A New Algorithm For Assessing The Xco2 Over Peninsular Malaysia Based On Gosat Data

Peningkatan kepekatan karbon dioksida (CO 2) yang disebabkan oleh aktiviti antropogenik telah menjadi tumpuan banyak kajian kerana kesan buruk pemanasan global dan perubahan iklim terhadap alam sekitar. The increasing carbon dioxide (CO 2) concentration induced by anthropogenic activities has been the focal point of many studies due to the adverse effects of global warming and climate change on the environment. To achieve a healthy environment, studying the transport, distributions and source regions of CO 2 in Malaysia is necessary

Source Characteristics of Atmospheric CO(2)and CH(4)in a Northeastern Highland Area of South Korea

This study aims to present the atmospheric CO(2)and CH(4)levels and analyze their source characteristics at an observation station in a northeastern highland area of Korea for the 2012-2014 period. We summarized the measured CO(2)and CH(4)concentrations for the 2012-2014 period. In addition, we characterized the major source of the rise of CO(2)and CH(4)in Ganseong (GS) by employing bivariate polar plots (BPP) and the concentration weighted trajectory (CWT) method together with currently available information on emission sources. For the three years, CO(2)was generally high in the order of winter, spring, autumn and summer and CH(4)high in the order of winter, autumn, spring and summer. The observed positive correlations between the hourly CO(2)and CH(4)in every season suggested the possibility of shared common emission sources, but there is a necessity for elucidation on this in the future. The BPP analysis indicated the local sources that are likely to be associated with the rise of greenhouse gases (GHGs) observed at GS (combustion in the village, plant respirations nearby GS, and mobile emissions on the nearby road for CO(2)and leakages from the gas stations along the road and agricultural activities for CH4). Synthesizing the CWT results together with emission source information from national and global emission inventories, we identified likely major source areas and characterized major emission sources. For example, the identified major sources for the winter CO(2)are coal combustion, coal washing and industrial activities in Inner Mongolia, northern and the northeastern China, fuel burning for the energy for the infrastructure of a northwestern city in South Korea, and the manufacturing industry and fuel combustion in the northern parts of North Korea. Hopefully, these kinds of results will aid environmental researchers and decision-makers in performing more in-depth studies for GHG sources in order to derive effective mitigation strategies

Studies of global cloud field using measurements of GOME, SCIAMACHY and GOME-2

Tropospheric clouds are main players in the Earth climate system. Characterization of long-term global and regional cloud properties aims to support trace-gases retrieval, radiative budget assessment, and analysis of interactions with particles in the atmosphere. The information needed for the determination of cloud properties can be optimally obtained with satellite remote sensing systems. This is because the amount of reflected solar light depends both on macro- and micro-physical characteristics of clouds. At the time of writing, the spaceborne nadir-viewing Global Ozone Monitoring Experiment (GOME), together with the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) and GOME-2, make available a unique record of almost 17 years (June 1996 throughout May 2012) of global top-of-atmosphere (TOA) reflectances and form the observational basis of this work. They probe the atmosphere in the ultraviolet, visible and infrared regions of the electromagnetic spectrum. Specifically, in order to infer cloud properties such as optical thickness (COT), spherical albedo (CA), cloud base (CBH) and cloud top (CTH) height, TOA reflectances have been selected inside and around the strong absorption band of molecular oxygen in the wavelength range at 758-772 nm (the O2 A-band). The retrieval is accomplished using the Semi-Analytical CloUd Retrieval Algorithm (SACURA). The physical framework relies on the asymptotic parameterizations of radiative transfer. The generated record has been throughly verified against synthetic datasets as function of cloud and surface parameters, sensing geometries, and instrumental specifications and validated against ground-based retrievals. The error budget analysis shows that SACURA retrieves CTH with an average accuracy of ±400 m, COT within ±20% (given that COT > 5) and places CTH closer to ground-based radar-derived CTH, as compared to independent satellite-based retrievals. In the considered time period the global average CTH is 5.2±3.0 km, for a corresponding average COT of 20.5±16.1 and CA of 0.62±0.11. Using linear least-squares techniques, global trend in deseasonalized CTH has been found to be -1.78±2.14 m * year-1 in the latitude belt ±60°, with diverging tendency over land ( 0.27±3.2 m * year-1) and water (-2.51±2.8 m * year-1) masses. The El Nino-Southern Oscillation (ENSO), observed through CTH and cloud fraction (CF) values over the Pacific Ocean, pulls clouds to lower altitudes. It is argued that ENSO must be removed for trend analysis. The global ENSO-cleaned trend in CTH amounts to -0.49±2.22 m * year-1. At a global scale, no explicit patterns of statistically significant trends (at 95% confidence level, estimated with bootstrap resampling technique) have been found, which are representative of peculiar natural climate variability. One exception is the Sahara region, which exhibits the strongest upward trend in CTH, sustained by an increasing trend in water vapor. Indeed, the representativeness of every trend is affected by the record length under study. 17 years of cloud data still might not be enough to provide any decisive answer to current open questions involving clouds. The algorithm used in this work can be applied to measurements provided by future planned Earth's observation missions. In this way, the existing cloud record will be extended and attribution of cloud property changes to natural or human causes and assessment of cloud feedback sign within the climate system can be investigated

