Regional Impact of Snow‐Darkening on Snow Pack and the Atmosphere During a Severe Saharan Dust Deposition Event in Eurasia

Light-absorbing impurities such as mineral dust can play a major role in reducing the albedo of snow surfaces. Particularly in spring, deposited dust particles lead to increased snow melt and trigger further feedbacks at the land surface and in the atmosphere. Quantifying the extent of dust-induced variations is difficult due to high variability in the spatial distribution of mineral dust and snow. We present an extension of a fully coupled atmospheric and land surface model system to address the impact of mineral dust on the snow albedo across Eurasia. We evaluated the short-term effects of Saharan dust in a case study. To obtain robust results, we performed an ensemble simulation followed by statistical analysis. Mountainous regions showed a strong impact of dust deposition on snow depth. We found a mean significant reduction of −1.4 cm in the Caucasus Mountains after 1 week. However, areas with flat terrain near the snow line also showed strong effects despite lower dust concentrations. Here, the feedback to dust deposition was more pronounced as increase in surface temperature and air temperature. In the region surrounding the snow line, we found an average significant surface warming of 0.9 K after 1 week. This study shows that the impact of mineral dust deposition depends on several factors. Primarily, these are altitude, slope, snow depth, and snow cover fraction. Especially in complex terrain, it is therefore necessary to use fully coupled models to investigate the effects of mineral dust on snow pack and the atmosphere

Modeling Snow Depth for Improved Simulation of Snow–Vegetation–Atmosphere Interactions

The presence of snow and its relationship to surrounding vegetation significantly impacts the surface energy balance. For accurate atmospheric model simulations, the degree to which a snowpack can cover vegetation must be realistically represented. Both vegetation height and snow depth must be reasonably known to determine the amount of masking. The Regional Atmospheric Modeling System/Land Ecosystem–Atmosphere Feedback, version two (RAMS/ LEAF-2) snow model was modified to simulate snow depth in addition to snow water equivalent and was driven offline with observed atmospheric forcing data. The model was run for five of the Boreal Ecosystem–Atmosphere Study (BOREAS) surface mesonet stations over the 1995/96 winter. The time evolution of simulated snow depth was compared with the observed snow depth. Averaged over the winter, the modeled snow depth at the four low-wind stations was within 0.09 m of the observations, and the average percent error was 27%, while the one wind-blown station was considerably worse. The average depth error at all five stations was 60.08 m. This is shown to be sufficient to reasonably account for the surface energy balance effects of vegetation protruding through the snow. 1

Analysis of information systems for hydropower operations

The operations of hydropower systems were analyzed with emphasis on water resource management, to determine how aerospace derived information system technologies can increase energy output. Better utilization of water resources was sought through improved reservoir inflow forecasting based on use of hydrometeorologic information systems with new or improved sensors, satellite data relay systems, and use of advanced scheduling techniques for water release. Specific mechanisms for increased energy output were determined, principally the use of more timely and accurate short term (0-7 days) inflow information to reduce spillage caused by unanticipated dynamic high inflow events. The hydrometeorologic models used in predicting inflows were examined to determine the sensitivity of inflow prediction accuracy to the many variables employed in the models, and the results used to establish information system requirements. Sensor and data handling system capabilities were reviewed and compared to the requirements, and an improved information system concept outlined

Satellite and in situ observations for advancing global Earth surface modelling: a review

In this paper, we review the use of satellite-based remote sensing in combination with in situ data to inform Earth surface modelling. This involves verification and optimization methods that can handle both random and systematic errors and result in effective model improvement for both surface monitoring and prediction applications. The reasons for diverse remote sensing data and products include (i) their complementary areal and temporal coverage, (ii) their diverse and covariant information content, and (iii) their ability to complement in situ observations, which are often sparse and only locally representative. To improve our understanding of the complex behavior of the Earth system at the surface and sub-surface, we need large volumes of data from high-resolution modelling and remote sensing, since the Earth surface exhibits a high degree of heterogeneity and discontinuities in space and time. The spatial and temporal variability of the biosphere, hydrosphere, cryosphere and anthroposphere calls for an increased use of Earth observation (EO) data attaining volumes previously considered prohibitive. We review data availability and discuss recent examples where satellite remote sensing is used to infer observable surface quantities directly or indirectly, with particular emphasis on key parameters necessary for weather and climate prediction. Coordinated high-resolution remote-sensing and modelling/assimilation capabilities for the Earth surface are required to support an international application-focused effort

HMMR (High-Resolution Multifrequency Microwave Radiometer) Earth observing system, volume 2e. Instrument panel report

Recommendations and background are provided for a passive microwave remote sensing system of the future designed to meet the observational needs of Earth scientist in the next decade. This system, called the High Resolution Multifrequency Microwave Radiometer (HMMR), is to be part of a complement of instruments in polar orbit. Working together, these instruments will form an Earth Observing System (EOS) to provide the information needed to better understand the fundamental, global scale processes which govern the Earth's environment. Measurements are identified in detail which passive observations in the microwave portion of the spectrum could contribute to an Earth Observing System in polar orbit. Requirements are established, e.g., spatial and temporal resolution, for these measurements so that, when combined with the other instruments in the Earth Observing System, they would yield a data set suitable for understanding the fundamental processes governing the Earth's environment. Existing and/or planned sensor systems are assessed in the light of these requirements, and additional sensor hardware needed to meet these observational requirements are defined

Changing Arctic snow cover: A review of recent developments and assessment of future needs for observations, modelling, and impacts

Open Access Journal (SHERPA RoMEO Green) DOI: 10.1007/s13280-016-0770-0Snow is a critically important and rapidly changing feature of the Arctic. However, snow-cover and snowpack conditions change through time pose challenges for measuring and prediction of snow. Plausible scenarios of how Arctic snow cover will respond to changing Arctic climate are important for impact assessments and adaptation strategies. Although much progress has been made in understanding and predicting snow-cover changes and their multiple consequences, many uncertainties remain. In this paper, we review advances in snow monitoring and modelling, and the impact of snow changes on ecosystems and society in Arctic regions. Interdisciplinary activities are required to resolve the current limitations on measuring and modelling snow characteristics through the cold season and at different spatial scales to assure human well-being, economic stability, and improve the ability to predict manage and adapt to natural hazards in the Arctic region

Remote Sensing of Environmental Changes in Cold Regions

This Special Issue gathers papers reporting recent advances in the remote sensing of cold regions. It includes contributions presenting improvements in modeling microwave emissions from snow, assessment of satellite-based sea ice concentration products, satellite monitoring of ice jam and glacier lake outburst floods, satellite mapping of snow depth and soil freeze/thaw states, near-nadir interferometric imaging of surface water bodies, and remote sensing-based assessment of high arctic lake environment and vegetation recovery from wildfire disturbances in Alaska. A comprehensive review is presented to summarize the achievements, challenges, and opportunities of cold land remote sensing