Commentary on Wang and Zender—MODIS snow albedo bias at high solar zenith angles relative to theory and to in situ observations in Greenland

The recent paper by Wang and Zender [Wang, X., & Zender, C. S. (2010). MODIS snow albedo bias at high solar zenith angles relative to theory and to in situ observations in Greenland. Remote Sensing of Environment.] draws erroneous conclusions about solar zenith angle biases at high latitudes by not making appropriate use of the extensive quality flags available with the MODIS BRDF/Albedo. Coarse resolution MODIS white-sky albedo data are compared with actual blue-sky field albedometer measurements from the Greenland GC-Net. By utilizing large area averages of the MODIS data product that combine both high quality and poor quality data indiscriminately, the authors erroneously conclude that the accuracy deteriorates for solar zenith angle (SZA) > 55° and often becomes physically unrealistic for SZA > 65°. Once the quality flags are considered, however, the comparisons demonstrate that the MODIS product performs quite well out to the recommended limit for product use of 70° SZA. This verifies the conclusions of an earlier more rigorous evaluation performed by Stroeve et al. [Stroeve, J., Box, J. E., Gao, F., Liang, S., Nolin, A., & Schaaf, C. B. (2005). Accuracy assessment of the MODIS 16-day albedo product for snow: comparisons with Greenland in situ measurements. Remote Sensing of Environment.]. With over a decade of observations and products now available from the MODIS instrument, these data are increasingly being used to evaluate and tune climate and biogeochemical models. However, such use should take into account the documented quality and limitations of the satellite-derived product.     

Detecting and measuring new snow accumulation on ice sheets by satellite remote sensing

A new technique is described that detects when and where new snow falls on ice sheets and then determines the thickness of new accumulation. Measurements of vertically polarized passive emission at 85 GHz are filtered with the Hilbert-Huang Transform to identify periods where the surface snow has changed significantly. These are shown to be commonly the result of new snow by comparison with both field observations and in situ instrumentation. Temperature, atmospheric emission and clouds all affect the passive microwave signal but each is examined and shown not to prevent the identification of new snow events. The magnitude of the brightness temperature change is not strongly correlated with snowfall amount. To quantify the amount of new snow, the spatial extent and timing of new snowfalls are examined with ICESat/GLAS laser altimetry data. Crossover differences between altimetric profiles taken before, during, and after the snowfall event provide a measure of the thickness of new snow. Specific cases are presented where 11 and 13 cm of new snow were detected over large regions.     

Evaluation of the MODIS (MOD10A1) daily snow albedo product over the Greenland ice sheet

This study evaluates the performance of the beta-test MODIS (MOD10A1) daily albedo product using in situ data collected in Greenland during summer 2004. Results indicate the beta-test product tracks the general seasonal variability in albedo but exhibits significant more temporal variability than observed at the stations. This may indicate problems with the cloud detection algorithm, and/or failure of the BRDF model to adequately model the bidirectional reflectance of snow. Comparisons with in situ observations at five automatic weather stations in Greenland indicate an overall RMSE of 0.067 for the Terra instrument and an RMSE of 0.075 on Aqua. The Terra-retrieved-albedo are slightly better correlated with the in situ data than the Aqua retrievals (r = 0.79 versus r = 0.77). Comparisons were also made between the MODIS daily albedo product and the MODIS 16-day albedo product (MOD43B3). Results indicate general correspondence between the two products, with better agreement found using the Terra-retrieved-albedo than the Aqua-retrieved albedo. The reason for the differences in albedo between the Aqua and Terra satellites remains unclear. At the stations examined, both the Terra and Aqua retrievals were made at nearly the same time of the day and therefore the differences in albedo between the satellites cannot be explained by differences in solar illumination. Finally, the albedo derived using MODIS data and the direct estimation algorithm (DEA) was also compared with 2004 Greenland in situ data. Results from this comparison suggest that the DEA performs well as long as the solar zenith angle of the observation is not greater than 70°.     

