Atmospheric Processes Research Articles

A hybrid physics-based and data-driven model for intra-day and day-ahead wind power forecasting considering a drastically expanded predictor search space

This work presents a novel hybrid (physics- and data-driven) model for short-term (intra-day and day-ahead, 3h-24h) wind power forecasting (STWPF). Traditionally, STWPF predictors admitted very few meteorological variables only from the grid points closest to the turbines. Here, with the aim to further capture the underlying atmospheric processes ruling the wind variability in the wind farm, the approach relies on drastically expanding the predictor space, composed of numerous meteorological variables throughout a large geographical domain, retrieved from a weather forecasting model (COSMO-1). An ad-hoc genetic algorithm that optimizes the selection of predictors is designed and combined with feed-forward artificial neural networks for its cost function evaluation. The introduced model is compared to multiple benchmark models in a 16-turbine wind farm in the Swiss Jura mountains. For +12h and +24h lead times, the new approach shows a root-mean squared error normalized to the installed wind farm capacity of 11% and 11.6%, respectively. These values entail ∼16% higher forecasting skill compared to state-of-the-art predictor frameworks. Results highlight the ability of the presented approach to systematically select as predictors different variables with a well-known impact on the wind farm performance, such as the turbulent kinetic energy or the vertical wind shear. Clustering the data according to the wind direction provides substantial benefit. In addition, it provides a better understanding of the attained improvement: largest performances occur in those wind directions affected by highly complex terrain. This indicates that the proposed model can be especially suitable for wind farms in complex terrain.

Inter‐comparison and validation of high‐resolution surface air temperature reanalysis fields over Italy

AbstractSurface air temperature (t2m) data are essential for understanding climate dynamics and assessing the impacts of climate change. Reanalysis products, which combine observations with retrospective short‐range weather forecasts, can provide consistent and comprehensive datasets. ERA5 represents the state‐of‐the‐art in global reanalyses and supplies initial and boundary conditions for higher‐resolution regional reanalyses designed to capture finer‐scale atmospheric processes. However, these products require validation, especially in complex terrains like Italy. This study analyses the capability of different reanalysis products to reproduce t2m fields over Italy during the 1991–2020 period. The analyses encompass ERA5, ERA5‐Land, the MEteorological Reanalysis Italian DAtaset (MERIDA), the Copernicus European Regional ReAnalysis (CERRA), and the Very High‐Resolution dynamical downscaling of ERA5 REAnalysis over ITaly (VHR‐REA_IT). The validation we conduct pertains to both the spatial distribution of 30‐year seasonal and annual normal values and the daily anomaly records. Each reanalysis is compared with observations projected onto its respective grid positions and elevations, overcoming any model bias resulting from an inaccurate representation of the real topography. Key findings reveal that normal values in reanalyses closely match observational values, with deviations typically below 1°C. However, in the Alps, winter cold biases sometimes exceed 3°C and show a relation with the elevation. Similar deviations occur in the Apennines, Sicily, and Sardinia. Conversely, VHR‐REA_IT shows a warm bias in the Po Valley up to 3°C in summer. Daily anomalies generally exhibit lower errors, with MERIDA showing the highest accuracy and correlation with observational fields. Moreover, when aggregating daily anomalies to annual time scales, the errors in the anomaly records rapidly decrease to <0.5°C. The results of this study empower reanalysis users across multiple sectors to gain a more profound insight into the capabilities and constraints of different reanalysis products. The knowledge and the characterization of the reanalyses t2m bias against observations can indeed be crucial when incorporating these products into their research and practical applications.

