Quantifying polynya ice production in the Laptev Sea with the COSMO model

Arctic flaw polynyas are considered to be highly productive areas for the formation of sea-ice throughout the winter season. Most estimates of sea-ice production are based on the surface energy balance equation and use global reanalyses as atmospheric forcing, which are too coarse to take into account the impact of polynyas on the atmosphere. Additional errors in the estimates of polynya ice production may result from the methods of calculating atmospheric energy fluxes and the assumption of a thin-ice distribution within polynyas. The present study uses simulations using the mesoscale weather prediction model of the Consortium for Small-scale Modelling (COSMO), where polynya area is prescribed from satellite data. The polynya area is either assumed to be ice-free or to be covered with thin ice of 10 cm. Simulations have been performed for two winter periods (2007/08 and 2008/09). When using a realistic thin-ice thickness of 10 cm, sea-ice production in Laptev polynyas amount to 30 km3 and 73 km3 for the winters 2007/08 and 2008/09, respectively. The higher turbulent energy fluxes of open-water polynyas result in a 50-70% increase in sea-ice production (49 km3 in 2007/08 and 123 km3 in 2008/09). Our results suggest that previous studies have overestimated ice production in the Laptev Sea.

Impact of Laptev Sea flaw polynyas on the atmospheric boundary layer and ice production using idealized mesoscale simulations

The interaction between polynyas and the atmospheric boundary layer is examined in the Laptev Sea using the regional, non-hydrostatic Consortium for Small-scale Modelling (COSMO) atmosphere model. A thermodynamic sea-ice model is used to consider the response of sea-ice surface temperature to idealized atmospheric forcing. The idealized regimes represent atmospheric conditions that are typical for the Laptev Sea region. Cold wintertime conditions are investigated with sea-ice–ocean temperature differences of up to 40 K. The Laptev Sea flaw polynyas strongly modify the atmospheric boundary layer. Convectively mixed layers reach heights of up to 1200 m above the polynyas with temperature anomalies of more than 5 K. Horizontal transport of heat expands to areas more than 500 km downstream of the polynyas. Strong wind regimes lead to a more shallow mixed layer with strong near-surface modifications, while weaker wind regimes show a deeper, well-mixed convective boundary layer. Shallow mesoscale circulations occur in the vicinity of ice-free and thin-ice covered polynyas. They are forced by large turbulent and radiative heat fluxes from the surface of up to 789 W m−2, strong low-level thermally induced convergence and cold air flow from the orographic structure of the Taimyr Peninsula in the western Laptev Sea region. Based on the surface energy balance we derive potential sea-ice production rates between 8 and 25 cm d−1. These production rates are mainly determined by whether the polynyas are ice-free or covered by thin ice and by the wind strength.

Assimilation of Meteosat Second Generation (MSG) satellite data in a regional numerical weather prediction model using a one-dimensional variational approach

The quality of temperature and humidity retrievals from the infrared SEVIRI sensors on the geostationary Meteosat Second Generation (MSG) satellites is assessed by means of a one dimensional variational algorithm. The study is performed with the aim of improving the spatial and temporal resolution of available observations to feed analysis systems designed for high resolution regional scale numerical weather prediction (NWP) models. The non-hydrostatic forecast model COSMO (COnsortium for Small scale MOdelling) in the ARPA-SIM operational configuration is used to provide background fields. Only clear sky observations over sea are processed. An optimised 1D–VAR set-up comprising of the two water vapour and the three window channels is selected. It maximises the reduction of errors in the model backgrounds while ensuring ease of operational implementation through accurate bias correction procedures and correct radiative transfer simulations. The 1D–VAR retrieval quality is firstly quantified in relative terms employing statistics to estimate the reduction in the background model errors. Additionally the absolute retrieval accuracy is assessed comparing the analysis with independent radiosonde and satellite observations. The inclusion of satellite data brings a substantial reduction in the warm and dry biases present in the forecast model. Moreover it is shown that the retrieval profiles generated by the 1D–VAR are well correlated with the radiosonde measurements. Subsequently the 1D–VAR technique is applied to two three–dimensional case–studies: a false alarm case–study occurred in Friuli–Venezia–Giulia on the 8th of July 2004 and a heavy precipitation case occurred in Emilia–Romagna region between 9th and 12th of April 2005. The impact of satellite data for these two events is evaluated in terms of increments in the integrated water vapour and saturation water vapour over the column, in the 2 meters temperature and specific humidity and in the surface temperature. To improve the 1D–VAR technique a method to calculate flow–dependent model error covariance matrices is also assessed. The approach employs members from an ensemble forecast system generated by perturbing physical parameterisation schemes inside the model. The improved set–up applied to the case of 8th of July 2004 shows a substantial neutral impact.

