铁牛传动及定位控制系统

翻车机中的铁牛和重推子系统是翻车机系统全线实现自动化的关键。主要论述了焦化厂铁牛和重推子系统的３及其构成和软件系统。     

Risks of visiting snow destinations: An analysis of skiers’ views

Very little research exists regarding the risks of visiting snow destinations. This study attempts to bridge this gap through insights into skiers’ perceptions of risk, of great relevance to snow industry stakeholders, by identifying factors that influence skiers. The research was conducted in the Spanish Pyrenees, using information gathered from adult members of two ski clubs that regularly visit this snow destination. Skiing risks, accommodation risks and public safety risks were found to have the most important bearing on visitors’ risk perceptions. However, actual figures demonstrate that respondents had some difficulties in assessing the typicality of the risk items and tended to misjudge the true level of the risks.     

Synthesis of waterborne polyurethane using snow as dispersant: Structures and properties controlled by polyols utilization

Waterborne polyurethane (WPU) dispersions have gained attention towards environmentally-friendly synthesis. In this article, a series of waterborne polyurethane emulsions was successfully synthesized and extensively characterized in terms of thermal, mechanical properties, hydrophilic behavior and morphology. Snow was chosen as dispersant instead of commonly used water. Preparation parameters such as intrinsic properties and molecular weight of polyols were discussed systematically. A chain structure was confirmed by Fourier transform infrared (FT-IR) spectroscopy. When comparing the nature of the polyols (PPG, PEG and PNA, 2000 g/mol) of this study, as-synthesized polyether waterborne polyurethane provided higher solid content, viscosity and water-resistance. However, polyester waterborne polyurethane performed differently and it exhibited higher thermal stability and crystallinity. When comparing the samples (WPU-N210, WPU-N220, WPU-N230 and WPU-N240) with different molecular weight of the same polyol (PPG) used as soft segment, the emulsion WPU-N220 with molecular weight of 2000 g/mol PPG provided the highest solid content and lowest viscosity. It was observed that particle size was uniform and highly dispersed for all samples from TEM images. Thermogravimetric, differential scanning calorimetry (DSC) and X-ray diffraction results demonstrated that the emulsion WPU-N230 with molecular weight of 3000 g/mol PPG possessed higher thermal stability and crystallinity than the other samples. The reason was that the T_g and thermal stability were increased with increasing molecular weight. When molecular weight increased, the arrangement of soft segment became more regular and so did the regularity of the molecular chains. This work demonstrated that different polyols as soft segment applied could lead to great differences in the structure and property of the resulting WPU.     

Retrieval of subpixel snow covered area, grain size, and albedo from MODIS

We describe and validate a model that retrieves fractional snow-covered area and the grain size and albedo of that snow from surface reflectance data (product MOD09GA) acquired by NASA's Moderate Resolution Imaging Spectroradiometer (MODIS). The model analyzes the MODIS visible, near infrared, and shortwave infrared bands with multiple endmember spectral mixtures from a library of snow, vegetation, rock, and soil. We derive snow spectral endmembers of varying grain size from a radiative transfer model specific to a scene's illumination geometry; spectra for vegetation, rock, and soil were collected in the field and laboratory. We validate the model with fractional snow cover estimates from Landsat Thematic Mapper data, at 30 m resolution, for the Sierra Nevada, Rocky Mountains, high plains of Colorado, and Himalaya. Grain size measurements are validated with field measurements during the Cold Land Processes Experiment, and albedo retrievals are validated with in situ measurements in the San Juan Mountains of Colorado. The pixel-weighted average RMS error for snow-covered area across 31 scenes is 5%, ranging from 1% to 13%. The mean absolute error for grain size was 51 µm and the mean absolute error for albedo was 4.2%. Fractional snow cover errors are relatively insensitive to solar zenith angle. Because MODSCAG is a physically based algorithm that accounts for the spatial and temporal variation in surface reflectances of snow and other surfaces, it is capable of global snow cover mapping in its more computationally efficient, operational mode.     

A satellite snow depth multi-year average derived from SSM/I for the high latitude regions

The hydrological cycle for high latitude regions is inherently linked with the seasonal snowpack. Thus, accurately monitoring the snow depth and the associated aerial coverage are critical issues for monitoring the global climate system. Passive microwave satellite measurements provide an optimal means to monitor the snowpack over the arctic region. While the temporal evolution of snow extent can be observed globally from microwave radiometers, the determination of the corresponding snow depth is more difficult. A dynamic algorithm that accounts for the dependence of the microwave scattering on the snow grain size has been developed to estimate snow depth from Special Sensor Microwave/Imager (SSM/I) brightness temperatures and was validated over the U.S. Great Plains and Western Siberia.

The purpose of this study is to assess the dynamic algorithm performance over the entire high latitude (land) region by computing a snow depth multi-year field for the time period 1987–1995. This multi-year average is compared to the Global Soil Wetness Project-Phase2 (GSWP2) snow depth computed from several state-of-the-art land surface schemes and averaged over the same time period. The multi-year average obtained by the dynamic algorithm is in good agreement with the GSWP2 snow depth field (the correlation coefficient for January is 0.55). The static algorithm, which assumes a constant snow grain size in space and time does not correlate with the GSWP2 snow depth field (the correlation coefficient with GSWP2 data for January is − 0.03), but exhibits a very high anti-correlation with the NCEP average January air temperature field (correlation coefficient − 0.77), the deepest satellite snow pack being located in the coldest regions, where the snow grain size may be significantly larger than the average value used in the static algorithm. The dynamic algorithm performs better over Eurasia (with a correlation coefficient with GSWP2 snow depth equal to 0.65) than over North America (where the correlation coefficient decreases to 0.29).     