Enhanced Solar Coronal Imaging: A GAN Approach with Fused Attention and Perceptual Quality Enhancement

The activity of the solar corona has a significant impact on all aspects of human life. People typically use images obtained from astronomical telescopes to observe coronal activities, among which the Atmospheric Imaging Assembly (AIA) of the Solar Dynamics Observatory (SDO) is particularly widely used. However, due to resolution limitations, we have begun to study the application of generative adversarial network super-resolution techniques to enhance the image data quality for a clearer observation of the fine structures and dynamic processes in the solar atmosphere, which improves the prediction accuracy of solar activities. We aligned SDO/AIA images with images from the High-Resolution Coronal Imager (Hi-C) to create a dataset. This research proposes a new super-resolution method named SAFCSRGAN, which includes a spatial attention module that incorporates channel information, allowing the network model to better capture the corona’s features. A Charbonnier loss function was introduced to enhance the perceptual quality of the super-resolution images. Compared to the original method using ESRGAN, our method achieved an 11.9% increase in Peak Signal-to-Noise Ratio (PSNR) and a 4.8% increase in Structural Similarity (SSIM). Additionally, we introduced two perceptual image quality assessment metrics, the Natural Image Quality Evaluator (NIQE) and Learned Perceptual Image Patch Similarity (LPIPS), which improved perceptual quality by 10.8% and 1.3%, respectively. Finally, our experiments demonstrated that our improved model surpasses other models in restoring the details of coronal images.

Application of advanced causal analyses to identify processes governing secondary organic aerosols

Understanding how different physical and chemical atmospheric processes affect the formation of fine particles has been a persistent challenge. Inferring causal relations between the various measured features affecting the formation of secondary organic aerosol (SOA) particles is complicated since correlations between variables do not necessarily imply causality. Here, we apply a state-of-the-art information transfer measure coupled with the Koopman operator framework to infer causal relations between isoprene epoxydiol SOA (IEPOX-SOA) and different chemistry and meteorological variables derived from detailed regional model predictions over the Amazon rainforest. IEPOX-SOA represents one of the most complex SOA formation pathways and is formed by the interactions between natural biogenic isoprene emissions and anthropogenic emissions affecting sulfate, acidity and particle water. Since the regional model captures the known relations of IEPOX-SOA with different chemistry and meteorological features, their simulated time series implicitly include their causal relations. We show that our causal model successfully infers the known major causal relations between total particle phase 2-methyl tetrols (the dominant component of IEPOX-SOA over the Amazon) and input features. We provide the first proof of concept that the application of our causal model better identifies causal relations compared to correlation and random forest analyses performed over the same dataset. Our work has tremendous implications, as our methodology of causal discovery could be used to identify unknown processes and features affecting fine particles and atmospheric chemistry in the Earth’s atmosphere.

The MOSAiC Distributed Network: Observing the coupled Arctic system with multidisciplinary, coordinated platforms

Central Arctic properties and processes are important to the regional and global coupled climate system. The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) Distributed Network (DN) of autonomous ice-tethered systems aimed to bridge gaps in our understanding of temporal and spatial scales, in particular with respect to the resolution of Earth system models. By characterizing variability around local measurements made at a Central Observatory, the DN covers both the coupled system interactions involving the ocean-ice-atmosphere interfaces as well as three-dimensional processes in the ocean, sea ice, and atmosphere. The more than 200 autonomous instruments (“buoys”) were of varying complexity and set up at different sites mostly within 50 km of the Central Observatory. During an exemplary midwinter month, the DN observations captured the spatial variability of atmospheric processes on sub-monthly time scales, but less so for monthly means. They show significant variability in snow depth and ice thickness, and provide a temporally and spatially resolved characterization of ice motion and deformation, showing coherency at the DN scale but less at smaller spatial scales. Ocean data show the background gradient across the DN as well as spatially dependent time variability due to local mixed layer sub-mesoscale and mesoscale processes, influenced by a variable ice cover. The second case (May–June 2020) illustrates the utility of the DN during the absence of manually obtained data by providing continuity of physical and biological observations during this key transitional period. We show examples of synergies between the extensive MOSAiC remote sensing observations and numerical modeling, such as estimating the skill of ice drift forecasts and evaluating coupled system modeling. The MOSAiC DN has been proven to enable analysis of local to mesoscale processes in the coupled atmosphere-ice-ocean system and has the potential to improve model parameterizations of important, unresolved processes in the future.

