基于CALIOP星载激光雷达探测数据的北京沙尘天气大气状况分析

基于2010年3、4月份的CALIOP (Cloud-Aerosol LiDAR with Orthogonal Polarization)激光雷达后向散射数据,给出了利用交互式数据语言IDL(Interactive Data Language)处理数据的新方法,得出大气后向散射及各参数的空间分布图;在此基础上深入研究了北京地区春季沙尘天气大气分布状况。对沙尘暴在可见光和红外光波段的后向散射、退偏振比以及分布高度的研究结果表明:大气对激光的后向散射信号及各参数的空间分布图可以描绘出大气各组分及其分布情况,如气溶胶、沙尘暴和云等的空间分布情况;利用NILU提供的沙尘暴追踪图,确定了沙尘暴来源,为研究沙尘暴对气候的影响提供了重要依据。     

Improving GERB scene identification using SEVIRI: Infrared dust detection strategy

The combination of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) and the Geostationary Earth Radiation Budget (GERB) instruments on Meteosat-8 provides a powerful new tool for detecting aerosols and estimating their radiative effect at high temporal and spatial resolution. However, at present no specific aerosol treatment is performed in the GERB processing chain, severely limiting the use of the data for aerosol studies. A particular problem relates to the misidentification of Saharan dust outbreaks as cloud which can bias the shortwave and longwave fluxes. In this paper an algorithm is developed which employs multiple-linear regression, using information from selected thermal infrared SEVIRI channels, to detect dust aerosol over ocean and provide an estimate of the optical depth at 0.55 μm (τ₀₅₅). To test the performance of the algorithm, it has been applied to a number of dust events observed by SEVIRI during March and June 2004. The results are compared to co-located MODIS observations taken from the Terra and Aqua platforms, and ground based observations from the Cape Verde AERONET site. In terms of detection capability, employing the algorithm results in a notable improvement in the routine GERB scene identification. Locations identified by MODIS as being likely to be dust contaminated were originally classified as cloud in over 99.5% of the cases studied. With the application of the detection algorithm approximately 60-70% of these points are identified as dusty depending on the dust model employed. The algorithm is also capable of detecting dust in regions and at times which would be excluded when using shortwave observations, due for example to the presence of sun-glint, or through the night. We further investigate whether the algorithm is capable of generating useful information concerning the aerosol loading. Comparisons with co-located retrievals from the SEVIRI 0.6 μm solar reflectance band observations show a level of agreement consistent with that expected from the simulations, with rms differences of between 0.5 and 0.8, and a mean bias ranging from − 0.5 to 0.3 dependent on the dust representation employed in the algorithm. Temporally resolved comparisons with observations from the Capo Verde AERONET site through the months of March and June reinforce these findings, but also indicate that the algorithm is capable of discerning the diurnal pattern in aerosol loading. The algorithm has now been incorporated within the routine GERB processing in detection mode, and will be used to provide an experimental aerosol product for assessment by the scientific community.     

200MW乏气送粉机组主蒸汽温度控制优化方案

本文在常规火电厂主蒸汽温度自动控制方案的基础上,对200MW乏气送粉机组的特性进行了分析,结合锅炉主要参数与主蒸汽温度自动控制的相互关系,以及相关的工艺要求,提出了一种新的优化方案。     

Estimating aerodynamic resistance of rough surfaces using angular reflectance

Current wind erosion and dust emission models neglect the heterogeneous nature of surface roughness and its geometric anisotropic effect on aerodynamic resistance, and over-estimate the erodible area by assuming it is not covered by roughness elements. We address these shortfalls with a new model which estimates aerodynamic roughness length (z₀) using angular reflectance of a rough surface. The new model is proportional to the frontal area index, directional, and represents the geometric anisotropy of z₀. The model explained most of the variation in two sets of wind tunnel measurements of aerodynamic roughness lengths (z₀). Field estimates of z₀ for varying wind directions were similar to predictions made by the new model. The model was used to estimate the erodible area exposed to abrasion by saltating particles. Vertically integrated horizontal flux (F_h) was calculated using the area not covered by non-erodible hemispheres; the approach embodied in dust emission models. Under the same model conditions, F_h estimated using the new model was up to 85% smaller than that using the conventional area not covered. These F_h simulations imply that wind erosion and dust emission models without geometric anisotropic sheltering of the surface, may considerably over-estimate F_h and hence the amount of dust emission. The new model provides a straightforward method to estimate aerodynamic resistance with the potential to improve the accuracy of wind erosion and dust emission models, a measure that can be retrieved using bi-directional reflectance models from angular satellite sensors, and an alternative to notoriously unreliable field estimates of z₀ and their extrapolations across landform scales.     

