基于多源数据的阿勒泰地区雪深反演研究

利用阿勒泰地区 2010~2012年冬季(11月~次年2月)3类积雪数据:风云三号微波成像仪(FY\|3/MWRI)反演的雪深数据、美国人机交互式多仪器冰雪制图系统(IMS)积雪面积数据、阿勒泰及周边地区实测雪深数据,进行积雪深度的反演研究。通过结合3类积雪数据的各自优势,建立修正模型,最终得到较准确的研究区雪深数据。同时通过编程实现了相应模型的操作平台,为今后研究区积雪业务化监测做好准备。结果表明:模型提高了FY\|3/MWRI数据反演阿勒泰地区积雪深度的准确性,改善了FY\|3/MWRI数据在阿勒泰地区雪深反演偏低的缺点,使微波与实测平均雪深误差由修正前的21.7~12.1 cm缩小为修正后的3.7~1.5 cm。     

一种汽车顶盖抗雪压动态仿真分析方法

目前顶盖雪压静态仿真分析方法的雪压计算值与测试值存在偏差,并且不能反映顶盖实际变形的位置和整体变形模式。为改进静态仿真分析方法,提出一种非线性显式计算的动态仿真分析方法,并以某乘用车为例,通过试验验证了其可靠性及有效性,为顶盖的研发设计提供了参考。     

Cellular-Automata Model for Dense-Snow Avalanches

This paper introduces a three-dimensional model for simulating dense-snow avalanches, based on the numerical method of cellular automata. This method allows one to study the complex behavior of the avalanche by dividing it into small elements, whose interaction is described by simple laws, obtaining a reduction of the computational power needed to perform a three-dimensional simulation. Similar models by several authors have been used to model rock avalanches, mud and lava flows, and debris avalanches. A peculiar aspect of avalanche dynamics, i.e., the mechanisms of erosion of the snowpack and deposition of material from the avalanche is taken into account in the model. The capability of the proposed approach has been illustrated by modeling three documented avalanches that occurred in Susa Valley (Western Italian Alps). Despite the qualitative observations used for calibration, the proposed method is able to reproduce the correct three-dimensional avalanche path, using a digital terrain model, and the order of magnitude of the avalanche deposit volume.     

On forecasting large and infrequent snow avalanches

Snow avalanches that threaten a highway or a residential area are often large avalanches that have a return period > 1 year. Danger assessment strongly relies on precipitation data since these avalanches are typically triggered by major snow storms. Given the extensive protection work that is in place in the European Alps, the avalanche control service (also called avalanche commission) responsible for danger assessment will usually monitor the avalanche situation throughout the winter, but only become active in case of a major snow fall. Related safety concepts describing the procedures and measures to be taken in a given danger situation are therefore often based on threshold values for new snow. By analysing the avalanche occurrence of a major avalanche path, we show that forecasting based on new snow amounts involves high uncertainty. Whereas the return period of an avalanche to, for example, the road was about 5 years, the return period for the corresponding new snow depth was substantially smaller, in our case slightly less than 2 years. Similar proportions were found for a number of other avalanche paths with different snow climate. The return period of the critical new snow depth was about 2–5 times smaller than the return period of the avalanche. This proportion is expected to increase with increasing return period. Hence, based on the return period of an avalanche path a first estimate for the critical new snow depth can be made. With a return period of the critical new snow depth of 1–2 years, avalanche prediction for individual avalanche path becomes very challenging since the false alarm ratio is expected to be high.     

