Consistency and differences between remotely sensed and surface observed total cloud cover over China

In this study, we conducted a comparison between surface-observed total cloud cover (TCCs) and Moderate Resolution Imaging Spectroradiometer (MODIS)-derived total cloud cover (TCCm) over China. A statistical method was applied to estimate the average field of view (FOV) of surface observers, and the radius range of FOV was 20–25, 25–35, 35–50, and 25–45 km for spring, summer, autumn, and winter, respectively. More differences would be added in the comparison when the satellite’s FOV was smaller or larger than the average FOV. Monthly mean TCCs was 74.78%, 74.41%, 66.5%, and 74.06% for each season and the corresponding TCCm was 75.27%, 78.34%, 73.82%, and 82.12%. The correlation between two data sets was stronger in spring (0.727) and summer (0.736) than in autumn (0.710) and winter (0.667). Over 60% of the differences were within the ?10% to 10% range, and more differences occurred for smaller TCCs. As a special feature, we found that the dust, haze, and snow cover over specific regions in China were the possible causes of the significant differences. Generally, these two data sets were in good agreement over China, and can complement each other especially in those significant difference cases to provide more accurate TCC data sets.     

Experimental evaluation of ALS point cloud ground extraction tools over different terrain slope and land-cover types

The article presents an evaluation of different terrain point extraction algorithms for airborne laser scanning (ALS) point clouds. The research area covers eight test sites with varying point densities in the range 3–15 points m^?2 and different surface topography as well as land-cover characteristics. In this article, existing implementations of algorithms were considered. Approaches that are based on mathematical morphology, progressive densification, robust surface interpolation, and segmentation are compared. The results are described based on qualitative and quantitative analyses. A quantification of the qualitative analyses is presented and applied to the data sets in this example. The achieved results show that the analysed algorithms give classification accuracy depending on the landscape and land cover. Although the results for flat and mountainous areas as well as for sparse and dense vegetation are in line with previous tests, this analysis provides an overview of situations in which the quantitative evaluation is not enough to correctly assess the classification results.     

Altitudinal and temporal evolution of raindrop size distribution observed over a tropical station using a K-band radar

Rain drop size distribution (DSD) measurements at different heights were made using a micro rain radar (MRR) at Thiruvananthapuram (latitude: 8.3° N, longitude: 76.9° E). Rain DSD data obtained from the MRR have been compared with a Joss–Waldvogel impact-type disdrometer (RD-80) deployed nearby and found to have good agreement. The analysis uses data collected during 16 continuous rainfall episodes during the southwest monsoon (June to September, JJAS) season. Since all the episodes behaved similarly, a single continuous rainfall episode occurring from 1610:01 to 1612:31 hours Indian Standard Time (IST) on 12 August 2006 is presented here. The fall velocity of those drops that contributed most to the rain rate was more or less constant at different altitudes and also with time during this episode, and the average value was 4.65 m s^?1. The rain rate (RR) was below 5 mm h^?1 for all the heights throughout the time. At the beginning of the rain episode, the number of drops at any given altitude was lower for larger drops. But towards the end of the episode, the number of drops in the smallest size class had reduced at almost all heights, whereas the number of drops in the larger size classes had increased. This suggests that the larger drops coming from above on colliding with smaller drops could coalesce, thus sweeping out the smaller drops as they fall. The reduction of small drops is seen with a corresponding increase in larger drops and without increase also during the course of a rainfall event. The former is an indication of coalescence while the latter is that of evaporation. All these observed phenomena in the natural rain support the observations made by Low and List in 1982.     

