Ocean eddy momentum fluxes at the latitudes of the Gulf Stream and the Kuroshio extensions as revealed by satellite data

Eddy momentum fluxes, i.e. Reynold stresses, are computed for the latitude bands of the Gulf Stream and Kuroshio extensions using 13 years of data from the merged satellite altimeter product of Le Traon et al. The spatial pattern and amplitude of the fluxes is remarkably similar to that found by Ducet and Le Traon using the 5 years of data that were available to them. In addition to updating the work of Ducet and Le Traon, we provide new insight into the role played by the underlying variable bottom topography, both for determining the structure of the eddy momentum fluxes seen in the satellite data and for influencing the way these fluxes feedback on the mean flow. While there is no clear evidence that eddies locally flux momentum into the eastward jets of the Gulf Stream and Kuroshio extensions, a clearer picture emerges after zonally integrating across each of the North Atlantic and North Pacific basins. We argue that the eddy momentum fluxes do indeed drive significant transport, a conclusion supported by preliminary results from a 3-D model calculation. We also present evidence that in the North Pacific, the Reynolds stresses are important for driving the recirculation gyres associated with the Kuroshio extension, taking advantage of new data from both observations and high-resolution model simulations.     

The Tropospheric Gas-Phase Degradation of NH3 and Its Impact on the Formation of N2O and NOx

The gas-phase degradation of NH3 in the atmosphere still has many uncertainties. One of them is the possible isomerisation of NH2O to NHOH, as indicated by kinetic studies. Since NH2O is formed during the gas-phase oxidation of ammonia in the troposphere, this reaction can potentially influence the subsequent production of N2O and NOx. So far, the isomerisation has never been implemented into current chemical schemes describing the atmospheric gas-phase degradation of NH3 and its atmospheric relevance has never been assessed. The N2O yield from NH3 degradation is calculated to be in the range of 10–43 %. It depends on the NO2 and O3 concentrations, but is independent of the NH3 concentration. Compared with the results from recent literature, the N2O yield derived from the new mechanism is 20–80% lower, implying a smaller global N2O source strength of 0.4 Tg yr- 1. The production of NH2SO2 seems to be less important for the atmospheric degradation of NH3. NH3 oxidation is a sink for NOx at NOx mixing ratios of more than about 1 ppb and a source at lower NOx burdens.     

Assessing ecological impairments in Neotropical rivers of Mexico: calibration and validation of the Biomonitoring Working Party Index

Freshwater ecosystems are one of the most endangered and threatened ecosystems worldwide, particularly in developing countries, where population growth, industrialization, urbanization, and changes in land use are increasing. In Mexico, water ecosystems are severely impacted and an easy-to-use tool is needed for monitoring freshwater ecosystems. This study presents the development of a calibration and validation procedure for the Biological Monitoring Working Party Index using physical–chemical and biological data from Neotropical rivers in Mexico (Apatlaco and Chalma-Tembembe). Water quality and macroinvertebrates were monitored in four campaigns including the dry and rainy seasons (2012–2013). Calibration of the index was performed in the following steps: determination of a physicochemical quality index, identification of the abundance class for each family of macroinvertebrates, incorporation of abundances in the corresponding physicochemical quality index interval, and determination of bioindication values for each macroinvertebrate family. With the calibrated values, the index was assessed for each study site and period. The calibrated index was validated by a statistical test. Ranges for water quality categories were defined by three statistical procedures. The range of the Biological Monitoring Working Party Index was tested with study sites from adjacent sub-basins. The Chalma-Tembembe River in agricultural areas comprised the categories “Bad polluted” to “regular and moderately polluted,” whereas the largest portion of the Apatlaco River in urban zones comprised “Bad, very polluted” to “Bad, extremely polluted.” Thus, the calibrated index is a suitable biomonitoring tool, allowing the detection of zones that need urgent management and a recovery plan.     

