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Modelling atmospheric composition at street level is challenging because pollutant concentration within street canyons depends on both local emissions and the transport of polluted air masses from remote areas. Therefore, regional-scale modelling and local applications must be combined to provide accurate simulations of the atmospheric composition at street locations. In our study, we compare two strategies: i) a subgrid-scale approach embedded in the chemistry-transport model or ii) the street-network model MUNICH. In both cases, the regional-scale chemistry-transport model CHIMERE provides the urban background concentrations, and the meteorological model WRF, coupled with CHIMERE, is used to provide meteorological fields. Simulation results for NOx, NO2, and PM2.5 concentrations over the city of Paris from both modelling approaches are compared with in-situ measurements in traffic air-quality stations. At stations located in downtown areas, with low traffic emissions, the street-network model MUNICH exhibits superior performance compared to the Subgrid approach for NOx concentrations, while comparable results are obtained for NO2. However, significant discrepancies between the two methods are observed for all analyzed pollutants at stations heavily influenced by road traffic. These stations are typically located near highways, where the bias between the two approaches can reach 58 %. The Subgrid approach's ability to estimate accurate emissions data is limited, leading to potential underestimation or overestimation of gas and fine particle concentrations based on the emission heterogeneity it handles. The performance of MUNICH appears to be highly sensitive to the friction velocity, a parameter influenced by the anthropogenic heat flux used in the WRF model. Street dimensions do contribute to the performance disparities observed between the two approaches, yet emissions remain the predominant factor. [ABSTRACT FROM AUTHOR]

Wind power production plays an important role in achieving UN's (United nations) Sustainable development goal (SDG) 7 - affordable and clean energy for all; and in the increasing global transition towards renewable and carbon neutral energy, understanding uncertainties associated with wind and turbulence is extremely important. Characterization of wind is not straightforward due to its intrinsic intermittency: activity of the becomes increasingly concentrated at smaller and smaller supports as the scale de15 creases. When it comes to power production by wind turbines, another complexity arises the influence of rainfall, which only a limited number of studies have addressed so far suggesting short term as well as long-term effects. To understand this, the project RWTurb (https://hmco.enpc.fr/portfolio-archive/rw-turb/; supported by the French National Research Agency, ANR-19-CE05-0022) employs multiple 3D sonic anemometers (man20 ufactured by Thies), mini meteorological stations (manufactured by Thies), and disdrometers Parsivel2, manufactured by OTT) on a meteorological mast in the wind farm of Pays d'Othe (110 km south-east of Paris, France; operated by Boralex). With this simultaneously measured data, it is possible to study wind power and associated atmospheric fields under various rain conditions. Variations of wind velocity, power available at the wind farm, power produced by wind turbines and air density are examined here during rain and dry conditions using the framework of Universal Multifractals (UM). UM is a widely used, physically based, scale invariant framework for characterizing and simulating geophysical fields over wide range of scales which accounts for the intermittency in the field. Since rated power acts like an upper threshold in statistical analysis of empirical wind power, efforts were made to use the theoretical available power as proxy to see the difference. From an event based analysis, differences in UM parameters were observed between rain and dry conditions for the fields illustrating the influence of rain. This is further explored using joint multifractal analy-sis and an increase in correlation exponent was observed between various fields with an increase in rain rate. Here we also examine the possibility of difference in power production according to type of rain (convective or stratiform) as well as various regimes of wind velocity. While examining time steps according to wind velocity, power curves showed different regions of departure from state curve according to the rain rate. [ABSTRACT FROM AUTHOR]

The inherent variability in atmospheric fields, which extends over a wide range of tem-poral and spatial scales, also gets transferred to energy fields extracted off them. In the specific case of wind power generation, this can be seen in the theoretical power available for extraction in the atmosphere as well as the empirical power produced by turbines. Further the power produced by turbines are affected by atmospheric turbulence as well as other fields it interact with. For modelling as well as analyzing them, quantification of their variability, intermittency and correlations with other interacting fields is important. To understand the uncertainties involved in power production, power outputs from four 2MW turbines are analyzed from an operational wind farm at Pay d'Othe, 110 km southeast of Paris, France. Using simultaneously measured wind velocity from the same location, the variability in power available at the wind farm, and power produced by wind turbines were analyzed. To accounts for the intermittency and variability in said fields, the framework of Universal Multifractals (UM) is used. UM is a widely used, physically based, scale invariant framework for characterizing and simulating geophysical fields over a wide range of scales. While statistically analysing the power produced by the turbine, rated power acts like an upper threshold resulting in biased estimators. This is identified and quantified here using the theoretical framework of UM along with the actual sampling resolution of instruments under study. The validity of this bias in framework is further tested and illustrated using numerical simulations of fields with the same multifractal properties. Understanding instrumental thresholds and their effect in analysis is important in retrieving actual fields and modelling them, more so, in the case of power production where the uncertainties due to turbulence are already a leading challenge. This is further expanded in the second part where the influence of rainfall in power production is studied using scale invariant tools of UM and joint multifractals. [ABSTRACT FROM AUTHOR]