Regional CO pollution over the Indian-subcontinent and various transport pathways as observed by MOPITT

We used day-side Measurement of Pollution in the Troposphere (MOPITT) carbon monoxide (CO) retrievals (2000–2007) to examine the regional CO emission and its transport pathways during the summer/winter monsoon, with a specific focus on the Indian-subcontinent. It is observed that MOPITT CO retrievals at 850 hPa level in general show large scale features of CO emission in India, as reflected in the bottom-up inventory. In particular, high CO mixing ratios over the eastern north-eastern part of India, along the Indo-Gangetic (IG) region, and low CO mixing ratios over central India are generally captured from the MOPITT data. A strong plume with enhanced CO mixing ratios at 350?hPa is observed during the summer monsoon, demonstrating large scale vertical transport of the boundary layer CO from the Indian region into the upper troposphere. During winter outflow CO from the Indian region is found to be transported over the Arabian Sea and Bay of Bengal and reaches up to Saudi Arabia and north-eastern Africa. It is observed that emissions from Southeast Asia and the eastern north-eastern Indian region have the greatest impact over the Bay of Bengal and the eastern Indian Ocean, while emissions from the rest of India dominate over the Arabian Sea and the western Indian Ocean.     

Automated cloud and shadow detection and filling using two-date Landsat imagery in the USA

A simple, efficient, and practical approach for detecting cloud and shadow areas in satellite imagery and restoring them with clean pixel values has been developed. Cloud and shadow areas are detected using spectral information from the blue, shortwave infrared, and thermal infrared bands of Landsat Thematic Mapper or Enhanced Thematic Mapper Plus imagery from two dates (a target image and a reference image). These detected cloud and shadow areas are further refined using an integration process and a false shadow removal process according to the geometric relationship between cloud and shadow. Cloud and shadow filling is based on the concept of the Spectral Similarity Group (SSG), which uses the reference image to find similar alternative pixels in the target image to serve as replacement values for restored areas. Pixels are considered to belong to one SSG if the pixel values from Landsat bands 3, 4, and 5 in the reference image are within the same spectral ranges. This new approach was applied to five Landsat path/rows across different landscapes and seasons with various types of cloud patterns. Results show that almost all of the clouds were captured with minimal commission errors, and shadows were detected reasonably well. Among five test scenes, the lowest producer's accuracy of cloud detection was 93.9% and the lowest user's accuracy was 89%. The overall cloud and shadow detection accuracy ranged from 83.6% to 99.3%. The pixel-filling approach resulted in a new cloud-free image that appears seamless and spatially continuous despite differences in phenology between the target and reference images. Our methods offer a straightforward and robust approach for preparing images for the new 2011 National Land Cover Database production.     

SFT下的云化概率和关键重要度分布的实现与研究

为了完善空间故障树(Space Fault Tree,SFT)理论,特别是离散型空间故障树(Discrete Space Fault Tree,DSFT)对于系统实际运行过程中产生的具有离散性、随机性和模糊性的可靠性数据的适应性,提出了云化SFT方法。该方法利用云模型能表示数据的离散性、随机性和模糊性特点,重构SFT的计算基础,即特征函数。进而在SFT计算中保留原始数据特征,使最终结果也能诠释原始数据特征。论文主要完成了云化SFT理论环节中的一部分,即云化概率重要度分布和关键重要度分布。论文论证了引入云模型表示系统可靠性数据的必要性和可行性。给出了云化概率重要度分布和关键重要度分布的计算推导过程。并通过实例对这两个云化概念进行了计算。研究表明云化SFT结果要比原SFT结果更为接近现实,包含了更多数据的原始特征。     

Spatio-temporal distribution of burned areas by ecoregions in Mexico and Central America

Fire activity in Mexico and Central America, and its associated emissions, has impacts across multiple scales. On the local-to-regional scale, fire activity impacts land use, productivity, and biodiversity. On the regional-to-global scale, fire activity impacts hydrological, biogeochemical, and atmospheric processes. A consistent, reliable, large-scale characterization of the spatial and temporal distribution of fire burned area is required to assess its environmental impacts and to support natural resources’ management. The spatial and temporal distributions of fire burned areas in ecoregions of Mexico and Central America are evaluated in this study for the period 2001–2014, using the satellite Moderate Resolution Imaging Spectroradiometer (MODIS) MCD45 Burned Area data set. The methodology combines the 500 m burned area product with a MODIS land cover product and a map of North American land cover to calculate the spatiotemporal variability of fire activity as a function of land-use type.

