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  1. 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.

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  2. null (Ed.)
    Climate change is extensively affecting ice sheets resulting in accelerating mass loss in recent decades. Assessment of this reduction and its causes is required to project future ice mass loss. Annual snow accumulation is an important component of the surface mass balance of ice sheets. While in situ snow accumulation measurements are temporally and spatially limited due to their high cost, airborne radar sounders can achieve ice sheet wide coverage by capturing and tracking annual snow layers in the radar images or echograms. In this paper, we use deep learning to uniquely identify the position of each annual snow layer in the Snow Radar echograms taken across different regions over the Greenland ice sheet. We train with more than 15,000 images generated from radar echograms and estimate the thickness of each snow layer within a mean absolute error of 0.54 to 7.28 pixels, depending on dataset. A highly precise snow layer thickness can help improve weather models and, thus, support glaciological studies. Such a well-trained deep learning model can be used with ever-growing datasets to aid in the accurate assessment of snow accumulation on the dynamically changing ice sheets. 
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  3. Abstract This paper provides an update and overview of the Center for Remote Sensing of Ice Sheets (CReSIS) radars and platforms, including representative results from these systems. CReSIS radar systems operate over a frequency range of 14–38 GHz. Each radar system's specific frequency band is driven by the required depth of signal penetration, measurement resolution, allocated frequency spectra, and antenna operating frequencies (often influenced by aircraft integration). We also highlight recent system advancements and future work, including (1) increasing system bandwidth; (2) miniaturizing radar hardware; and (3) increasing sensitivity. For platform development, we are developing smaller, easier to operate and less expensive unmanned aerial systems. Next-generation platforms will further expand accessibility to scientists with vertical takeoff and landing capabilities. 
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