Algorithm for retrieval of the effective snow grain size and pollution amount from satellite measurements

We present an algorithm for retrieval of the effective Snow Grain Size and Pollution amount (SGSP) from satellite measurements. As well as our previous version (Zege et al., 2008, 1998), the new algorithm is based on the analytical solution for snow reflectance within the asymptotic radiative transfer theory. The SGSP algorithm does not use any assumptions on snow grain shape and allows for the snow pack bidirectional reflectance distribution function (BRDF). The algorithm includes a new atmospheric correction procedure that allows for snow BRDF. This SGSP algorithm has been thoroughly validated with computer simulations. Its sensitivity to the atmosphere model has been investigated. It is shown that the inaccuracy of the snow characteristic retrieval due to the uncertainty in the aerosol and molecular atmosphere model is negligible, as compared to that due to the measurement errors at least for aerosol loads typical for polar regions. The significant advantage of the SGSP over conventional algorithms, which use a priori assumptions about particle shape and (or) not allow for the BRDF of the individual snow pack, is that the developed retrieval still works at low sun elevations, which are typical for polar regions.     

In-situ measured spectral directional emissivity of snow and ice in the 8–14 μm atmospheric window

The directional emissivity of snow and ice surfaces in the 8–14 μm thermal infrared (TIR) atmospheric window was determined from spectral radiances obtained by field measurements using a portable Fourier transform infrared spectrometer in conjunction with snow pit work. The dependence of the directional emissivity on the surface snow type (grain size and shape) was examined. We obtained emissivity spectra for five different surface types, i.e., fine dendrite snow, medium granular snow, coarse grain snow, welded sun crust snow, and smooth bare ice. The derived emissivities show a distinct spectral contrast at wavelengths λ = 10.5–12.5 μm which is enhanced with increasing the snow grain size. For example, emissivities at both 10.5 μm and 12.5 μm for the nadir angle were 0.997 and 0.984 for the fine dendrite snow, 0.996 and 0.974 for the medium granular snow, 0.995 and 0.971 for the coarse grain snow, 0.992 and 0.968 for the sun crust, and 0.993 and 0.949 for the bare ice, respectively. In addition, the spectral contrast exhibits a strong angular dependence, particularly for the coarser snow and bare ice, e.g., the emissivity at λ = 12.5 μm for the off-nadir angle of 75° reaches down to 0.927, 0.896, and 0.709 for the coarse grain snow, sun crust, and bare ice cases, respectively. The angular dependent emissivity spectra of the bare ice were quite consistent with the spectra predicted by the Fresnel reflectance theory. The observed results firmly demonstrate that the directional emissivity of snow in the TIR can vary depending upon the surface snow type. The high variability of the spectral emissivity of snow also suggests the possibility to discriminate between snow and ice types from space using the brightness temperature difference in the atmospheric window.     

An algorithm for the retrieval of 30-m snow-free albedo from Landsat surface reflectance and MODIS BRDF

We present a new methodology to generate 30-m resolution land surface albedo using Landsat surface reflectance and anisotropy information from concurrent MODIS 500-m observations. Albedo information at fine spatial resolution is particularly useful for quantifying climate impacts associated with land use change and ecosystem disturbance. The derived white-sky and black-sky spectral albedos may be used to estimate actual spectral albedos by taking into account the proportion of direct and diffuse solar radiation arriving at the ground. A further spectral-to-broadband conversion based on extensive radiative transfer simulations is applied to produce the broadband albedos at visible, near infrared, and shortwave regimes. The accuracy of this approach has been evaluated using 270 Landsat scenes covering six field stations supported by the SURFace RADiation Budget Network (SURFRAD) and Atmospheric Radiation Measurement Southern Great Plains (ARM/SGP) network. Comparison with field measurements shows that Landsat 30-m snow-free shortwave albedos from all seasons generally achieve an absolute accuracy of ±0.02-0.05 for these validation sites during available clear days in 2003-2005, with a root mean square error less than 0.03 and a bias less than 0.02. This level of accuracy has been regarded as sufficient for driving global and regional climate models. The Landsat-based retrievals have also been compared to the operational 16-day MODIS albedo produced every 8-days from MODIS on Terra and Aqua (MCD43A). The Landsat albedo provides more detailed landscape texture, and achieves better agreement (correlation and dynamic range) with in-situ data at the validation stations, particularly when the stations include a heterogeneous mix of surface covers.     

