Real‐Time Calibration of Radar Precipitation Estimates

One of the main concerns with precipitation measurements is that gage networks are almost always too sparse to provide an adequate spatial coverage of storm‐scale precipitation. Gage measurements are representative only at the measurement site and are biased underestimates of the actual precipitation, mainly as a result of the effect of wind on the gage. Consequently, storm‐scale, real‐time assessments using only gage‐measured precipitation are frequently inadequate. With the advent of the WSR‐88D (formerly NEXRAD) weather radars, precipitation estimates at higher spatial resolutions (4 km by 4 km) are now available in real time. These radars use the reflectivity of S‐band (10 cm) microwaves to provide an estimate of precipitation. Unfortunately, reflectivity is a function of the surface area of the raindrops and not their volume. As a result of this and other sources of error, radar precipitation estimates using fixed reflectivity‐to‐rainfall relationships are subject to substantial biases. To provide better high‐resolution precipitation estimates, a gage‐radar precipitation compositing procedure has been developed to enhance real‐time precipitation assessments. Radar estimates provide the spatial ‘footprint’ of the storm while gage data are used to enhance accuracy. This procedure calibrates each radar separately (since biases usually vary by radar), provides a composite mosaic of multiple radars for regions that lie under more than one radar umbrella, and determines an estimate of the uncertainty of the calibration.     

An assessment of three satellite-based precipitation data sets as applied to the Thailand region

The study examines three satellite-based data sets to estimate long-term precipitation for the Thailand region: the Tropical Rainfall Mapping Mission (TRMM) 3B43, the Climate Prediction Centre morphing technique (CMORPH), and a locally developed regression model using the geostationary meteorological satellite (GMS) covering the Thailand region. Data for the first two sets were available at a spatial resolution of 0.25° × 0.25°, while the local regression model used data from the GMS at a resolution of 5 km × 5 km. The statistical regression model was developed by relating long-term monthly average precipitation from 27 rain gauge stations with concurrent satellite data in the visible and thermal infrared bands. The model was then tested against independent data from 27 rain gauge stations. Satellite/rain gauge ratios were estimated, and a smooth spline surface was used to correct the error from the model. Data from the three approaches were compared with a rain gauge network. The TRMM relation performed better than CMORPH, and the performance for GMS was comparable to TRMM with root mean square different and mean bias difference of 33.6 and 4.2%, respectively. The locally developed regression model was used to produce monthly and yearly total rainfall maps from the GMS data for the entire country.     

Spatial Patterns in Surface Energy Balance Components Derived from Remotely Sensed Data

A relatively simple modeling approach for estimating spatially distributed surface energy fluxes was applied to two small watersheds, one in a semi‐arid climate region and one in a sub‐humid region. This approach utilized a combination of ground‐based meteorological data and remotely sensed data to estimate ‘instantaneous’ surface energy fluxes at the time of the satellite or aircraft overpasses. The spatial resolution in the watershed grid cells, which was on the order of 100‐400 km, was selected to be compatible with ground measurements used for validation. The model estimates of surface energy fluxes compared well with ground‐based measurements of surface flux (typically within approximately 40 Wm^?2). The model accuracy may be slightly less for bare soil surfaces due to an overestimation of the soil heat flux. In addition to demonstrating the feasibility of computing spatially distributed values of surface energy fluxes, these maps were used to qualitatively infer the dominant factors controlling the energy fluxes for the time period shortly following precipitation events in the basins. For the semi‐arid watershed, values of sensible heat flux varied considerably over the watershed and displayed a pattern very similar to that of the spatially variable cumulative precipitation for at least one to eight days prior to the image acquisition. Due to the large fraction of exposed bare soil in a semi‐arid ecosystem, even very small precipitation events had a strong influence on the pattern of sensible heat fluxes observed shortly after the event (less than 24 hours). For the sub‐humid watershed, the fluxes tended to be more uniform across the watershed, and were influenced by a combination of precipitation total and land cover type.     

