Aerosol optical depth variability over the southeastern Arabian Sea

Aerosol optical depth (AOD), an index of aerosol concentration, is used to study atmospheric features. Accordingly, spatiotemporal variability of AOD in the atmospheric column over the southeastern Arabian Sea (SEAS) is investigated utilizing monthly data obtained from both the Sea-viewing Wide Field-of-view Sensor (SeaWiFS), from September 1997 to December 2010, and the Moderate Resolution Imaging Spectroradiometer (MODIS) on board Aqua, from July 2002 to December 2010. A comparison between the data from both sensors showed similar averages (~0.13), but with different standard deviations (0.03 and 0.02) over the SEAS. The AOD distribution was found to be dominated by an annual cycle controlled by the monsoon climate, with maximum aerosol concentration during July/August (~0.2) and minimum during November/December (<0.1). Empirical mode decomposition (EMD) analysis revealed the influence of the Quasi-Biennial Oscillation (QBO) and El Niño Southern Oscillation (ENSO) in producing inter-annual variability of 2% and 1%, respectively. Simulated backwards trajectories of aerosols, using Hybrid Single-Particle Lagrangian Integrated Trajectory models, indicated two main remote sources, i.e. sea salt from the Arabian Sea and dust particles from the Arabian Peninsula are the key factors contributing to an increase in the concentration of aerosols over the study area during the southwest monsoon period, irrespective of the opposing phases of QBO.     

Aerosol optical depth spatio-temporal characterization over the Canadian BOREAS domain

A complete set of Advanced Very High Resolution Radiometer (AVHRR) data (75 images) is used to retrieve aerosol optical depth (AOD) over dense vegetation and over lake water in the visible AVHRR channel. The present approach for remote sensing of aerosols from the National Oceanic and Atmospheric Administration (NOAA)-11 AVHRR sensor is based on the detection of atmospherically dominated signals over dark surface covers such as dense dark vegetation (DDV). Such targets were identified using the reflective portion of the middle-wave AVHRR channel 3 signal. When a fixed DDV surface reflectance is subtracted from the observed reflectance after correction for all other atmospheric effects, the remaining part, which is due to aerosols, is inverted to derive aerosol optical thickness using a look-up table (LUT) similar to that used in water surface inversion. The algorithm was applied to the daily afternoon NOAA-11 AVHRR (1?km×1?km) data acquired from the end of May to mid-August 1994 over the Canadian 1000?km×1000?km Boreal Ecosystem Atmosphere Study (BOREAS) domain. A validation analysis using five ground-based Sun photometers within the studied area shows the good performance of the retrieval algorithm. The approach allows detailed analysis of the AOD spatio-temporal behaviour at the regional scale useful for climate and transport model validation.     

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.     

基于并行计算的气溶胶定量遥感反演模型实现

为了加快气溶胶光学厚度(AOD)反演计算速度,基于SYNTAM串行算法,提出了循环分块划分和聚合通信的策略,利用消息传递模型,在中国气象局的IBM Cluster 1600高性能计算机系统上,并行实现了从MODIS双星(TERRA和AQUA)卫星数据反演AOD。试验结果表明该方法大大减少了计算时间,与地面太阳光度计实测AOD数据进行对比验证,发现所有站点处的AOD反演相对误差小于22%,表明这种并行方法可以满足高精度监测空气质量要求。     

Graph-regularized low-rank representation for aerosol optical depth retrieval

In this article, we propose a novel data-driven regression model for aerosol optical depth (AOD) retrieval. First, we adopt a low-rank representation (LRR) model to learn a powerful representation of the spectral response. Then, a graph regularization is incorporated into the LRR model to capture the local structure information and the non-linear property of the remote-sensing data. Since it is easy to acquire the rich results retrieved by satellite, we use them as a baseline to construct the graph. Finally, the learned feature representation is fed into Support Vector Machines (SVMs) to retrieve AOD. Experiments are conducted on two widely used data sets acquired by different sensors, and the experimental results show that the proposed method can achieve superior performance compared to the physical models and other state-of-the-art empirical models.     

