基于时空不确定性的对流尺度集合预报效果评估检验

针对对流尺度天气系统的高度非线性特征和高分辨率模式预报结果存在时、空不确定性现象,以及当前邻域概率法主要考虑高分辨率预报结果的空间位移误差,而不能有效解决预报结果存在时间超前与滞后问题,将时间因素引入到邻域概率法中,结合一次强飑线过程进行对流尺度集合预报试验,并基于改进后的新型邻域概率法与分数技巧评分,对降水预报进行了不同时、空尺度的效果评估检验。结果表明:（1）邻域集合概率法和概率匹配平均法在极端降水的分数技巧评分远高于传统集合平均,弥补了集合平均对极端降水预报能力偏低的缺陷。（2）对于此类飑线过程的对流尺度天气系统而言,邻域半径为15—45 km的空间尺度能够改善降水位移误差的空间不确定性,并使其预报效果达到最优,其中15—30 km的邻域半径对于尺度更小的大量级降水事件预报能力更强。（3）对流尺度降水预报考虑时间尺度与降水强度存在着对应关系,不同时间尺度可以捕获到不同量级降水的时间不确定性。同时,时间尺度与空间尺度对于降水预报效果的影响是相互关联的。（4）改进的邻域概率法能够同时体现高分辨率模式预报结果在对流尺度降水事件上存在的时、空不确定性,实现了对流尺度降水在时、空尺度上的综合评估,并能为不同量级降水提供与其时、空尺度相匹配的概率预报结果。      

River flux uncertainties predicted by hydrological variability and riverine material behaviour

Data on riverine fluxes are essential for calculating element cycles (carbon, nutrients, pollutants) and erosion rates from regional to global scales. At most water‐quality stations throughout the world, riverine fluxes are calculated from continuous flow data (q) and discrete concentration data (C), the latter being the main cause of sometimes large uncertainties. This article offers a comprehensive approach for predicting the magnitude of these uncertainties for water‐quality stations in medium to large basins (drainage basin area > 1000 km²) based on the commonly used discharge‐weighted method. Uncertainty levels – biases and imprecisions – for sampling intervals of 3 to 60 days are correlated first through a nomograph with a flux variability indicator, the quantity of riverine material discharged in 2% of time (M_2%). In turn, M_2% is estimated from the combination of a hydrological reactivity index, W_2% (the cumulative flow volume discharged during the upper 2% of highest daily flow) and the truncated b_50sup exponent, quantifying the concentration versus discharge relationship for the upper half of flow values (C = a q ^b50sup, for q > q₅₀, where q₅₀ is the median flow): M_2% = W_2% + 27.6b_50sup. W_2% can be calculated from continuous flow measurements, and the b_50sup indicator can be calculated from infrequent sampling, which makes it possible to predict a priori the level of uncertainty at any station, for any type of riverine material either concentrated (b_50sup > 0) or diluted (b_50sup > 0) with flow. A large data base of daily surveys, 125 station variables of suspended particulate matter (SPM), total dissolved solids (TDS) and dissolved and particulate nutrients, was used to determine uncertainties from simulated discrete surveys and to establish relationships between indicators. Results show, for example, that for the same relatively reactive basin (W_2% > 25%), calculated fluxes from monthly sampling would yield uncertainties approaching ±100% for SPM (b_50sup > 1.4) fluxes and ±10% for TDS (b_50sup = ?0.2). The application to the nitrate survey of the river Seine shows significant trends for the 1972–2009 records. Copyright © 2012 John Wiley & Sons, Ltd.     

Characteristics of tropical Pacific SST predictability in coupled GCM forecasts using the NCEP CFS

The limits of predictability of El Niño and the Southern Oscillation (ENSO) in coupled models are investigated based on retrospective forecasts of sea surface temperature (SST) made with the National Centers for Environmental Prediction (NCEP) coupled forecast system (CFS). The influence of initial uncertainties and model errors associated with coupled ENSO dynamics on forecast error growth are discussed. The total forecast error has maximum values in the equatorial Pacific and its growth is a strong function of season irrespective of lead time. The largest growth of systematic error of SST occurs mainly over the equatorial central and eastern Pacific and near the southeastern coast of the Americas associated with ENSO events. After subtracting the systematic error, the root-mean-square error of the retrospective forecast SST anomaly also shows a clear seasonal dependency associated with what is called spring barrier. The predictability with respect to ENSO phase shows that the phase locking of ENSO to the mean annual cycle has an influence on the seasonal dependence of skill, since the growth phase of ENSO events is more predictable than the decay phase. The overall characteristics of predictability in the coupled system are assessed by comparing the forecast error growth and the error growth between two model forecasts whose initial conditions are 1 month apart. For the ensemble mean, there is fast growth of error associated with initial uncertainties, becoming saturated within 2 months. The subsequent error growth follows the slow coupled mode related the model’s incorrect ENSO dynamics. As a result, the Lorenz curve of the ensemble mean NINO3 index does not grow, because the systematic error is identical to the same target month. In contrast, the errors of individual members grow as fast as forecast error due to the large instability of the coupled system. Because the model errors are so systematic, their influence on the forecast skill is investigated by analyzing the erroneous features in a long simulation. For the ENSO forecasts in CFS, a constant phase shift with respect to lead month is clear, using monthly forecast composite data. This feature is related to the typical ENSO behavior produced by the model that, unlike the observations, has a long life cycle with a JJA peak. Therefore, the systematic errors in the long run are reflected in the forecast skill as a major factor limiting predictability after the impact of initial uncertainties fades out.     