Observed and modelled effects of ice lens formation on passive microwave brightness temperatures over snow covered tundra

Immediately before an April 2007 snow survey and passive microwave radiometer field campaign in the Northwest Territories, Canada, a rain-on-snow event deposited a thin (~ 3 mm) continuous layer of ice on the surface of the snowpack. At eight sites the brightness temperature (T_b) of the undisturbed snow pack was measured with a multi-frequency dual polarization (6.9, 19, 37, and 89 GHz) ground based radiometer system. The ice lens was then carefully removed and the T_bs were measured again. The individual V-pol channels and the 37 V − 19 V difference were largely unaffected by the presence of the ice lens, exhibiting a systematic shift of about 3 K. In comparison, the ice lens had a considerable effect on the H-pol T_b at all frequencies, with a mean difference (ice lens present − ice lens removed) of − 9 K (± 5.3 K) at 6.9 GHz, − 40 K (± 11.3 K) at 19 GHz, − 33 K (± 7.6 K) at 37 GHz, and − 19 K (± 8.0 K) at 89 GHz. The effect of the ice lens on H-pol measurements was also observed with spaceborne data from the Advanced Microwave Scanning Radiometer (AMSR-E) satellite data.Simulations of T_b were produced for each site using a new two layer formulation of the Helsinki University of Technology (HUT) snow emission model. The ice lens was used as the top layer and the underlying snowpack considered as a homogenous second layer. The agreement between observations and simulations was variable, with agreement strongest at 19 GHz. A comparison with simulations produced using the Microwave Emission Model of Layered Snowpacks (MEMLS) suggests HUT model uncertainty is related not to the ice lens, but to difficulties in simulating emission from deep snow. Overall, the observations and simulations suggest H-pol measurements are capable of detecting new ice layers across the tundra snowpack, while V-pol measurements are more appropriate for snow water equivalent (SWE) retrievals due to their relative insensitivity to ice layers.     

A survey of models and algorithms for winter road maintenance. Part II: system design for snow disposal

This is the second part of a four-part survey of optimization models and solution algorithms for winter road maintenance planning. The first part addresses system design problems for spreading and plowing operations. The aim of this paper is to provide a comprehensive survey of optimization models and solution methodologies for the design of systems for snow disposal operations. These problems include partitioning a region or road network into sectors, locating snow disposal sites, allocating sectors to snow disposal sites, and allocating sectors to private companies or governmental agencies. The two last parts of the survey mainly concentrate on vehicle routing for winter road maintenance.     

A survey of models and algorithms for winter road maintenance. Part IV: Vehicle routing and fleet sizing for plowing and snow disposal

This is the last part of a four-part survey of optimization models and solution algorithms for winter road maintenance planning. The two first parts of the survey address system design problems for winter road maintenance. The third part concentrates mainly on vehicle routing problems for spreading operations. The aim of this paper is to provide a comprehensive survey of optimization models and solution methodologies for the routing of vehicles for plowing and snow disposal operations. We also review models for the fleet sizing and fleet replacement problems.     

Feature conversion based on decomposition and combination of swept volumes

In this paper, an approach is proposed for converting design features into machining features based on decomposition and combination of swept volumes. In the approach, delta volumes between a negative swept volume and a positive swept volume, which are directly generated from original design features, are decomposed into a set of smaller negative swept volumes first. Then, the negative swept volumes are combined to form maximal swept features, each of which represents a set of machining features with the same cutting-paths. Since sweeping is a common characteristic of different type features, the maximal swept features can serve as an intermediate representation for the different types of feature-based models; in particular, they can be conveniently converted into milling features or turning features, common features or user-specific features, and their alternatives.     

Artificial neural network-based techniques for the retrieval of SWE and snow depth from SSM/I data

The retrieval of snow water equivalent (SWE) and snow depth is performed by inverting Special Sensor Microwave Imager (SSM/I) brightness temperatures at 19 and 37 GHz using artificial neural network ANN-based techniques. The SSM/I used data, which consist of Pathfinder Daily EASE-Grid brightness temperatures, were supplied by the National Snow and Ice Data Centre (NSIDC). They were gathered during the period of time included between the beginning of 1996 and the end of 1999 all over Finland. A ground snow data set based on observations of the Finnish Environment Institute (SYKE) and the Finnish Meteorological Institute (FMI) was used to estimate the performances of the technique. The ANN results were confronted with those obtained using the spectral polarization difference (SPD) algorithm, the HUT model-based iterative inversion and the Chang algorithm, by comparing the RMSE, the R², and the regression coefficients. In general, it was observed that the results obtained through ANN-based technique are better than, or comparable to, those obtained through other approaches, when trained with simulated data. Performances were very good when the ANN were trained with experimental data.     