A comparative study of Chang and HUT models for UK snow depth retrieval

For the development of passive microwave remote sensing techniques, brightness temperature information on the medium covering the Earth's surface under different conditions is required. An emission model is a useful tool for the estimation of the brightness temperature of the medium. If the medium is a snow pack, the microwave radiation emitted will depend on the physical temperature, crystal characteristics, stratification and density of the snow. The parameters of microwave emission models available for the retrieval of snow characteristics are highly dependent on local environmental and climatological conditions. The aim of this study was to compare the empirical Chang model with the semiempirical radiative transfer model of snow developed at Helsinki University of Technology (HUT) for snow depth (SD) retrieval for UK snow packs. In the first step we used the HUT model. The root mean square error (RMSE) was used to validate the accuracy of model estimates. The snow events from different days in 1995, 1996 and 1997 were used in this study. In the second step a revised form of the Chang model, which was originally calibrated for global snow monitoring, was applied to estimate the SD. It is evident from this study that the Chang model underestimates the SD whereas the HUT model both underestimates and overestimates the SD for UK snow cover. This study also demonstrates that the application of algorithms for snow pack monitoring requires local calibration for effective and reasonable results.     

Estimation of snow depth in the UK using the HUT snow emission model

Snow cover characteristics have significant effects on upwelling naturally emitted microwave radiation through processes of forward scattering. This study simulates numerically the electromagnetic responses from snow in the UK using the radiative transfer‐based semiempirical model developed at the Helsinki University of Technology (HUT), which takes into account the influence of soil surface, forest canopy and atmosphere on space‐borne observed brightness temperature by using empirical and semiempirical formulas. A sensitivity analysis of the HUT model was conducted to determine the most sensitive parameter affecting upwelling radiation from snow in the UK. The model‐based results were compared with observed Special Sensor Microwave Imager (SSM/I) brightness temperatures to better understand the SSM/I response to snow. The available ensemble of data required for input to the HUT model comprise surface physical temperature, ground level pressure and water vapour content, forest stem volume and land cover water fraction. Based on the sensitivity analyses, numerical parameters representing physical snow pack quantities (e.g. snow grain size, snow moisture and snow depth (SD)) were varied and the method of root mean square error (RMSE) minimization was used to invert the SD. The HUT model was applied to different days in 3 months (23–31 January, 1–5 and 26–27 February and 1–7 March 1995) of records of daily SD and SSM/I observations. The results show that the HUT model both underestimates and overestimates SD prediction. For the month of January 1995, the HUT model underestimated SD with a bias of ?0.59 cm, whereas for February and March 1995 the HUT model overestimated the SD with a bias of 1.89 cm and 1.64 cm, respectively. This study demonstrates that microwave remote sensing of snow can be used successfully in the UK, where most research on snow cover is conducted by using a visible and infrared radiometer. It is also evident from this work that application of algorithms to snow pack monitoring needs local calibration for effective and reasonable results.     

Human skeleton tracking from depth data using geodesic distances and optical flow

In this paper, we present a method for human full-body pose estimation from depth data that can be obtained using Time of Flight (ToF) cameras or the Kinect device. Our approach consists of robustly detecting anatomical landmarks in the 3D data and fitting a skeleton body model using constrained inverse kinematics. Instead of relying on appearance-based features for interest point detection that can vary strongly with illumination and pose changes, we build upon a graph-based representation of the depth data that allows us to measure geodesic distances between body parts. As these distances do not change with body movement, we are able to localize anatomical landmarks independent of pose. For differentiation of body parts that occlude each other, we employ motion information, obtained from the optical flow between subsequent intensity images. We provide a qualitative and quantitative evaluation of our pose tracking method on ToF and Kinect sequences containing movements of varying complexity.     

