Bayesian area-to-point kriging using expert knowledge as informative priors

Area-to-point (ATP) kriging is a common geostatistical framework to address the problem of spatial disaggregation or downscaling from block support observations (BSO) to point support (PoS) predictions for continuous variables. This approach requires that the PoS variogram is known. Without PoS observations, the parameters of the PoS variogram cannot be deterministically estimated from BSO, and as a result, the PoS variogram parameters are uncertain. In this research, we used Bayesian ATP conditional simulation to estimate the PoS variogram parameters from expert knowledge and BSO, and quantify uncertainty of the PoS variogram parameters and disaggregation outcomes. We first clarified that the nugget parameter of the PoS variogram cannot be estimated from only BSO. Next, we used statistical expert elicitation techniques to elicit the PoS variogram parameters from expert knowledge. These were used as informative priors in a Bayesian inference of the PoS variogram from BSO and implemented using a Markov chain Monte Carlo algorithm. ATP conditional simulation was done to obtain stochastic simulations at point support. MODIS (Moderate Resolution Imaging Spectroradiometer) atmospheric temperature profile data were used in an illustrative example. The outcomes from the Bayesian ATP inference for the Matérn variogram model parameters confirmed that the posterior distribution of the nugget parameter was effectively the same as its prior distribution; for the other parameters, the uncertainty was substantially decreased when BSO were introduced to the Bayesian ATP estimator. This confirmed that expert knowledge brought new information to infer the nugget effect at PoS while BSO only brought new information to infer the other parameters. Bayesian ATP conditional simulations provided a satisfactory way to quantify parameters and model uncertainty propagation through spatial disaggregation.     

Spatiotemporal patterns of tropical deforestation and forest degradation in response to the operation of the Tucuruí hydroelectric dam in the Amazon basin

The planned construction of hundreds of hydroelectric dams in the Amazon basin has the potential to provide invaluable ‘clean’ energy resources for aiding in securing future regional energy needs and continued economic growth. These mega-structures, however, directly and indirectly interfere with natural ecosystem dynamics, and can cause noticeable tree loss. To improve our understanding of how hydroelectric dams affect the surrounding spatiotemporal patterns of forest disturbances, this case study integrated remote sensing spectral mixture analysis, GIS proximity analysis and statistical hypothesis testing to extract and evaluate spatially-explicit patterns of deforestation (clearing of entire forest patch) and forest degradation (reduced tree density) in the 80,000 km² neighborhoods of the Brazil's Tucuruí Dam, the first large-scale hydroelectric project in the Amazon region, over a period of 25 years from 1988 to 2013. Results show that the average rates of deforestation were consistent during the first three time periods 1988–1995 (620 km² per year), 1995–2001 (591 km² per year), and 2001–2008 (660 km² per year). However, such rate dramatically fell to half of historical levels after 2008, possibly reflecting the 2008 global economic crisis and enforcement of the Brazilian Law of Environmental Crimes. The rate of forest degradation was relatively stable from 1988 to 2013 and, on average, was 17.8% of the rate of deforestation. Deforestation and forest degradation were found to follow similar spatial patterns across the dam neighborhoods, upstream reaches or downstream reaches at the distances of 5 km–80 km, suggesting that small and large-scale forest disturbances may have been influencing each other in the vicinity of the dam. We further found that the neighborhoods of the Tucuruí Dam and the upstream region experienced similar degrees of canopy loss. Such loss was mainly attributed to the fast expansion of the Tucuruí town, and the intensive logging activities alongside major roads in the upstream reservoir region. In contrast, a significantly lower level of forest disturbance was discovered in the downstream region.     

青藏高原与东亚地区暖季MCSs统计特征的对比分析

利用1996-2010年逐时红外云顶亮温(TBB)数据,对青藏高原(下称高原)和东亚地区暖季(3-9月)中尺度对流系统(MCSs)进行了普查,获得MCSs各项特征数据集;结合CMORPH降水资料对MCSs降水特征进行了分析,在此基础上又对高原和东亚地区的MCSs特征进行了对比分析.结果表明:(1)高原是东亚地区MCSs高频发生区域,高原MCSs的平均发生频次远高于东亚25°N以北地区,是东亚25°N以北地区唯一的MCSs活动高频区.高原腹地是高原地区MCSs发生的高频区,在31°N,88°E附近MCSs的发生频次最高.(2)高原地区 MCSs分布有明显的月际变化,春季主要出现在高原北部,夏季主要出现在高原中东部和南部.高原地区MCSs的月际变化特征与东亚地区基本一致,东亚夏季风是其月际变化的重要影响因子.(3)高原地区MCSs存在明显的单峰型日变化特征,但春、夏季日变化特征略有不同,高原MCSs的日变化较东亚地区更为显著.(4)高原地区MCSs的降水频次为5.6％,降水贡献率为10.1％,最大降水频次为12％,最大降水贡献率为27％;与东亚地区相比,高原的MCSs降水偏小.(5)高原地区的MCSs大多为向东移动且移速缓慢以及短生命史的MβCS,平均生命史为4.6h,平均面积约为11.2×10-1km2,平均移速为31.5 km·h-1,东移的MCSs占59.4％;与整个东亚地区的MCSs相比,高原的MCSs面积和尺度都较小,生命史略短且移速慢,云顶平均TBB和平均最低TBB均偏高.     