全球降水计划IMERG和GSMaP反演降水在四川地区的精度评估

IMERG和GSMaP是全球降水计划（GPM）时代最主要的高分辨率降水产品。为研究其在中国四川地区的适用性,以中国气象局提供的自动气象站融合降水数据为参考基准,采用6种统计指数分析了IMERG（IMERG_Uncal, IMERG_Cal）和GSMaP（GSMaP_MVK, GSMaP_Gauge）系列产品在四川的误差特征。结果表明：① 在日和小时尺度上,GSMaP系列产品均高估地面降水观测,GSMaP_MVK高估最显著,校正产品GSMaP_Gauge的相关系数（CC）、相对偏差（BIAS）和均方根误差（RMSE）较GSMaP_MVK均有较大提高,尤其对川西高原降水的高估现象改善明显,而IMERG_Uncal存在低估川西高原降水、轻微高估四川盆地降水的问题,校正产品IMERG_Cal一定程度上降低了对川西高原降水的低估现象,但整体精度（CC, RMSE）提高不明显。② IMERG系列产品对降水事件的探测准确性更好,GSMaP_Gauge虽然在四川表现出较高的命中率（POD）,但存在较多的误报降水,在盆地和四川南部各产品均表现出较高的POD和关键成功指数（CSI）以及低误报率（FAR）,而四川西北部表现最差,尤其是在无自动站分布地区。③ 4套降水产品中,IMERG_Cal表现出最好的探测强降水和弱降水的能力,具有一定的监测极端降水的潜力。总体上,IMERG和GSMaP在盆地的反演精度优于高原山区,校正产品精度优于纯卫星产品,不同地形地区精度差异明显,表明对卫星降水产品进行不同地形误差订正仍是未来降水反演工作的重点和难点。     

The Coefficient of Localization—An Addendum

Geographical data sets sometimes contain missing observations that need to be estimated. A statistical approach to the problem is discussed for multivariate normal spatial data sets satisfying the first-order spatial Markov property with constant mean, where the information at neighboring or contiguous observed sites is used to estimate the missing values. The completed data are used to estimate the parameters of the distribution. The procedure is iterative. The approach is a special case of the Orchard and Woodbury missing information principle. The paper concludes with an illustrative empirical example using rainfall data from an area of Kansas and Nebraska. The quality of the estimates for different sites are compared.     

A STATISTICAL APPROACH TO THE PROBLEM OF MISSING SPATIAL DATA USING A FIRST-ORDER MARKOV MODEL*

Geographical data sets sometimes contain missing observations that need to be estimated. A statistical approach to the problem is discussed for multivariate normal spatial data sets satisfying the first-order spatial Markov property with constant mean, where the information at neighboring or contiguous observed sites is used to estimate the missing values. The completed data are used to estimate the parameters of the distribution. The procedure is iterative. The approach is a special case of the Orchard and Woodbury missing information principle. The paper concludes with an illustrative empirical example using rainfall data from an area of Kansas and Nebraska. The quality of the estimates for different sites are compared.     

Increasing the spatial resolution of agricultural land cover maps using a Hopfield neural network

Land cover class composition of remotely sensed image pixels can be estimated using soft classification techniques increasingly available in many GIS packages. However, their output provides no indication of how such classes are distributed spatially within the instantaneous field of view represented by the pixel. Techniques that attempt to provide an improved spatial representation of land cover have been developed, but not tested on the difficult task of mapping from real satellite imagery. The authors investigated the use of a Hopfield neural network technique to map the spatial distributions of classes reliably using information of pixel composition determined from soft classification previously. The approach involved designing the energy function to produce a ‘best guess’ prediction of the spatial distribution of class components in each pixel. In previous studies, the authors described the application of the technique to target identification, pattern prediction and land cover mapping at the sub-pixel scale, but only for simulated imagery. We now show how the approach can be applied to Landsat Thematic Mapper (TM) agriculture imagery to derive accurate estimates of land cover and reduce the uncertainty inherent in such imagery. The technique was applied to Landsat TM imagery of small-scale agriculture in Greece and largescale agriculture near Leicester, UK. The resultant maps provided an accurate and improved representation of the land covers studied, with RMS errors for the Landsat imagery of the order of 0.1 in the new fine resolution map recorded. The results showed that the neural network represents a simple efficient tool for mapping land cover from operational satellite sensor imagery and can deliver requisite results and improvements over traditional techniques for the GIS analysis of practical remotely sensed imagery at the sub pixel scale.     

