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NASA NTRS1992

What can we learn about impact mechanics from large craters on Venus?

More than 50 unequivocal peak-ring craters and multiringed impact basins have been identified on Venus from Earth-based Arecibo, Venera 15/16, and Magellan radar images. These ringed craters are relatively pristine, and so serve as an important new dataset that will further understanding of the structural and rheological properties of the venusian surface and of impact mechanics in general. They are also the most direct analogues for craters formed on the Earth in Phanerozoic time. Finite-element simulations of basin collapse and ring formation were undertaken in collaboration with V. J. Hillgren (University of Arizona). These calculations used an axisymmetric version of the viscoelastic finite element code TECTON, modeled structures on the scale of Klenova or Meitner, and demonstrated two major points. First, viscous flow and ring formation are possible on the timescale of crater collapse for the sizes of multiringed basins seen on Venus and heat flows appropriate to the plant. Second, an elastic lithosphere overlying a Newtonian viscous asthenosphere results mainly in uplift beneath the crater. Inward asthenospheric flow mainly occurs at deeper levels. Lithospheric response is dominantly vertical and flexural. Tensional stress maxima occur and ring formation by normal faulting is predicted in some cases, but these predicted rings occur too far out to explain observed ring spacings on Venus (or on the Moon). Overall, these estimates and models suggest that multiringed basin formation is indeed possible at the scales observed on Venus. Furthermore, due to the strong inverse dependence of solid-state viscosity on stress, the absence of Cordilleran-style ring faulting in craters smaller than Meitner or Klenova makes sense. The apparent increase in viscosity of shock-fluidized rock with crater diameter, greater interior temperatures accessed by larger, deeper craters, and decreased non-Newtonian viscosity associated with larger craters may conspire to make the transition with diameter from peak-ring crater to Orientale-type multiringed basin rather abrupt.

Mckinnon, William B.↗

NASA NTRS2020

The Evolution of Lidar Networks: A US Perspective

Atmospheric aerosols and clouds play an important role in climate by directly scattering and absorbing sunlight. Aerosol-cloud interactions modify the particle properties causing indirect effects, alter aerosol deposition and rainfall, and contribute substantially to the uncertainties in predicting climate effects. Aerosols also affect air quality and both constituents modulate boundary layer dynamics to a certain extent, in turn impacting aerosol transport and aerosol-cloud interactions. The spatiotemporal distribution of aerosol and cloud layers is thus important, and lidar remains the primary instrument for determining the vertical distribution of aerosols and thin clouds. Lidar still provides important information for opaque cloud layers by determining base heights. Combined lidar and radar data provide a comprehensive coverage of aerosol and cloud vertical distributions, and thus locations where and when they interact. Lidar data provide a means of constraining column aerosol loading observations (e.g. AOD) to vertical extents. In addition, lidar has proven effective at determining a proxy for boundary layer height by examining aerosol gradients and/or cloud base heights in lidar profiles. Polarized lidars provide additional information on particle shape, allowing estimates of cloud phase and separation of dust or smoke from sulfate and sea salt aerosols. Given the importance of lidar observations and a critical maturation in technology and retrieval techniques, several organizations began to build operational lidar networks around 2000. The European Aerosol Research Lidar Network (EARLINET) was started as a research focused network of advanced lidars across Europe. The Asian Dust Lidar Network (ADNET) was also developed as a regional network providing lidar profiles of dust and pollution across Eastern Asia. In the US, the NASA Micro Pulse Lidar Network (MPLNET) was created to provide global lidar profiling at key sites in the NASA Aerosol Robotic Network (AERONET). The Network for the Detection of Atmospheric Composition Change (NDACC) pre-dates these networks and many sites have lidar, but it is not strictly a lidar network and at the time focused less on the lower troposphere. Finally, there were already existing ceilometer networks operated by various meteorological agencies, but in particular here in the US the profile data has not been available. The ceilometer networks were used to provide only clouds base heights and estimates of PBL height. Thus, the distinguishing feature between lidar and ceilometer networks was historically the ability to actually provide profile information (in addition to differences in wavelength, and advanced retrievals such as the raman technique). As time progressed, each lidar network matured and developed more operational capabilities and data sets, coupled with viable data centers providing DAAC services and access to near-real-time (NRT) data. In 2008 under WMO guidance, the Global Atmospheric Watch (GAW) Aerosol Lidar Observation Network (GALION) was formed as a global network made up of the existing lidar networks. The goal was to share information, best practices, and develop frameworks and techniques for quality data. GALION grew to include several other regional lidar networks and this has led to a vast increase in quality lidar sites worldwide.

