Technical Note: The air quality modeling system Polyphemus

International audiencePolyphemus is an air quality modeling platform which aims at covering the scope and the abilities of modern air quality systems. It deals with applications from local scale to continental scale, using two Gaussian models and two Eulerian models. It manages passive tracers, radioactive decay, photochemistry and aerosol dynamics. The structure of the system includes four independent levels with data management, physical parameterizations, numerical solvers and high-level methods such as data assimilation. This enables sensitivity and uncertainty analysis, primarily through multimodel approaches. On top of the models, drivers implement advanced methods such as model coupling or data assimilation

Accounting for meteorological biases in simulated plumes using smarter metrics

In the next few years, numerous satellites with high-resolution instruments dedicated to the imaging of atmospheric gaseous compounds will be launched, to finely monitor emissions of greenhouse gases and pollutants. Processing the resulting images of plumes from cities and industrial plants to infer the emissions of these sources can be challenging. In particular traditional atmospheric inversion techniques, relying on objective comparisons to simulations with atmospheric chemistry transport models, may poorly fit the observed plume due to modelling errors rather than due to uncertainties in the emissions. The present article discusses how these images can be adequately compared to simulated concentrations to limit the weight of modelling errors due to the meteorology used to analyse the images. For such comparisons, the usual pixel-wise ℒ2 norm may not be suitable, since it does not linearly penalise a displacement between two identical plumes. By definition, such a metric considers a displacement as an accumulation of significant local amplitude discrepancies. This is the so-called double penalty issue. To avoid this issue, we propose three solutions: (i) compensate for position error, due to a displacement, before the local comparison; (ii) use non-local metrics of density distribution comparison; and (iii) use a combination of the first two solutions. All the metrics are evaluated using first a catalogue of analytical plumes and then more realistic plumes simulated with a mesoscale Eulerian atmospheric transport model, with an emphasis on the sensitivity of the metrics to position error and the concentration values within the plumes. As expected, the metrics with the upstream correction are found to be less sensitive to position error in both analytical and realistic conditions. Furthermore, in realistic cases, we evaluate the weight of changes in the norm and the direction of the four-dimensional wind fields in our metric values. This comparison highlights the link between differences in the synoptic-scale winds direction and position error. Hence the contribution of the latter to our new metrics is reduced, thus limiting misinterpretation. Furthermore, the new metrics also avoid the double penalty issue.</p

Carotenoid accumulation during tomato fruit ripening is modulated by the auxin-ethylene balance

Background : Tomato fruit ripening is controlled by ethylene and is characterized by a shift in color from green to red, a strong accumulation of lycopene, and a decrease in β-xanthophylls and chlorophylls. The role of other hormones, such as auxin, has been less studied. Auxin is retarding the fruit ripening. In tomato, there is no study of the carotenoid content and related transcript after treatment with auxin. Results : We followed the effects of application of various hormone-like substances to “Mature-Green” fruits. Application of an ethylene precursor (ACC) or of an auxin antagonist (PCIB) to tomato fruits accelerated the color shift, the accumulation of lycopene, α-, β-, and δ-carotenes and the disappearance of β-xanthophylls and chlorophyll b. By contrast, application of auxin (IAA) delayed the color shift, the lycopene accumulation and the decrease of chlorophyll a. Combined application of IAA + ACC led to an intermediate phenotype. The levels of transcripts coding for carotenoid biosynthesis enzymes, for the ripening regulator Rin, for chlorophyllase, and the levels of ethylene and abscisic acid (ABA) were monitored in the treated fruits. Correlation network analyses suggest that ABA, may also be a key regulator of several responses to auxin and ethylene treatments. Conclusions : The results suggest that IAA retards tomato ripening by affecting a set of (i) key regulators, such as Rin, ethylene and ABA, and (ii) key effectors, such as genes for lycopene and β-xanthophyll biosynthesis and for chlorophyll degradation