Interaction of microphysics and dynamics in a warm conveyor belt simulated with the ICOsahedral Nonhydrostatic (ICON) model

Warm conveyor belts (WCBs) produce a major fraction of precipitation in extratropical cyclones and modulate the large-scale extratropical circulation. Diabatic processes, in particular associated with cloud formation, influence the cross-isentropic ascent of WCBs into the upper troposphere and additionally modify the potential vorticity (PV) distribution, which influences the larger-scale flow. In this study we investigate heating and PV rates from all diabatic processes, including microphysics, turbulence, convection, and radiation, in a case study that occurred during the North Atlantic Waveguide and Downstream Impact Experiment (NAWDEX) campaign using the Icosahedral Nonhydrostatic (ICON) modeling framework. In particular, we consider all individual microphysical process rates that are implemented in ICON\u27s two-moment microphysics scheme, which sheds light on (i) which microphysical processes dominate the diabatic heating and PV structure in the WCB and (ii) which microphysical processes are the most active during the ascent and influence cloud formation and characteristics, providing a basis for detailed sensitivity experiments. For this purpose, diabatic heating and PV rates are integrated for the first time along online trajectories across nested grids with different horizontal resolutions. The convection-permitting simulation setup also takes the reduced aerosol concentrations over the North Atlantic into account. Our results confirm that microphysical processes are the dominant diabatic heating source during ascent. Near the cloud top longwave radiation cools WCB air parcels. Radiative heating and corresponding PV modification in the upper troposphere are non-negligible due to the longevity of the WCB cloud band. In the WCB ascent region, the process rates from turbulent heating and microphysics partially counteract each other. From all microphysical processes condensational growth of cloud droplets and vapor deposition on frozen hydrometeors most strongly influence diabatic heating and PV, while below-cloud evaporation strongly cools WCB air parcels prior to their ascent and increases their PV value. PV production is the strongest near the surface with substantial contributions from condensation, melting, evaporation, and vapor deposition. In the upper troposphere, PV is reduced by diabatic heating from vapor deposition, condensation, and radiation. Activation of cloud droplets as well as homogeneous and heterogeneous freezing processes have a negligible diabatic heating contribution, but their detailed representation is important for, e.g., hydrometeor size distributions. Generally, faster-ascending WCB trajectories are heated markedly more than more slowly ascending WCB trajectories, which is linked to larger initial specific humidity content providing a thermodynamic constraint on total microphysical heating. Yet, the total diabatic heating contribution of convectively ascending trajectories is relatively small due to their small fraction in this case study. Our detailed case study documents the effect of different microphysical processes implemented in ICON\u27s two-moment scheme for heating and PV rates in a WCB from a joint Eulerian and Lagrangian perspective. It emphasizes the predominant role of microphysical processes and provides a framework for future experiments on cloud microphysical sensitivities in WCBs

EuLerian Identification of ascending AirStreams (ELIAS 2.0) in numerical weather prediction and climate models - Part 2: Model application to different datasets

Warm conveyor belts (WCBs) affect the atmospheric dynamics in midlatitudes and are highly relevant for total and extreme precipitation in many parts of the extratropics. Thus, these airstreams and their effect on midlatitude weather should be well represented in numerical weather prediction (NWP) and climate models. This study applies newly developed convolutional neural network (CNN) models which allow the identification of footprints of WCB inflow, ascent, and outflow from a limited number of predictor fields at comparably low spatiotemporal resolution. The goal of the study is to demonstrate the versatile applicability of the CNN models to different datasets and that their application yields qualitatively and quantitatively similar results as their trajectory-based counterpart, which is most frequently used to objectively identify WCBs. The trajectory-based approach requires data at higher spatiotemporal resolution, which are often not available, and is computationally more expensive. First, an application to reanalyses reveals that the well-known relationship between WCB ascent and extratropical cyclones as well as between WCB outflow and blocking anticyclones is also found for WCB footprints identified with the CNN models. Second, the application to Japanese 55-year reanalyses shows how the CNN models may be used to identify erroneous predictor fields that deteriorate the models\u27 reliability. Third, a verification of WCBs in operational European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble forecasts for three Northern Hemisphere winters reveals systematic biases over the North Atlantic with both the trajectory-based approach and the CNN models. The ensemble forecasts\u27 skill tends to be lower when being evaluated with the trajectory approach due to the fine-scale structure of WCB footprints in comparison to the rather smooth CNN-based WCB footprints. A final example demonstrates the applicability of the CNN models to a convection-permitting simulation with the ICOsahedral Nonhydrostatic (ICON) NWP model. Our study illustrates that deep learning methods can be used efficiently to support process-oriented understanding of forecast error and model biases and opens numerous directions for future research

Visual Analysis of Multiple Dynamic Sensitivities along Ascending Trajectories in the Atmosphere

Numerical weather prediction models rely on parameterizations for subgrid-scale processes, e.g., for cloud microphysics. These parameterizations are a well-known source of uncertainty in weather forecasts that can be quantified via algorithmic differentiation, which computes the sensitivities of prognostic variables to changes in model parameters. It is particularly interesting to use sensitivities to analyze the validity of physical assumptions on which microphysical parameterizations in the numerical model source code are based. In this article, we consider the use case of strongly ascending trajectories, so-called warm conveyor belt trajectories, known to have a significant impact on intense surface precipitation rates in extratropical cyclones. We present visual analytics solutions to analyze interactively the sensitivities of a selected prognostic variable, i.e. rain mass density, to multiple model parameters along such trajectories. We propose a visual interface that enables to a) compare the values of multiple sensitivities at a single time step on multiple trajectories, b) assess the spatio-temporal relationships between sensitivities and the shape and location of trajectories, and c) a comparative analysis of the temporal development of sensitivities along multiple trajectories. We demonstrate how our approach enables atmospheric scientists to interactively analyze the uncertainty in the microphysical parameterizations, and along the trajectories, with respect to a selected prognostic variable. We apply our approach to the analysis of convective trajectories within the extratropical cyclone "Vladiana", which occurred between 22-25 September 2016 over the North Atlantic