Arctic Sea ice leads detected using sentinel-1B SAR image and their responses to atmosphere circulation and sea ice dynamics

Arctic sea ice leads are linear fractures in pack ice, which provide narrow windows for the enhanced exchange of mass, energy, and momentum between the atmosphere and ocean. However, the parameterisations of the sub-grid (< 1 km) lead distribution and its associated dynamic processes are still challenging for numerical sea ice modelling. This study explores the dynamics governing lead formation in the Arctic Ocean using multiple data sources including Sentinel-1 synthetic aperture radar (SAR) images, buoy array, and atmospheric reanalysis. Random forest (RF) models trained based on the polarisation and texture features of SAR images were compared, and an optimal RF model sensitive to narrow leads was selected with implementation of screening based on lead geometry. As a case study, the lead distribution along the trajectory of buoy array in the central Arctic Ocean during the freezing period of 2018–2019 was obtained. An enhanced lead opening was observed as the sea ice motion swerved from clockwise circulation following the Beaufort Gyre (BG) to meridional advection when assembled into the Transpolar Drift (TPD). Synoptically, active lead-opening events associated with ice divergence were driven by episodic cyclones. We noticed a dramatic change in the backscatter signal over the leads caused by the growth of thin ice and formation of frost flowers. The identification and the evolution of narrow leads during the freezing season given in this study are conducive to increasing our understanding of the response mechanism of sea ice to atmospheric dynamic processes and supporting the numerical simulation of leads.

Impacts of Fengyun-4A and Ground-Based Observation Data Assimilation on the Forecast of Kaifeng’s Heavy Rainfall (2022) and Mechanism Analysis of the Event

The advancement of Numerical Weather Prediction (NWP) is pivotal for enhancing high-impact weather forecasting and warning systems. However, due to the high spatial and temporal inhomogeneity, the moisture field is difficult to describe by initial conditions in NWP models, which is the essential thermodynamic variable in the simulation of various physical processes. Data Assimilation techniques are central to addressing these challenges, integrating observational data with background fields to refine initial conditions and improve forecasting accuracy. This study evaluates the effectiveness of integrating observations from the Fengyun-4A (FY-4A) Advanced Geosynchronous Radiation Imager (AGRI) and ground-based microwave radiometer (MWR) in forecasts and mechanism analysis of a heavy rainfall event in the Kaifeng region of central China. Our findings reveal that jointly assimilating AGRI radiance and MWR data significantly enhances the model’s humidity profile accuracy across all atmospheric layers, resulting in improved heavy rainfall predictions. Analysis of the moisture sources indicates that the storm’s water vapor predominantly originates from westward air movement ahead of a high-altitude trough, with sustained channeling towards the rainfall zone, ensuring a continuous supply of moisture. The storm’s development is further facilitated by a series of atmospheric processes, including the interplay of high and low-level vorticity and divergence, vertical updrafts, the formation of a low-level jet, and the generation of unstable atmospheric energy. Additionally, this study examines the influence of Tai-hang Mountain’s terrain on precipitation patterns in the Kaifeng area. Our experiments, comparing a control setup (CTL) with varied terrain heights, demonstrate that reducing terrain height by 50–60% significantly decreases precipitation coverage and intensity. In contrast, increasing terrain height enhances precipitation, although this effect plateaus when the elevation increase exceeds 100%, closely mirroring the precipitation changes observed with a 75% terrain height increment.

