glaciers based on the snow strata and radarbackscattering characteristics. Our analysis focuses on the measured strataof multiple years at the caldera of Mount Wrangell (K'elt'aeni) to estimate the localsnow accumulation rate. We developed a method for using our radar readingsof multi-year strata to constrain the uncertain parameters of interpretationmodels with the assumption that most of the snow layers detected by theradar at the caldera are annual accumulation layers. At a 2004 ice core and2005 temperature sensor tower site, the locally estimated average snowaccumulation rate is ∼2.89 m w.e. a−1 between the years2003 and 2021. Our estimate of the snow accumulation rate between 2005 and2006 is 2.82 m w.e. a−1, which matches closely to the 2.75 m w.e. a−1 inferred from independent ground-truth measurements made the sameyear. The snow accumulation rate between the years 2003 and 2021 also showeda linear increasing trend of 0.011 m w.e. a−2. This trend iscorroborated by comparisons with the surface mass balance (SMB) derived forthe same period from the regional atmospheric climate model MAR (ModèleAtmosphérique Régional). According to MAR data, which show anincrease of 0.86 ∘C in this area for the period of 2003–2021, thelinear upward trend is associated with the increase in snowfall and rainfallevents, which may be attributed to elevated global temperatures. Thefindings of this study confirmed the viability of our methodology, as wellas its underlying assumptions and interpretation models.