Validation of the Climate-SAF surface broadband albedo product: Comparisons with in situ observations over Greenland and the ice-covered Arctic Ocean

This paper describes a validation study performed by comparing the Climate-SAF Surface Albedo Product (SAL) to ground truth observations over Greenland and the ice-covered Arctic Ocean. We compare Advanced Very High Resolution Radiometer (AVHRR)-based albedo retrievals to data from the Greenland Climate Network (GCN) weather stations and the floating ice station Tara for polar summer 2007. The AVHRR dataset consists of 2755 overpasses. The overpasses are matched to in situ observations spatially and temporally. The SAL algorithm presented here derives the surface broadband albedo from AVHRR channels 1 and 2 using an atmospheric correction, temporal sampling of an empirical Bidirectional Reflectance Distribution Function (BRDF), and a narrow-to-broadband conversion algorithm. The satellite product contains algorithms for snow, sea ice, vegetation, bare soil, and water albedo. At the Summit and DYE-2 stations on the Greenland ice sheet, instantaneous SAL RMSE is 0.073. The heterogeneous surface conditions at satellite pixel scale over the stations near the Greenland west coast increase RMSE to > 0.12. Over Tara, the instantaneous SAL RMSE is 0.069. The BRDF sampling approach reduces RMSE over the ice sheet to 0.053, and to 0.045 over Tara. Taking into account various sources of uncertainty for both satellite retrievals and in situ observations, we conclude that SAL agrees with in situ observations within their limits of accuracy and spatial representativeness.     

Assessing the coupling between surface albedo derived from MODIS and the fraction of diffuse skylight over spatially-characterized landscapes

In this effort, the MODerate Resolution Imaging Spectroradiometer (MODIS) (Collection V005) Bidirectional Reflectance Distribution Function (BRDF)/Albedo algorithm is used to retrieve instantaneous surface albedo at a point in time and under specific atmospheric conditions. These retrievals are then used to study the role that the fraction of diffuse skylight plays under realistic scenarios of anisotropic diffuse illumination and multiple scattering between the surface and atmosphere. Simulations of the sky radiance using the MODTRAN®5.1 radiative transfer model were performed under different aerosol optical properties, illumination conditions, and surface characteristics to describe these effects on surface albedo retrievals from MODIS. This technique was examined using a validation scheme over four measurement sites with varied aerosol levels and landscapes, ranging from croplands to tundra ecosystems, and over extended time periods. Furthermore, a series of geostatistical analyses were performed to examine the types of spatial patterns observed at each measurement site. In particular, Enhanced Thematic Mapper Plus (ETM+) retrievals of surface albedo were acquired to analyze the change in variogram model parameters as a function of increased window-size. Results were then used to assess the degree to which a given point measurement is able to capture the intrinsic variability at the scale of MODIS observations. Assessments of MODIS instantaneous albedos that account for anisotropic multiple scattering, over snow-free and snow-covered lands and at all diurnal solar zenith angles, show a slight improvement over the albedo formulations that treat the downwelling diffuse radiation as isotropic. Comparisons with field measurements show biases improving by 0.004-0.013 absolute units (root-mean-squared error) or 0.1%-2.0% relative error.     

复杂掩模图数据处理与转换的研究

将数字图像处理理论和算法引入到掩模版图数据处理与转换系统中,开发了相应的数据处理与转换软件模块.实现了bmp图像文件的输入、图像的灰度量化、对比度拉伸、反转、边缘检测、多边形近似及版图文件的输出.实验结果证明,该软件能将含有较复杂图形的bmp文件成功地转换为GDSII版图文件.     

Predicting the probability of ice storm damages to electricity transmission facilities based on ELM and Copula function

In this paper, a novel method based on extreme learning machine (ELM) and Copula function is proposed to predict the damages to electricity transmission facilities during ice storms. The ELM is firstly trained based on the historical data of wind speed, freezing precipitation, temperature, as well as the distribution parameters of wind and ice loads. The ELM can then be employed to predict the distributions of the real-time wind and ice loads on electricity transmission facilities. Furthermore, the correlation between wind load and ice load is modeled with Copula functions. On the basis of ELM and Copula function, the joint probability distribution of wind and ice loads can be finally formulated and applied to predict the potential damages to electricity transmission facilities such as transmission lines and towers. The proposed method is tested with a real dataset to demonstrate its effectiveness.     