Micro-PINGUIN: microtiter-plate-based instrument for ice nucleation detection in gallium with an infrared camera

Abstract. Ice nucleation particles play a crucial role in atmospheric processes; for example, they can trigger ice formation in clouds and thus influence their lifetime and optical properties. The quantification and characterization of these particles require reliable and precise measurement techniques. In this publication, we present a novel droplet freezing instrument to measure the immersion freezing of biotic and abiotic ice-nucleating particles within the temperature range of 0 to −25 °C. Immersion freezing of the samples is investigated using 384-well PCR plates with a sample volume of 30 µL. Nucleation events are detected with high precision using a thermal camera that records the increase in infrared emission due to the latent heat release. To maximize the thermal contact between the PCR plate and the surrounding cooling unit, we use a gallium bath as a mount for the PCR plate. The instrument was validated relative to a calibrated temperature standard and through reproducibility measurements employing the same suspension. We find that the combination of good thermal connectivity and precise temperature recording enables accurate (±0.81 °C at −10 °C) and reproducible (±0.20 °C) detection of the nucleation temperatures. Consequently, the results that are produced using the MICROtiter-Plate-based instrument for Ice Nucleation detection in GalliUm with an INfrared camera (micro-PINGUIN) are of good quality and the instrument can be used to study the immersion freezing of various ice-nucleating particles. For comparison with already existing instruments, Snomax® (hereafter Snomax) and illite NX suspensions are measured with the new ice nucleation instrument, micro-PINGUIN. Further, we investigated the reproducibility of experiments using Snomax suspensions and found poor reproducibility when suspensions were prepared freshly even if the same batch of Snomax is used. This could be attributed to substrate heterogeneity, aging effects, and dilution errors. The reproducibility of the measurements is greatly improved for Snomax suspensions that are prepared in advance and stored frozen in aliquots. Thus, we suggest the use of suspensions frozen in aliquots for further reproducibility measurements and intercomparison studies.

Oxidation-induced variations in the physical and fragmentation characteristics of exhaust soot from DMC-diesel blend: Effect of NO presence in the air atmosphere

This study investigated the changes in physical properties (including morphology, primary particle diameter, fractal dimension, and nanostructure) and fragmentation characteristics of exhaust soot from a modern direct injection diesel engine fueled with diesel (D100) and its blend with 7 vol% of dimethyl carbonate (DMC7) during oxidation processes in air and air-NO atmospheres. Soot particles (D100 soot and DMC7 soot) with specific oxidation degrees (0%, 20%, 50%, and 80%) were produced using a thermogravimetric analyzer, then applied to a high-resolution transmission electron microscopy to generate images. The results exhibited that the morphology for D100 soot and DMC7 soot transformed similarly through oxidation, experiencing an increase in fractal dimension, and a decrease in primary particle diameter, while the introduction of NO to the air accelerated these transformations. Additionally, the nanostructure of both soot particles was gradually ordered through oxidation, as evidenced by longer fringe length, narrower separation distance, and lower tortuosity, while DMC7 soot showed lower order degree compared to D100 soot. Interestingly, the ordering processes of soot nanostructure were decelerated as NO was involved in the oxidation process. Furthermore, the aggregate fragmentation rate for both soot particles declined over the oxidation process, while the possibility for primary particle fragmentation increased overall. Moreover, DMC7 soot exhibited significantly lower aggregate fragmentation rate and higher possibility for primary particle fragmentation than D100 soot at the same oxidation stage. This suggests that DMC7 soot is more susceptible to internal oxidation compared to D100 soot. Furthermore, the presence of NO in the air promoted aggregate fragmentation, while reducing the possibility of primary particle fragmentation.

Evaporation driven by Atmospheric Boundary Layer Processes over a Shallow Salt-Water Lagoon in the Altiplano

