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

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.

As the climate warms and the transition from a perennial to a seasonal Arctic sea-ice cover becomes imminent, knowledge of melt ponding is central to understanding changes in the new Arctic. The Density Dimension Algorithm for Bifurcating Sea-Ice Reflectors (DDA-bifurcate-seaice), applied to NASA’s Ice, Cloud and land Elevation (ICESat-2) photon data, has the ability to provide measurements and monitoring of the onset of melt in the Arctic and on melt progression through its ability to detect multiple surfaces within the photon cloud. The DDA-bifurcate-seaice detects and characterizes melt ponds by retrieving two surface heights, pond surface and bottom, and measurements of depth and width of melt ponds at near sensor resolution. Ponds are generally missed in the standard ICESat-2 Sea Ice Height data product (ATL07) due to its lower resolution and inability to detect multiple surfaces.

The objective is to demonstrate that spatially variable ice-surface roughness is an important, and so-far overlooked, component of melt processes in the Greenland Ice Sheet (GrIS), linking the analysis of spatial ice-surface roughness (SISR) derived from satellite laser altimeter data with a new regional energy balance model. Specific results are: (1) SISR can be calculated from ICESat Geoscience Laser Altimeter System (GLAS) data for the GrIS. (2) Seasonal and interannual changes of SISR are reflected in satellite-altimetry-based roughness maps, demonstrated for GLAS data. (3) Current regional climate models largely utilize roughness as a constant value in space and time. Here, we develop a new Regional Energy Balance Model (REBM) that is sensitive to SISR, using the same physical principles as the Integrated Forecasting System (IFS), constrained by climate fields of the Regional Atmospheric Climate Model (RACMO2). (4) A control study is carried out to ensure that REBM works correctly. (5) Melt energy of the GrIS calculated by REBM using SISR results in much higher melt values than predicted by regional climate models (RACMO2, IFS), which underestimate melting. (6) Application of the approach using REBM to the GrIS, driven by seasonally and interannually variable SISR, highlights regional and temporal differences in sensible heat flux and thus melt differences. In summary, SISR explains some of the discrepancy between observed and modeled melting in the GrIS. This study serves as a proof of concept for an approach that establishes mathematical and physical concepts for linking satellite altimeter measurements, SISR and energy balance modeling.

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.

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.