Predicting the fracture character of weak layers from snowpack penetrometer signals

Digital penetrometers provide reliable assessments of snow penetration resistance with depth. However, extracting useful information from the signals relating to snow stability has proved to be challenging. In this study, penetrometer profiles were collected in close proximity to compression tests. A scheme for predicting the fracture character of weak layers in the compression tests from the penetrometer signals is presented. When a two-group classification between sudden (Q1) (an indicator of instability) and other fracture character groups was performed, potential failure layers were correctly classified 80% of the time. The variables offering the best discrimination between sudden and other categories were weak layer thickness, average force gradient above the weak layer, and both the average and the maximum force gradient below the weak layer. The effect of introducing randomly selected layers into the prediction scheme was also investigated. When such layers were introduced, the classification rate dropped to 67%, indicating that more effective fracture character prediction occurred when weak layers were manually pre-identified. This suggests that this scheme should be used in conjunction with a weak layer detection model rather than as a stand alone analytical technique for the purpose of critical weak layer identification. The classification rate dropped further to 55% when a more detailed, four-group classification scheme was used.     

Upward-looking ground-penetrating radar for monitoring snowpack stratigraphy

Operational remote monitoring of snowpack stratigraphy, melt water intrusions and their evolution with time for forecasting snowpack stability is not possible to date. Determination of the spatial variability of snowpack conditions on various scales requires a number of point measurements with various methods. These methods are either destructive or do not provide information about the internal structure of the snowpack. The application of a remotely controlled non-destructive sensor system would help to gain a higher spatio-temporal resolution about information of the snowpack. In this study we present results from upward-looking ground-penetrating radar (GPR) surveys from horizontal caves dug in the front wall of snow pits at the bottom of the snowpack. GPR data are compared with vertical profiles of snow hardness and density, obtained in the snow pit. Data were acquired in different areas with varying snow conditions with various GPR impulse systems, frequencies and polarizations. Radar experiments with high frequencies (> 1 GHz) detect internal layers in the snowpack in dry snow, but fail to provide clear reflections at the upper snow-air transition because of attenuation. In wet snow, the radar signals < 1 GHz are capable to penetrate a meter-thick snowpack and detect the snow surface, although the signal is strongly attenuated. Analysis of reflection phases and magnitudes allows interpretation of their physical origin in terms of changes in electrical permittivity. Varying antenna polarization causes a strongly different signal response, likely caused by the snow-pit wall present in our set-up. Forward calculation of density-based reflection coefficients between neighboring layers of varying hardness yields ambiguous results in terms of correspondence with observed radar reflections apart except for interferences of neighboring reflections. Moreover, we identify several pitfalls for future applications. The system set-up used here represents a basis for further developments towards a system, which is capable of improving information on the spatial and temporal snowpack characteristics.     

Quantifying changes in weak layer microstructure associated with artificial load changes

Researchers and practitioners have long utilized a variety of penetrometers to investigate the snowpack. Identifying definitive relationships between penetrometer-derived microstructural information and stability has been challenging. The purpose of this study is two-fold: 1. We propose a simple field test to establish relationships between load and penetrometer-derived microstructural estimates, 2. We utilize the SnowMicroPen (SMP) to quantify changes in weak layer residual strength and microstructural dimension associated with an artificial loading event. Our dataset is from Moonlight Basin, Montana and includes three modified loaded-column tests, each paired with 5 SMP profiles. Depth hoar comprised the targeted weak layer. Results indicate that loading caused the residual strength and rupture frequency to decrease significantly. Much like a compression test at a micro-scale, the force required for the SMP to rupture individual structures as well as the micro-scale strength decreased significantly when the slab stress was increased by artificially adding blocks of snow. A decrease in observed rupture frequency within the weak layer (or an increase in the distance between ruptured structures) also occurred after the loading event, probably because some structures within the weak layer had already failed or were so close to failing that the penetrometer could not detect their rupture. Due in part to the large difference in loads, microstructural differences between the natural and loaded columns were significant enough that only one profile would have been necessary to determine a significant difference in residual strength. Artificial removal of slab stress resulted in greater rupture forces and larger microstructures, likely due to elastic rebound.     

