An approach of surface coal fire detection from ASTER and Landsat-8 thermal data: Jharia coal field,India

Radiant temperature images from thermal remote sensing sensors are used to delineate surface coal fires, by deriving a cut-off temperature to separate coal-fire from non-fire pixels. Temperature contrast of coal fire and background elements (rocks and vegetation etc.) controls this cut-off temperature. This contrast varies across the coal field, as it is influenced by variability of associated rock types, proportion of vegetation cover and intensity of coal fires etc. We have delineated coal fires from background, based on separation in data clusters in maximum v/s mean radiant temperature (13th band of ASTER and 10th band of Landsat-8) scatter-plot, derived using randomly distributed homogeneous pixel-blocks (9 × 9 pixels for ASTER and 27 × 27 pixels for Landsat-8), covering the entire coal bearing geological formation. It is seen that, for both the datasets, overall temperature variability of background and fires can be addressed using this regional cut-off. However, the summer time ASTER data could not delineate fire pixels for one specific mine (Bhulanbararee) as opposed to the winter time Landsat-8 data. The contrast of radiant temperature of fire and background terrain elements, specific to this mine, is different from the regional contrast of fire and background, during summer. This is due to the higher solar heating of background rocky outcrops, thus, reducing their temperature contrast with fire. The specific cut-off temperature determined for this mine, to extract this fire, differs from the regional cut-off. This is derived by reducing the pixel-block size of the temperature data. It is seen that, summer-time ASTER image is useful for fire detection but required additional processing to determine a local threshold, along with the regional threshold to capture all the fires. However, the winter Landsat-8 data was better for fire detection with a regional threshold.     

Hemispheric asymmetry of sunspot area in solar cycle 23 and rising phase of solar cycle 24: Comparison of three data sets

Analysis of long-term solar data from different observatories is required to compare and confirm the various level of solar activity in depth. In this paper, we study the north–south asymmetry of monthly mean sunspot area distribution during the cycle-23 and rising phase of cycle-24 using the data from Kodaikanal Observatory (KO), Michelson Doppler Imager (MDI) and Solar Optical Observing Network (SOON). Our analysis confirmed the double peak behavior of solar cycle-23 and the dominance of southern hemisphere in all the sunspot area data obtained from three different resources. The analysis also showed that there is a 5–6 months time delay in the activity levels of two hemispheres. Furthermore, the wavelet analysis carried on the same data sets showed several known periodicities (e.g., 170–180 days, 2.1 year) in the north–south difference of sunspot area data. The temporal occurrence of these periods is also the same in all the three data sets. These results could help in understanding the underlying mechanism of north–south asymmetry of solar activity.     

Accuracy of the deep convection intensity from a limited number of casts

Deep convection is one of the key components of the Atlantic Meridional Overturning Circulation. The intensity of deep convection (DC) is traditionally estimated as the maximum mixed layer depth (MMLD). In this study, we developed a criterion of the minimum number of casts needed for obtaining the MMLD in the Greenland Sea with a pre-defined accuracy. The criterion depends on convection intensity.For gridded datasets, we introduce a complementary measure for the DC intensity: the area of the region with the mixed layer depth over a predefined value (800 m for the Greenland Sea, notated as S₈₀₀). For a weak or a moderate DC, variations of its intensity is more clear from variations of the MMLD (cluster 1 in the MMLD - S₈₀₀ parameter space). Then the MMLD can be obtained with the 25 % accuracy for at minimum 40 casts during winter. For a well developed DC (cluster 2), variations of the DC intensity are better accessed from variations of S₈₀₀ and minimum 10 casts are required.In the central Greenland Sea, the number of casts is sufficient for obtaining the interannual variations of the convection intensity only since 1986. If only Argo floats are available, minimum 4 floats should simultaneously operate in the Greenland Sea gyre during winter to reach the abovementioned accuracy. Up to present, the number of floats has been insufficient during most of the winters.     

基于FY-2G产品的江西省总云量最小二乘法建模与分析

使用2016年1月至2018年12月FY-2G卫星逐小时总云量产品和江西省26个测站的地面总云量观测资料,分析了两者在江西地区相关性的时空分布特征。结果表明:江西地区卫星总云量和地面观测总云量在数值和演变趋势上一致性较好,两者的总体相关系数超过0.60,但空间分布不均匀,大型水体(鄱阳湖和柘林湖)附近相关系数较低,低于0.45,江西省南部地区相关系数也不高,低于0.50。两者的相关系数在时间上也分布不均,其中14时的相关系数最高。此外,基于FY-2G卫星总云量产品和最小二乘法采用分段建模的方式,构建了江西省地面观测云的回归模型,且模拟的地面总云量在数值和演变趋势上更加接近观测的地面云量。     

准噶尔盆地西北缘一次大到暴雨天气过程分析

摘要：通过分析2010年6月２8～２9日发生在准噶尔盆地西北缘的一次大到暴雨天气,揭示了形成这次强降水天气的环流背景特点、物理量和雷达回波特征。结果显示：冷暖气团在克拉玛依地区附近相遇产生的中尺度锋生现象触发了本次暴雨天气过程。表现最明显的特征是：高空槽移动缓慢,中层切变线不断南压、低层中尺度系统维持,冷暖气团在区域强烈交汇。暴雨区雷达速度场有明显的风场幅合。对流降水云团强度、移向变化的预测,是此类天气临近预报的关键和难点。     

