ALBERT

Description: [1]  In our recent paper [6], the sensitivity of infrasound to the upper atmosphere is investigated using impulsive signals from the Tungurahua volcano in Ecuador. We reported on the coherent variability of thermospheric travel times, with periods equal to those of the tidal harmonics. Moreover, it was shown that the error in predicted thermospheric travel time is in accord with typical uncertainties in the upper atmospheric wind speed models. Given the observed response of the infrasound celerities to upper atmospheric tidal variability, it was suggested that infrasound observations may be used to reduce uncertainty in the knowledge of the atmospheric specifications in the upper atmosphere. In this paper, we discuss the estimation of upper atmospheric wind model updates from the infrasound data described in the aforementioned paper. The parameterization of the model space by empirical orthogonal functions is described; it is found that the wind model in the upper mesosphere and lower thermosphere can be described by a four-parameter model. Due to the small dimensionality of the model space, a grid search method can be used to solve the inverse problem. A Bayesian method is used to assess the uncertainty in the inverse solution given the a priori uncertainty in the data and model spaces and the non-linearity of the inverse problem at hand. We believe that this is the first study in which such methods are applied to real infrasound data, allowing for a rigorous analysis of this inverse problem. It is found that the complexity of the a posteriori model distribution increases for a larger dimensional model space and larger uncertainties in the data. A case study is presented in which the non-linear propagation from source to receiver is simulated using an updated wind model and non-linear ray theory. As non-linear propagation effects further constrain the propagation path, this is a way to check the physical self-consistency of the travel time inversion approach. We obtain excellent agreement between the simulated and observed waveforms.

Description: The annual peak-flow series of the Polish rivers are mixtures of summer and winter flows. In this part I of a sequence of two papers, theoretical aspects of applicability of seasonal approach to flood frequency analysis (FFA) in Poland are discussed. A testing procedure is introduced for the seasonal model and the data overall fitness. Conditions for objective comparative assessment of accuracy of AM (annual maxima) and SM (seasonal maxima) approaches to FFA are formulated and finally Gumbel (EV1) distribution is chosen as seasonal distribution for detailed investigation. Sampling properties of AM quantile x ( F ) estimates are analyzed and compared for the SM and AM models for equal seasonal variances. For this purpose 4 estimation methods were used, employing both asymptotic approach and sampling experiments. Superiority of the SM over AM approach is stated evident in the upper quantile range, particularly for the case of no seasonal variation in the parameters of Gumbel distribution. In order to learn whether the standard two- and three-parameter flood frequency distributions can be used to model the samples generated from the Two-Component EV1 distribution, the shape of TCEV1 pdf has been tested in terms of bimodality. Then the use of upper quantile estimate got from the dominant season of extreme floods (DEFS) as AM upper quantile estimate is studied and respective systematic error is assessed. The second part of the paper deals with advantages and disadvantages of SM and AM approach when applied to real flow data of Polish rivers. Copyright © 2011 John Wiley & Sons, Ltd.

Description: The annual peak-flow series of Polish rivers are mixtures of summer and winter flows. As part II of a sequence of two papers, practical aspects of applicability of seasonal approach to flood frequency analysis (FFA) of Polish rivers are discussed. Taking Two-Component Extreme Value ( TCEV 1) model as an example it was shown in the first part that regardless of estimation method, the seasonal approach can give profit in terms of upper quantile estimation accuracy that rises with the return period of the quantile and is the greatest for no seasonal variation. In this part, an assessment of AM (annual maxima) versus SM (seasonal maxima) approach to FFA was carried out with respect to seasonal and annual peak-flow series of 38 Polish gauging stations. First, the assumption of mutual independence of the seasonal maxima has been tested. The smoothness of SM and AM empirical probability distribution functions was analysed and compared. The TCEV 1 model with seasonally estimated parameters was found to be not appropriate for most Polish data as it considerably underrates the skewness of AM distributions and upper quantile values as well. Consequently, the discrepancies between the SM and AM estimates of TCEV 1 are observed. Taking SM and TCEV 1 distribution, the dominating season in AM series was confronted with predominant season for extreme floods. The key argument for presumptive superiority of SM approach that SM samples are more statistically homogeneous than AM samples has not been confirmed by the data. An analysis of fitness to SM and AM of Polish data sets made for seven distributions pointed to Pearson (3) distribution as the best for AM and Summer Maxima, whereas it was impossible to select a single best model for winter samples. In the multi-model approach to FFA, the tree functions, i.e., Pe(3), CD3 and LN3, should be involved for both SM and AM. As the case study, Warsaw gauge on the Vistula River was selected. While most of AM elements are here from winter season, the prevailing majority of extreme annual floods are the summer maxima. The upper quantile estimates got by means of classical annual and two-season methods happen to be fairly close; what's more they are nearly equal to the quantiles calculated just for the season of dominating extreme floods. Copyright © 2011 John Wiley & Sons, Ltd.

Description: [1]  Due, in part, to the challenging environment of Earth's high-latitude regions, available information on cold climate effects on aeolian processes in these areas remains limited. Data from these areas, however, provide insight into the physics of sediment transport by wind and the controls on erosive winds in proximity to ice caps and topographic influences. This study presents a 2 year record of meteorological, saltation activity, horizontal saltation flux, and particle size distribution data from four sites in the McMurdo Dry Valleys of Antarctica, 2008 to 2010. Saltation measurements revealed daily and seasonal patterns with spring and summer sediment transport events occurring between 09:00 and 24:00 hours due to thermally generated winds. Fall and winter events occur at any time of day with the strongest associated with foehn winds. Threshold wind speed at 4.2 m in all seasons for all locations was ≈10 m s −1 . Saltation occurred in the temperature range −40°C to +5°C. Westerly winds in the fall/winter and easterly winds in spring/summer are associated with the majority of transport events. The sand in transport is mainly 250 to 500 μ m in diameter and poorly sorted. The integrated saltation flux varies over three orders of magnitude among the sites, with the lowest mean flux recorded in the Taylor Valley (2.9 kg m −1 day −1 ) and the highest in the eastern Victoria Valley (2271 kg m −1 day −1 ) for 24 hours of continuous saltation. The percentage of time saltation active at these locations annually is ≈2%, ≈4%, and ≈13%, respectively, for the Victoria, Taylor, and Wright Valleys.