The total burned area over Mexico and Central America over the period 2001–2014 was found to be 614,243.5 km², but with significant interannual variability over the 14 years included in the study. Indeed, the minimum burned area over the period was 9892.25 km² in 2014 and the maximum was 37,669.50 km² in 2011, a fourfold increase. Burned areas were found to be concentrated in northern Mexico and on the Pacific coast, mainly from October to June. Agricultural burned areas accounted for 37% and 43% of total detected burns in Mexico and Central America, respectively. The largest extent of burned surface occurs in May for most land-cover types. The maximum density of burned areas occurred in the tropical dry forests ecoregion during the dry season. Both in Mexico and Central America, burned area anomalies have significant anti-correlation with precipitation anomalies.     

Aerosol optical and microphysical properties observed by the lidar technique from a forest-fire smoke event over Madrid

On 27 August 2012, a wildfire occurred in the western zone of the Madrid region. Consequently, a significant release of smoke and aerosols was injected into the free troposphere and advected by the synoptic circulation in trajectories that passed over the capital. This event was detected by the Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT; Research Centre for Energy, Environment and Technology), Madrid lidar station at about 5 km height. While the main aerosol plume did not pass over the lidar station, the eastern portion of the dispersed emissions did. No aerosol layers were detected by lidar in the free-troposphere for the next few days. Since few observations of fresh biomass burning aerosol have been carried out by lidar so far, this study contributes to previous studies on biomass burning aerosols, in particular regarding the first stages of the smoke transport.

This article studies the optical and microphysical properties of the smoke aerosols retrieved by the lidar technique along with the recently developed Lidar/Radiometer Inversion Code (LiRIC), which combines both lidar and sun–sky photometer data. The sun–sky photometer data are provided by the Agencia Estatal de Meteorología (AEMET; Spanish Meteorological Agency) station close to the CIEMAT site. We suggest that the aerosol properties retrieved might be linked to the water uptake phenomenon in view of the fact that simultaneous water vapour lidar measurements exhibited a close relationship with backscatter coefficient profiles. This article is concerned not only with showing the capability of combining different remote-sensing techniques and the LiRIC code to provide aerosol vertical distribution for fine and coarse modes, but also with providing signs about the plausible interaction between aerosols and humidity that leads fresh biomass burning aerosols to act as cloud condensation nuclei. This key role, known as the indirect effect, remains the major source of uncertainty when the global radiation budget is assessed.     

Improved cloud detection in AVHRR daytime and night-time scenes over the ocean

Accurate cloud detection is a requirement of many geophysical applications that use visible and infrared satellite data (e.g. cloud climatologies, multichannel sea surface temperature (MCSST)). Unfortunately, a significant source of residual error in such satellite-based products is undetected cloud. Here, a new, computationally efficient cloud detection procedure for both daytime and night-time Advanced Very High Resolution Radiometer (AVHRR) data is developed. It differs substantially from our prior related work. First, a new clustering procedure is used, which produces more homogeneous and distinct clusters than those produced by either our previous work or the ISODATA algorithm of Ball and Hall. Second, the input information vector is reduced in size, incorporates both radiance and spatial components and each component is normalized. These changes improve the clustering/subsequent classification, tend to decrease execution time, and simplify post-processing of the classified (cloud, clear ocean) data to remove any residual outliers. Third, the enhanced performance makes possible the use of a multipass procedure which is very effective in identifying the complex multilayer cloud structures common in satellite data. Validation with independent lidar observations confirms the accuracy of the new procedure. Marine low stratiform clouds (LSCs-fog, stratus and stratocumulus) are also detected effectively. This advance is important because LSCs are a major source of residual cloud contamination in contemporary sea surface temperature (SST) products. Finally, the method is sufficiently general that it can be adapted to other sensors (e.g. the Along-Track Scanning Radiometer (ATSR), the Moderate Resolution Imaging Spectroradiometer (MODIS)).     