Evaluation of SMMR satellite-derived snow depth using ground-based measurements

Daily snow depth data for 284 first-order hydromet stations located in the former Soviet Union have been made available for climatological analysis by the U.S.S.R. State Committee for Hydrometeorology. The data are compared with the snow depth product derived from Scanning Multichannel Microwave Radiometer (SMMR) passive microwave brightness temperatures for the year 1980. Results suggest some discrepancies between the two data sources. Anomalies are considered to emanate from a variety of sources. These may include: (a) problems comparing a point measurement to one averaged over a pixel area; (b) an algorithm bias given deep snow conditions; (c) geographical influences such as elevation and forest cover; and (d) presence of depth hoar.     

Enhancement of OMI aerosol optical depth data assimilation using artificial neural network

A regional chemical transport model assimilated with daily mean satellite and ground-based aerosol optical depth (AOD) observations is used to produce three-dimensional distributions of aerosols throughout Europe for the year 2005. In this paper, the AOD measurements of the Ozone Monitoring Instrument (OMI) are assimilated with Polyphemus model. In order to overcome missing satellite data, a methodology for preprocessing AOD based on neural network (NN) is proposed. The aerosol forecasts involve two-phase process assimilation and then a feedback correction process. During the assimilation phase, the total column AOD is estimated from the model aerosol fields. The main contribution is to adjust model state to improve the agreement between the simulated AOD and satellite retrievals of AOD. The results show that the assimilation of AOD observations significantly improves the forecast for total mass. The errors on aerosol chemical composition are reduced and are sometimes vanished by the assimilation procedure and NN preprocessing, which shows a big contribution to the assimilation process.     

Aerosol optical thickness over Pearl River Delta region,China

We validated moderate resolution imaging spectroradiometer (MODIS) Level 2 aerosol products with ground-based sun photometer (CE-318) measurements over the Pearl River Delta (PRD) region. MODIS aerosol products are also used to investigate the temporal and spatial variations of aerosol optical thickness (AOT). The results show that MODIS AOT is validated quantitatively with a higher correlation coefficient (r = 0.88, 0.80 at Guangzhou and r = 0.95, 0.92 at Hong Kong) and lower root mean square errors (RMSE = 0.15, 0.16 at Guangzhou and RMSE = 0.07, 0.08 at Hong Kong), while the Ångström exponent (α) is still in doubt (r = 0.09). The MODIS AOT values are generally higher than those of the CE-318 values in Guangzhou and smaller than those in Hong Kong. The regional multi-year monthly (July 2002–December 2012) mean AOT values are 0.66 ± 0.20 and 0.64 ± 0.18 for Terra- and Aqua-MODIS, respectively. From month to month, the values of Terra-MODIS AOT are larger than those of Aqua-MODIS during most of the month. This implies that AOT in the morning is generally larger than that in the afternoon. The largest monthly AOT occurred in April at 0.85 ± 0.16 and 0.88 ± 0.17 for Terra-MODIS and Aqua-MODIS, respectively, and the smallest occurred in November for both Terra- and Aqua-MODIS at 0.47 ± 0.13 and 0.47 ± 0.10, respectively. The spatial distribution of AOT in spring and summer shows more variation than in autumn and winter. This can be partially attributed to the cleansing effect of precipitation which clears aerosol particles over the whole region in spring and summer and results in a lower AOT outside urban areas, while AOT in urban areas is higher where anthropogenic aerosols build up quickly despite the cleansing effect of the rain.     

Comparing MODIS and AERONET aerosol optical depth over China

The newest daily and monthly Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depths (AOD or τ) dataset over land, C005, retrieved using the second-generation operational algorithm, were evaluated using a ground-based Aerosol Robotic Network (AERONET) dataset from 13 sites over China. The dataset covers the period 2003–2006. Daily MODIS C005 AODs over China were found to have a positive bias with a relationship of τ_MODIS?=?0.135?+?1.022τ_AERONET, for which the offset is larger than reported global validation results. However, the relationship τ_MODIS?=?0.021?+?0.929τ_AERONET showed that monthly MODIS C005 AODs were an overestimation for small AOD and underestimation for high AOD. Both daily and monthly MODIS AOD retrievals showed poor performance in extreme aerosol conditions, e.g. under dust events or heavy urban/industrial haze. Nevertheless, both daily and monthly MODIS C005 AOD datasets can be used for investigation of aerosol spatial distribution and temporal variation over China.     