Description: We present a model order reduction technique that overcomes the computational burden associated with the application of Monte Carlo methods to the solution of the groundwater flow equation with random hydraulic conductivity. The method is based on the Galerkin projection of the high-dimensional model equations onto a subspace, approximated by a small number of pseudo-optimally chosen basis functions (principal components). To obtain an efficient reduced order model, we develop an offline algorithm for the computation of the parameter-independent principal components. Our algorithm combines a greedy algorithm for the snapshot selection in the parameter space and an optimal distribution of the snapshots in time. Moreover, we introduce a residual-based estimation of the error associated with the reduced model. This estimation allows a considerable reduction of the number of full system model solutions required for the computation of the principal components. We demonstrate the robustness of our methodology by way of numerical examples, comparing the empirical statistics of the ensemble of the numerical solutions obtained using the traditional Monte Carlo method and our reduced model. The numerical results show that our methodology significantly reduces the computational requirements (CPU time and storage) for the solution of the Monte Carlo simulation, ensuring a good approximation of the mean and variance of the head. The analysis of the empirical probability density functions at the observation wells suggests that our reduced model produces good results and is most accurate in the regions with large drawdown.

Description: The ability of a particular model to accurately predict how a system responds to forcing is predicated on various model parameters that must be appropriately identified. There are many algorithms whose purpose is to solve this inverse problem, which is often computationally intensive. In this study, we propose a new algorithm that significantly reduces the computational burden associated with parameter identification. The algorithm is an extension of the quasilinearization approach where the governing system of differential equations is linearized with respect to the parameters. The resulting inverse problem therefore becomes a linear regression or quadratic programming problem (QP) for minimizing the sum of squared residuals; the solution becomes an update on the parameter set. This process of linearization and regression is repeated until convergence takes place. This algorithm has not received much attention, as the QPs can become quite large, often infeasible for real-world systems. To alleviate this drawback, proper orthogonal decomposition is applied to reduce the size of the linearized model, thereby reducing the computational burden of solving each QP. In fact, this study shows that the snapshots need only be calculated once at the very beginning of the algorithm, after which no further calculations of the reduced-model subspace are required. The proposed algorithm therefore only requires one linearized full-model run per parameter at the first iteration followed by a series of reduced-order QPs. The method is applied to a groundwater model with about 30,000 computation nodes where as many as 15 zones of hydraulic conductivity are estimated.

Description: We describe a global atmospheric data assimilation scheme that has been adapted for use with a Martian General Circulation Model (GCM), with the ultimate goal of creating globally and temporally interpolated “reanalysis” data sets from planetary atmospheric observations. The system uses the Data Assimilation Research Testbed (DART) software to apply an Ensemble Kalman Filter (EnKF) to the MarsWRF GCM. Specific application to Mars also required the development of a radiance forward model for near-nadir Thermal Emission Spectrometer (TES) observations. Preliminary results from an assimilation of 40 sols of TES radiance data, taken around Ls = 150° (August 1999, Mars Year 24), are provided. 1.3 million TES observations are ingested and used to improve the state prediction by the GCM, with bias and error reductions obtained throughout the state vector. Results from the assimilation suggest steepening of the latitudinal and vertical thermal gradients with concurrent strengthening of the mid-latitude zonal jets, and a slower recession of the southern polar ice edge than predicted by the unaided GCM. Limitations of the prescribed dust model are highlighted by the presence of an atmospheric radiance bias. Preliminary results suggest the prescribed dust vertical profile might not be suitable for all seasons, in accordance with more recent observations of the vertical distribution of dust by the Mars Climate Sounder. The tools developed using this DA system are available at http://www.marsclimatecenter.com. A tutorial and example TES radiance assimilation are also provided.

Description: The response of the second-generation Canadian earth system model (CanESM2) to historical (1850–2005) and future (2006–2100) natural and anthropogenic forcing is assessed using the newly-developed representative concentration pathways (RCPs) of greenhouse gases (GHGs) and aerosols. Allowable emissions required to achieve the future atmospheric CO2 concentration pathways, are reported for the RCP 2.6, 4.5 and 8.5 scenarios. For the historical 1850–2005 period, cumulative land plus ocean carbon uptake and, consequently, cumulative diagnosed emissions compare well with observation-based estimates. The simulated historical carbon uptake is somewhat weaker for the ocean and stronger for the land relative to their observation-based estimates. The simulated historical warming of 0.9°C compares well with the observation-based estimate of 0.76 ± 0.19°C. The RCP 2.6, 4.5 and 8.5 scenarios respectively yield warmings of 1.4, 2.3, and 4.9°C and cumulative diagnosed fossil fuel emissions of 182, 643 and 1617 Pg C over the 2006–2100 period. The simulated warming of 2.3°C over the 1850–2100 period in the RCP 2.6 scenario, with the lowest concentration of GHGs, is slightly larger than the 2°C warming target set to avoid dangerous climate change by the 2009 UN Copenhagen Accord. The results of this study suggest that limiting warming to roughly 2°C by the end of this century is unlikely since it requires an immediate ramp down of emissions followed by ongoing carbon sequestration in the second half of this century.