Evaluation of spring snow covered area depletion in the Canadian Arctic from NOAA snow charts

The National Oceanic and Atmospheric Administration (NOAA) weekly snow cover dataset (1966-) is the longest available record of snow cover extent (SCE) over the Northern Hemisphere (NH). This dataset has been used extensively to derive trends in continental SCE and in climate-related studies, but it has received only limited validation, particularly in high latitude areas of the NH. This study evaluated spring snow cover depletion in the NOAA dataset over a study area in the Canadian Arctic mainland north of the tree line. The evaluation used four sources of information: (1) surface snow depth and snow survey observations, (2) snow cover extent produced from the Advanced Very High Resolution Radiometer (AVHRR), (3) snow cover extent derived from Special Sensor Microwave/Imager (SSM/I), and (4) Landsat 5 TM browse images. Six spring seasons from the period 1981-2000 with low (1984, 1988, and 1998) and high (1985, 1995, and 1997) spring snow cover extent were evaluated. The evaluation revealed that the NOAA weekly dataset consistently overestimated snow cover extent during the spring melt period, with delays of up to 4 weeks in melt onset. A number of possible reasons for this delay were investigated. The most likely causes for the delayed melt onset were frequent cloud cover in the spring melt period, and the low frequency of data coverage over higher latitudes. The results suggest that caution should be exercised when using this dataset in any studies related to the timing of snowmelt in the high latitudes of the Northern Hemisphere.     

Factors affecting remotely sensed snow water equivalent uncertainty

State-of-the-art passive microwave remote sensing-based snow water equivalent (SWE) algorithms correct for factors believed to most significantly affect retrieved SWE bias and uncertainty. For example, a recently developed semi-empirical SWE retrieval algorithm accounts for systematic and random error caused by forest cover and snow morphology (crystal size — a function of location and time of year). However, we have found that climate and land surface complexities lead to significant systematic and random error uncertainties in remotely sensed SWE retrievals that are not included in current SWE estimation algorithms. Joint analysis of independent meteorological records, ground SWE measurements, remotely sensed SWE estimates, and land surface characteristics have provided a unique look at the error structure of these recently developed satellite SWE products. We considered satellite-derived SWE errors associated with the snow pack mass itself, the distance to significant open water bodies, liquid water in the snow pack and/or morphology change due to melt and refreeze, forest cover, snow class, and topographic factors such as large scale root mean square roughness and dominant aspect. Analysis of the nine-year Scanning Multichannel Microwave Radiometer (SMMR) SWE data set was undertaken for Canada where many in-situ measurements are available. It was found that for SMMR pixels with 5 or more ground stations available, the remote sensing product was generally unbiased with a seasonal maximum 20 mm average root mean square error for SWE values less than 100 mm. For snow packs above 100 mm, the SWE estimate bias was linearly related to the snow pack mass and the root mean square error increased to around 150 mm. Both the distance to open water and average monthly mean air temperature were found to significantly influence the retrieved SWE product uncertainty. Apart from maritime snow class, which had the greatest snow class affect on root mean square error and bias, all other factors showed little relation to observed uncertainties. Eliminating the drop-in-the-bucket averaged gridded remote sensing SWE data within 200 km of open water bodies, for monthly mean temperatures greater than − 2 °C, and for snow packs greater than 100 mm, has resulted in a remotely sensed SWE product that is useful for practical applications.     

Snow-cover mapping in forests by constrained linear spectral unmixing of MODIS data

A snow-cover mapping method accounting for forests (SnowFrac) is presented. SnowFrac uses spectral unmixing and endmember constraints to estimate the snow-cover fraction of a pixel. The unmixing is based on a linear spectral mixture model, which includes endmembers for snow, conifer, branches of leafless deciduous trees and snow-free ground. Model input consists of a land-cover fraction map and endmember spectra. The land-cover fraction map is applied in the unmixing procedure to identify the number and types of endmembers for every pixel, but also to set constraints on the area fractions of the forest endmembers. SnowFrac was applied on two Terra Moderate Resolution Imaging Spectroradiometer (MODIS) images with different snow conditions covering a forested area in southern Norway. Six experiments were carried out, each with different endmember constraints. Estimated snow-cover fractions were compared with snow-cover fraction reference maps derived from two Landsat Enhanced Thematic Mapper Plus (ETM+) images acquired the same days as the MODIS images. Results are presented for non-forested areas, deciduous forests, coniferous forests and mixed deciduous/coniferous forests. The snow-cover fraction estimates are enhanced by increasing constraints introduced to the unmixing procedure. The classification accuracy shows that 96% of the pixels are classified with less than 20% error (absolute units) on 7 May 2001 when all forested and non-forested areas are included. The corresponding figure for 4 May 2000 is 88%.     

今后我国露天矿山装运设备的探讨

通过国内外露天矿山开采规模、装运设备的对比分析，指出我国露天矿山当前存在矿山规模小、装运设备规格小且大部分已进入凹陷或深凹开采的特点。因此，研究认为大倾角运输机是今后我国露天矿山装运设备的主要发展方向。     

深凹露天矿高效运输系统及关键技术探讨

随着我国大中型露天矿开采深度的下降,现有单一运输系统已不能满足矿山生产的需要,本文叙述了如何选择高效的运输系统以及系统中的关键技术。