The impact of deforestation on cloud cover over the Amazon arc of deforestation

Atmospheric general circulation model (AGCM) simulations predict that a complete deforestation of the Amazon basin would lead to a significant climate change; however, it is more difficult to determine the amount of deforestation that would lead to a detectable climate change. This paper examines whether cloudiness has already changed locally in the Brazilian arc of deforestation, one of the most deforested regions of the Amazon basin, where over 15% of the primary forest has been converted to pasture and agriculture. Three pairs of deforested/forested areas have been selected at a scale compatible with that of climate model grids to compare changes in land cover with changes in cloudiness observed in satellite data over a 10-year period from 1984 to 1993. Analysis of cloud cover trends suggests that a regional climate change may already be underway in the most deforested part of the arc of deforestation. Although changes in cloud cover over deforested areas are not significant for interannual variations, they are for the seasonal and diurnal distributions. During the dry season, observations show more low-level clouds in early afternoon and less convection at night and in early morning over deforested areas. During the wet season, convective cloudiness is enhanced in the early night over deforested areas. Generally speaking, the results suggest that deforestation may lead to increased seasonality; however, some of the differences observed between deforested and forested areas may be related to their different geographical locations.     

Remote sensing of cloud distributions over the Bayanhar Mountains- watershed of the Yangtze and Yellow rivers

Although the two largest rivers in China originate in the same region separated only by the Bayanhar Mountains as a watershed, the Yangtze and Yellow rivers behave in quite different ways. Most of the warm and humid air currents from the Arabian Sea and the Bay of Bengal are blocked by the Bayanhar Mountains. As a result the amount of water in the Yellow River is only 5 per cent of that in the Yangtze River. Based on the cloud coverage area and the cloud volumetric distributions, and also the thickness above 9-4 km of the cumulus clouds located north and south of the Bayanhar Mountains from the geosynchronous satellite infrared imagery, the results suggest that a more detailed investigation is warranted in the hope that the proper modification of cumuli north of the Bayanhar Mountains would enhance the rainfall over the fountainhead of the Yellow River.     

Estimation and distribution of near-surface meteorological elements over complex terrains: a case study in the Tibetan areas of West Sichuan Province,China

ABSTRACT

Meteorological elements are important for various fields related to human activities, including scientific research. Using the Tibetan Areas of West Sichuan Province (TAOWS) as an example, this study examined the estimation methods for near-surface air temperature (T_a), vapour pressure deficit (VPD), and atmospheric pressure (P) and their distribution characteristics in areas with complex terrains and sparse stations. An improved satellite-based approach, combining an artificial neural network and inverse distance weighting (ANN-IDW), is proposed for estimating T_a and VPD with high-accuracy under all weather conditions from Moderate Resolution Imaging Spectroradiometer (MODIS) data. The data of 41 meteorological stations in TAOWS and its adjacent areas were used for the training and validation of the ANN-IDW. For T_a and VPD, the mean absolute errors (MAEs) of the ANN-IDW are 1.45°C to 2.15°C and 0.54 hPa to 0.87 hPa, respectively. Also, the detailed features of the distribution of the estimated T_a and VPD are prominent and closely related to the terrain. The accuracy of the method was also verified indirectly. In addition, the improved method based on the existing method was applied for estimating P. The results confirm that (1) the ANN-IDW is suitable for estimating T_a and VPD in areas with complex terrain and sparse stations under all weather conditions; (2) the improved method is more suitable for estimating P at high-elevation. Moreover, the distribution characteristics of meteorological elements in TAOWS were also analysed. These elements influence agricultural production and animal husbandry and have a high application value. The results further show that topography is the most important factor affecting the spatial distribution and complexity of meteorological elements over complex terrains, but the degree of influence of topography varies greatly across different seasons.     

点云空间与反射强度融合的非结构化道路可行驶区域检测

可行驶区域检测旨在检测和提取智能车辆在道路上可行进的区域, 目前主流的检测方法主要基于三维激光雷达的空间特征实现, 难以处理路面边缘无清晰空间特征的非结构化道路. 为此, 本文提出了一种基于点云空间和反射强度融合的非结构化道路可行驶区域检测方法. 首先, 通过融合反射强度因子改进了基于空间特征的柱坐标系检测模型; 然后, 使用强度和降维空间检测对检测精度较低的环形检测模型进行优化, 并将其与柱坐标系检测模型联合使用以提高方法检测准确率; 最后, 在自录实际道路数据集上进行对比实验. 实验结果表明本文方法显著提高了非结构化道路可行驶区域检测的成功率与精确率, 在结构化道路上也具有良好效果.     

