Data assimilation (4D-VAR) to forecast flood in shallow-waters with sediment erosion

In this paper, the four-dimensional variational data assimilation technique (4D-VAR) is presented as a tool to forecast floods. Our study is limited to purely hydrological flows and supposes that the weather, here a big rain, has been already forecasted by meteorological services. The technique consists in minimizing, in the sense of Lagrange, the cost function: a measure of the difference between calculated data and available observations, here the water level. This is done under constraints that are the equations of the physical model. In our case, we modified the shallow-water equations to include a simplified sediment transport model. The steepest descent algorithm is then used to find the minimum. This is made possible because we can compute analytically the gradient of the cost function by using the adjoint equations of the model. As an application of the 4D-VAR technique, the overflowing of the Chicoutimi River at the Chute-Garneau dam, during the 1996 flood, is investigated. It is found that the 4D-VAR method reduces the error in the water height forecast even when the erosion model is not activated. In terms of Lyapunov exponents, we estimate the predictability horizon of such an event to be about half-an-hour after a big rain. However, this limit of predictability can be increased by using more observations or by using a finer computational grid.     

Model identification for hydrological forecasting under uncertainty

Methods for the identification of models for hydrological forecasting have to consider the specific nature of these models and the uncertainties present in the modeling process. Current approaches fail to fully incorporate these two aspects. In this paper we review the nature of hydrological models and the consequences of this nature for the task of model identification. We then continue to discuss the history (“The need for more POWER‘’), the current state (“Learning from other fields”) and the future (“Towards a general framework”) of model identification. The discussion closes with a list of desirable features for an identification framework under uncertainty and open research questions in need of answers before such a framework can be implemented.     

Ocean current estimation using a Multi-Model Ensemble Kalman Filter during the Grand Lagrangian Deployment experiment (GLAD)

In the summer and fall of 2012, during the GLAD experiment in the Gulf of Mexico, the Consortium for Advanced Research on Transport of Hydrocarbon in the Environment (CARTHE) used several ocean models to assist the deployment of more than 300 surface drifters. The Navy Coastal Ocean Model (NCOM) at 1 km and 3 km resolutions, the US Navy operational NCOM at 3 km resolution (AMSEAS), and two versions of the Hybrid Coordinates Ocean Model (HYCOM) set at 4 km were running daily and delivering 72-h range forecasts. They all assimilated remote sensing and local profile data but they were not assimilating the drifter’s observations. This work presents a non-intrusive methodology named Multi-Model Ensemble Kalman Filter that allows assimilating the local drifter data into such a set of models, to produce improved ocean currents forecasts. The filter is to be used when several modeling systems or ensembles are available and/or observations are not entirely handled by the operational data assimilation process. It allows using generic in situ measurements over short time windows to improve the predictability of local ocean dynamics and associated high-resolution parameters of interest for which a forward model exists (e.g. oil spill plumes). Results can be used for operational applications or to derive enhanced background fields for other data assimilation systems, thus providing an expedite method to non-intrusively assimilate local observations of variables with complex operators. Results for the GLAD experiment show the method can improve water velocity predictions along the observed drifter trajectories, hence enhancing the skills of the models to predict individual trajectories.     

High precision analysis of an embryonic extensional fault-related fold using 3D orthorectified virtual outcrops: The viewpoint importance in structural geology

Image-based 3D modeling has recently opened the way to the use of virtual outcrop models in geology. An intriguing application of this method involves the production of orthorectified images of outcrops using almost any user-defined point of view, so that photorealistic cross-sections suitable for numerous geological purposes and measurements can be easily generated. These purposes include the accurate quantitative analysis of fault-fold relationships starting from imperfectly oriented and partly inaccessible real outcrops. We applied the method of image-based 3D modeling and orthorectification to a case study from the northern Apennines, Italy, where an incipient extensional fault affecting well-layered limestones is exposed on a 10-m-high barely accessible cliff. Through a few simple steps, we constructed a high-quality image-based 3D model of the outcrop. In the model, we made a series of measurements including fault and bedding attitudes, which allowed us to derive the bedding-fault intersection direction. We then used this direction as viewpoint to obtain a distortion-free photorealistic cross-section, on which we measured bed dips and thicknesses as well as fault stratigraphic separations. These measurements allowed us to identify a slight difference (i.e. only 0.5°) between the hangingwall and footwall cutoff angles. We show that the hangingwall strain required to compensate the upward-decreasing displacement of the fault was accommodated by this 0.5° rotation (i.e. folding) and coeval 0.8% thickening of strata in the hangingwall relatively to footwall strata. This evidence is consistent with trishear fault-propagation folding. Our results emphasize the viewpoint importance in structural geology and therefore the potential of using orthorectified virtual outcrops.     