Monitoring snow cover variability in an agropastoral area in the Trans Himalayan region of Nepal using MODIS data with improved cloud removal methodology

Monitoring the extent and pattern of snow cover in the dry, high altitude, Trans Himalayan region (THR) is significant to understand the local and regional impact of ongoing climate change and variability. The freely available Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover images, with 500 m spatial and daily temporal resolution, can provide a basis for regional snow cover mapping, monitoring and hydrological modelling. However, high cloud obscuration remains the main limitation. In this study, we propose a five successive step approach — combining data from the Terra and Aqua satellites; adjacent temporal deduction; spatial filtering based on orthogonal neighbouring pixels; spatial filtering based on a zonal snowline approach; and temporal filtering based on zonal snow cycle — to remove cloud obscuration from MODIS daily snow products. This study also examines the spatial and temporal variability of snow cover in the THR of Nepal in the last decade. Since no ground stations measuring snow data are available in the region, the performance of the proposed methodology is evaluated by comparing the original MODIS snow cover data with least cloud cover against cloud-generated MODIS snow cover data, filled by clouds of another densely cloud-covered product. The analysis indicates that the proposed five-step method is efficient in cloud reduction (with average accuracy of > 91%). The results show very high interannual and intra-seasonal variability of average snow cover, maximum snow extent and snow cover duration over the last decade. The peak snow period has been delayed by about 6.7 days per year and the main agropastoral production areas of the region were found to experience a significant decline in snow cover duration during the last decade.     

New generalized conversion method of the MDCT to MDST coefficients in the frequency domain for arbitrary symmetric windowing function

A new generalized conversion method of the MDCT to MDST coefficients directly in the frequency domain is proposed for arbitrary symmetric windowing function. Based on the compact block matrix representation of the MDCT and MDST filter banks, on their properties and on relations among transform sub-matrices, a relation in the matrix-vector form between the MDCT and MDST coefficients in the frequency domain is derived. Given MDCT coefficients of three consecutive data blocks at a decoder, the MDST coefficients of the current data block can be obtained by combining the MDCT coefficients of the previous, current and next blocks via conversion matrices. Since the forms of conversion matrices depend on the employed windowing function, a specific solution for each windowing function is derived. Because the conversion matrices have a very regular structure, the matrix-vector products are reduced to simple analytical formulas. The new generalized conversion method is more efficient and structurally simpler both in terms of arithmetic complexity and memory requirements compared to existing exact frequency domain-based conversion methods. Although the new generalized conversion method enables us to compute the exact MDST coefficients only in specified one or more frequency ranges, the computation of complete set of MDST coefficients still requires a high number of arithmetic operations. As an alternative, an efficient and flexible approximate conversion method is constructed. With properly selected parameters it can produce acceptable approximated results with much lower computational complexity. Therefore, the approximate conversion method has a potential to be used in many MDCT-based audio decoders, and particularly at resource-limited and low-cost decoders for spectral analysis to obtain the magnitude and phase information.     

基于核函数的PLS丁苯橡胶聚合转化率软测量

针对企业对丁苯橡胶聚合转化率需在线高精度预测的需求,考虑实际工况复杂性和偏最小二乘(PLS)算法非线性处理能力的不足,分别建立了引入单核和混合核函数的丁苯橡胶聚合转化率PLS预测模型。工业数据仿真结果表明:基于核的PLS模型均可满足企业生产对预测精度的要求,即聚合转化率预测绝对误差大于1.5的比例不大于样本总数的10%,尤其是混合核PLS因兼有局部和全局特性,表现出更优的性能。     

基于Simulink的数字下变频系统的设计与仿真

针对数字下变频中低通抽取滤波器通带失真大、资源消耗严重等问题,采用锐化技术和二阶多项式插值补偿滤波器设计了基于Simulink的数字下变频系统,并利用Simulink构建系统模型。仿真结果表明,该系统不仅可以达到降低信号频率和速率的目的,同时可以有效改善抽取滤波器的通带与阻带特性,并减少了系统工作量。