Hydration of C3S, C2S and their Blends. Micro- and Nanoscale Characterization

This study forms part of wider research conducted under a EU 7 th Framework Programme (COmputationally Driven design of Innovative CEment-based materials or CODICE). The ultimate aim is the multi-scale modelling of the variations in mechanical performance in degraded and non-degraded cementitious matrices. The model is being experimentally validated by hydrating the main tri-calcium silicate (T1-C3S) and bi-calcium silicate (β-C2S), phases present in Portland cement and their blends. The present paper discusses micro- and nanoscale studies of the cementitious skeletons forming during the hydration of C3S, C2S and 70 % / 30 % blends of both C3S/C2S and C2S/C3S with a water/cement ratio of 0.4. The hydrated pastes were characterized at different curing ages with 29 Si NMR, SEM/TEM/EDS, BET, and nanoindentation. The findings served as a basis for the micro- and nanoscale characterization of the hydration products formed, especially C-S-H gels. Differences were identified in composition, structure and mechanical behaviour (nanoindentation), depending on whether the gels formed in C3S or C2S pastes. The C3S gels had more compact morphologies, smaller BET-N2 specific surface area and lesser porosity than the gels from C2S-rich pastes. The results of nanoindentation tests appear to indicate that the various C-S-H phases formed in hydrated C3S and C2S have the same mechanical properties as those formed in Portland cement paste. Compared to the C3S sample, the hydrated C2S specimen was dominated by the loose-packed (LP) and the low-density (LD) C-S-H phases, and had a much lower content of the high density (HD) C-S-H phase

Large-eddy simulation of heat transfer downstream of a backward-facing step

Large-eddy simulation is used to predict heat transfer in the separated and reattached flow regions downstream of a backward-facing step. Simulations were carried out at a Reynolds number of 28 000 (based on the step height and the upstream centreline velocity) with a channel expansion ratio of 1.25. The Prandtl number was 0.71. Two subgrid-scale models were tested, namely the dynamic eddy-viscosity, eddy-diffusivity model and the dynamic mixed model. Both models showed good overall agreement with available experimental data. The simulations indicated that the peak in heat-transfer coefficient occurs slightly upstream of the mean reattachment location, in agreement with experimental data. The results of these simulations have been analysed to discover the mechanisms that cause this phenomenon. The peak in heat-transfer coefficient shows a direct correlation with the peak in wall shear-stress fluctuations. It is conjectured that the peak in these fluctuations is caused by an impingement mechanism, in which large eddies, originating in the shear layer, impact the wall just upstream of the mean reattachment location. These eddies cause a 'downwash', which increases the local heat-transfer coefficient by bringing cold fluid from above the shear layer towards the wall.

Land cover classification of Samoylov Island and Landsat subpixel water cover of Lena River Delta, Siberia, with links to ESRI grid files

Ignoring small-scale heterogeneities in Arctic land cover may bias estimates of water, heat and carbon fluxes in large-scale climate and ecosystem models. We investigated subpixel-scale heterogeneity in CHRIS/PROBA and Landsat-7 ETM+ satellite imagery over ice-wedge polygonal tundra in the Lena Delta of Siberia, and the associated implications for evapotranspiration (ET) estimation. Field measurements were combined with aerial and satellite data to link fine-scale (0.3 m resolution) with coarse-scale (upto 30 m resolution) land cover data. A large portion of the total wet tundra (80%) and water body area (30%) appeared in the form of patches less than 0.1 ha in size, which could not be resolved with satellite data. Wet tundra and small water bodies represented about half of the total ET in summer. Their contribution was reduced to 20% in fall, during which ET rates from dry tundra were highest instead. Inclusion of subpixel-scale water bodies increased the total water surface area of the Lena Delta from 13% to 20%. The actual land/water proportions within each composite satellite pixel was best captured with Landsat data using a statistical downscaling approach, which is recommended for reliable large-scale modelling of water, heat and carbon exchange from permafrost landscapes.

Sea ice thickness distribution (ITD) Model for the Arctic, links to NetCDF files

Sea ice models contain many different parameterizations of which one of the most commonly used is a subgrid-scale ice thickness distribution (ITD). The effect of this model component and the associated ice strength formulation on the reproduction of observed Arctic sea ice is assessed. To this end the model's performance in reproducing satellite observations of sea ice concentration, thickness and drift is evaluated. For an unbiased comparison, different model configurations with and without an ITD are tuned with an automated parameter optimization. The original combination of ITD and ice strength parameterization does not lead to better results than a simple single category model. Yet changing to a simpler ice strength formulation, which depends linearly on the mean ice thickness across all thickness categories, allows to clearly improve the model-data misfit when using an ITD. In the original formulation, the ice strength depends strongly on the number of thickness categories, so that introducing more categories can lead to thicker albeit weaker ice on average.

