The Redwood Project is a collaboration of computer scientists and Earth scientists at the University of California, whose goal is to design, build, and put into practical use a distributed information system that effectively supports intensive computational and storage requirements, such as those evidenced by Earth science applications. A key objective is to support "power harnessing", the ability to concentrate as much of the distributed power available in a large-scale system to meet the demands of any single application. A service-based network software architecture provides the framework for network-integrated computing with power harnessing. In addition, Redwood technologies include a hierarchical wide-area distributed storage architecture, a wide-area high-speed network with quality of service provisions, and an "Experimentalist's Workstation" that uses an active document paradigm as the user's interface to the rich tool and data environment provided by Redwood. The project is currently seeking to develop partnerships, both financial and intellectual, with industry and the state and federal government.
Abstract Major processes controlling the existence of a large sub-continental glacier system were identified on the basis of glaciological, meteorological and isotopic analyses using expeditionary and long-term data. Observations were made on the southern Inylchek glacier located in the Pobeda-Khan Tengry massif, the largest sub-continental glacier system on the northern periphery of central Asia. More than 1200 glaciers with a total area of about 4320 km 2 comprise the massif. Melt is for the most part caused by radiation and is most intensive during periods of anticyclonic weather with fohn development. The proportion of solar radiation input in relation to heat balance is more than 90%. Evaporation and condensation are negligible during most times and comprise 7% of heat expenditure. Accumulation was associated with cold cyclonic weather. Four ice-formation zones were identified, the upper boundary of liquid runoff is at 5200 m and the recryslallization zone is above 5900 m. The calculated net glacier mass is negative, −318 kg m −2 a −1 , and indicates the degradation of modern Pobeda-Khan Tengry glaciers.
Abstract. Given the tradeoffs between spatial and temporal resolution, questions about resolution optimality are fundamental to the study of global snow. Answers to these questions will inform future scientific priorities and mission specifications. Heterogeneity of mountain snowpacks drives a need for daily snow cover mapping at the slope scale (≤ 30 m) that is unmet for a variety of scientific users, ranging from hydrologists to the military to wildlife biologists. But finer spatial resolution usually requires coarser temporal or spectral resolution. Thus, no single sensor can meet all these needs. Recently, constellations of satellites and fusion techniques have made noteworthy progress. The efficacy of two such recent advances is examined: 1) a fused MODIS - Landsat product with daily 30 m spatial resolution; and 2) a harmonized Landsat 8 - Sentinel 2A/B (HLS) product with 2–3 day temporal and 30 m spatial resolution. State-of-art spectral unmixing techniques are applied to surface reflectance products from 1 & 2 to create snow cover and albedo maps. Then an energy balance model was run to reconstruct snow water equivalent (SWE). For validation, lidar-based Airborne Snow Observatory SWE estimates were used. Results show that reconstructed SWE forced with 30 m resolution snow cover has lower bias, a measure of basin-wide accuracy, than the baseline case using MODIS (463 m cell size), but higher mean absolute error, a measure of per-pixel accuracy. However, the differences in errors may be within uncertainties from scaling artifacts e.g., basin boundary delineation. Other explanations are 1) the importance of daily acquisitions and 2) the limitations of downscaled forcings for reconstruction. Conclusions are: 1) spectrally unmixed snow cover and snow albedo from MODIS continue to provide accurate forcings for snow models; and 2) finer spatial and temporal resolution through sensor design, fusion techniques, and satellite constellations are the future for Earth observations.
For pt.I see ibid., vol.38, no.6, p.2465-74 (2000). The relationship between snow water equivalence (SWE) and SAR backscattering coefficients at C- and X-band (5.5 and 9.6 GHz) can be either positive or negative. Therefore, discovery of the relationship with an empirical approach is unrealistic. Instead, the authors estimate snow depth and particle size using SIR-C/X-SAR imagery from a physically-based first order backscattering model through analyses of the importance of each scattering term and its sensitivity to snow properties. Using numerically simulated backscattering values, the authors develop semi-empirical models for characterizing the snow-ground interaction terms, the relationships between the ground surface backscattering components, and the snowpack extinction properties at C-band and X-band. With these relationships, snow depth and optical equivalent grain size can be estimated from SIR-C/X-SAR measurements. Validation using three SIR-C/X-SAR images shows that the algorithm performs usefully for incidence angles greater than 300, with root mean square errors (RMSEs) of 34 cm and 0.27 mm for estimating snow depth and ice optical equivalent particle radius, respectively.
In order to more accurately study the change trend of land surface temperature in North America in recent years, we combined remote sensing and meteorological station data and used various restoration models to generate more accurate and more complete remote sensing land surface temperature data. Our data covered the North American continent from 2002 to 2018, with a spatial resolution of 0.05°×0.05°. In order to facilitate the statistics of the data, we set the projection mode of the data as World_Cylindrical_Equal_Area. We collated the data from different time dimensions, including month, season and year.
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