Atmospheric errors in interferometric synthetic aperture radar (InSAR)-derived estimates of surface deformation often obscure real displacement signals, especially in mountainous regions. As climate change disproportionately impacts the mountain cryosphere, developing a technique for atmospheric correction that performs well in high-relief terrain is increasingly important. Here, we developed and implemented a statistical machine learning-based atmospheric correction that relies on the differing spatial and topographic characteristics of periglacial features and atmospheric noise. Our correction is applied at the native spatial and temporal resolution of the InSAR data (40 m, 12 days), does not require external atmospheric data, and can correct both stratified and turbulent atmospheric noise. Using Sentinel-1 data from 2015-2022, we trained a convolutional neural network (CNN) on atmospheric noise from 136 short-baseline interferograms and displacement signals from time-series inversion of 337 interferograms. The CNN correction was then tested on a densely connected network of 202 Sentinel-1 interferograms which were inverted to create a displacement time series. We used the Rocky Mountains in Colorado as our training, validation, and testing areas. When applied to our validation data, our correction offers a 690% improvement in performance over a global meteorological reanalysis-based correction and a 209% improvement over a high-pass filter correction. We found that our correction reveals previously hidden time-dependent kinematic behavior of three representative rock glaciers in our testing dataset. Our flexible, robust approach can be used to correct arbitrary InSAR data to analyze subtle surface deformation signals for a range of science and engineering applications.

In this presentation, we will describe the [Pangeo Project](http://pangeo.io), a coordinated community effort with support from NASA, NSF, AWS, Microsoft Azure and Google Cloud, to develop interactive and reproducible open source workflows for discovery, visualization, and quantitative analysis of large datasets used for research in the Earth Sciences. The Pangeo computational platform is based on JupyterHub and deployed wherever the data is stored. Python libraries such as Xarray, Rasterio, and Dask enable distributed parallel computations on HPC and Kubernetes clusters. We will discuss the design concepts central to the Pangeo platform and highlight specific applications using NASA satellite data archives on AWS. We will discuss recent progress in the integration of data discovery tools (e.g. STAC, CMR, Intake) with cloud-native storage formats for multidimensional data types (Cloud-Optimized Geotiff, Zarr, etc.) and highlight how they can be used to construct elegant, robust and reproducible scientific workflows. Finally, we will discuss performance, security, transferability across public cloud platforms, cost to operate, and approaches to encourage a cultural shift in scientific computation through educational events.

Uturuncu volcano in southern Bolivia is a member of a distinctive class of volcanoes – systems that show unrest despite not having erupted in the Holocene. Uturuncu has not erupted in 250 kyr, but has been deforming (uplift with a moat of subsidence) for several decades, along with seismic swarms and active, sulfur-encrusted fumaroles. Our work builds on previous geophysical imaging at Uturuncu by jointly analyzing multidisciplinary datasets, focusing on imaging the shallow (<15 km depth below surface) structure of the system with geophysical and geochemical data. Whereas previous research pointed to andesite melt at depths >15 km depth, results were ambiguous as to what proportions of melts vs. brines are present at shallower depths. Identifying fluids (melt, brine, etc.) and structures at shallow depths is key for evaluating the hazard potential of the volcano and understanding the source of the unrest. We present new results from gravimetry, seismology (hypocenter relocation, seismic velocity and attenuation tomography), gas geochemistry, and InSAR observations. The results point to an extensive and active hydrothermal system extending ~20 km laterally and ~10 km vertically from Uturuncu, with possible connections at depth to the deeper magmatic system. A combined view of the new density, seismic velocity and attenuation models, and the existing resistivity model is crucial for revealing key features of the hydrothermal system: a vapour-rich conduit beneath Uturuncu (low resistivity/high attenuation column extending from 1.5 to 12.5 km depth), an extensive alteration zone surrounding Uturuncu (complex zone of annular shaped anomalies surrounding Uturuncu from 1.5 to 12.5 km depth), and a possible zone of sulfide deposition just below the western flank of Uturuncu at 1.5 km depth (high density/low resistivity/high attenuation). High fluxes of diffuse CO2 degassing at sub-magmatic temperatures and a small area directly above a low resistivity anomaly subsiding from 2014 to 2017 show that the hydrothermal system is currently active. Analyzed jointly, this multidisciplinary data set suggests that current activity within the shallow structure at Uturuncu is dominated by hydrothermal, rather than magmatic processes.

Image feature tracking with medium-resolution optical satellite imagery (e.g., Landsat-8) offers measurements of glacier surface velocity on a global scale. However, for slow-moving glaciers (<0.1 m/day), the larger pixel sizes (~15-30 m) and longer repeat intervals (minimum of 16 days, assuming no cloud cover) limit temporal sampling, often precluding analysis of sub-annual velocity variability. As a result, detailed records of short-term glacier velocity variations are limited to a subset of glaciers, often from dedicated SAR image tasking and/or field observations. To address these issues, we are leveraging large archives of very-high-resolution (~0.3-0.5 m) DigitalGlobe WorldView/GeoEye imagery with ~monthly repeat interval and high-resolution (~3-5 m) Planet PlanetScope imagery with ~daily-weekly repeat interval for the period from 2014 to 2019. We are using automated, open-source tools to develop corrections for sensor geometry and image geolocation, and integrating new, high resolution DEMs for improved orthorectification, reducing the uncertainty of short-term (monthly to seasonal) velocity measurements. These temporally dense records will be integrated with other velocity products (e.g., NASA ITS_LIVE), which will allow us to study the evolution of glacier dynamics, and its relationships with local climatology, geomorphology, and hydrology on a regional scale. In this study, we present initial results for surface velocity mapping for glaciers in Khumbu Himalaya, Nepal and Mt. Rainier, USA. We are using high-performance computing environments to scale this analysis to larger glacierized regions in High Mountain Asia and Continental U.S.