MIXTURE ANGLYSIS USING FACTOR ANGLYSIS I: CALIBRATION AND QUANTITATION

One of the major application areas of factor analysis, multivariate calibration and quantitation, is coveredin this review. The algorithms, methodologies and applications covered include principal componentregression, target transformation factor analysis, singular value decomposition and rank annihilationfactor analysis. Many important areas of research having relevance to multivariate calibration andquantitation problems are also covered in this review, including background correction, measurementerror, rank determination, cross-validation, figures of merit, detection of invalid samples, experimentaldesign, sample selection, statistical inference and wavelength selection.     

连云港市暴雨天气形势分析

用连云港市7个气象观测站的常规观测资料,对1971-2008年的38 a中29次区域性暴雨天气过程进行数理统计,对连云港市的暴雨天气变化趋势进行探讨,连云港市暴雨天气主要发生在6-9月,7-8月次数最多,占总数79％,雨量极值出现在西连岛站,暴雨日数及雨量极值趋于增大且趋势比较明显.同时,对全市区域性暴雨天气的高空影响系统进行了分类,划分为4种类型:台风减弱型、低压型、横槽类型、低槽类型,各类型几乎各占总暴雨数的1/4.     

边界层温度廓线遥感反演的本地化改进研究

采用MODIS资料和美国发展的MODIS大气温、湿度廓线统计反演算法,估算大气温度、湿度廓线作为初始场,应用101层快速透过率模式(PFAAST)估算了大气透过率,并采用Newton非线性迭代算法反演中国西北荒漠戈壁地区大气温度廓线。结果表明:该方法对边界层高度及以上部分的大气温度反演得比较好,误差基本都在2 K范围内,边界层范围内的温度反演误差较大,反演误差与气溶胶光学厚度增量和地表温度估算误差呈显著正相关关系,与大气水汽混合比的关系较差。文中从敏感性试验和理论分析角度阐述了地表温度和气溶胶光学厚度估算误差对大气温度反演误差的影响,发现不同光谱波段的地表温度权重均随地表温度的增加有不同程度增加,地表温度反演误差增加将增加地表温度权重,提高地表温度估算误差有助于提高地表温度权重的精度;荒漠戈壁地区大气边界层中气溶胶浓度较高,光学厚度较大,使边界层大气透过率降低,进而降低卫星红外遥感波段的地表温度权重和空气温度权重。由于该模式没有很好地考虑边界层中沙尘气溶胶的影响,使卫星反演的大气透过率偏高,以至于高估地表温度权重和大气温度权重,使得反演的表面温度和空气温度偏低。该研究结合太阳光度计获得的光学厚度资料,采用统计方法对气溶胶效应引起的大气透过率误差和表面温度估算误差进行校正,并对物理算法进行本地化改进,实现了边界层温度廓线的反演。     

南城自动站土壤水分资料的统计学订正分析

对2006—2009年南城站5—10cm、10—20cm、20—30cm、30—40cm和40—50cm等5个土层各90组样本的HYA-SF型土壤水分自动观测数据与同期人工观测数据进行了偏差与相关性分析,采用线性方程建立拟合模式,对自动土壤水分观测数据进行订正,并以相对误差±5%、±10%为指标对订正前后的数据合格情况进行了统计。结果表明,各土层自动土壤水分观测数据与人工观测数据虽然存在较大偏差,但二者具有一致的变化趋势,相关性好;分别利用5个土层同期对比观测数据建立的一元线性回归模型;拟合订正后的自动站各层数据偏差不同程度减小,数据合格率明显上升。     

统计降尺度方法在青海省冬季最低温度预测中的应用

利用2003-2007年国家气象中心T213L31全球中期数值预报模式逐日输出产品与青海地区25个气象站的观测数据作为试验资料, 利用相关系数和逐步回归进行因子选择, 并以单隐层神经网络和多元回归作为降尺度方法进行对比研究, 用2003-2006年间的11月1日～次年3月1日的资料作为训练样本, 以数值预报产品和前一日观测的最低温度作为因子, 建立青海省25个气候站的冬季最低温度的24, 48, 72 h预报模型, 并且以2006年12月和2007年的1、 2月作为24, 48, 72 h逐日最低温度预报试验时段。试验表明, 对于青海地区来说, 青海北部地区的预报命中率总体好于南部高原地区; 在4种对比方案中, 以选择数值预报资料结合前一日地面观测的最低温度作为主要因子的方法相对较优, 随着预报时效的延长, 24 h历史实况的作用逐渐减弱; 对于所有台站来说, 这4种方案各有优缺点, 没有一种方案可以完全代替其他所有方案; 在实际业务运行中, 对不同的台站应采用不同的预报方案进行实际业务预报。     

一次人工增雨作业中CA—FCM与其他评估方案的比较研究

根据2002年4月5日河南省人工催化增雨作业数据,分别采用作业区域趋势对比双比分析评估方案、区域趋势相关回归评估方案、区域趋势协变量多元回归评估方案、FCM（浮动对比区历史回归）评估方案、以降水量为协变量的CA—FCM评估方案和以降水量与整层大气可降水量为协变量的CA—FCM评估方案进行效果评估。结果表明：协变量由降水量和整层大气降水量2个组成的CA—FCM方法,由于引入了不受催化影响的物理协变量作为控制因子．以及采用了聚类分析提高了对比区和影响区相关性,其评估效果最好,显著水平高于0．05;其他评估方法评估效果较差。