DETECTION OF CLOUDS IN MEDIUM-RESOLUTION SATELLITE IMAGERY USING DEEP CONVOLUTIONAL NEURAL NETS

Extensive field observations were conducted in the Upper Susitna basin in central Alaska in 2012–2014. This paper describes the weather, glacier mass balance, snow cover, and soils of the basin. We found that temperatures over the glacier are cooler than over land at the same elevation. The glaciers have been losing mass faster in recent years than in the 1980s. Measurements of glacier mass change with traditional methods closely matched radar measurements.

Snow has a profound impact on permafrost and ecosystem functioning in the Arctic tundra. This paper aims to characterize the variability of end-of-winter snow depth and its relationship to topography in ice-wedge polygon tundra of Arctic Alaska. In addition, we develop a Bayesian geostatistical method to integrate multiscale observational platforms (a snow probe, ground penetrating radar, unmanned aerial system and airborne lidar) for estimating snow depth in high resolution over a large area.

This study produced a high-resolution dynamical downscaling data set for the Alaskan North Slope and surrounding areas. It helps to resolve the problem of the sparse observation over this region, where routinely and accurately measuring climatic variables is extremely difficult. This data set boosts up multiple research projects that explore the various climatic impacts over the Alaskan North Slope of the past and the future.

This paper introduces the development process of a data set that specifically made for climatic impacts research over the Alaskan North Slope. This data set can offset to some extent the sparseness of observation on spatial and temporal scales, retrieving high-resolution climatic backgrounds that enable various studies in the fields of climatology, hydrology, ecology, etc.

Development and calibration of a process-rich model representation of thaw-depth dynamics in Arctic tundra is presented. Improved understanding of polygonal tundra thermal hydrology processes, of thermal conduction, surface and subsurface saturation and snowpack dynamics is gained by using measured field data to calibrate and refine model structure. The refined model is then used identify future data needs and observational studies.