DCS在焦炉地面除尘系统中的应用

以和利时DCS系统在某100万t捣固焦炉地面除尘站系统中的应用为例,介绍了袋式除尘系统的工艺流程、工艺联锁与控制要求,并依此制订了基于和利时MACSV系统的详细实现方案。系统具有免维护、除尘效果良好等特点。系统投运后,不仅减少了工人劳动强度,改善了工厂周边环境,并且取得了良好的经济效益和社会效益。     

Dust particle detection in traffic surveillance video using motion singularity analysis

Dust particle detection in video aims to automatically determine whether the video is degraded by dust particle or not. Dust particles are usually stuck on the camera lends and typically temporally static in the images of a video sequence captured from a dynamic scene. The moving objects in the scene can be occluded by the dusts; consequently, the motion information of moving objects tends to yield singularity. Motivated by this, a dust detection approach is proposed in this paper by exploiting motion singularity analysis in the video. First, the optical model of dust particle is theoretically studied in by simulating optical density of artifacts produced by dust particles. Then, the optical flow is exploited to perform motion singularity analysis for blind dust detection in the video without the need for ground truth dust-free video. More specifically, a singularity model of optical flow is proposed in this paper using the direction of the motion flow field, instead of the amplitude of the motion flow field. The proposed motion singularity model is further incorporated into a temporal voting mechanism to develop an automatic dust particle detection in the video. Experiments are conducted using both artificially-simulated dust-degraded video and real-world dust-degraded video to demonstrate that the proposed approach outperforms conventional approaches to achieve more accurate dust detection.     

Dust source identification using MODIS: A comparison of techniques applied to the Lake Eyre Basin, Australia

The impact of mineral aerosol (dust) in the Earth's system depends on particle characteristics which are initially determined by the terrestrial sources from which the sediments are entrained. Remote sensing is an established method for the detection and mapping of dust events, and has recently been used to identify dust source locations with varying degrees of success. This paper compares and evaluates five principal methods, using MODIS Level 1B and MODIS Level 2 aerosol data, to: (a) differentiate dust (mineral aerosol) from non-dust, and (2) determine the extent to which they enable the source of the dust to be discerned. The five MODIS L1B methods used here are: (1) un-processed false colour composite (FCC), (2) brightness temperature difference, (3) Ackerman's (1997: J.Geophys. Res., 102, 17069-17080) procedure, (4) Miller's (2003:Geophys. Res. Lett. 30, 20, art.no.2071) dust enhancement algorithm and (5) Roskovensky and Liou's (2005: Geophys. Res. Lett. 32, L12809) dust differentiation algorithm; the aerosol product is MODIS Deep Blue (Hsu et al., 2004: IEEE Trans. Geosci. Rem. Sensing, 42, 557-569), which is optimised for use over bright surfaces (i.e. deserts). These are applied to four significant dust events from the Lake Eyre Basin, Australia. OMI AI was also examined for each event to provide an independent assessment of dust presence and plume location. All of the techniques were successful in detecting dust when compared to FCCs, but the most effective technique for source determination varied from event to event depending on factors such as cloud cover, dust plume mineralogy and surface reflectance. Significantly, to optimise dust detection using the MODIS L1B approaches, the recommended dust/non-dust thresholds had to be considerably adjusted on an event by event basis. MODIS L2 aerosol data retrievals were also found to vary in quality significantly between events; being affected in particular by cloud masking difficulties. In general, we find that OMI AI and MODIS AQUA L1B and L2 data are complementary; the former are ideal for initial dust detection, the latter can be used to both identify plumes and sources at high spatial resolution. Overall, approaches using brightness temperature difference (BT10-11) are the most consistently reliable technique for dust source identification in the Lake Eyre Basin. One reason for this is that this enclosed basin contains multiple dust sources with contrasting geochemical signatures. In this instance, BTD data are not affected significantly by perturbations in dust mineralogy. However, the other algorithms tested (including MODIS Deep Blue) were all influenced by ground surface reflectance or dust mineralogy; making it impossible to use one single MODIS L1B or L2 data type for all events (or even for a single multiple-plume event). There is, however, considerable potential to exploit this anomaly, and to use dust detection algorithms to obtain information about dust mineralogy.     

基于直方图相似度度量的中央空调风管附尘度检测

对于主从控制中央空调清洁机器人,操作人员依据视频很容易就可判断出风管是否清扫干净。在机器人自主清洗中央空调风管时,为了能够使机器人识别管道内灰尘附着程度,文章采用了直方图相似度作为清洁机器人自动判断管道附尘度和清洁与否的标准。文章给出了相关的原理和算法,并进行了试验验证,结果表明该方法具有快速性、实时性和准确性等特点。     

可编程控制器在锅炉吹灰系统中的应用

本文给出ＰＬＣ在锅炉吹灰器控制中的应用,并成功地实现了若干台石油裂解炉吹灰器的集中控制。     

除尘器智能控制器的开发

针对市场上传统的脉冲袋式除尘器控制器在实际应用中存在的灵活性差、可靠性低等问题，借助于高性能的单片机开发了一种功能完善的通用型控制器。具体介绍了系统的组成和工作原理，详细讨论了通用控制器的硬件组成和软件设计，阐述了系统的性能指标和功能特点，在设计中采用光电耦合器将外部负载驱动电路和控制电路隔离，同时结合看门狗技术提高整个系统的可靠性。