Validation of MODIS retrieval aerosol optical depth and an investigation of aerosol transport over Mohal in north western Indian Himalaya

This study deals with the optical properties of aerosols during 2007 over Mohal (31.9º N, 77.12º E) in north western Indian Himalaya, investigated using ground-based measurements and multi-satellite data. The daily average (mean ± standard deviation) aerosol optical depth (AOD) at 500 nm, Ångström exponent and turbidity coefficient values were 0.2 ± 0.1, 1.1 ± 0.3 and 0.1 ± 0.1, respectively. About 84% of AOD values retrieved from satellites were found to be within an uncertainty limit with a significant correlation coefficient around 0.70. The present study suggests that AOD retrieval using the Moderate Resolution Imaging Spectroradiometer (MODIS) is able to characterize AOD distribution over Mohal. However, to eliminate systematic errors, the existing MODIS algorithm needs to be modified in view of the changing aerosol optical properties, especially during the biomass-burning period. To investigate the influence of aerosol transport, a multi-sensor approach in conjunction with back-trajectory analysis was used. The observed higher values of AOD during dust-loading days with simultaneous study of the space-borne lidar measurements as well as back-trajectory analysis suggest the influx of desert aerosols. Transport of dust aerosols on 12 April, 27 April and 1 June caused a significant reduction in surface-reaching solar irradiance by 43, 40 and 39 W m^–2, respectively. Atmospheric forcing during these days increased by 33.8, 33.0 and 33.2 W m^–2, which translates into atmospheric heating rates of 0.95, 0.93 and 0.93 K day^–1, respectively. This indicates significant climatic implications due to arriving aerosols in north-western Indian Himalaya.     

利用PowerBuilder实现数据的万能查询

数据查询操作是数据库应用系统中最常用的操作。在数据库结构比较复杂的情况下，为终端用户提供友好的数据查询平台尤为重要。设计了一种在PowerBuilerER环境下实现高效，灵活的万能查询的方法。该方法在珠海市公共汽车公司人事、户籍计算机管理系统的开发中得到了成功应用。     

Snow optical properties for different particle shapes with application to snow grain size retrieval and MODIS/CERES radiance comparison over Antarctica

We investigated the single scattering optical properties of snow for different ice particle shapes and degrees of microscopic scale roughness. These optical properties were implemented and tested in a coupled atmosphere-snow radiative transfer model. The modeled surface spectral albedo and radiance distribution were compared with surface measurements. The results show that the reflected radiance and irradiance over snow are sensitive to the snow grain size and its vertical profile. When inhomogeneity of the particle size distribution in the vertical is taken into account, the measured spectral albedo can be matched, regardless of the particle shapes. But this is not true for the modeled radiance distribution, which depends a lot on the particle shape. The usual “equivalent spheres” assumption significantly overestimates forward reflected radiances, and underestimates backscattering radiances, around the principal plane. On average, the aggregate shape assumption has the best agreement with the measured radiances to a mean bias within 2%.The snow optical properties with the aggregate assumption were applied to the retrieval of snow grain size over the Antarctic plateau. The retrieved grain sizes of the top layer showed similar and large seasonal variation in all years, but only small year to year variation. Using the retrieved snow grain sizes, the modeled spectral and broadband radiances showed good agreements with MODIS and CERES measurements over the Antarctic plateau. Except for the MODIS 2.13 μm channel, the mean relative model-observation differences are within few percent. The modeled MODIS radiances using measured surface reflectance at Dome C also showed good agreement in visible channels, where radiation is not sensitive to snow grain size and the measured surface bidirectional reflectance is applicable over the Antarctic plateau. But modeled radiances using local, surface-measured reflectance in the near infrared yielded large errors because of the high sensitivity to the snow grain size, which varies spatially and temporally. The CERES broadband shortwave radiance is moderately sensitive to the snow grain size, comparable to the MODIS 0.86 μm channel. The variation of broadband snow reflectance due to the seasonal variation in snow grain size is about 5% in a year over the Antarctic plateau. CERES broadband radiances simulated with grain sizes retrieved using MODIS are about 2% larger than those observed.     