Because optimal transport acts as displacement interpolation in physical space rather than as interpolation in value space, it can potentially avoid double penalty errors. As such it provides a very attractive metric for non-negative physical fields comparison – the Wasserstein distance – which could further be used in data assimilation for the geosciences. The algorithmic and numerical implementations of such distance are however not straightforward. Moreover, its theoretical formulation within typical data assimilation problems face conceptual challenges, resulting in scarce contributions on the topic in the literature. We formulate the problem in a way that offers a unified view on both classical data assimilation and optimal transport. The resulting OTDA framework accounts for both the classical source of prior errors, background and observation, together with a Wasserstein barycentre in between states that stand for these background and observation. We show that the hybrid OTDA analysis can be decomposed as a simpler OTDA problem involving a single Wasserstein distance, followed by a Wasserstein barycentre problem which ignores the prior errors and can be seen as a McCann interpolant. We also propose a less enlightening but straightforward solution to the full OTDA problem, which includes the derivation of its analysis error covariance matrix. Thanks to these theoretical developments, we are able to extend the classical 3D-Var/BLUE paradigm at the core of most classical data assimilation schemes. The resulting formalism is very flexible and can account for sparse, noisy observations and non-Gaussian error statistics. It is illustrated by simple one– and two–dimensional examples that show the richness of the new types of analysis offered by this unification. [ABSTRACT FROM AUTHOR]

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 properly compared to simulated concentrations to limit the weight modelling errors due to the meteorology used to analyse the images. For such comparisons, the usual pixel-wise L2 norm may not be a good option, because it is subject to the double penalty issue inherent to its local definition. This issue is characterised by a mutation of any position shift into significant amplitude discrepancies. To circumvent this issue, we propose to either provide an upstream correction of the position misfit between the observed and simulated plumes in the usual L2 norm or use a non-local metric based on the optimal transport theory, such as the Wasserstein distance. All the metrics are evaluated using first a catalogue of analytical plumes and then more realistic plumes simulated with mesoscale Eulerian atmospheric transport model, with an emphasis on the sensitivity of the metrics to position mismatch and the concentration values within the plumes. As expected, the metrics with the upstream correction are found to be less sensitive to position errors 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. It is found that discrepancies between two plume images due to wind direction errors in the meteorological conditions are less penalised by our new metrics with the upstream correction than without, thus avoiding the double penalty issue. [ABSTRACT FROM AUTHOR]

The accident at the Fukushima-Daiichi nuclear power plant yielded massive and rapidly varying atmospheric radionuclide releases. The assessment of these releases and of the corresponding uncertainties can be performed using inverse modelling methods that combine an atmospheric transport model with a set of observations and have proven to be very effective for this type of 5 problem. In the case of Fukushima-Daiichi, a Bayesian inversion is particularly suitable because it allows errors to be modelled rigorously and a large amount of observations of different natures to be assimilated at the same time. More specifically, one of the major sources of uncertainty in the source assessment of the Fukushima-Daiichi releases stems from the temporal representation of the source. To obtain a well time-resolved estimate, we implement a MCMC sampling algorithm within a Bayesian framework, the Reversible-Jump MCMC, in order to retrieve the distributions of the magnitude of the Fukushima-Daiichi 137Cs source as well as its temporal discretisation. In addition, we develop Bayesian methods allowing to combine air concentration and deposition measurements, as well as to assess the spatio-temporal information of the air concentration observations in the definition of the observation error matrix. These methods are applied to the reconstruction of the posterior distributions of the magnitude and temporal evolution of the 137Cs release. They yield a source estimate between 11 and 24 March, as well as an assessment of the uncertainties associated with the observations, the model and the source estimate. The total released reconstructed activity is estimated to be between 10 and 20 PBq, although it increases when taking into account the deposition measurements. Finally, the variable discretisation of the source term yields an almost hourly profile over certain intervals of high temporal variability, signaling identifiable portions of the source term. [ABSTRACT FROM AUTHOR]