Sensitivity analysis of geologic computer models: A formal procedure based on Latin hypercube sampling

Mathematical models in which a response variableY is calculated, usually via a computer program, as a function of input variablesX ₁ ,X ₂, ...,X _k are encountered frequently in earth sciences literature. In many cases, uncertainty in the knowledge of the correct values of the input variables exists, leading to uncertainty in the correct value of the response. Sensitivity analysis procedures can be used to identify which input variable uncertainties contribute most to uncertainty in the response variable.This paper presents the technique of Latin hypercube sampling, a structured, formal sampling process used in the sensitivity analysis procedure. Output resulting from the sample is analyzed using stepwise regression analysis, leading to identification of key input variables. Future data gathering activities can then be done efficiently, focusing on these variables. The technique is applied to a model, developed by Sudicky and Frind, which represents the flow of a contaminant through a porous media.     

Position and velocity reliability testing in degraded GPS signal environments

Reliability testing, namely receiver autonomous integrity monitoring (RAIM), consists of statistical testing of least-squares residuals of observations, e.g., on an epoch-by-epoch basis aiming towards reliable navigation fault detection and exclusion (FDE). In this paper, classic RAIM and FDE methods are extended with testing of range-rate residuals to find inconsistent velocity solutions in order to contribute to the reliability of the system with special focus on degraded signal environments. Reliability enhancement efforts discussed include a Backward-FDE scheme based on statistical outlier detection and an iteratively reweighted robust estimation technique, a modified Danish method. In addition, measurement weighting assigned to code and Doppler observations is assessed in the paper in order to allow fitting a priori variance models to the estimation processes. The schemes discussed are also suitable in terms of computational convenience for a combined GPS/Galileo system. The objective of this paper is to assess position and velocity reliability testing and enhancement in urban and indoor conditions and to analyze the navigation accuracy conditions with high sensitivity GPS (HSGPS) tests. The results show the necessity of weighted estimation and FDE for reliability enhancement in degraded signal-environment navigation.     

台儿庄生态敏感性GIS评价

生态敏感性评价是进行生态规划的重要依据,本文以遥感影像和生态环境调查数据为基础,选取具有代表性的8类生态因子并确定其对生态环境影响的分级标准和权重,使用GIS技术,运用加权叠加的方法,对枣庄市台儿庄区生态敏感性进行评价。通过模糊聚类和小面积图斑自动归类合并处理,将区域土地生态敏感性划分5类。研究结果表明:台儿庄区生态极度敏感区占全区面积的7.04%,高度敏感地区占全区面积的8.5%,中度敏感区占全区面积的21.63%,轻度敏感区占全区面积的51.02%,不敏感区占全区面积的11.82,生态敏感性总体较低。     

Validation of the effects of temperature simulated by multi-model ensemble and prediction of mean temperature changes for the next three decades in China

Using series of daily average temperature observations over the period of 1961–1999 of 701 meteorological stations in China, and simulated results of 20 global climate models (such as BCCR_BCM2.0, CGCM3T47) during the same period as the observation, we validate and analyze the simulated results of the models by using three factor statistical method, achieve the results of multi- model ensemble, test and verify the results of multi-model ensemble by using the observation data during the period of 1991–1999. Finally, we analyze changes of the annual mean temperature result of multi-mode ensemble prediction for the period of 2011–2040 under the emission scenarios A2, A1B and B1. Analyzed results show that: (1) Global climate models can reproduce Chinese regional spatial distribution of annual mean temperature, especially in low latitudes and eastern China. (2) With the factor of the trend of annual mean temperature changes in reference period, there is an obvious bias between the model and the observation. (3) Testing the result of multi-model ensemble during the period of 1991–1999, we can simulate the trend of temperature increase. Compared to observation, the result of different weighing multi-model ensemble prediction is better than the same weighing ensemble. (4) For the period of 2011–2040, the growth of the annual mean temperature in China, which results from multi-mode ensemble prediction, is above 1 °C. In the spatial distribution of annual mean temperature, under the emission scenarios of A2, A1B and B1, the trend of growth in South China region is the smallest, the increment is less than or equals to 0.8 °C; the trends in the northwestern region and south of the Qinghai-Tibet Plateau are the largest, the increment is more than 1 °C.     

区域集合预报系统2 m温度预报的校准技术

采用非齐次高斯回归 (NGR) 技术对国家气象中心区域集合预报系统的2 m温度预报结果开展了一阶偏差和二阶离散度的校准研究。对预报结果比较详尽的检验分析表明:校准后的2 m温度预报可靠性和预报技巧均显著提高,表现为校准后集合预报成员的均方根误差与离散度更为接近;原Talagrand直方图中的“L”形分布现象得到有效改善;Brier评分、最小连续分级概率评分 (CRPS) 明显减小,相对作用特征 (ROC) 面积增大,说明校准后的2 m温度预报表现出更好的预报技能。此外,NGR技术与自适应误差订正技术的对比试验表明,NGR在消除集合平均偏差和提高集合离散度两个方面均有优势。     