Role of sea-surface wind and transport on enhanced aerosol optical depth observed over the Arabian Sea

The objective of this study is to understand the reasons for the enhancement in aerosol optical depth (AOD) over the Arabian Sea observed during June, July and August. During these months, high values of AOD are found over the sea beyond 10° N and adjacent regions. The Arabian Sea is bounded by the lands of Asia and Africa on its three sides. So the region is influenced by transported aerosols from the surroundings as well as aerosols of local origin (marine aerosols). During the summer monsoon season in India, strong surface winds with velocities around 15 m s^?1 are experienced over most parts of the Arabian Sea. These winds are capable of increasing sea spray activity, thereby enhancing the production of marine aerosols. The strong winds increase the contribution of marine aerosols over the region to about 60% of the total aerosol content. The main components of marine aerosols include sea salt and sulphate particles. The remaining part of the aerosol particles comes from the western and northern land masses around the sea, of which the main component is transported dust particles. This transport is observed at higher altitudes starting from 600 m. At low levels, the transport occurs mainly from the Indian Ocean and the Arabian Sea itself, indicating the predominance of marine aerosols at these levels. The major portion of the total aerosol loading was contributed by coarse-mode particles during the period of study. But in the winter season, the concentration of coarse-mode aerosols is found to be less. From the analysis, it is concluded that the increase in marine aerosols and dust particles transported from nearby deserts results in an increase in aerosol content over the Arabian Sea during June, July and August.     

Temporal and spatial variability in biomass burned areas across the USA derived from the GOES fire product

Burned area is a critical input to the algorithms of biomass burning emissions and understanding variability in fire activity due to climate change but it is difficult to estimate. This study presents a robust algorithm to reconstruct the patterns in burned areas across Contiguous United States (CONUS) in diurnal, seasonal, and interannual scales from 2000-2006. Specifically, burned areas in individual fire pixels are empirically calculated using diurnal variations in instantaneous fire sizes from the Geostationary Operational Environmental Satellites (GOES) WF_ABBA (Wildfire Automated Biomass Burning Algorithm) fire product. GOES burned areas exhibit diurnal variability with a temporal scale of half hours. The cumulative burned area during 9:00-16:00 local solar time accounts for 65%-81% of the total daily burned area. The diurnal variability is strongest in croplands compared to shrublands, grasslands, savannas, and forests. Analysis on a seasonal scale indicates that over 56% of burning occurs during summer (June-August). On average, the total annual burned area during the last seven years is 2.12 × 10⁴ ± 0.41 × 10⁴ km². The algorithm developed in this study can be applied to obtain burned area from the detections of GOES active fires at near real time, which can greatly improve the estimates of biomass burning emissions needed for predicting air quality.     

Physical-based soil moisture retrieval method over bare agricultural areas by means of multi-sensor SAR data

The main objective of this research is to develop, test and validate soil moisture retrieval method based on multi-source SAR (Synthetic Aperture Radar) data for bare agricultural areas. The Radardat-2, TerraSAR-X and Sentinel-1A SAR data were applied to retrieve soil moisture content in combination with the integral equation model (IEM) or calibrated integral equation model (CIEM). A straightforward inversion scheme was developed, which does not require the prior knowledge of surface roughness. The soil moisture content can be directly estimated using a look-up table (LUT) optimization method with multi-source SAR data as inputs. For validation purpose, in situ soil moisture content was measured during the period of SAR data acquisitions. The effectiveness and reliability of the soil moisture retrieval methods were evaluated based on the in situ measurements and cost function distribution graph. The experimental results indicate that the developed approach provided accurate soil moisture estimates with root mean square errors (RMSE) ranging from 0.047 cm³ cm^?3 to 0.079 cm³ cm^?3 over the experimental areas. The distribution graphs of the cost function demonstrate the uniqueness and convergence of the estimated results based on multi-source SAR data. Either IEM or CIEM was employed to estimate soil moisture content, more accurate results were obtained with Radarsat-2, TerraSAR-X and Sentinel-1A data as inputs. The experimental results preliminary illustrate that the multi-source SAR data are promising for soil moisture retrieval over bare agricultural areas. The novelty of the presented research can be summarized as two aspects. Firstly, the multi-sensor SAR with different incidence angle, different frequency and different polarization were combined to estimate soil moisture content by means of the physical-based methods. The combination of the multi-sensor SAR data can effectively solve the ill-posed problem of soil moisture retrieval using physical models. Secondly, the CIEM was utilized to establish the soil moisture retrieval model, which transforms the three unknown parameters to two unknown parameters. Furthermore, the convergence and uniqueness of the estimated soil moisture were validated through distribution graphs of the cost function.     