The Impact of Different Support Vectors on GOSAT-2 CAI-2 L2 Cloud Discrimination

Greenhouse gases Observing SATellite-2 (GOSAT-2) will be launched in fiscal year 2018. GOSAT-2 will be equipped with two sensors: the Thermal and Near-infrared Sensor for Carbon Observation (TANSO)-Fourier Transform Spectrometer 2 (FTS-2) and the TANSO-Cloud and Aerosol Imager 2 (CAI-2). CAI-2 is a push-broom imaging sensor that has forward- and backward-looking bands to observe the optical properties of aerosols and clouds and to monitor the status of urban air pollution and transboundary air pollution over oceans, such as PM2.5 (particles less than 2.5 micrometers in diameter). CAI-2 has important applications for cloud discrimination in each direction. The Cloud and Aerosol Unbiased Decision Intellectual Algorithm (CLAUDIA1), which applies sequential threshold tests to features is used for GOSAT CAI L2 cloud flag processing. If CLAUDIA1 is used with CAI-2, it is necessary to optimize the thresholds in accordance with CAI-2. However, CLAUDIA3 with support vector machines (SVM), a supervised pattern recognition method, was developed, and then we applied CLAUDIA3 for GOSAT-2 CAI-2 L2 cloud discrimination processing. Thus, CLAUDIA3 can automatically find the optimized boundary between clear and cloudy areas. Improvements in CLAUDIA3 using CAI (CLAUDIA3-CAI) continue to be made. In this study, we examined the impact of various support vectors (SV) on GOSAT-2 CAI-2 L2 cloud discrimination by analyzing (1) the impact of the choice of different time periods for the training data and (2) the impact of different generation procedures for SV on the cloud discrimination efficiency. To generate SV for CLAUDIA3-CAI from MODIS data, there are two times at which features are extracted, corresponding to CAI bands. One procedure is equivalent to generating SV using CAI data. Another procedure generates SV for MODIS cloud discrimination at the beginning, and then extracts decision function, thresholds, and SV corresponding to CAI bands. Our results indicated the following. (1) For the period from November to May, it is more effective to use SV generated from training data from February while for the period from June to October it is more effective to use training data from August; (2) In the preparation of SV, features obtained using MODIS bands are more effective than those obtained using the corresponding GOSAT CAI bands to automatically extract cloud training samples

Thermal imaging study to determine the operational condition of a conveyor belt drive system structure

The paper discusses the results of a study carried out to determine the thermal condition of a conveyor power unit using a thermal imaging camera. The tests covered conveyors in the main haulage system carrying coal from a longwall. The measurements were taken with a thermal imaging diagnostic method which measures infrared radiation emitted by an object. This technology provides a means of assessing the imminence and severity of a possible failure or damage. The method is a non-contact measuring technique and offers great advantages in an underground mine. The thermograms were analysed by comparing the temperature distribution. An analysis of the operating time of the conveyors was also carried out and the causes of the thermal condition were determined. The main purpose of the research was to detect changes in thermal state during the operation of a belt conveyor that could indicate failure and permit early maintenance and eliminate the chance of a fire. The article also discusses the construction and principle of operation of a thermal imaging camera. The findings obtained from the research analysis on determining the thermal condition of the conveyor drive unit are a valuable source of information for the mine's maintenance service.Web of Science1411art. no. 325

Simulating urban soil carbon decomposition using local weather input from a surface model

Non peer reviewe

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This Open Access handbook published at the IAMG's 50th anniversary, presents a compilation of invited path-breaking research contributions by award-winning geoscientists who have been instrumental in shaping the IAMG. It contains 45 chapters that are categorized broadly into five parts (i) theory, (ii) general applications, (iii) exploration and resource estimation, (iv) reviews, and (v) reminiscences covering related topics like mathematical geosciences, mathematical morphology, geostatistics, fractals and multifractals, spatial statistics, multipoint geostatistics, compositional data analysis, informatics, geocomputation, numerical methods, and chaos theory in the geosciences

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Feature Papers of Drones - Volume II

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