Abstract Observations over a salt-water lagoon in the Altiplano show that evaporation (E) is triggered at noon, concurrent to the transition of a shallow, stable atmospheric boundary layer (ABL) into a deep mixed layer. We investigate the coupling between the ABL and E drivers using a land-atmosphere conceptual model, observations and a regional model. Additionally, we analyze the ABL interaction with the aerodynamic and radiative components of evaporation using the Penman equation adapted to salt-water. Our results demonstrate that non-local processes are dominant in driving E. In the morning the ABL is controlled by the local advection of warm air (∼5 Kh−1), which results in a shallow (&lt;350 m), stable ABL, with virtually no mixing and no E (&lt;50 Wm−2). The warm-air advection ultimately connects the ABL with the residual layer above, increasing the ABL height (h) by ∼1-km. At midday a thermally-driven regional flow arrives to the lagoon, which first advects a deeper ABL from the surrounding desert (∼1500 mh−1) that leads to an extra ∼700-m h increase. The regional flow also causes an increase in wind (∼12 ms−1) and an ABL collapse due to the entrance of cold air (∼−2 Kh−1) with a shallower ABL (∼−350 mh−1). The turbulence produced by the wind decreases the aerodynamic resistance and mixes the water body releasing the energy previously stored in the lake. The ABL feedback on E through vapor pressure enables high evaporation values (∼450 Wm−2 at 1430 LT). These results contribute to the understanding of E of water bodies in semi-arid conditions and emphasize the importance of understanding ABL processes when describing evaporation drivers.

The Troll Observing Network (TONe): plugging observation holes in Dronning Maud Land, Antarctica

Antarctica and the Southern Ocean play vital roles in the Earth system, exerting significant influence on global atmospheric, cryospheric, and oceanic processes. Understanding the ongoing changes in Antarctica and their broader impact on global dynamics is imperative. Achieving this understanding necessitates dedicated and coordinated observations of environmental parameters in the region. The Troll Observing Network (TONe) is Norway's proactive initiative to the call for sustained, coordinated, complementary, geographically spread, and societally relevant long-term observations from Antarctica. Centered around the Troll Research Station in Dronning Maud Land, a region characterized by limited data availability, TONe constitutes a comprehensive state-of-the-art observatory network, specifically designed for environmental observations for studying and monitoring of the atmosphere, terrestrial, and marine environments. TONe aims to facilitate broad and open access to observational data and shared services to the international research communities as a foundation for acquiring knowledge of societal significance. TONe will, however, be a limited contribution the broader international data gathering effort required to underpin a strengthened understanding of Antarctica in the Earth system. TONE is an important initiative focused on a data sparse region of Antarctica, but has only substantial added value when seen in the context of similar ongoing and future efforts around the continent.

Technical note: Characterization of a single-beam gradient force aerosol optical tweezer for droplet trapping, phase transition monitoring, and morphology studies

Abstract. Single particle analysis is essential for a better understanding of the particle transformation process and to predict its environmental impact. In this study, we developed an aerosol optical tweezer (AOT) Raman spectroscopy system to investigate the phase state and morphology of suspended aerosol droplets in real time. The system comprises four modules: optical trapping, reaction, illumination and imaging, and detection. The optical trapping module utilizes a 532 nm laser and a 100 × oil immersion objective to stably trap aerosol droplets within 30 s. The reaction module allows us to adjust relative humidity (RH) and introduce reaction gases into the droplet levitation chamber, facilitating experiments to study liquid–liquid phase transitions. The illumination and imaging module employs a high-speed camera to monitor the trapped droplets, while the detector module records Raman scattering light. We trapped sodium chloride (NaCl) and 3-methyl glutaric acid (3-MGA) mixed droplets to examine RH-dependent morphology changes. Liquid–liquid phase separation (LLPS) occurred when RH was decreased. Additionally, we introduced ozone and limonene/pinene to generate secondary organic aerosol (SOA) particles in situ, which collided with the trapped droplet and dissolved in it. To determine the trapped droplet's characteristics, we utilized an open-source program based on Mie theory to retrieve diameter and refractive index from the observed whispering gallery modes (WGMs) in Raman spectra. It is found that mixed droplets formed core–shell morphology when RH was decreased, and the RH dependence of the droplets' phase transitions generated by different SOA precursors varied. Our AOT system serves as an essential experimental platform for in situ assessment of morphology and phase state during dynamic atmospheric processes.