Comparative study of aircraft- and satellite-derived aerosol and cloud microphysical parameters during CAIPEEX-2009 over the Indian region

This article presents the spatial and vertical distribution of aerosols and cloud microphysical parameters from the combined data sets of aircraft and satellites. The aircraft-based Cloud Aerosol Interactions and Precipitation Enhancement Experiment (CAIPEEX) was conducted in India during May to September 2009. During the experimental period, 3 days were identified on which space-borne lidar (CALIPSO) and radar (CloudSat) were nearby/over passed the observational regions, which covered north, south central, and southern parts of the Indian subcontinent. The results obtained from these three cases are explored. Similar features of aerosol layering and water/ice cloud signatures are observed by both aircraft and CALIPSO. In addition, events where dust aerosols acting as ice nuclei and polluted aerosols increase the depth of warm rain initiation are observed. The CloudSat profiles of liquid water content, droplet number concentration, and effective radii are underestimated when compared with the corresponding aircraft profiles. The aircraft measurements are able to bring out fine variability in vertical distribution, which would be more useful for regional parameterization schemes and model evaluation.     

INSAT-3D and MODIS retrieved sea surface temperature validation and assessment over waters surrounding the Indian subcontinent

The INSAT-3D imager (4 km) and Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on-board Aqua and Terra space-platforms level-2 (1 km) sea surface temperature (SST_skin) product accuracy has been analysed over waters surrounding the Indian subcontinent by indirect comparison method using collocated bulk in-situ measurements (SST_depth) for 3 years (October 2013–October 2016). Statistical results show that root mean square error of all the three satellites is in range of around 0.60–0.70°C. Retrieval error is found to be slightly more in case of validation against iQuam data set. INSAT-3D is showing more underestimation with bias ranging from about ?0.16°C to ?0.20°C than MODIS sensor having bias in range of about 0.06°C to ?0.12°C. All the three missions are slightly underestimating over open-ocean with bias ranging in 0–0.17°C. INSAT-3D is significantly underestimating in-situ observations over the Arabian Sea (approximate bias = 0.27°C). Seasonal validation analysis reveals relatively high retrieval error during monsoon season than pre-monsoon and post-monsoon seasons. MODIS sensor is showing significant underestimation during monsoon with bias ranging from approximately ?0.29°C to ?0.58°C. Overall, all the three missions are performing similarly well over the study area.     

Variability and mechanisms of vertical distribution of aerosols over the Indian region

The present study investigates the seasonal variability in the vertical distribution of aerosol over the Indian region and its surroundings, and the possible mechanisms in the atmosphere that give rise to vertical transport of the aerosols. During boreal summer months, the aerosols reach a higher altitude of above 5 km over the Indian region. In the winter season, especially during December, January, and February, the aerosols remain at low levels of the atmosphere, extending to about 3 km. The low-level atmospheric conditions are favourable for lifting of aerosols associated with the organized convection in the atmosphere during the months from May to September. The shifting of the Inter Tropical Convergence Zone (ITCZ) towards the northern hemisphere and the monsoon activity associated with it makes the atmosphere turbulent over the region during the period. The vorticity and convergence patterns are favourable for the vertical transport of aerosols during the period from May to November. High vertical wind shear, which leads to the generation of turbulence during the monsoon season, enhances the mixing of aerosols in the atmosphere and supports the lifting motion. Over the Arabian Sea, during the summer months, the aerosols reach a higher altitude of about 6 km. The production of marine aerosols is increased by the monsoon winds over the sea, and the turbulent atmosphere lifts the particles to high altitudes. The transportation of dust aerosols from west and northwest parts is found at high altitudes towards the destination regions in north and south India. This also dominates the total aerosol content over the region.