The objective of this paper is to demonstrate a new capability to detect faint stratospheric aerosols in atmospheric lidar data from NASA’s Cloud-Aerosol Lidar Infrared Pathfinder Satellite Observations (CALIPSO) Mission (12 June 2006 30 June 2023) at high resolution. Faint aerosols from wildfires, distant volcanic eruptions, and Asian and Saharan dust storms play important roles in the Earth-Atmosphere system, as they provide essential air quality and pollution, airline safety, climate radiative forcing, monitoring of fires and volcanic eruptions, environmental safety, and weather forecasting. Tenuous aerosols are recorded in the CALIOP atmospheric lidar data from the CALIPSO mission, but especially extremely faint stratospheric aerosol layers often escape detection and classification partly or entirely in the current CALIOP data analysis scheme. To solve this problem, we introduce a new algorithm for detection and height determination of atmospheric layers, the Density-Dimension Algorithm for CALIOP data analysis (CALIOP-DDA). The DDA is an auto-adaptive algorithm that builds on concepts from artificial intelligence and spatial statistics. Core steps are calculation of a density field and application of a threshold function for signal-noise separation. Stratospheric aerosol detection is aided by the tropopause split concept. The CALIOP-DDA facilitates detection of extremely faint stratospheric aerosols from various sources, including distant wildfires and volcanic eruptions, in nighttime and day-time CALIOP data, even in presence of complex types of other cloud and aerosol layers across a large range of optical thicknesses, while retaining the full 334m along-track, 30m height resolution, without creating false positives. CALIOPDDA results are evaluated by comparison to layer heights derived from airborne validation experiments, conducted using the Cloud Physics Lidar (CPL). In conclusion, the CALIOP-DDA holds promise as the algorithmic basis for a future improved, high-resolution CALIPSO data product.

The Negribreen Glacier System on the east coast of Spitsbergen, Svalbard, has been actively surging since 2016, i.e., during the entire lifetime of ICESat-2 (launched in September 2018). The progression of Negribreen's surge throughout the glacier system has resulted in large-scale elevation changes and wide-spread crevassing, which is ideally mapped and analyzed using ICESat-2 measurements processed by the Density Dimension Algorithm for Ice (DDA-ice) (see Herzfeld et al. 2016, IEEE TGRS, and Herzfeld et al., 2022, Science of Remote Sensing).    In this analysis, we quantify how Negribreen has been evolving in its mature surge phase over the course of 2019 and 2020. Using ICESat-2 data, together with airborne field data and Sentinel-1-derived velocity data, we quantify large-scale effects such as elevation-change and mass transfer through the system, as well as smaller-scale effects afforded by high-resolution data products of the DDA-ice such as crevasse characterization, surface roughness and changes thereof.     Results show the expansion of the surge in upper Negribreen where increased crevassing has occurred along with height change rates nearing 30 m/year. In addition, fresh surge crevasses formed along the margin between the surging ice of Negribreen and non-surging ice of neighboring Ordonnansbreen. Finally, increased surge activity found on inflowing glaciers from the Filchnerfonna accumulation zone suggest that surge effects may continue to expand up glacier leading to further disintegration of the ice system with continued mass loss.

A document by Camden Opfer. Click on the document to view its contents.

The objectives of this paper are to investigate the tradeoffs between a physically constrained neural network and a deep, convolutional neural network and to design a combined ML approach (“VarioCNN”). Our solution is provided in the framework of a cyberinfrastructure that includes a newly designed ML software, GEOCLASS-image, modern high-resolution satellite image datasets (Maxar WorldView data) and instructions/descriptions that may facilitate solving similar spatial classification problems. Combining the advantages of the physically-driven connectionist-geostatistical classification method with those of an efficient CNN, VarioCNN, provides a means for rapid and efficient extraction of complex geophysical information from submeter resolution satellite imagery. A retraining loop overcomes the difficulties of creating a labeled training data set.Computational analyses and developments are centered on a specific, but generalizable, geophysical problem: The classification of crevasse types that form during the surge of a glacier system. A surge is a glacial catastrophe, an acceleration of a glacier to typically 100-200 times its normal velocity, which for a marine-terminating glacier leads to sudden and substantial mass transfer from the cryosphere to the oceans, contributing significantly to sea-level-rise. The sudden and rapid acceleration characteristic of a surge results in formation of crevasses, whose spatial characteristics provide informants on the ice-dynamic processes that occur during the surge. GEOCLASS-image is applied to study the current (2016-2024) surge in the Negribreen Glacier System, Svalbard. The geophysical result is a description of the structural evolution and expansion of the surge, based on crevasse types that capture ice deformation in 6 simplified classes.

A document by Thomas Trantow. Click on the document to view its contents.