基于层次分析法与频率比模型的采空塌陷危险性评价

采空塌陷危险性评价是编制地质灾害防治规划、开展地质灾害防治与监测预警工作的重要依据。本文采用层次分析法与频率比模型相结合构建了采空塌陷危险性评价模型（AHP-PF组合模型）。以沈阳市蒲河-清水矿区为例,考虑了地质条件、地表特征、开采条件等3方面影响因素,选取了第四纪覆盖类型、第四纪松散层厚度、地质构造复杂程度、可采煤层顶板强度指标、煤层倾角、地表沉陷速率、采深采厚比、采空区叠置层数等8个评价指标,利用AHP-PF组合模型计算各指标权重及频率比,最后进行采空塌陷危险性分区。评价结果表明,采空塌陷危险性高区主要集中在采深采厚比小、沉陷速率大及目前仍在开采的区域,该区域是地质灾害防治、搬迁避让的重点区域。     

山西省煤矿采空地下空间评估与再利用研究

山西省煤炭资源丰富,年煤炭产量占全国产量的近1/4,煤炭资源开采形成大量的采空地下空间。随着碳中和目标的提出和山西风电、光伏发电的大力发展,如何合理利用煤矿采空地下空间,尤其是将地下空间与新能源结合将成为煤炭产业低碳发展的关键。本次研究运用比例系数法和采空地下空间守恒定律,测算出2022年山西省开采煤矿井巷可利用空间为1.54亿m3,“十五”到“十三五”期间关闭/退出煤矿的井巷可利用空间为1.05亿m3,估算1949至2021年山西省煤矿因工作面开采形成的采空地下空间约38.98亿m3,预测到2030年山西省煤矿工作面开采还可形成采空地下空间约19.56亿m3。根据山西省能源低碳发展需求提出了山西省煤矿采空地下空间未来可利用的四种模式：煤矿地下旅游或地下仓储、煤矿地下抽水蓄能、煤矿地下压缩空气储能和煤矿地下封存二氧化碳等。     

基于MonoRTM模型的微波辐射计反演方法研究

基于辐射传输模型MonoRTM计算天空亮温度,使用多元线性统计回归方法和BP神经网络方法分别对大气温度和水汽密度廓线进行了反演,检验并分析了两种方法的反演精度.结果表明,多元线性回归方法反演的温度偏差总体不大于6K,反演的水汽密度偏差小于4 g/m3;神经网络方法反演的温度偏差小于2K,反演的水汽密度误差总体不大于2 g/m3.与探空数据的对比表明,对于大气温度和水汽密度反演,BP神经网络方法的反演结果都要比多元线性回归方法的反演结果更接近探空资料值.     

US institutional pathways to clean coal and shale gas: lessons for China

China's 12th Five-Year Plan (2011–2015) envisages that shale gas and coal will be central to its energy future. However, for China to meet the energy security and climate change objectives set out in its 12th Five-Year Plan it will be reliant on the widespread commercial deployment of two key technologies; hydraulic fracturing combined with horizontal drilling for shale gas, and carbon capture and storage (CCS) for coal. China is moving to acquire these technologies through technology transfer and diffusion from the US, but progress has been slow, and neither is currently available in China on a commercial scale. Drawing on interviews in the US and China, this article argues that China's expectation of technology from the US may well be disappointed because of factors unique to the US institutional environment that have made the development of fracking technology possible and hinder the development of CCS technology at a commercial scale.

Policy relevance

If China is to meet the energy security and climate change objectives set out in its 12th Five-Year Plan it will be reliant on the widespread commercial deployment of fracking and clean coal technologies. While China expects to acquire these technologies via technology transfer and diffusion from the US, progress has been slow. Because of factors unique to the US institutional environment the availability of both technologies on a commercial scale in China is unlikely in the coming years. As a result, Chinese policy makers would be well-advised not to count on these technologies to meet their energy and climate goals.     

Investment risk and return under renewable decarbonization of a power market

How does financial performance risk affect investments in low-carbon electricity-generating technologies to achieve climate policy targets? A detailed risk simulation of price formation in the Great Britain wholesale power market is used to show that the increasing replacement of fossil facilities with wind, ceteris paribus, may cause a deterioration of the financial risk–return performance metrics for incremental investments. Low-carbon investments appear to be high risk, low return, and as such may require a progressively higher level of support over time than envisaged by the conventional degression trajectories. The increasing riskiness of the wholesale market will to some extent offset the benefits of lower capital costs and operational efficiencies if investors need to satisfy cautious debt coverage ratios alongside positive expected returns. This increased risk is additional to the well-known ‘merit order effect’ of low-carbon investments progressively depressing wholesale prices and hence their expected investment returns.

Policy relevance

Policy support for renewable technologies such as wind is usually based upon levelized costs and is expected to reduce over time as capital costs and operational efficiencies improve. However, levelized costs do not take full account of the risk aversion that investors may have in practice. Expected policy support reductions may be moderated to some extent by the increased financial performance risk that intermittent technologies bring to the power market. The annual risk-return profiles for incremental investments deteriorate for all technologies as wind replaces fossil fuels. This extra risk premium will need to be incorporated into evaluating policy incentives for new investments in a decarbonizing power market.