Impacts of Atmospheric Internal Variations on the Variability of Sea Surface Temperature Based on the Hydra‐SINTEX Model

AbstractOcean–atmosphere interactions largely control the variabilities of the climate system on Earth. However, how much atmospheric internal signals contribute to climate variabilities remains uncertain over many parts of the globe. Here, we develop an interactive ensemble coupled model (called Hydra‐SINTEX) to investigate the influences of atmospheric internal variations (AIVs) on the mean‐states and variability of the climate system. The results show that, while climatological mean‐states are little affected, the AIVs can largely influence climate variabilities over the globe. We pay particular attention to two regions, that is, the tropical eastern Indian Ocean, which is the key area of the Indian Ocean Dipole (IOD), and the subtropical North Pacific. We found that sea surface temperature (SST) variabilities in these two regions are much reduced without the AIVs but with distinct mechanisms. Without the AIVs, the intensity of the IOD is largely reduced in association with weakened air–sea coupling in the tropics. This indicates the importance of atmospheric noise forcing on the development of the IOD. In contrast, the reduction of SST variability in the subtropical North Pacific is caused by the absence of the AIVs that are generated by both mid‐latitude atmospheric processes and weakened remote influence of the tropical SST in accordance with the reduced SST signals there.

Frost quakes in wetlands in northern Finland during extreme winter weather conditions and related hazard to urban infrastructure

Abstract. The paper reports the first results of an experiment in northern Finland during winter 2022–2023 that aimed at studying seismic events caused by seasonal freezing in the so-called critical zone (CZ) of the Earth. These events have attracted public attention recently, as multiple reports about them from local inhabitants in Arctic and sub-Arctic areas appeared on social networks. To make an instrumental study of such events, to reveal the relationship between their occurrence and winter weather conditions, and to evaluate the possible hazard, we installed two high-resolution seismic arrays with co-located soil temperature stations at two sites in Finland, one of them being in the city of Oulu in the sub-Arctic area (65.04° N, 25.61° E) and the other one above the polar circle in the municipality of Sodankylä (67.36° N, 26.63° E). The equipment recorded continuous seismic and soil temperature data during November 2022–April 2023. Based on reports from the inhabitants of Talvikangas (Oulu) about the ground shaking and unusual noises on 6 January 2023 and their observations of new fractures on the roads there, we selected the time interval for the identification of frost quakes that originated during that day from continuous seismic records in Talvikangas and in Sodankylä. During the selected time interval, the extremely rapid air temperature drops of about −1.4 °C h−1 in Talvikangas and −0.88 °C h−1 in Sodankylä were observed. We identified and located two types of seismic events – namely, frost quakes with frequencies of about 10–20 Hz, with waveforms like those of tectonic events, and irregularly shaped frost tremors with frequencies of about 120–180 Hz. The sources of frost quakes in Talvikangas are mainly located on irrigated wetland, while in Sodankylä, about 50 % of registered frost quakes were caused by ice fracturing on the Kitinen River. However, several relatively strong events, with their origin in the wetlands, were also recorded. A significant number of sources of frost tremors are confined to wetland areas cut by irrigation channels and to roads cleaned from snow during winter both in Talvikangas and in Sodankylä. We calculated ground accelerations and ground velocities for the strongest events from both groups and compared them to equivalent properties of other seismic signals, like distant local earthquakes in the area, mining production blasts, and cargo train vibration. Our study shows that high-frequency frost tremors corresponding to surface fracture opening in the uppermost frozen surface layer of a thickness of up to 5 cm can directly damage infrastructure objects like roads and basements of buildings. Surface waves, produced by frost quakes and propagating inside the shallow soil layer, have large enough ground accelerations at epicentral distances of hundreds of metres. Therefore, frost quakes should be considered a phenomenon that can potentially damage infrastructures, and they have to be taken into account in seismic hazard assessments. Our research is the first instrumental study of seismic events originating from wetland areas. These events occur as a result of the interaction between the uppermost layer of the solid Earth's CZ and atmosphere processes that deserves further study.

