基于GIS的城区雨洪监控系统应用

在GIS技术支持下建立的雨洪监控系统,为及时、合理地利用雨洪,节约开支提供了很好的保障,本文介绍了雨洪监控系统的应用和工程实践中的问题。     

全球观测资料质量监视评估

全球观测资料质量监视评估系统是为监视评估国家气象中心所接收的全球气象观测资料的质量而设计开发的。该系统以资料同化的6小时预报场为背景场，计算观测资料与背景场的偏差；运用质量控制技术排除观测资料中的可能是错误的资料；实时和非实时地统计分析各类观测报告的状况和观测资料的质量。采用的质量控制技术和统计分析方法都尽量和国际上发达气象中心相一致。所有产品都以图表形式在气象系统内部web网页上发布。该系统应用于日常观测资料质量监视评估及中国区域探空资料问题的详细分析、探空站综合评分、探空观测资料归档、探空观测资料偏差订正效果检验、L波段探空资料质量分析等，取得了显著效果。对加强观测业务管理、保证观测资料质量、改进观测技术发挥了积极的作用。     

气象遥感资料变分校准方案改进及应用试验

针对常见气象遥感资料变分校准方案的不足，提出了一个改进方案。该方案不需要将校准参照量插值到遥测资料的格点上，既避免了对稀疏的校正参照量进行客观分析的困难，也避免了校正参照量稀缺区域不准确插值分析带来的不利影响，使遥感资料的整体偏差得到修订的同时，能够更好地保持遥测资料的空间分布形态不变。利用国家卫星气象中心通过9210下发的ATOVS反演产品进行了试验，结果表明改进方案校准效果好；并将校准后的ATOVS反演资料用于Grapes三维变分同化系统，应用试验表明校准后的ATOVS反演资料在三维变分同化系统中具有应用价值。     

河北省冬麦区土壤水分监测预测系统及其应用

河北省冬小麦生育阶段正值少雨时期,一般年份只有在灌溉的情况下才能满足小麦生长发育的需求,麦区土壤水分实时监测预测对于灌溉决策十分重要。在冬小麦单站多层次土壤水分动态模型(VSMB模型)的基础上,根据河北省麦区从南到北冬小麦发育期和土壤类型的不同进行参数调整和修正,并对数据库进行设计,形成河北省整个冬麦区麦田土壤水分监测预测系统(RSM-MFS)。文中介绍了该系统的基本原理和功能,并对实际应用进行了效果分析。从近年在河北省冬麦区土壤水分的监测预测结果来看,监测相对误差在10%左右,风险预测相对误差在20%左右。     

利用MODIS资料监测和预警干旱新方法

为了提高遥感监测土壤水分和干旱的技术水平,在对常用遥感监测土壤水分和干旱方法进行评价的基础上,根据土壤热力学理论,提出了利用EOS/MODIS资料遥感监测农业干旱的新方法———能量指数模式。实际监测应用结果表明,能量指数模式更适合农作物土壤水分和干旱的监测,监测效果明显优于已经业务化的其它模式。农作物生长季节干旱的预警应当在土壤水分遥感监测的基础上,考虑未来预警期内的预报降水量和最高气温,并且把这个预报降水量和最高气温定量转化为土壤水分订正值,按照当地的标准划分成干旱预警等级。     

2004年广东省干旱灾害遥感监测应用研究

针对广东省气候湿润、植被覆盖度高的特点,以NOAA/AVHRR资料为数据源,采用植被供水指数法,对2004年广东省发生的50年来最严重的一次干旱灾害进行遥感动态监测,并结合土地利用分类数据,采用掩膜技术提取干旱分类信息.研究结果表明,10月上旬全省部分地区出现旱情,10月下旬全省受旱总面积达到峰值,占全省面积的72.6%;11月上旬后期受旱面积有所减少,中旱、重旱区域有所增加,其后旱情总体呈减缓趋势,但在12月中旬出现了短暂的增加趋势.以11月上旬为例的干旱灾害分类研究还表明,水田、旱地、灌木、林地等不同的用地类型,其受旱比例依次递减.该研究结果较好地反映了本次干旱灾害的空间分布及发展过程,为相关部门进行干旱灾情评估、制定和实施抗旱减灾措施提供了科学依据.     

Sensor Calibration in Support for NOAA’s Satellite Mission

Sensor calibration, including its definition, purpose, traceability options, methodology, complexity, and importance, is examined in this paper in the context of supporting NOAA's satellite mission. Common understanding of sensor calibration is essential for the effective communication among sensor vendors, calibration scientists, satellite operators, program managers, and remote sensing data users, who must cooperate to ensure that a nation's strategic investment in a sophisticated operational environmental satellite system serves the nation's interest and enhances the human lives around the world. Examples of calibration activities at NOAA/NESDIS/ORA are selected to further illustrate these concepts and to demonstrate the lessons learned from the past experience.     

Assessment with satellite data of the urban heat island effects in Asian mega cities

This study focuses on using remote sensing for comparative assessment of surface urban heat island (UHI) in 18 mega cities in both temperate and tropical climate regions. Least-clouded day- and night-scenes of TERRA/MODIS acquired between 2001 and 2003 were selected to generate land-surface temperature (LST) maps. Spatial patterns of UHIs for each city were examined over its diurnal cycle and seasonal variations. A Gaussian approximation was applied in order to quantify spatial extents and magnitude of individual UHIs for inter-city comparison. To reveal relationship of UHIs with surface properties, UHI patterns were analyzed in association with urban vegetation covers and surface energy fluxes derived from high-resolution Landsat ETM+ data. This study provides a generalized picture on the UHI phenomena in the Asian region and the findings can be used to guide further study integrating satellite high-resolution thermal data with land-surface modeling and meso-scale climatic modeling in order to understand impacts of urbanization on local climate in Asia.     

Remotely sensed estimation of forest canopy density: A comparison of the performance of four methods

In recent years, a number of alternative methods have been proposed to predict forest canopy density from remotely sensed data. To date, however, it remains difficult to decide which method to use, since their relative performance has never been evaluated. In this study the performance of: (1) an artificial neural network, (2) a multiple linear regression, (3) the forest canopy density mapper and (4) a maximum likelihood classification method was compared for prediction of forest canopy density using a Landsat ETM+ image. Comparison of confusion matrices revealed that the regression model performed significantly worse than the three other methods. These results were based on a z-test for comparison of weighted kappa statistics, which is an appropriate statistic for analysis of ranked categories. About 89% of the variance of the observed canopy density was explained by the artificial neural networks, which outperformed the other three methods in this respect. Moreover, the artificial neural networks gave an unbiased prediction, while other methods systematically under or over predicted forest canopy density. The choice of biased method could have a high impact on canopy density inventories.