基于静止气象卫星观测的降水时间降尺度研究

高分辨率降水时空分布估计对于陆面数值模拟和同化,天气、气候与环境及其相关领域研究具有重要意 义。长时间序列降水分布常使用时间和空间插值技术进行估计。本文提出了一种基于每小时静止卫星云图云分类 信息的累积降水估计时间加权插值方法,它通过利用高时空分辨率静止卫星云图所反映的云的变化,与卫星降水 估计和地面观测降水相结合,以获得时空分布比较合理的地面降水分布。将该方法应用于FY2C 云图和国家卫星 气象中心FY2C 的6 小时降水估计业务产品,得到FY2C 卫星发射(2005 年)以来具有10KM空间分辨率以及1 小 时分辨率的降水数据集,并利用中国区域自动雨量观测资料对该降水估计数据集进行检验和评估,以表明该方法 的合理性。     

基于CLDAS融合降水的中国降雨侵蚀力研究

气象站和卫星降雨资料估算降雨侵蚀力时存在无法反映空间异质性且精度差的问题,基于CLDAS多源融合降水,利用EI₆₀模型从不同的时空尺度对中国的降雨侵蚀力进行评估,并结合降雨量、侵蚀性降雨次数、侵蚀密度等指标,探讨降雨对土壤侵蚀的潜在作用。结果表明：（1） CLDAS降雨侵蚀力与地面实测数据在不同的时间尺度均有良好的回归关系,相关系数达到0.8以上,与CMORPH降雨侵蚀力相比,其相对误差显著降低,可以准确反映全国范围的降雨侵蚀力季节性变异。（2） 在2001—2020年,不同雨量区的降雨侵蚀力、降雨量和侵蚀性降雨次数的变化趋势基本一致,高雨量区的年际变化波动剧烈,侵蚀性降雨次数和暴雨过程协同影响降雨侵蚀力的大小。（3） 空间上,中国的降雨侵蚀力值的特点为东南沿海地区高、西北内陆地区低。时间上,侵蚀性降雨集中在5—8月,夏、秋两季对土壤造成的侵蚀影响更大。（4） 通过对年降雨量、年侵蚀密度和年暴雨量进行分区定量分析,结果表明暴雨量与侵蚀密度成正相关关系,即年降雨量一定,暴雨事件越多,降雨侵蚀密度越大。     

降雨的空间不均性对模拟产流量和产沙量不确定的影响

在传统的水文/水质模型中,降雨被认为是在空间上均匀分布并且对模型输出的不确定性不产生影响。本文的目的是评价由于降雨的空间分布不均匀性对模型输出-产流量和产沙量-不确定性的影响。本文选取卢氏流域为研究区域,使用SWAT模型和流域内24个雨量站的降雨作为模型的输入。基于降雨空间分布均匀的假定下,每次用一个雨量站的点雨量来作为流域的面平均降雨量,模拟的产流量和产沙量的不确定性来自于降水的不均匀性。模拟的产流量和产沙量的不确定性大于降雨的不确定性,结果表明,在运用水文/水质模型时,为了准确的模拟、预测产流量和产沙量必须掌握降雨的空间分布特性并将其应用于水文、水质模拟之中。     

A Review of “Hazards,Vulnerability, and Environmental Justice”

