Satellite Observations of Lava Lake Activity at Nyiragongo Volvano, Ex-Zaire, during the Rwandan Refugee Crisis

In June 1994 the summit crater of Nyiragongo volcano, located in the Great Lakes region of central Africa, began to fill with new lava, ending nearly 12 years of quiescence. An earlier eruption of the volcano in 1977 had culminated in the catastrophic draining of a lava lake through fissures in the crater wall, feeding highly mobile lava flows which reached the outskirts of Goma and killed more than 70 people. By July 1994, as many as 20,000 Hutu refugees were arriving in Goma every hour, only 18km south from the summit of Nyiragongo. The exodus brought more than one million people to the camps near the town raising fears of a repeat of the 1977 eruption. This paper examines the role that satellite remote sensing could have played in surveillance of the volcano during this time, and demonstrates the potential for monitoring this and other volcanoes in the future. Images recorded by the spaceborne Advanced Very High Resolution Radiometer (AVHRR) – freely available over the Internet – provide semi-quantitative information on the activity of the volcano. The aim of this paper is to promote the wider use of readily available technologies.     

Water Resources Modeling of the Ganges‐Brahmaputra‐Meghna River Basins Using Satellite Remote Sensing Data1

Nishat, Bushra and S.M. Mahbubur Rahman, 2009. Water Resources Modeling of the Ganges‐Brahmaputra‐Meghna River Basins Using Satellite Remote Sensing Data. Journal of the American Water Resources Association (JAWRA) 45(6):1313‐1327. Abstract: Large‐scale water resources modeling can provide useful insights on future water availability scenarios for downstream nations in anticipation of proposed upstream water resources projects in large international river basins (IRBs). However, model set up can be challenging due to the large amounts of data requirement on both static states (soils, vegetation, topography, drainage network, etc.) and dynamic variables (rainfall, streamflow, soil moisture, evapotranspiration, etc.) over the basin from multiple nations and data collection agencies. Under such circumstances, satellite remote sensing provides a more pragmatic and convenient alternative because of the vantage of space and easy availability from a single data platform. In this paper, we demonstrate a modeling effort to set up a water resources management model, MIKE BASIN, over the Ganges, Brahmaputra, and Meghna (GBM) river basins. The model is set up with the objective of providing Bangladesh, the lowermost riparian nation in the GBM basins, a framework for assessing proposed water diversion scenarios in the upstream transboundary regions of India and deriving quantitative impacts on water availability. Using an array of satellite remote sensing data on topography, vegetation, and rainfall from the transboundary regions, we demonstrate that it is possible to calibrate MIKE BASIN to a satisfactory level and predict streamflow in the Ganges and Brahmaputra rivers at the entry points of Bangladesh at relevant scales of water resources management. Simulated runoff for the Ganges and Brahmaputra rivers follow the trends in the rated discharge for the calibration period. However, monthly flow volume differs from the actual rated flow by (?) 8% to (+) 20% in the Ganges basin, by (?) 15 to (+) 12% in the Brahmaputra basin, and by (?) 15 to (+) 19% in the Meghna basin. Our large‐scale modeling initiative is generic enough for other downstream nations in IRBs to adopt for their own modeling needs.     

Effects of Soil Moisture Variations on Deposition Velocities Above Vegetation

The parameterized subgrid-scale surface flux (PASS) modelprovides a simplified means of using remote sensing data from satellites and limited surface meteorological information to estimate the influence of soil moisture on bulk canopy stomatalresistances to the uptake of gases over extended areas.PASS-generated estimates of bulk canopy stomatal resistance were usedin a dry deposition module to compute gas deposition velocitieswith a horizontal resolution of 200 m for approximately 5000 km² of agricultural crops and rangeland. Results were compared with measurements of O₃ flux and concentrations made during April and May 1997 at two surface stations and from an aircraft. The trend in simulated O₃ deposition velocityduring soil moisture drydown over a period of a few days matchedthe trend observed at the two surface stations. For areas underthe aircraft flight paths, the variability in simulated O₃ deposition velocity was substantially smaller than the observedvariability, while the averages over tens of kilometers were usually in agreement within 0.1 cm s^-1. Model results indicated that soil moisture can have a major role in depositionof O₃ and other substances strongly affected by canopy stomatal resistance.     

基于FY2C卫星数据的藏北高原降水估算研究

如何确切的掌握降水的时空分布,对区域气候、水文和生态应用等至关重要。以藏北高原典型区为研究区,在大量地面实况降水观测数据与对长时间序列FY 2C 影像光谱特征和云图特征分析的基础上,获取卫星降水模拟参数特征集以刻画云降水的发生与发展过程,选用最值归一化方法对不同量纲云图特征参数进行归一化处理。构建基于三层前向型反向传播神经网络的卫星降水估算模型,用于该地域降水估算,并采用多指标体系分析模型的降水模拟精度。结果表明：静止气象卫星红外波段能较精确地揭示云的降水机理,较高时间分辨率遥感图像可以监测云图的变化细节,并获取能够反映云图降水特征的降水模拟参数;人工神经网络能较好地刻画该地域卫星降水特征的非线性规律;三层前向型反向传播神经网络卫星降水估算模型的估算结果与雨量计实测值间的相关性可以达到0.57。模型估算结果系统性的低估偏小,预示着对该地域弱降水强度将有较好的指示性     

The Importance of Mixing Height in Characterising Pollution Levels from Aerosol Optical Thickness Derived by Satellite

In the present study the horizontal distribution of columnar aerosol optical thicknessderived at high spatial resolution from Earth observation satellite data in the Lombardy area (Italy) was converted to the horizontal distribution of optically effective aerosols concentration at the ground level. This was achieved by incorporating information on atmosphere's mixing height, at which pollutants released at ground level are vertically dispersed by convection or mechanical turbulence. The resulted fields compared favourably to pollutant concentration measurements provided by the ground stations. These results show that it is possible to calculate mean concentration fields by using the spatial distribution of aerosol optical thickness (AOT) measured by satellite normalized by the atmospheric mixing height. The advantage of satellites in measuring AOT is that they can capture all actual emissions compared to the models, which are based on inventoried data.     