Application of the representer method for parameter estimation in numerical reservoir models

A data assimilation method was applied to estimate poorly known parameters (permeabilities) in a numerical reservoir model. Most variational methods for data assimilation are based on the assumption that the model is perfect except for the poorly known parameters. The representer method allows also for model errors, i.e. for uncertainties in the state variables (pressures and saturations). The method is based on minimizing a cost functional, assuming all the errors and parameters to be multivariate Gaussian random variables with given mean and covariances. The uncertain parameters and variables are expanded into a finite sum of basis functions called representers, and the gradients of the cost functional are obtained with an adjoint method. This approach gives an optimal parametrization in the sense that the final result is equal to the solution of the full inverse problem. The method was tested on a simple one-dimensional model to simulate two-phase (oil-water) flow through a heterogeneous reservoir. The results show that the method is able to provide an acceptable estimate of the permeability field. We used pressure measurements from a small number of observation wells in between the injection and production wells, but the representer method could be used equally well to assimilate data from other sources. The method appears to be a promising data assimilation tool for applications in reservoir engineering.     

Model-reduced gradient-based history matching

Gradient-based history matching algorithms can be used to adapt the uncertain parameters in a reservoir model using production data. They require, however, the implementation of an adjoint model to compute the gradients, which is usually an enormous programming effort. We propose a new approach to gradient-based history matching which is based on model reduction, where the original (nonlinear and high-order) forward model is replaced by a linear reduced-order forward model and, consequently, the adjoint of the tangent linear approximation of the original forward model is replaced by the adjoint of a linear reduced-order forward model. The reduced-order model is constructed with the aid of the proper orthogonal decomposition method. Due to the linear character of the reduced model, the corresponding adjoint model is easily obtained. The gradient of the objective function is approximated, and the minimization problem is solved in the reduced space; the procedure is iterated with the updated estimate of the parameters if necessary. The proposed approach is adjoint-free and can be used with any reservoir simulator. The method was evaluated for a waterflood reservoir with channelized permeability field. A comparison with an adjoint-based history matching procedure shows that the model-reduced approach gives a comparable quality of history matches and predictions. The computational efficiency of the model-reduced approach is lower than of an adjoint-based approach, but higher than of an approach where the gradients are obtained with simple finite differences.     

Influence of preferred orientation on the powder pattern of deerite

Attention is drawn to the differences in the relative intensities of the reflections of deerite measured on a Guinier photograph and those obtained from diffractometer data (Langer et al., 1977), which were probably collected without taking adequate precautions to reduce the orientation effect. Powder data for deerite, which are in conformity with the structure, are presented.     

Reduced-order optimal control of water flooding using proper orthogonal decomposition

Model-based optimal control of water flooding generally involves multiple reservoir simulations, which makes it into a time-consuming process. Furthermore, if the optimization is combined with inversion, i.e., with updating of the reservoir model using production data, some form of regularization is required to cope with the ill-posedness of the inversion problem. A potential way to address these issues is through the use of proper orthogonal decomposition (POD), also known as principal component analysis, Karhunen–Loève decomposition or the method of empirical orthogonal functions. POD is a model reduction technique to generate low-order models using ‘snapshots’ from a forward simulation with the original high-order model. In this work, we addressed the scope to speed up optimization of water-flooding a heterogeneous reservoir with multiple injectors and producers. We used an adjoint-based optimal control methodology that requires multiple passes of forward simulation of the reservoir model and backward simulation of an adjoint system of equations. We developed a nested approach in which POD was first used to reduce the state space dimensions of both the forward model and the adjoint system. After obtaining an optimized injection and production strategy using the reduced-order system, we verified the results using the original, high-order model. If necessary, we repeated the optimization cycle using new reduced-order systems based on snapshots from the verification run. We tested the methodology on a reservoir model with 4050 states (2025 pressures, 2025 saturations) and an adjoint model of 4050 states (Lagrange multipliers). We obtained reduced-order models with 20–100 states only, which produced almost identical optimized flooding strategies as compared to those obtained using the high-order models. The maximum achieved reduction in computing time was 35%.