The three-dimensional structure of fronts in mid-latitude weather systems in numerical weather prediction models

Atmospheric fronts are a widely used conceptual model in meteorology, most encountered as two-dimensional (2-D) front lines on surface analysis charts. The three-dimensional (3-D) dynamical structure of fronts has been studied in the literature by means of “standard” 2-D maps and cross-sections and is commonly sketched in 3-D illustrations of idealized weather systems in atmospheric science textbooks. However, only recently has the feasibility of the objective detection and visual analysis of 3-D frontal structures and their dynamics within numerical weather prediction (NWP) data been proposed, and such approaches are not yet widely known in the atmospheric science community. In this article, we investigate the benefit of objective 3-D front detection for case studies of extra-tropical cyclones and for comparison of frontal structures between different NWP models. We build on a recent gradient-based detection approach, combined with modern 3-D interactive visual analysis techniques, and adapt it to handle data from state-of-the-art NWP models including those run at convection-permitting kilometre-scale resolution. The parameters of the detection method (including data smoothing and threshold parameters) are evaluated to yield physically meaningful structures. We illustrate the benefit of the method by presenting two case studies of frontal dynamics within mid-latitude cyclones. Examples include joint interactive visual analysis of 3-D fronts and warm conveyor belt (WCB) trajectories, as well as identification of the 3-D frontal structures characterizing the different stages of a Shapiro–Keyser cyclogenesis event. The 3-D frontal structures show agreement with 2-D fronts from surface analysis charts and augment the surface charts by providing additional pertinent information in the vertical dimension. A second application illustrates the relation between convection and 3-D cold-front structure by comparing data from simulations with parameterized and explicit convection. Finally, we consider “secondary fronts” that commonly appear in UK Met Office surface analysis charts. Examination of a case study shows that for this event the secondary front is not a temperature-dominated but a humidity-dominated feature. We argue that the presented approach has great potential to be beneficial for more complex studies of atmospheric dynamics and for operational weather forecasting

3D mapping of the neutral X-ray absorption in the local interstellar medium: The Gaia and XMM-Newton synergy

We present a three-dimensional map of the hydrogen density distribution in the Galactic interstellar medium. The hydrogen equivalent column densities were obtained from the Exploring the X-ray Transient and variable Sky project ({\sc EXTraS}) which provides equivalent $N_{\rm H}$ values from X-ray spectral fits of observations within the {\it XMM-Newton} Data Release. {\sc EXTraS} include multiple fits for each source, allowing an accurate determination of the equivalent column densities, which depends on the continuum modeling of the spectra. A cross-correlation between the {\sc EXTraS} catalogue and the first {\it Gaia} Data Release was performed in order to obtain accurate parallax and distance measurements. We use a Bayesian method explained in \citet{rez17} in order to predict the most probable distribution of the density at any arbitrary point, even for lines of sight along which there are no initial observation. The resulting map shows small-scale density structures which can not been modeled by using analytic density profiles. In this paper we present a proof of concept of the kind of science possible with the synergy of these catalogs. However, given the systematic uncertainties connected to the source identification and to the dependence of $N_{\rm H}$ on the spectral model, the present maps should be considered qualitatively at this point

Плазменное получение тепловой энергии из сульфатного лигнина

This article shows an overview and analysis of the literature on methods of using sludge lignin. This product obtained after treatment of pulp. As a result of calculating the optimum composition of water, organic materials with mechanical impurities from the adiabatic combustion temperature of about 1200 K were determined. Using the obtained results of experimental studies have been carried out in a plasma reactor of the catalytic reactor and has been optimized. The obtained results can be used to create industrial enterprises based on plasma catalytic reactors for waste sludge lignin for the purpose of obtaining heat

3D mapping of the neutral X-ray absorption in the local interstellar medium: the Gaia and XMM-Newton synergy

We present a three-dimensional map of the hydrogen density distribution in the Galactic interstellar medium. The hydrogen-equivalent column densities were obtained from the Exploring the X-ray Transient and variable Sky project (EXTRAS) which provides equivalent N_H values from X-ray spectral fits of observations within the XMM-Newton Data Release. EXTRAS include multiple fits for each source, allowing an accurate determination of the equivalent column densities, which depends on the continuum modelling of the spectra. A cross-correlation between the EXTRAS catalogue and the first Gaia Data Release was performed in order to obtain accurate parallax and distance measurements. We use a Bayesian method explained in Rezaei Kh. et al. (2017) in order to predict the most probable distribution of the density at any arbitrary point, even for lines of sight along which there are no initial observation. The resulting map shows small-scale density structures which could not have been modelled by using analytic density profiles. In this paper, we present a proof of concept of the kind of science possible with the synergy of these catalogues. However, given the systematic uncertainties connected to the source identification and to the dependence of N_H on the spectral model, the present maps should be considered qualitatively at this point