青藏高原东南部及其邻近地区FY-2E卫星晴空大气可降水量评估

为了解青藏高原东南部及邻近地区FY-2E卫星大气可降水量(FY-2ETPW)的可靠性,利用2010~2012年探空计算值(RS TPW)对其进行对比分析。结果表明:青藏高原地区月平均FY-2ETPW除7月偏小外,其余月份都偏大,相对偏差仅有6月和7月在25%以内,其余月份都在50%以上,尤其是1月高达780.45%,FY-2ETPW与RS TPW之间为负相关系数,FY-2ETPW在青藏高原地区不可靠;低海拔地区,6月和7月平均FY-2ETPW偏小,其余月份都偏大,5~8月相对偏差不足4%,但1月相对偏差却高达105.84%;低海拔地区4~10月,FY-2E TPW与RS TPW相关系数大于0.5,FY-2ETPW订正模型估计标准误差为6.57mm,个别站点误差较大,还有待进一步的改进。     

中国2013年1月PM2.5重污染过程卫星反演研究

利用第三代空气质量模型CMAQ(community multiscale air quality modelling system)模拟的PM2.5垂直分层数据和中尺度气象模型WRF(weather research and forcasting model)模拟的高分辨率湿度数据,分别对MODIS AOD(aerosol optical depth)资料进行垂直与湿度订正,建立了订正后的AOD数据与PM2.5地面监测数据之间的线性拟合模型,其线性相关系数r=0.77(n=57,P0.01).基于此线性拟合模型,首次反演了2013年1月全国10 km分辨率PM2.5月均浓度的空间分布特征,并分析了人口暴露水平.结果表明,2013年1月我国PM2.5月均浓度大于100μg·m-3、200μg·m-3的面积占国土面积的比例分别高达10.99%、1.34%,暴露人口占全国总人口的比例分别高达45.01%、6.31%.     

卫星导航接收机高低温工作环境试验与评定方法研究

为了完善适用于实验室的卫星导航接收机高低温环境适应性测试与评定方法,提出了实验室条件下模拟接收机实际工作状态的适应性测试条件、评估方法和标准流程。该方法能够获得接收机预期使用情况,剔除产品早期故障,弥补了现有卫星导航接收机环境试验及评定方法的不足,提高了设备检测的可靠性。     

大气环境分区管理:以广东省为例

从区域大气环境管理的角度出发,提出大气环境分区管理的理念.以广东省地区为例,利用空气质量数值模拟和卫星遥感反演分析相结合的方法,从气象要素模拟场、浓度要素模拟场和卫星图像解译中筛选出关键因子,形成大气环境管理分区指标体系,在此基础上,利用层次聚类分析法将广东省划分为严格控制区、持续改善区和协调发展区这3种大气环境管理分区类型.结果表明,广东省大气环境管理严格控制区、持续改善区和协调发展区分别占16.3%、28.0%和55.7%.严格控制区在珠三角、粤东、粤西和粤北所占的比例分别为27.9%、19.3%、4.4%和12.5%,区域内应实施最严格的大气环境管理政策以推动空气质量改善;持续改善区在珠三角、粤东、粤西和粤北所占的比例分别为34.4%、15.8%、7.8和34.5%,区域内应实施相对严格的大气环境管理政策以保障持续稳定达标;协调发展区在珠三角、粤东、粤西和粤北所占的比例分别为37.7%、64.9%、87.8%和53.0%,区域内应实施较为宽松的大气环境管理政策以保障相对良好的空气质量.总体而言,广东省大气环境管理压力主要集中在珠三角地区,粤北、粤东地区次之,粤西地区大气环境管理压力最小.     

卫星遥感监测海南地区对流层CO2浓度时空变化特征

为了探索海南地区对流层CO₂浓度[以φ(CO₂)计]时空变化特征,采用2002年9月—2012年2月AIRS反演的对流层中层CO₂产品,利用北半球全球本底站瓦里关站和飞机观测φ(CO₂)对该产品进行验证,结合统计分析方法对海南地区φ(CO₂)的月、季、年平均值的时空变化特征进行了研究.结果表明:AIRS反演φ(CO₂)与地基和不同纬度带海洋上空飞机观测数据对比均具有很好的一致性,并且与飞机观测验证偏差更小,二者相关系数均在0.9以上,总体月均值偏差小于2×10-6;全国φ(CO₂)呈现北高南低的分布规律,并且存在较为明显的分界线,形成4个高值中心(塔克拉玛干沙漠、塔里木盆地、内蒙古西部和东北平原)和2个低值中心(青藏高原西南部和云南地区),海南地区平均φ(CO₂)为382.67×10-6,略高于云南低值中心的381.45×10-6;全国φ(CO₂)呈现明显逐年增加趋势,其年均增长速率为2.16×10-6,而海南地区亦呈现显著增加趋势,年均增长速率为2.11×10-6,低于全国水平;φ(CO₂)呈季节性波动特性,全国φ(CO₂)最高值出现在春季,而海南地区为夏季,最低值均出现在秋季;海南地区西部海域、陆地和东部海域上空φ(CO₂)年增长速率分别为2.09×10-6、2.14×10-6和2.11×10-6,表明海南陆地上空增速略大于海洋地区,西部和东部海域上空增长速率基本保持一致.