A numerical study of smoke concentration distribution near low-intensity fires: Sensitivity to vertical canopy structure and fire heat source strength

Abstract Broadly speaking, prediction of the negative impacts of prescribed fire on air quality is limited by gaps in our understanding of the underlying fire, fuels, and atmospheric processes. These knowledge gaps hinder our ability to accurately predict smoke concentration distributions, leading to unintended smoke intrusions into nearby communities and subsequent threats to public health and safety. In this study, numerical simulations are performed using a Lagrangian particle dispersion model (FLEXPART-WRF), with particle motion driven by output from a full-physics atmospheric model with a forest canopy sub-model and 10-m horizontal grid spacing (ARPS-CANOPY), to address two research questions. First, what is the relationship between near-fire (within ~50–150 m of fire) smoke concentration distribution and (i) vertical canopy structure, (ii) fire heat source strength? Second, what roles do mean transport (i.e., transport by the mean wind) and turbulent diffusion play in shaping the near-fire smoke concentration distribution? To address these questions, simulations are run with 25 combinations of plant area density profile and fire sensible heat flux magnitude, and smoke is represented by particles with diameters ≤ 2.5 µm (PM2.5). Results show that near-fire PM2.5 concentration distribution is primarily controlled by vertical canopy structure, with fire heat source strength primarily controlling the PM2.5 concentration magnitude. Analysis of the underlying ARPS-CANOPY variables driving FLEXPART-WRF particle dispersion helps elucidate the roles of mean transport and turbulent diffusion. In total, the study findings suggest that the vertical distribution of canopy vegetation and fire heat source strength are important factors influencing PM2.5 dispersion and concentration distribution near low-intensity fires.

OPC-Pod: A New Sensor Payload to Measure Aerosol Particles for Small Uncrewed Aircraft Systems

Abstract Atmospheric aerosols affect human health and influence atmospheric and biological processes. Dust can be transported long distances in the atmosphere, and the mechanisms that influence dust transport are not fully understood. To improve the data base for numerical models that simulate dust transport, measurements are needed that cover both the vertical distribution of the dust and its size distribution. In addition to measurements with crewed aircraft, uncrewed aircraft systems (UAS) provide a particularly suitable platform for this purpose. In this paper we present the OPC-Pod, a payload for the small fixed-wing UAS of the type MASC-3 for aerosol particle measurements. The OPC-Pod is based on the optical particle counter (OPC) OPC-N3 (Alphasense, UK), modified by the addition of a dryer and a passive aspiration system. Based on field tests with a reference instrument in Mannheim, Germany, wind tunnel tests and a comparison measurement with the UAS-mounted aerosol particle measurement system UCASS during a dust event over Cyprus, we show that the OPC-Pod can measure particle number concentrations in the range 0.66 - 31 μm as well as particle size distributions. The agreement of ther OPC-Pod with UCASS is good. Both instruments resolve a vertical profile of the Saharan dust event, with a prominent dust layer between 1500 m and 2800 m above sea level, with particle number concentrations up to 35 cm−3 for particles between 0.66 - 31 μm.

Microstructural and profilometric analysis of thermally sprayed cylinder wall coatings

AbstractCylinder bore coatings are widely used in automotive internal combustion engines to replace cylinder liners. During the atmospheric plasma spraying process, the coatings are oxidized and controlled by the Si content of the steel powder used as the coating raw material. This phenomenon affects the technological process, the microstructure, and the properties of the formed coatings. The research aims to investigate how the Si content of two commercial grades of steel powder commonly used in industry affects the undesirable consequences observed in practice, such as clogging of powder nozzles or large variations in coating hardness. The analyses and industrial experiments show that increasing the Si content can contribute significantly to these undesirable phenomena.