沙尘输送模式的不确定性分析

利用一个远距离输送的沙尘模式估计了由于参数化过程 (干沉降速度) 和输入资料 (源强和水平风场) 的误差造成沙尘模拟的不确定性。通过对以上参数分别进行敏感性试验, 分析了模式对2002年3月15～24日期间中国东部地区两次主要沙尘过程模拟的不确定性。结果显示, 源区的潜在源强和气象水平风场的不确定性对模拟结果的影响最大, 而干沉降速度的影响相对较小。同时, 对不同区域 ［西部 (＜95°E)、中部 (95°E～110°E) 和东部 (＞110°E)］ 的潜在源强和干沉降速度参数进行敏感性试验发现, 中部区域的参数设置对模拟结果的影响最大, 而西部和东部区域的参数变化对模拟结果的影响很小。此外, 不同高度的风场影响也不一样: 地面风速影响最大, 中层的影响较小, 而高层 (约6 km高度以上) 的风场几乎没有影响。     

最优子集回归方法在季节气候预测中的应用

利用DEMETER计划多个模式的模拟资料研究1959～2001年多模式集合预报的季节降水在中国区域的表现, 并结合最优子集回归(OSR)方法对中国区域的季节降水进行降尺度预报, 比较其与多模式集合预报的技巧。研究表明: 多个单模式在中国区域对季节降水的模拟性能普遍较差, 多元线性回归(MLR)集合的预报技巧不如集合平均(EM)。利用OSR方法进行降尺度预报可以极大改善中国区域季节降水的预报技巧。夏季, 降水距平相关系数(ACC)在长江以南、西藏以及内蒙古中部等地区提高很显著, ACC在中国区域的平均达到0.29, 明显高于多模式集合平均与多元线性回归集合。冬季, OSR方法可以改善多模式集合在中国北方地区较低的预报技巧。概率Brier技巧评分(BSS)也表明了OSR方法对季节降水预报的改善。需要说明的是, 虽然OSR方法在中国区域能明显提高季节降水的预报技巧, 但是其选取的预报因子与中国区域季节降水的物理机制问题仍有待于进一步的研究。     

基于TIGGE资料的地面气温多模式超级集合预报

基于TIGGE资料, 采用均方根误差分别对欧洲中期天气预报中心、日本气象厅、美国国家环境预报中心和英国气象局4个中心集合预报的地面气温场集合平均结果进行检验评估, 比较各中心地面气温的预报效果。并利用超级集合、多模式集合平均和消除偏差集合平均3种方法对4个中心的地面气温预报进行集成, 同时对预报结果进行分析。结果表明: 2007年夏季日本气象厅与欧洲中期天气预报中心在北半球大部分地区预报效果最好, 各中心在不同地区预报效果不同。超级集合与消除偏差集合平均降低了预报误差, 预报效果优于最好的单个中心预报和多模式集合平均。对于较长的预报时效, 消除偏差集合平均表现出了更好的预报性能。     

Current status of ENSO prediction skill in coupled ocean–atmosphere models

The overall skill of ENSO prediction in retrospective forecasts made with ten different coupled GCMs is investigated. The coupled GCM datasets of the APCC/CliPAS and DEMETER projects are used for four seasons in the common 22 years from 1980 to 2001. As a baseline, a dynamic-statistical SST forecast and persistence are compared. Our study focuses on the tropical Pacific SST, especially by analyzing the NINO34 index. In coupled models, the accuracy of the simulated variability is related to the accuracy of the simulated mean state. Almost all models have problems in simulating the mean and mean annual cycle of SST, in spite of the positive influence of realistic initial conditions. As a result, the simulation of the interannual SST variability is also far from perfect in most coupled models. With increasing lead time, this discrepancy gets worse. As one measure of forecast skill, the tier-1 multi-model ensemble (MME) forecasts of NINO3.4 SST have an anomaly correlation coefficient of 0.86 at the month 6. This is higher than that of any individual model as well as both forecasts based on persistence and those made with the dynamic-statistical model. The forecast skill of individual models and the MME depends strongly on season, ENSO phase, and ENSO intensity. A stronger El Niño is better predicted. The growth phases of both the warm and cold events are better predicted than the corresponding decaying phases. ENSO-neutral periods are far worse predicted than warm or cold events. The skill of forecasts that start in February or May drops faster than that of forecasts that start in August or November. This behavior, often termed the spring predictability barrier, is in part because predictions starting from February or May contain more events in the decaying phase of ENSO.     

自然湿地甲烷排放模拟研究——模型的灵敏度分析与应用

对甲烷湿地排放模型CH4MODwetland进行灵敏度分析表明,环境驱动因素——温度和地表水深是影响甲烷排放通量的主要因素,地表水深对季节性积水沼泽甲烷排放通量的灵敏度大于常年积水沼泽。模型对植物和土壤输入参数的灵敏度响应依次为Wmax(地上生物量最大值)FR(地下净初级生产力占植物总净初级生产力的比例)fV(植被类型系数)TAmax(植物从生长初期到最大地上生物量所需有效积温)ρ(容量),OM(土壤有积质含量)SAND(土壤砂粒含量)。以经验水位模型驱动CH4MODwetland,模拟三江平原毛果苔草和小叶章沼泽(1950年代～2000年代),以及若尔盖高原木里苔草和乌拉苔草沼泽(1960年代～2000年代)多年甲烷排放通量的变化。结果表明,年代际甲烷排放通量的变化主要受降水量的影响,但气候变暖使得降水量基本相同的年代甲烷排放通量增加:三江平原毛果苔草沼泽和小叶章沼泽(1980年代～2000年代比1950年代～1970年代模拟的甲烷排放通量分别增加了9.5%和8.3%;若尔盖高原乌拉苔草沼泽和木里苔草沼泽(1990年代～2000年代比1960年代～1970年代)分别增加了6.0%和5.5%。该结论能够为评估未来气候变化对中国湿地甲烷排放的影响提供依据。