Welton, Ellsworth J.↗

NASA NTRS1995

(abstract) A Polarimetric Model for Effects of Brine Infiltrated Snow Cover and Frost Flowers on Sea Ice Backscatter

A polarimetric scattering model is developed to study effects of snow cover and frost flowers with brine infiltration on thin sea ice. Leads containing thin sea ice in the Artic icepack are important to heat exchange with the atmosphere and salt flux into the upper ocean. Surface characteristics of thin sea ice in leads are dominated by the formation of frost flowers with high salinity. In many cases, the thin sea ice layer is covered by snow, which wicks up brine from sea ice due to capillary force. Snow and frost flowers have a significant impact on polarimetric signatures of thin ice, which needs to be studied for accessing the retrieval of geophysical parameters such as ice thickness. Frost flowers or snow layer is modeled with a heterogeneous mixture consisting of randomly oriented ellipsoids and brine infiltration in an air background. Ice crystals are characterized with three different axial lengths to depict the nonspherical shape. Under the covering multispecies medium, the columinar sea-ice layer is an inhomogeneous anisotropic medium composed of ellipsoidal brine inclusions preferentially oriented in the vertical direction in an ice background. The underlying medium is homogeneous sea water. This configuration is described with layered inhomogeneous media containing multiple species of scatterers. The species are allowed to have different size, shape, and permittivity. The strong permittivity fluctuation theory is extended to account for the multispecies in the derivation of effective permittivities with distributions of scatterer orientations characterized by Eulerian rotation angles. Polarimetric backscattering coefficients are obtained consistently with the same physical description used in the effective permittivity calculation. The mulitspecies model allows the inclusion of high-permittivity species to study effects of brine infiltrated snow cover and frost flowers on thin ice. The results suggest that the frost cover with a rough interface significantly increases the backscatter from thin saline ice and the polarimetric signature becomes closer to the isotropic characteristics. The snow cover also modifies polarimetric signatures of thin sea ice depending on the snow mixture and the interface condition.

thin sea ice radar backscatter frost flowers snow ↗

NASA NTRSDate not supplied

FloodPlanet: High-Resolution Commercial Imagery for Training and Validation of Deep Learning-Based Models of Inundation Extent

Flooding events are becoming increasingly frequent worldwide and are known to cause extensive damage. Public optical and radar satellite imagery can be used to detect large areas of inundation in rural areas, however, long revisit times and coarse spatial resolution limit applications for short-lived events and urban areas. Commercial constellations such as those operated by Planet offer increased spatial and temporal resolution and can supplement mapping efforts to provide more information to disaster response, relief, and mitigation efforts. Deep learning requires high quality labeled data for training across coincident sensors. The FloodPlanet dataset presented here contains labeled surface water for 18 events across the world based on Planetscope imagery with coincident Harmonized Landsat Sentinel-2 ( HLS) or Sentinel-1 and builds upon the previously existing Sen1Floods11, xBD, and NASA Sentinel-1 datasets. Sen1Floods11 includes 4,831 512x512 pixel overlapping tiles of coincident Sentinel-1 and Sentinel-2 data observing 11 flood events across the world from 2017-2019. The dataset contains a combination of automated and hand-labeled surface water for use in training and validation of inundation modeling efforts. The xBD dataset identifies flood-damaged buildings and indicates the scale of damage to each (none, minor, moderate, and major) from four flood events which occurred in the United States, India, Nepal, and Bangladesh from the same time period. The NASA dataset contains hand-labeled water bodies observed in Sentinel-1 imagery during five flood events within the 2017-2019 period. The effort presented here utilizes observations from these previously investigated flood events to generate labels of surface water at the 3-5m spatial resolution provided by Planetscope and facilitate the comparison between public and commercial data. A data pipeline was built which uses clustering algorithms to pick the most suitable overlapping chips between the public data and PlanetScope data for manual labeling. Labels were created manually using NASA’s ImageLabeler tool and include areas of high- and low-confidence water. The high confidence designation is reserved for areas of open, unobstructed water while low confidence is used for areas of suspected water beneath vegetation, clouds, or cloud shadows. Expected to be released in late 2022, the FloodPlanet dataset will include tiled imagery with a unique ID for each 1024x1024 pixel tile, 7 bands of HLS data, and high- and low-confidence flood labels in both shapefile and tiff formats. The authors will follow Spatial Temporal Access Catalog (STAC) guidelines to release FloodPlanet on the Radiant Earth ML hub, which hosts public datasets for machine learning.

Alexander Melancon↗