自动气象观测数据的质量控制

精细化气候变化分析及气候预测服务亟待发展,对高分辨率优质量的气候资料需求不断增长。本文利用缺测率、对比差值、空间一致性、粗差率、一致率5个指标对2009—2014年浙江省2 209个自动站温度数据进行评估分析,运用开放式可调整的评分标准,选取数据质量稳定可靠的站点,满足气候研究与业务的发展需求。共取得91个一级可用站,499个二级可用站,363个三级可用站,一级站主要分布于宁波和嘉兴地区,二级和三级站在各地都有较均匀的分布。在全省气候统计分析中,可以根据需求利用可用站。此外,有可用自动站参与的温度分布图完美呈现了地形对温度的影响,也使城市化发展突显出来,精细化的气候分析能更有效的为高山地区耕种制度及城市建设与规划提供科学依据。     

EnSRF雷达资料同化在一次飑线过程中的应用研究

本文利用包含复杂冰相微物理过程的WRF（Weather Research and Forecasting）模式,针对2007年4月23日发生在我国华南地区的一次典型飑线天气过程,分别进行了确定性预报和集合预报试验,发现确定性预报能大致捕捉到飑线系统的发生发展过程,但对飑线后部的层云区模拟效果较差。集合预报能够有效地减少模式的不确定性,大部分集合成员对飑线的模拟效果优于确定性预报。进一步将集合预报得到的40个成员作为背景场,采用EnSRF（Ensemble Square Root Filter）同化多普勒天气雷达资料,并将分析得到的集合作为初始场进行集合预报,通过与未同化雷达资料的集合对比,考察了EnSRF同化多部雷达资料对飑线系统的影响。结果表明:EnSRF雷达资料同化增加了模式初始场的中小尺度信息,大部分集合成员的分析场能够较准确地再现飑线的热力场、动力场和微物理场的细致特征,并且模拟出飑线后部的层云结构。通过对EnSRF分析的集合进行模拟发现,大部分集合成员较未同化雷达资料时模拟效果有明显改善。同化后的集合预报ETS（Equitable Threat Score）评分最高,其次是未同化的集合预报,确定性预报的最低。     

中国东部夏季暴雨的年代际跃变及其大尺度环流背景

本文利用1960~2011年中国东部地面测站的逐日降水资料和JRA-55再分析资料探讨了夏季暴雨分布的年代际跃变及其相关联的大尺度环流异常特征。基于暴雨频数和占比(夏季暴雨占比是指5~8月暴雨降水量对总降水量的贡献百分比)的分析结果表明:中国东部夏季暴雨分布在20世纪70年代末和90年代初经历两次反相的经向"三极子"跃变。中国东部夏季暴雨的年代际演变过程可分为三个时段:1960~1979年为华南和华北暴雨偏多、江淮流域暴雨偏少的经向"三极子"分布;1980~1991年为南方和华北暴雨偏少、江淮流域暴雨偏多的经向三极子"分布;1992~2011年为南方暴雨显著偏多、华北暴雨持续偏少,逐渐形成经向"偶极子"分布,并导致近十多年我国夏季"南涝北旱"的整体格局。1970年代末(1990年代初)跃变相关联的大尺度环流异常配置:东亚夏季风的减弱(增强),西太平洋副高的增强西伸但南撤(北抬),南亚高压的减弱南缩(增强东扩),以及蒙古高原中低层的气旋式(反气旋式)环流异常。与此同时,低层局地环流也发生调整:华北和黄淮地区以及华南和江南地区均为反气旋式(气旋式)环流异常,而江淮流域和四川盆地受控于风场切变式辐合(辐散)异常;涡度场发生相应变化,南北方大部分地区的负(正)涡度异常不(有)利于低涡的发展,而江淮流域和四川盆地的正(负)涡度异常有(不)利于低涡的发展,进而引发江南和华南暴雨减少(增加)、江淮流域和四川盆地暴雨增加(减少)、黄淮和华北暴雨减少(增加)的经向"三极子"跃变。     