A relatively simple modeling approach for estimating spatially distributed surface energy fluxes was applied to two small watersheds, one in a semi-arid climate region and one in a sub-humid region. This approach utilized a combination of ground-based meteorological data and remotely sensed data to estimate ‘instantaneous’ surface energy fluxes at the time of the satellite or aircraft overpasses. The spatial resolution in the watershed grid cells, which was on the order of 100-400 km, was selected to be compatible with ground measurements used for validation. The model estimates of surface energy fluxes compared well with ground-based measurements of surface flux (typically within approximately 40 Wm^?2). The model accuracy may be slightly less for bare soil surfaces due to an overestimation of the soil heat flux. In addition to demonstrating the feasibility of computing spatially distributed values of surface energy fluxes, these maps were used to qualitatively infer the dominant factors controlling the energy fluxes for the time period shortly following precipitation events in the basins. For the semi-arid watershed, values of sensible heat flux varied considerably over the watershed and displayed a pattern very similar to that of the spatially variable cumulative precipitation for at least one to eight days prior to the image acquisition. Due to the large fraction of exposed bare soil in a semi-arid ecosystem, even very small precipitation events had a strong influence on the pattern of sensible heat fluxes observed shortly after the event (less than 24 hours). For the sub-humid watershed, the fluxes tended to be more uniform across the watershed, and were influenced by a combination of precipitation total and land cover type.     

The aboveground biomass of desert steppe and its spatiotemporal variation in western Inner Mongolia

A precise understanding of the aboveground biomass of desert steppe and its spatio-temporal variation is important to understand how arid ecosystems respond to climate change and to ensure that scarce grassland resources are used rationally. On the basis of 756 ground survey quadrats sampled in western Inner Mongolia steppe in 2005–2011 and remote sensing data from the Moderate Resolution Imaging Spectroradiometer (MODIS)—the normalized difference vegetation index (NDVI) dataset for the period of 2001–2011—we developed a statistical model to estimate the aboveground biomass of the desert steppe and further explored the relationships between aboveground biomass and climate factors. The conclusions are as follows: (1) the aboveground biomass of the steppe in the research area was 5.27 Tg (1 Tg=10¹² g) on average over 11 years; between 2001 and 2011, the aboveground biomass of the western Inner Mongolia steppe exhibited fluctuations, with no significant trend over time; (2) the aboveground biomass of the steppe in the research area exhibits distinct spatial variation and generally decreases gradually from southeast to northwest; and (3) the important factor causing interannual variations in aboveground biomass is precipitation during the period from January to July, but we did not find a significant relationship between the aboveground biomass and the corresponding temperature changes. The precipitation in this period is also an important factor influencing the spatial distribution of aboveground biomass (R²=0.39, P<0.001), while the temperature might be a minor factor (R²=0.12, P<0.01). The uncertainties in our estimate are primarily due to uncertainty in converting the fresh grass yield estimates to dry weight, underestimates of the biomass of shrubs, and error in remote sensing dataset.     

考虑多大气参数不确定性的地面太阳短波辐射估算误差数学期望

以香港地面站观测的AOD与WVC数据为参照,首先分析了MISR/AOD和MODIS/WVC产品的不确定性描述方法,然后在考虑二者联合不确定性的影响下,推导了辐射传输方程法估算地面太阳短波辐射的相对误差分布函数。通过统计2005―2013年香港MISR/AOD和MODIS/WVC反演值的分布情况,得出两者的联合概率密度函数,然后将AOD和WVC的联合概率密度函数作为权重函数与相对误差分布函数积分,得到对辐射传输方程法估算地面太阳短波辐射的相对误差数学期望。结果表明,夏季在太阳天顶角为0°时,使用MISR/AOD和MODIS/WVC产品估算地面太阳短波辐射的相对误差期望为3.17%,并且有90%的把握使估算结果相对误差＜3.53%;不同季节不同太阳天顶角下、不同置信水平下,地面太阳短波辐射估算的相对误差大小,可为不同应用研究提供基础数据,为评估亚热带地面太阳短波辐射估算结果是否符合应用需求提供依据。     

