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

The North Atlantic Waveguide and Downstream Impact Experiment

The North Atlantic Waveguide and Downstream Impact Experiment (NAWDEX) explored the impact of diabatic processes on disturbances of the jet stream and their influence on downstream high-impact weather through the deployment of four research aircraft, each with a sophisticated set of remote sensing and in situ instruments, and coordinated with a suite of ground-based measurements. A total of 49 research flights were performed, including, for the first time, coordinated flights of the four aircraft: the German High Altitude and Long Range Research Aircraft (HALO), the Deutsches Zentrum für Luft- und Raumfahrt (DLR) Dassault Falcon 20, the French Service des Avions Français Instrumentés pour la Recherche en Environnement (SAFIRE) Falcon 20, and the British Facility for Airborne Atmospheric Measurements (FAAM) BAe 146. The observation period from 17 September to 22 October 2016 with frequently occurring extratropical and tropical cyclones was ideal for investigating midlatitude weather over the North Atlantic. NAWDEX featured three sequences of upstream triggers of waveguide disturbances, as well as their dynamic interaction with the jet stream, subsequent development, and eventual downstream weather impact on Europe. Examples are presented to highlight the wealth of phenomena that were sampled, the comprehensive coverage, and the multifaceted nature of the measurements. This unique dataset forms the basis for future case studies and detailed evaluations of weather and climate predictions to improve our understanding of diabatic influences on Rossby waves and the downstream impacts of weather systems affecting Europe

A web service based tool to plan atmospheric research flights

We present a web service based tool for the planning of atmospheric research flights. The tool provides online access to horizontal maps and vertical cross-sections of numerical weather prediction data and in particular allows the interactive design of a flight route in direct relation to the predictions. It thereby fills a crucial gap in the set of currently available tools for using data from numerical atmospheric models for research flight planning. A distinct feature of the tool is its lightweight, web service based architecture, requiring only commodity hardware and a basic Internet connection for deployment. Access to visualisations of prediction data is achieved by using an extended version of the Open Geospatial ConsortiumWeb Map Service (WMS) standard, a technology that has gained increased attention in meteorology in recent years. With the WMS approach, we avoid the transfer of large forecast model output datasets while enabling on-demand generated visualisations of the predictions at campaign sites with limited Internet bandwidth. Usage of the Web Map Service standard also enables access to thirdparty sources of georeferenced data. We have implemented the software using the open-source programming language Python. In the present article, we describe the architecture of the tool. As an example application, we discuss a case study research flight planned for the scenario of the 2010 Eyjafjalla volcano eruption. Usage and implementation details are provided as Supplement

Neural network satellite retrievals of nocturnal stratocumulus cloud properties

I investigate the feasibility of retrieving cloud top droplet effective radius, optical thickness and cloud top temperature of nocturnal marine stratocumulus clouds by inverting infrared satellite measurements using an artificial neural network. For my study, I use the information contained in the three infrared channels centred at 3.7, 11.0 and 12.0 μm of the Moderate Resolution Imaging Spectroradiometer (MODIS) on-board NASA ' s Terra satellite, as well as sea surface temperature. A database of simulated top-of-atmosphere brightness temperatures of a range of cloud parameters is computed using a correlated-k parameterisation which I have embedded in the radiative transfer package libRadtran. The database is used to train feed-forward neural networks of different architecture to perform the inversion of the satellite measurements for the cloud properties. I investigate the application of Bayesian methods to estimate the retrieval uncertainties, and analyse the Jacobian of the networks in order to gain information about the functional dependence of the retrieved parameters on the inputs. A high variability in the Jacobian indicates that the nocturnal retrieval problem is ill-posed. My experiments show that because the problem is ill-conditioned, it is very difficult to find a network that approximates the database of simulated brightness temperatures well. Sea surface temperature proves to be a necessary input. I compare the retrievals of a selected network architecture with in-situ cloud measurements taken during the second Dynamics and Chemistry of Marine Stratocumulus experiment. The results show general agreement between retrievals and in-situ observations, although no collocated comparsions are possible because of a time lag of five hours between both measurements. I establish that the uncertainty estimates are prone to numerical problems and their results are questionable. I show that the Jacobian is a valuable tool in evaluating the retrieval networks.Science, Faculty ofEarth, Ocean and Atmospheric Sciences, Department ofGraduat