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OpenMRG : Open data from Microwave links, Radar, and Gauges for rainfall quantification in Gothenburg, Sweden
No full textPotential and Limitations of a Commercial Broadband Echo Sounder for Remote Observations of Turbulent Mixing
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In situ incubations with the Gothenburg benthic chamber landers : Applications and quality control
No full textAssessment of the European Climate Projections as Simulated by the Large EURO-CORDEX Regional and Global Climate Model Ensemble
No full textValidation of wind measurements of two mesosphere-stratosphere-troposphere radars in northern Sweden and in Antarctica
No full textThe effect of current and future maternal exposure to near-surface ozone on preterm birth in 30 European countries-an EU-wide health impact assessment
No full textConvective Mixing Driven by Non-monotonic Density
No full textCO2 injection for enhanced oil recovery (CO2-EOR) or for storage in depleted oil and gas reservoirs can be a means for disposing of anthropogenic CO2 emissions to mitigate climate change. Fluid flow and mixing of CO2 and hydrocarbons in such systems are governed by the underlying physics and thermodynamics. Gravity effects such as gravity override and convection are mechanisms that can alter fluid flow dynamics, impacting CO2 migration, oil production and eventual CO2 storage at the field scale. This study focuses on convection in a miscible setting caused by non-monotonicity in oil density when mixed with CO2, i.e., a maximum mixture density occurs at an intermediate CO2 concentration. We perform high-resolution simulations to quantify the convective behavior in a simple box system where gravity effects are isolated. We show that convection of CO2 in oil is dependent on whether CO2 originates from above or below the oil zone. From above, convection follows classic convective mixing but is accelerated by viscosity decrease with increasing CO2. From below, convection flows upward due to CO2 buoyancy, but is countered by downward convection due to the heavier mixture density. This convective system is significantly more complex and efficient than from above. We characterize the instabilities in both early- and late-time regimes and quantify mixing rates. For a 100 mD reservoir, convective fingers would be on the order of centimeters in width and mix over a meter length scale within days to a month, depending on the placement of CO2. The simulations are performed in non-dimensional form and thus can be rescaled to a different reservoir parameters. Our results give important insights into field-scale impacts of convective mixing and can guide future work in development of upscaled models and experimental design
Tailoring circulation type classification outcomes
No full textEfforts to intercompare many existing circulation type classification (CTC) methods have found no consistency in their outcomes. Therefore, when confronted with a task to classify atmospheric circulation types, it is difficult to find clear guidelines. This study explores the ways of increasing consistency between existing methods and obtaining physically meaningful and practically useful results. By applying a range of CTC methods to sea-level pressure fields over a Scandinavian domain, it is shown that CTC methods using the same similarity measure (pattern correlation (CORR) or Euclidean distance (DIST)) have higher consistency. It is further shown that CTC outcomes can be tailored towards specific user requirements by properly manipulating the input data. Using unprocessed input data in DIST-based CTC methods frequently results in classes containing physically inconsistent members because the classification procedure is obfuscated by circulation-irrelevant information in the data. Using spatially standardized data in DIST-based methods leads to considerably improved agreement with CORR-based methods and brings high physical consistency within the individual classes. However, standardizing the input data removes too much of the circulation-relevant information and results in no clear improvement in partitioning dependent variables such as precipitation. Best performance is achieved with DIST-based methods using the input data with the spatial mean removed. This simple procedure focuses the CTC methods to use only the circulation-relevant information and hence results both in physically consistent classes and in optimally performing partitioning of dependent variables. Consequently, the recommended guideline would be to use DIST-based methods with spatial-mean-removed input data as the generally most effective classification approach