Review of snow water equivalent retrieval methods using spaceborne passive microwave radiometry

ABSTRACT

The physical properties of a snowpack strongly influence the emissions from the substratum, making snow property retrievals feasible by means of the surface brightness temperature observed by passive microwave sensors. Depending on the spatial resolution observed, time series records of daily snow coverage and critical snowpack properties such as snow depth (SD) and snow water equivalent (SWE) could be helpful in applications ranging from modelling snow variations for water resources management in a catchment to global climatologic studies. However, the challenge of including spaceborne SWE products in operational hydrological and hydroclimate modelling applications is very demanding with limited uptake by these systems, mostly attributed to insufficient SWE estimation accuracy. The root causes of this challenge include the coarse spatial resolution of passive microwave (PM) observations that observe highly aggregated snowpack properties at the spaceborne scale, and inadequacies during the retrieval process caused by uncertainties with the forward emission modelling of snow and challenges to find robust parameterizations of the models. While the spatial resolution problem is largely in the realm of engineering design and constrained by physical restrictions, a better understanding of developed and adopted retrieval methodologies can provide the clarity needed to enhance our knowledge in this field. In this paper, we review snow depth and SWE retrieval methods using PM observations, taking only dry snow retrieval processes into consideration. Snow properties using PM observations can be modelled by purely empirical relations based on underlying physical processes, and SD and SWE can be estimated by regression-based approaches. Snow property retrievals have been refined gradually throughout four decades use of PM observations in tandem with better understanding of physical processes, inclusion of better snowpack parameterizations, improved uncertainty analysis frameworks, and applying better inversion algorithms. Studying available methods, we conclude that snowpack parameterization is key to accurate retrieval. By improving retrieval algorithm architectures to better capture dynamic snowpack evolution processes, SWE estimates are likely to improve. We conclude that this challenge can be addressed by coupling emission models and land surface models or integrating weather-driven snowpack evolution into emission models and performing inversion in a Bayesian framework.     

Validation and analysis of aerosol optical thickness retrieval over land

Aerosol optical thickness (AOT) retrieval from Moderate Resolution Imaging Spectroradiometer (MODIS) data has been well established over oceans, but this is not the case over land. In this article, the AOT data sets retrieved by exploiting the synergy of TERRA and AQUA MODIS data (SYNTAM) over land are validated with ground-based measurements from Aerosol Robotic Network (AERONET) data, as well as from the National Aeronautics and Space Administration (NASA) AOT products, amended with a DeepBlue algorithm in Asian (15–60° N and 35–150° E) and American areas (30–40° N and 100–120° W). Overall, AOT retrieval errors of around 10–20% against AERONET data are found at both 1 and 10 km resolutions. The spectral and spatial sensitivities of the AOT correlation are explicitly addressed at both 1 and 10 km resolutions. Three window sizes, 1?×?1, 3?×?3 and 5?×?5, are tested for SYNTAM to evaluate the effect of window size on parameter statistics, and it is found that the accuracy of the SYNTAM method decreases with increasing window size. The validations at three spectral bands of 0.47, 0.55 and 0.66 μm show that the accuracies of different bands are 80–90% similar, and that the band at 0.47 μm has the highest accuracy most of the time. Comparisons between AOT data sets derived from the SYNTAM and AOT products from the NASA Dark Dense Vegetation (DDV) and the DeepBlue algorithms are also conducted using data from the USA. More pixels with AOT values for the area could be retrieved using the SYNTAM method with the NASA DeepBlue algorithm. The AOT values of more than 90% of pixels derived by both methods are very close. This clearly shows that AOT data from SYNTAM are very close to the AOT data set from the NASA DeepBlue algorithm in cloud-free areas. The synergic use of both the SYNTAM and DeepBlue algorithms could produce AOT values over much greater land areas.     