Stochastic simulations of fault networks in 3D structural modeling

3D structural modeling is a major instrument in geosciences, e.g. for the assessment of groundwater and energy resources or nuclear waste underground storage. Fault network modeling is a particularly crucial step during this task, for faults compartmentalize rock units and plays a key role in subsurface flow, whether faults are sealing barriers or drains. Whereas most structural uncertainty modeling techniques only allow for geometrical changes and keep the topology fixed, we propose a new method for creating realistic stochastic fault networks with different topologies. The idea is to combine an implicit representation of geological surfaces which provides new perspectives for handling topological changes with a stochastic binary tree to represent the spatial regions. Each node of the tree is a fault, separating the space in two fault blocks. Changes in this binary tree modify the fault relations and therefore the topology of the model.     

Maximum likelihood estimation of Gutenberg-Richterb parameter for uncertain magnitude values

The maximum likelihood estimation of theb parameter in the Gutenberg-Richter relation is extended to the case of uncertain magnitude. An interval which contains the real unknown magnitude is used rather than the uncertain magnitude itself. The proposed approach is very flexible, it allows for the combination of the parts of a catalog of different quality into a single minimally biased set of recurrence parameters.On leave from the Institute of Geophysics, Polish Academy of Sciences, 00-973 Warsaw, Pasteura 3, Poland     

Mixing Methodologies in Seismic Hazard Assessment via a Logic Tree Procedure: An Application for Eastern Spain

This study provides a procedure for assessing seismic hazardand uncertainties in regions that are characterised by a large non-instrumental earthquakedatabase and a seismic and tectonic behaviour which doesn't allow an evident seismic zonation.This procedure is a synthesis of the non-zoning or non-parametric methodology (using extremevalues distribution functions as proposed by Epstein and Lomnitz, 1966) and the zoning orparametric methodology (using the theorem of total probability as proposed by Cornell, 1968)via a logic tree procedure taking into consideration the advantages offered by each of these.Taking the area which we shall describe as the east coast of Spain and surrounding inland areas,an application was made and a specific logic tree was developed in order to solve the problems anduncertainties related to the evaluation of the seismic hazard using both methodologies. The use of thelogic tree allowed the systematisation of a large number of solutions obtained. A number of relevantresults were obtained which show that in some cases there are great differences in the seismichazard results provided by the non-zoning and the zoning methodologies. In these cases, mean value andstandard deviation of the obtained results provide an intermediate solution to the over-conservativeestimation provided by the non-zoning methodology and the lowest results provided by the zoningmethodology. In other cases results provided by both methodologies are significantly closer.In any case, synthesis among both methodologies gives a wider knowledge of the uncertaintiesassociated with the seismic hazard results. Finally uncertainties increase with the decreaseof the annual probability of exceedence and in sites with a seismic history of large size earthquakes.     

Uncertainties in Facies Proportion Estimation I. Theoretical Framework: The Dirichlet Distribution

To simulate geological models comprising several litho-types—or facies—we need first to estimate their proportions, which are often poorly known. The corresponding uncertainties can be modelled using a Bayesian approach for inverting the multinomial distribution. The result obtained is known as the Dirichlet distribution. It can be simulated by decomposition into independent conditional distributions. Application of the model is extended to the case of nonstationary proportions and, with some approximation, to the case of correlated spatial data. The mathematical developments presented in the appendices provide a more precise and general definition of the distribution, several decomposition formulae into independent variables, the determination of remarkable stability properties, and the resulting consequences for the conditional and marginal distributions.     

气候变化国家评估报告(Ⅱ):气候变化的影响与适应

Significant and various impacts of climate change have been observed in China, showing both positive and adverse effects, dominantly the latter, in different sectors and regions. It is very likely that future climate change would cause significant adverse impacts on the ecosystems, agriculture, water resources, and coastal zones in China. Adoption of adaptive measures to climate change can alleviate the adverse impact, therefore such measures should be incorporated into the medium-and long-term national economic and social development plans. Because China has done relatively limited research on impact assessment and our understanding of climate change is incomplete, the current impact assessment methodologies used and results obtained contain many uncertainties. To reduce the uncertainties and develop effective and practical climate change adaptive measures in China, it is necessary to emphasize regional case studies on adaptive measures, enlarge the scope of climate change research, and strengthen the assessment of the impacts resulted from extreme weather/climate events.     

Seasonal Sensitivity on COBEL-ISBA Local Forecast System for Fog and Low Clouds

Skillful low visibility forecasts are essential for air-traffic managers to effectively regulate traffic and to optimize air-traffic control at international airports. For this purpose, the COBEL-ISBA local numerical forecast system has been implemented at Paris CDG international airport. This local approach is robust owing to the assimilation of detailed local observations. However, even with dedicated observations and initialization, uncertainties remain in both initial conditions and mesoscale forcings. The goal of the research presented here is to address the sensitivity of COBEL-ISBA forecast to initial conditions and mesoscale forcings during the winter season 2002–2003. The main sources of uncertainty of COBEL-ISBA input parameters have been estimated and the evaluation of parameter uncertainty on the forecasts has been studied. A budget strategy is applied during the winter season to quantify COBEL-ISBA sensitivity. This study is the first step toward building a local ensemble prediction system based on COBEL-ISBA. The conclusions of this work point out the potential for COBEL-ISBA ensemble forecasting and quantify sources of uncertainty that lead to dispersion.