Cleaning Air Streams from Fine Particles in Paint Booths

To solve the problem of increasing the efficiency of cleaning the exhaust air of painting chambers from fine particles, a separation device with elements of a square and rectangular shape has been developed and described by the principles of its operation. A method for calculating the design dimensions of the developed separation device is presented. Different versions of the height of the separation zone are considered, depending on the size of dispersed particles. It is shown that the device is able to capture particles smaller than 10 microns with an efficiency close to 100 %, provided they adhere to the walls. The peculiarity of the design of the separation device is that a set of small-diameter vortices is created, in which the flow regime is close to laminar, and to achieve large values of centrifugal force, it is not necessary to create high speeds in the crevices.

EuLerian Identification of Ascending air Streams (ELIAS 2.0) in Numerical Weather Prediction and Climate Models. Part II: Model application to different data sets

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 air streams 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 spatio-temporal resolution. The goal of the study is to demonstrate the versatile applicability of the CNN models to different data sets and that their application yields qualitatively and quantitatively similar results as their trajectory-based counterpart which is most frequently used to objectively identify WCBs but requires data at higher spatio-temporal resolution which is 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' 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' 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.

EuLerian Identification of ascending Air Streams (ELIAS 2.0) in Numerical Weather Prediction and Climate Models. Part I: Development of deep learning model

Physical processes on the synoptic scale are important modulators of the large-scale extratropical circulation. In particular, rapidly ascending air streams in extratropical cyclones, so-called warm conveyor belts (WCBs), modulate the upper-tropospheric Rossby wave pattern and are sources and magnifiers of forecast uncertainty. Thus, from a process-oriented perspective, numerical weather prediction (NWP) and climate models should adequately represent WCBs. The identification of WCBs usually involves Lagrangian air parcel trajectories that ascend from the lower to the upper troposphere within two days. This requires numerical data with high spatial and temporal resolution which is often not available from standard output and requires expensive computations. This study introduces a novel framework that aims to predict the footprints of the WCB inflow, ascent, and outflow stages over the Northern Hemisphere from instantaneous gridded fields using convolutional neural networks (CNNs). With its comparably low computational costs and relying on standard model output alone the new diagnostic enables the systematic investigation of WCBs in large data sets such as ensemble reforecast or climate model projections which are mostly not suited for trajectory calculations. Building on the insights from a logistic regression approach of a previous study, the CNNs are trained using a combination of meteorological parameters as predictors and trajectory-based WCB footprints as predictands. Validation of the networks against the trajectory-based data set confirms that the CNN models reliably replicate the climatological frequency of WCBs as well as their footprints at instantaneous time steps. The CNN models significantly outperform previously developed logistic regression models. Including time-lagged information on the occurrence of WCB ascent as a predictor for the inflow and outflow stages further improves the models' skill considerably. A companion study demonstrates versatile applications of the CNNs in different data sets including the verification of WCBs in ensemble forecasts. Overall, the diagnostic demonstrates how deep learning methods may be used to investigate the representation of weather systems and of their related processes in NWP and climate models in order to shed light on forecast uncertainty and systematic biases from a process-oriented perspective.

Investigation on the Effect of Different Channel Geometries of Thermal Wheel for Energy Transfer Efficiency

Inside buildings, heating, ventilation, and air conditioning systems are utilized to provide a comfortable environment. However, they account for a significant percentage of overall total energy consumption: in the United States, they account for about 50% of building final energy consumption and 20% of total energy consumption. [1]. The installation of a heat exchanger between the exhaust and fresh air streams is critical, owing to the significant energy savings. [2], [3]. Thermal wheels have recently gotten a lot of attention because of their high efficiency and low-pressure loss when compared to other energy recovery solutions [4]. The goal of this research is to give a comprehensive study and optimization of Thermal wheel design, with the goal of enhancing sensible effectiveness while reducing pressure loss based on channel shape.

Moisture origin, transport pathways, and driving processes of intense wintertime moisture transport into the Arctic

Poleward moisture transport occurs in episodic, high-amplitude events with strong impacts on the Arctic and its climate system components such as sea ice. This study focuses on the origin of such events and examines the moisture sources, moisture transport pathways, and their linkage to the large-scale circulation. For that purpose, 597 events of intense zonal mean poleward moisture transport at 70° N (exceeding the 90th anomaly percentile) are identified and kinematic backward trajectories from 70° N are computed to pinpoint the moisture sources and characterize the air-streams accomplishing the transport. The bulk of the moisture transported into the polar cap during these events originates in the eastern North Atlantic with an uptake maximum poleward of 50° N. This asymmetry between ocean basins is a direct consequence of the fact that most of the moisture transport into the polar cap occurs in this sector. As a result of the fairly high-latitude origin of the moisture, the median time moisture spends in the atmosphere prior to reaching 70° N amounts to about 2.5 days. Trajectories further reveal an inverse relationship between moisture uptake latitude and the level at which moisture is injected into the polar cap, consistent with ascent of poleward flowing air in a baroclinic atmosphere. Focusing on events for which 75 % of the zonal mean moisture transport takes place in the North Atlantic east of Greenland (424 events) reveals that lower tropospheric moisture transport results predominantly from two types of air-streams: (i) cold, polar air advected from the Canadian Arctic over the North Atlantic and around Greenland, whereby the air is warmed and moistened by surface fluxes, and (ii) air subsiding from the mid-troposphere into the boundary layer. Both air-streams contribute about 36 % each to the total transport. The former dominates the moisture transport during events associated with an anomalously high frequency of cyclones east of Greenland (218 events), whereas the latter is more important in the presence of atmospheric blocking over Scandinavia and the Ural (145 events). A substantial portion of the moisture sources associated with both types of air-streams are located between Iceland, the British Isles, and Norway. Long-range moisture transport, accounting for 17 % of the total transport, is the dominant type of air-stream during events with weak forcing by baroclinic weather systems (64 events). Finally, mid-tropospheric moisture transport is invariably associated with (diabatically) ascending air and moisture origin in the central and western North Atlantic, including the Gulf Stream front, accounting for roughly 10 % of the total transport. In summary, our study reveals that moisture injections into the polar atmosphere are not primarily caused by the poleward transport of warm and humid air from low latitudes – a conclusion that applies in particular to cases where the transport is driven by baroclinic weather systems such as extratropical cyclones. Instead, it results from a combination of air-streams with pre-dominantly high-latitude or high-altitude origin and their interplay with large-scale weather systems (e.g., cyclones, blocks).

A new argument against cooling by convective air eddies formed above sunlit zebra stripes

There is a long-lasting debate about the possible functions of zebra stripes. According to one hypothesis, periodical convective air eddies form over sunlit zebra stripes which cool the body. However, the formation of such eddies has not been experimentally studied. Using schlieren imaging in the laboratory, we found: downwelling air streams do not form above the white stripes of light-heated smooth or hairy striped surfaces. The influence of stripes on the air stream formation (facilitating upwelling streams and hindering horizontal stream drift) is negligible higher than 1–2 cm above the surface. In calm weather, upwelling air streams might form above sunlit zebra stripes, however they are blown off by the weakest wind, or even by the slowest movement of the zebra. These results forcefully contradict the thermoregulation hypothesis involving air eddies.