On the Mechanisms Driving Latent Heat Flux Variations in the Northwest Tropical Atlantic

AbstractThe Northwest Tropical Atlantic (NWTA) is a region of complex surface ocean circulation. The most prominent feature is the North Brazil Current (NBC) and its retroflection at 8°N, which leads to the formation of numerous mesoscale eddies known as NBC rings. The NWTA also receives the outflow of the Amazon River, generating freshwater plumes that can extend up to 100,000 km2. We show that these two processes influence the spatial variability of the region's surface latent heat flux (LHF). On the one hand, the presence of surface freshwater modifies the vertical stratification of the ocean, the mixed layer heat budget, and thus the air‐sea heat exchanges. On the other hand, NBC rings create a highly heterogeneous mesoscale sea surface temperature (SST) field that directly influences the near‐surface atmospheric circulation. These effects are illustrated by observations from the ElUcidating the RolE of Cloud‐Circulation Coupling in ClimAte ‐ Ocean Atmosphere (EUREC4A‐OA) and Atlantic Tradewind Ocean‐Atmosphere Interaction Campaign (ATOMIC) experiments, satellite and reanalysis data. We decompose the LHF budget into several terms controlled by different atmospheric and oceanic processes to identify the mechanisms leading to LHF changes. We find LHF variations of up to 160 W m2, of which 100 W m2 are associated with wind speed changes and 40 W m2 with SST variations. Surface currents or heat release associated with stratification changes remain as second‐order contributions with LHF variations of less than 10 W m2 each. This study highlights the importance of considering these three components to properly characterize LHF variability at different spatial scales, although it is limited by the scarcity of collocated observations.

MALTA: A Zonally Averaged Global Atmospheric Transport Model for Long‐Lived Trace Gases

AbstractWe present a two‐dimensional, zonally averaged global model of atmospheric transport named MALTA: Model of Averaged in Longitude Transport in the Atmosphere. It aims to be accessible to a broad community of users, with the primary function of quantifying emissions of greenhouse gases and ozone depleting substances. The model transport is derived from meteorological reanalysis data and flux‐gradient experiments using a three‐dimensional transport model. Atmospheric sinks are prescribed loss frequency fields. The zonally averaged model simulates important large‐scale transport features such as the influence on trace gas concentrations of the quasi‐biennial oscillation and variations in inter‐hemispheric transport rates. Stratosphere‐troposphere exchange is comparable to a three‐dimensional model and inter‐hemispheric transport is faster by up to 0.3 years than typical transport times of three‐dimensional models, depending on the metric used. Validation of the model shows that it can estimate emissions of CFC‐11 from an incorrect a priori emissions field well using three‐dimensional (3D) mole fraction fields generated using a different 3D model than which the flux gradient relationships were derived. The model is open source and is expected to be applicable to a wide range of studies requiring a fast, simple model of atmospheric transport and chemical processes for estimating associated emissions or mole fractions.

The Northeast Water Polynya, Greenland: Climatology, Atmospheric Forcing and Ocean Response

AbstractThe Northeast Water Polynya is a significant annually recurring summertime Arctic polynya, located off the coast of Northeast Greenland. It is important for marine wildlife and affects local atmospheric and oceanic processes. In this study, over 40 years of observational and reanalysis products (ERA5 and ORAS5) are analyzed to characterize the polynya's climatology and ascertain forcing mechanisms. The Northeast Water Polynya has high spatiotemporal variability; its location, size and structure vary interannually, and the period for which it is open is changing. We show this variability is largely driven by atmospheric forcing. The polynya extent is determined by the direction of the near‐surface flow regime, and the relative locations of high and low sea‐level pressure centers over the region. The surface conditions also impact the oceanic water column, which has a strong seasonal cycle in potential temperature and salinity, the amplitude of which decreases with depth. The ocean reanalyses also show a significant warming trend at all depths and a freshening near the surface consistent with greater ice melt, but salinification at lower depths (∼200 m). As the Arctic region changes due to anthropogenic forcing, the sea‐ice edge is migrating northwards and the Northeast Water Polynya is generally opening earlier and closing later in the year. This could have significant implications for both the atmosphere and ocean in this complex and rapidly changing environment.