Development of a tundra-specific snow water equivalent retrieval algorithm for satellite passive microwave data

Airborne and satellite brightness temperature (T_B) measurements were combined with intensive field observations of sub-Arctic tundra snow cover to develop the framework for a new tundra-specific passive microwave snow water equivalent (SWE) retrieval algorithm. The dense snowpack and high sub-grid lake fraction across the tundra mean that conventional brightness temperature difference approaches (such as the commonly used 37 GHz-19 GHz) are not appropriate across the sub-Arctic. Airborne radiometer measurements (with footprint dimensions of approximately 70 × 120 m) acquired across sub-Arctic Canada during three field campaigns during the 2008 winter season were utilized to illustrate a slope reversal in the 37 GHz T_B versus SWE relationship. Scattering by the tundra snowpack drives a negative relationship until a threshold SWE value is reached near 130 mm at which point emission from the snowpack creates a positive but noisier relationship between 37 GHz T_B and SWE.The change from snowpack scattering to emission was also evident in the temporal evolution of 37 GHz T_B observed from satellite measurements. AMSR-E brightness temperatures (2002/03-2006/07) consistently exhibited decreases through the winter before reaching a minimum in February or March, followed by an increase for weeks or months before melt. The cumulative absolute change (Σ|Δ37V|) in vertically polarized 37 GHz T_B was computed at both monthly and pentad intervals from a January 1 start date and compared to ground measured SWE from intensive and regional snow survey campaigns, and climate station observations. A greater (lower) cumulative change in |Δ37V| was significantly related to greater (lower) ground measured SWE (r² = 0.77 with monthly averages; r² = 0.67 with pentad averages). Σ|Δ37V| was only weakly correlated with lake fraction: monthly r² values calculated for January through April 2003-2007 were largely less than 0.2. These results indicate that this is a computationally straightforward and viable algorithmic framework for producing tundra-specific SWE datasets from the complete satellite passive microwave record (1979 to present).     

Secondary attribute retrieval using tree data structures

Several techniques are known for searching an ordered collection of data. The techniques and analyses of retrieval methods based on primary attributes are straightforward. Retrieval using secondary attributes depends on several factors. For secondary attribute retrieval, the linear structures—inverted lists, multilists, doubly linked lists—and the recently proposed nonlinear tree structures—multiple attribute tree (MAT), K-d tree (kdT)—have their individual merits.It is shown in this paper that, of the two tree structures, MAT possesses several features of a systematic data structure for external file organisation which make it superior to kdT. Analytic estimates for the complexity of node searchers, in MAT and kdT for several types of queries, are developed and compared.     

Continuous retrieval of multimedia data using parallelism

Most implementations of workstation-based multimedia information systems cannot support a continuous display of high resolution audio and video data and suffer from frequent disruptions and delays termed hiccups. This is due to the low I/O bandwidth of the current disk technology, the high bandwidth requirement of multimedia objects, and the large size of these objects, which requires them to be almost always disk resident. A parallel multimedia information system and the key technical ideas that enable it to support a real-time display of multimedia objects are described. In this system, a multimedia object across several disk drives is declustered, enabling the system to utilize the aggregate bandwidth of multiple disks to retrieve an object in real-time. Then, the workload of an application is distributed evenly across the disk drives to maximize the processing capability of the system. To support simultaneous display of several multimedia objects for different users, two alternative approaches are described. The first approach multitasks a disk drive among several requests while the second replicates the data and dedicates resources to each individual request. The trade-offs associated with each approach are investigated using a simulation model