NPP-VIIRS热红外数据地表温度反演方法研究

地表温度作为监测陆地表面与大气变化的重要参数,对于研究地表能量平衡和全球气候变化具有重要作用。可见光红外成像辐射仪套件（Visible Infrared Imaging Radiometer Suite,VIIRS）是搭载在新一代对地观测卫星NPP上的一个重要传感器。与其他传感器相比,VIIRS拥有更高的空间分辨率。分裂窗算法是反演地表温度最常用的方法,主要是利用两个热红外通道来反演地表温度,经过多年的研究和改进,发展了多种形式的分裂窗算法。由于过去很少有人利用VIIRS数据对多种分裂窗算法进行对比分析,因此利用VIIRS传感器上M15和M16两个热红外通道数据计算辐亮度和星上亮温,采用多种形式的分裂窗算法反演获得多组地表温度数据,再利用海拉尔野外观测站点的实测数据对结果进行验证,对比各算法精度,得到反演精度较高的算法。结果显示PR84算法的反演误差最大达到1.8 K,其余各算法反演地表温度的RMSE都在1.5 K以内,算法中加入二次项和水汽项可以提高算法精度,其中BL95的算法精度最高达到了1.23 K。研究结果表明,BL95算法更适用于VIIRS热红外数据地表温度反演。     

Estimating the spatial and temporal impacts of climate change on rainfall reliability: An example in a Mediterranean agricultural region

Rainfall is the major driver of crop growth in Mediterranean agricultural regions and its spatial and temporal distributions determine yield potential. This study uses a long term spatial archive of rainfall observations for the Eyre Peninsula (South Australia) to estimate the spatial and temporal impacts of climate change on wheat yield. The three step process involved: (1) cluster analysis and statistical comparison to spatially distinguish heterogeneous “hazardscapes” (places that represent the physical susceptibility to hazards (Khan, 2012)); (2) using historical rainfall reliabilities to estimate the probability of receiving rainfall within a range of predefined thresholds and season for each hazardscape; (3) applying 2030 and 2070 climate change projections to determine the potential future impacts on rainfall. Nine hazardscapes were spatially differentiated each having temporally different historical seasonal rainfall reliabilities. Variations over space and time mean that the impacts of climate change will be spatially explicit. Projected rainfall reductions for 2030 showed marginal impact on hazardscapes with low seasonal reliabilities, primarily in winter and spring. The 2070 projections showed that some hazardscapes were unlikely to receive past rates of rainfall thus limiting the ongoing prospects of current and perhaps the potential adoption of alternative rain-fed land uses. Reductions in rainfall for hazardscapes with higher historical rainfall reliabilities will cause negative impacts on crop development. The ability to quantify the potential spatial and temporal impacts of climate change on seasonal trends will inform land managers' climate change mitigation and adaptation pathways.     

区域尺度蒸散发遥感估算——反演与数据同化研究进展

遥感技术近年来在估算区域尺度蒸散发中应用广泛。不同方法在驱动数据、模型机理和适用范围往往存在很大差别。鉴于此,阐述了基于传统方法空间尺度扩展的遥感模型,经验统计公式,特征空间法,单源、双源垂向能量平衡余项法等几类的遥感蒸散发反演方法,简要介绍了三温模型、非参数化模型、半经验模型、集成模型等常用模型。同时,分析了遥感数据同化实现连续估算区域蒸散发的主要思路,综述了基于能量平衡和基于复杂过程模型的数据同化的原理、方法演进及常用同化算法等。最后,探讨了各类区域蒸散发遥感方法的优劣、展望了模型机理完善、不确定性研究、结果验证等与蒸散发直接反演和数据同化相关的研究方向。     

不同概率分布函数降雨极值的适用性分析

极端降雨极值发生的重现期是流域与城市防洪设施规划设计标准需要参考的最重要参数之一。利用常用的5种水文统计学分布函数,选取中国十大流域内10个站点不同时段的最大降雨极值序列进行拟合,并检验筛选不同站点的适用性分布函数。结果表明：10个站点拟合优度检验拟合效果较好,曲线差异度较小的分布依次为广义极值分布、对数正态分布、皮尔逊III分布;不同站点适宜性曲线的差异程度不同。研究结果可为区域降雨极值序列的拟合提供参考,即不同的区域、不同的季节、不同时长的降雨极值序列都应寻找其较适宜的分布函数并采用多种检验方法来拟合,以降低不确定性。     