Estimating winter wheat biomass by assimilating leaf area index derived from fusion of Landsat-8 and MODIS data

A sufficient number of satellite acquisitions in a growing season are essential for deriving agronomic indicators, such as green leaf area index (GLAI), to be assimilated into crop models for crop productivity estimation. However, for most high resolution orbital optical satellites, it is often difficult to obtain images frequently due to their long revisit cycles and unfavorable weather conditions. Data fusion algorithms, such as the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and the Enhanced STARFM (ESTARFM), have been developed to generate synthetic data with high spatial and temporal resolution to address this issue. In this study, we evaluated the approach of assimilating GLAI into the Simple Algorithm for Yield Estimation model (SAFY) for winter wheat biomass estimation. GLAI was estimated using the two-band Enhanced Vegetation Index (EVI2) derived from data acquired by the Operational Land Imager (OLI) onboard the Landsat-8 and a fusion dataset generated by blending the Moderate-Resolution Imaging Spectroradiometer (MODIS) data and the OLI data using the STARFM and ESTARFM models. The fusion dataset had the temporal resolution of the MODIS data and the spatial resolution of the OLI data. Key parameters of the SAFY model were optimised through assimilation of the estimated GLAI into the crop model using the Shuffled Complex Evolution-University of Arizona (SCE-UA) algorithm. A good agreement was achieved between the estimated and field measured biomass by assimilating the GLAI derived from the OLI data (GLAI_L) alone (R² = 0.77 and RMSE = 231 g m⁻²). Assimilation of GLAI derived from the fusion dataset (GLAI_F) resulted in a R² of 0.71 and RMSE of 193 g m⁻² while assimilating the combination of GLAI_L and GLAI_F led to further improvements (R² = 0.76 and RMSE = 176 g m⁻²). Our results demonstrated the potential of using the fusion algorithms to improve crop growth monitoring and crop productivity estimation when the number of high resolution remote sensing data acquisitions is limited.     

Predictive geochemical mapping using environmental correlation

The distribution of chemical elements at and near the Earth's surface, the so-called critical zone, is complex and reflects the geochemistry and mineralogy of the original substrate modified by environmental factors that include physical, chemical and biological processes over time.Geochemical data typically is illustrated in the form of plan view maps or vertical cross-sections, where the composition of regolith, soil, bedrock or any other material is represented. These are primarily point observations that frequently are interpolated to produce rasters of element distributions. Here we propose the application of environmental or covariate regression modelling to predict and better understand the controls on major and trace element geochemistry within the regolith. Available environmental covariate datasets (raster or vector) representing factors influencing regolith or soil composition are intersected with the geochemical point data in a spatial statistical correlation model to develop a system of multiple linear correlations. The spatial resolution of the environmental covariates, which typically is much finer (e.g. ∼90 m pixel) than that of geochemical surveys (e.g. 1 sample per 10-10,000 km²), carries over to the predictions. Therefore the derived predictive models of element concentrations take the form of continuous geochemical landscape representations that are potentially much more informative than geostatistical interpolations.Environmental correlation is applied to the Sir Samuel 1:250,000 scale map sheet in Western Australia to produce distribution models of individual elements describing the geochemical composition of the regolith and exposed bedrock. As an example we model the distribution of two elements – chromium and sodium. We show that the environmental correlation approach generates high resolution predictive maps that are statistically more accurate and effective than ordinary kriging and inverse distance weighting interpolation methods. Furthermore, insights can be gained into the landscape processes controlling element concentration, distribution and mobility from analysis of the covariates used in the model. This modelling approach can be extended to groups of elements (indices), element ratios, isotopes or mineralogy over a range of scales and in a variety of environments.     

A note on “Origin of components in Chilean thermal waters” by

Risacher et al. (2011) have presented voluminous data on thermal waters of Chile that is quite appreciable; however, their work still falls short on several counts. The most notable shortcoming of the work is the presentation and treatment of data. The interpretations are based on questionable premises (viz., extent of seawater intrusion) and considerations (viz., using average chemical composition of rock types for geochemical modeling, considering volcanic rocks as the only rock type in contact with the geothermal fluids) and assumptions not always substantiated by facts (with proper references) could have been corroborated. Use of Cl/Br ratio for discrimination purpose is unconvincing, considering the uncertainty in the measurement of low (<1 mg/L) Br concentration.