Characterization of seasonal variation of forest canopy in a temperate deciduous broadleaf forest, using daily MODIS data

In this paper, we present an improved procedure for collecting no or little atmosphere- and snow-contaminated observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The resultant time series of daily MODIS data of a temperate deciduous broadleaf forest (the Bartlett Experimental Forest) in 2004 show strong seasonal dynamics of surface reflectance of green, near infrared and shortwave infrared bands, and clearly delineate leaf phenology and length of plant growing season. We also estimate the fractions of photosynthetically active radiation (PAR) absorbed by vegetation canopy (FAPAR_canopy), leaf (FAPAR_leaf), and chlorophyll (FAPAR_chl), respectively, using a coupled leaf-canopy radiative transfer model (PROSAIL-2) and daily MODIS data. The Markov Chain Monte Carlo (MCMC) method (the Metropolis algorithm) is used for model inversion, which provides probability distributions of the retrieved variables. A two-step procedure is used to estimate the fractions of absorbed PAR: (1) to retrieve biophysical and biochemical variables from MODIS images using the PROSAIL-2 model; and (2) to calculate the fractions with the estimated model variables from the first step. Inversion and forward simulations of the PROSAIL-2 model are carried out for the temperate deciduous broadleaf forest during day of year (DOY) 184 to 201 in 2005. The reproduced reflectance values from the PROSAIL-2 model agree well with the observed MODIS reflectance for the five spectral bands (green, red, NIR₁, NIR₂, and SWIR₁). The estimated leaf area index, leaf dry matter, leaf chlorophyll content and FAPAR_canopy values are close to field measurements at the site. The results also showed significant differences between FAPAR_canopy and FAPAR_chl at the site. Our results show that MODIS imagery provides important information on biophysical and biochemical variables at both leaf and canopy levels.     

Accuracy assessment of the MODIS 16-day albedo product for snow: comparisons with Greenland in situ measurements

The accuracy of the Moderate Resolution Imaging Spectroradiometer (MODIS) 16-day albedo product (MOD43) is assessed using ground-based albedo observations from automatic weather stations (AWS) over spatially homogeneous snow and semihomogeneous ice-covered surfaces on the Greenland ice sheet. Data from 16 AWS locations, spanning the years 2000-2003, were used for this assessment. In situ reflected shortwave data were corrected for a systematic positive spectral sensitivity bias of between 0.01 and 0.09 on a site-by-site basis using precise optical black radiometer data. Results indicate that the MOD43 albedo product retrieves snow albedo with an average root mean square error (RMSE) of ±0.07 as compared to the station measurements, which have ±0.035 RMSE uncertainty. If we eliminate all satellite retrievals that rely on the backup algorithm and consider only the highest quality results from the primary bidirectional reflectance distribution function (BRDF) algorithm, the MODIS albedo RMSE is ±0.04, slightly larger than the in situ measurement uncertainty. There is general agreement between MODIS and in situ observations for albedo <0.7, while near the upper limit, a −0.05 MODIS albedo bias is evident from the scatter of the 16-site composite.     

A New Method of Simulating Bright Temperature of Snow Cover based on Snow Grain Size Evolution Process

This research used HUT model, DMRT model and MEMLS model to simulate interactions(absorption and extinction) between snow grainsfor different wave bands (18.7 GHz and 36.5 GHz) of microwave which were used for radiative transfer model. Obtaining the snow grain size is always a difficulty. So this research used Jordan91 snow grain size evolution model to evolve snow grain size which was regarded as input parameter of radiative transfer model, and used measured data to simulate spaceborne brightness temperature for 18.7 GHz horizontal polarization and 36.5 GHz horizontal polarization in a mixed pixel. The results showed that the bias of simulation brightness temperature using extinction coefficient of HUT model, DMRT model and MEMLS model for 18.7 GHz horizontal polarization were -3.6 K、-1.8 K and -0.7 K respectively, and for 36.5 GHz horizontal polarization were 4.0 K、10.4 K and 14.4 K respectively. For 18.7 GHz horizontal polarization and 36.5 GHz horizontal polarization, the bright temperature simulation based on effective snow grain size shows a good linear relationship with the brightness temperature simulation basedon snow grain size evolution process. Therefore, the method based on the snow grain size evolution process is a suitable method for obtaining the snow grain size parameter in the radiative transfer model.