Evaluation of satellite-based precipitation estimation over Iran

Precipitation in semi-arid countries such as Iran is one of the most important elements for all aspects of human life. In areas with sparse ground-based precipitation observation networks, the reliable high spatial and temporal resolution of satellite-based precipitation estimation might be the best source for meteorological and hydrological studies. In the present study, four different satellite rainfall estimates (CMORPH, PERSIANN, adjusted PERSIANN, and TRMM-3B42 V6) are evaluated using a relatively dense Islamic Republic of Iran's Meteorological Organization (IRIMO) rain-gauge network as reference. These evaluations were done at daily and monthly time scales with a spatial resolution of 0.25° × 0.25° latitude/longitude. The topography of Iran is complicated and includes different, very diverse climates. For example, there is an extremely wet (low-elevation) Caspian Sea coastal region in the north, an arid desert in the center, and high mountainous areas in the west and north. Different rainfall regimes vary between these extremes. In order to conduct an objective intercomparison of the various satellite products, the study was designed to minimize the level of uncertainties in the evaluation process. To reduce gauge uncertainties, only the 32 pixels, which include at least five rain gauges, are considered. Evaluation results vary by different areas. The satellite products had a Probability of Detection (POD) greater than 40% in the southern part of the country and the regions of the Zagros Mountains. However, all satellite products exhibited poor performance over the Caspian Sea coastal region, where they underestimated precipitation in this relatively wet and moderate climate region. Seasonal analysis shows that spring precipitations are detected more accurately than winter precipitation, especially for the mountainous areas all over the country. Comparisons of different satellite products show that adj-PERSIANN and TRMM-3B42 V6 have better performance, and CMORPH has poor estimation, especially over the Zagros Mountains. The comparison between PERSIANN and adj-PERSIANN shows that the bias adjustment improved the POD, which is a daily scale statistic.     

Uncertainty Estimate in Resources Assessment: A Geostatistical Contribution

For many decades the mining industry regarded resources/reserves estimation and classification as a mere calculation requiring basic mathematical and geological knowledge. Most methods were based on geometrical procedures and spatial data distribution. Therefore, uncertainty associated with tonnages and grades either were ignored or mishandled, although various mining codes require a measure of confidence in the values reported. Traditional methods fail in reporting the level of confidence in the quantities and grades. Conversely, kriging is known to provide the best estimate and its associated variance. Among kriging methods, Ordinary Kriging (OK) probably is the most widely used one for mineral resource/reserve estimation, mainly because of its robustness and its facility in uncertainty assessment by using the kriging variance. It also is known that OK variance is unable to recognize local data variability, an important issue when heterogeneous mineral deposits with higher and poorer grade zones are being evaluated. Altenatively, stochastic simulation are used to build local or global uncertainty about a geological attribute respecting its statistical moments. This study investigates methods capable of incorporating uncertainty to the estimates of resources and reserves via OK and sequential gaussian and sequential indicator simulation The results showed that for the type of mineralization studied all methods classified the tonnages similarly. The methods are illustrated using an exploration drill hole data sets from a large Brazilian coal deposit.     

Comparison Between Kriging Variance and Interpolation Variance as Uncertainty Measurements in the Capanema Iron Mine, State of Minas Gerais—Brazil

The Capanema Mine, an iron ore deposit, is located in the central portion of the Quadrilátero Ferrífero, State of Minas Gerais, southeastern Brazil. Mine development data from approximately 7000 drillholes were used for a comparative study between kriging variance and interpolation variance as uncertainty measurements associated with ordinary kriging estimates. As known, the traditional kriging variance does not depend on local data and, therefore, does not measure the actual dispersion of data. On the other hand, the interpolation variance measures adequately the local dispersion of data used for an ordinary kriging estimate. This paper presents an application of the concept of interpolation variance for measuring uncertainties associated with ordinary kriging estimates of Fe and silica grades. These data were selected for their distinct statistical characteristics with Fe presenting a negatively skewed distribution and, consequently, a low dispersion, and silica a positively skewed distribution and, therefore, a high variability. Comparative studies between the two uncertainty measurements associated with ordinary kriging estimates of Fe and silica proved the superiority of the interpolation variance as a reliable and precise alternative to the kriging variance.