Design for uncertainty of aerospace structures

The aim of this work is to present a general overview of state-of-the-art related to design for uncertainty with a focus on aerospace structures. In particular, a simulation on a FCCZ lattice cell and on the profile shape of a nozzle will be performed. Optimization under uncertainty is characterized by the need to make decisions without complete knowledge of the problem data. When dealing with a complex problem, non-linearity, or optimization, two main issues are raised: the uncertainty of the feasibility of the solution and the uncertainty of the objective value of the function. In the first part, the Design Of Experiments (DOE) methodologies, Uncertainty Quantification (UQ), and then Uncertainty optimization will be deepened. The second part will show an application of the previous theories on through a commercial software. Nowadays multiobjective optimization on high non-linear problem can be a powerful tool to approach new concept solutions or to develop cutting-edge design. In this thesis an effective improvement have been reached on a rocket nozzle. Future work could include the introduction of multi scale modelling, multiphysics approach and every strategy useful to simulate as much possible real operative condition of the studied design.

Air Bubble Entrainment in Hydraulic Jumps: Physical Modelling and Scale Effects

A hydraulic jump is characterised by strong energy dissipation and air entrainment. In the present study, new air-water flow measurements were performed in hydraulic jumps with partially-developed flow conditions in relatively large-size facilities with phase-detection probes. The experiments were conducted with identical Froude numbers, but a range of Reynolds numbers and relative channel widths. The results showed drastic scale effects at small Reynolds numbers in terms of void fraction and bubble count rate distributions. The void fraction distributions implied comparatively greater detrainment at low Reynolds numbers leading to a lesser overall aeration of the jump roller, while dimensionless bubble count rates were drastically lower especially in the mixing layer. The experimental results suggested also that the relative channel width had little effect on the air-water flow properties for identical inflow Froude and Reynolds numbers.

Combined hydraulic and biological modelling and full-scale validation of SBR process

The biological reactions during the settling and decant periods of Sequencing Batch Reactors (SBRs) are generally ignored as they are not easily measured or described by modelling approaches. However, important processes are taking place, and in particular when the influent is fed into the bottom of the reactor at the same time (one of the main features of the UniFed process), the inclusion of these stages is crucial for accurate process predictions. Due to the vertical stratification of both liquid and solid components, a one-dimensional hydraulic model is combined with a modified ASM2d biological model to allow the prediction of settling velocity, sludge concentration, soluble components and biological processes during the non-mixed periods of the SBR. The model is calibrated on a full-scale UniFed SBR system with tracer breakthrough tests, depth profiles of particulate and soluble compounds and measurements of the key components during the mixed aerobic period. This model is then validated against results from an independent experimental period with considerably different operating parameters. In both cases, the model is able to accurately predict the stratification and most of the biological reactions occurring in the sludge blanket and the supernatant during the non-mixed periods. Together with a correct description of the mixed aerobic period, a good prediction of the overall SBR performance can be achieved.

Modelling of the kinetics of municipal solid waste composting in full-scale mechanical-biological treatment plants

A thesis submitted in fulfilment of the requirements for the Degree of Doctor of Philosophy in Sanitary Engineering in the Faculty of Sciences and Technology of the New University of Lisbon

Mathematical modelling of shallow flows: closure models drawn from grain-scale mechanics of sediment transport and flow hydrodynamics

Canadian Journal of Civil Engineering 36(10) 1605–16

Full-scale biological phosphorus removal: quantification of storage polymers, microbial performance and metabolic modelling

Dissertação para obtenção do Grau de Doutor em Engenharia Química e Bioquímica

Multi-scale support vector algorithms for hot spot detection and modelling

The algorithmic approach to data modelling has developed rapidly these last years, in particular methods based on data mining and machine learning have been used in a growing number of applications. These methods follow a data-driven methodology, aiming at providing the best possible generalization and predictive abilities instead of concentrating on the properties of the data model. One of the most successful groups of such methods is known as Support Vector algorithms. Following the fruitful developments in applying Support Vector algorithms to spatial data, this paper introduces a new extension of the traditional support vector regression (SVR) algorithm. This extension allows for the simultaneous modelling of environmental data at several spatial scales. The joint influence of environmental processes presenting different patterns at different scales is here learned automatically from data, providing the optimum mixture of short and large-scale models. The method is adaptive to the spatial scale of the data. With this advantage, it can provide efficient means to model local anomalies that may typically arise in situations at an early phase of an environmental emergency. However, the proposed approach still requires some prior knowledge on the possible existence of such short-scale patterns. This is a possible limitation of the method for its implementation in early warning systems. The purpose of this paper is to present the multi-scale SVR model and to illustrate its use with an application to the mapping of Cs137 activity given the measurements taken in the region of Briansk following the Chernobyl accident.