Atmospheric Dynamics Research Articles

Global climate modelling of Saturn’s atmosphere, Part V: Large-scale vortices

This paper presents an analysis of large-scale vortices in the atmospheres of gas giants, focusing on a detailed study conducted using the Saturn-DYNAMICO global climate model (GCM). Large-scale vortices, a prominent feature of gas giant atmospheres, play a critical role in their atmospheric dynamics. By employing three distinct methods – manual detection, machine learning via artificial neural networks (ANN), and dynamical detection using the Automated Eddy-Detection Algorithm (AMEDA) – we characterise the spatial, temporal, and dynamical properties of these vortices within the Saturn-DYNAMICO GCM. Our findings reveal a consistent production of vortices due to well-resolved eddy-to-mean flow interactions, exhibiting size and intensity distributions broadly in agreement with observational data. However, notable differences in vortex location, size, and concentration highlight the model’s limitations and suggest areas for further refinement. The analysis underscores the importance of zonal wind conditions in influencing vortex characteristics and suggests that more accurate modelling of giant planet vortices may require improved representation of moist convection and jet structure. This study not only provides insights into the dynamics of Saturn’s atmosphere as simulated by the GCM but also offers a framework for comparing vortex characteristics across observations and models of planetary atmospheres.

Lev Gutman—A Pioneer in Theoretical Mesoscale Meteorology

Abstract On 5 March 2023, Professor Lev Gutman would have been 100 years old. This article describes Professor Gutman’s legacy in the field of dynamic mesoscale meteorology and numerical weather prediction. Gutman developed his career as a mathematician and meteorologist in the Soviet Union, where he built a school of specialists in mesoscale meteorology from the 1950s through the 1970s. He primarily worked on analytical methods to solve complex nonlinear problems, such as the structure of sea breezes, mountain–valley circulations, and thermal convection over heated terrain. Gutman pioneered the development of theories of cumulus clouds, tornadoes, and other atmospheric phenomena. In the 1960s, he carried out numerous research investigations on these topics with his doctoral students and collaborators at the High-Altitude Geophysical Institute in Nalchik in the northern Caucasus and later at the Siberian Scientific Center near Novosibirsk. Gutman compiled the results from these studies into a monograph titled “Introduction to the Nonlinear Theory of Mesoscale Meteorological Processes,” which was published in Russian in 1969 and later translated into English, Chinese, and Japanese. This monograph became a major textbook for specialists in mesoscale meteorology, remaining relevant to this day. After Professor Gutman immigrated to Israel in 1978, his collaborations expanded to include Israeli and western scientists from Europe and the United States. Gutman did not receive the recognition he deserved due to the political realities of the time. His book and his seminal analytical solutions should still be useful for early career scientists in mesoscale meteorology and atmospheric dynamics.

A case study on the dust storm that occurred on March 13–18, 2022, over the Algerian Sahara, using satellite remote sensing

This study investigates the dynamics of a significant dust storm that occurred in Algeria in March 2022, employing data derived from the Sentinel-5P and CALIPSO satellite instruments. We examine the Aerosol Absorbing Index (AAI) to detect n absorbing aerosols, with a focus on desert dust, and analyze the attenuation coefficient. Additionally, we employ the HYSPLIT trajectory analyze to study dust transport and MERRA-2 to examine wind patterns wind. The key findings unveil a detailed trajectory of a prominent dust storm in Algeria in March 2022. The Aerosol Absorbing Index (AAI) effectively identifies absorbing aerosols, particularly desert dust, through thorough analyses of dust trajectory and wind patterns; augmenting these findings, CALIPSO satellite data has provided a detailed vertical profile of aerosols within the dust plume, emphasizing spatial and altitudinal extents. This research significantly contributes to advancing scientific discussions on atmospheric dynamics in arid regions and enhances our understanding and forecasting capabilities related to Saharan dust storm initiation and trajectory.

The atmospheric composition of the ultra-hot Jupiter WASP-178 b observed with ESPRESSO

Context. Ultra-hot Jupiters (UHJ) have emerged as ideal testbeds for new techniques for studying exoplanet atmospheres. Only a limited number of them are currently well studied, however. Aims. We search for atmospheric constituents for the UHJ WASP-178 b with two ESPRESSO transits. Additionally, we show parallel photometry that we used to obtain updated and precise stellar, planetary, and orbital parameters. Methods. The two transits we obtained were analysed with narrow-band transmission spectroscopy and with the cross-correlation technique to provide detections at different altitude levels. We focused on searching for Na I, Hα, Hβ, Hγ, Mg I, and Li I lines in narrow-band data, as well as Fe I and Fe II, and attempted to confirm Mg I with the cross-correlation technique. We corrected for the Rossiter-McLaughlin effect and regions with a low signal-to-noise ratio due to Na I absorption in the interstellar medium. We then verified our results via bootstrapping. Results. We report the resolved line detections of Na I (5.5σ and 5.4σ), Hα (13σ), Hβ (7.1σ), and tentatively Mg I (4.6σ). With a cross-correlation, we confirm the Mg I detection (7.8 σ and 5.8 σ), and we additionally report the detections of Fe I (12σ and 10σ) and Fe II (11σ and 8.4σ) on both nights separately. The detection of Mg I remains tentative, however, because the results on the two nights differ. The results also differ compared with the properties derived from the narrow-band data. Conclusions. None of our resolved spectral lines probing the middle to upper atmosphere shows significant shifts relative to the planetary rest frame. Hα and Hβ exhibit a respective line broadening of 39.6 ± 2.1 km s−1 and 27.6 ± 4.6 km s−1, however, indicating the onset of possible escape. WASP-178 b differs from similar UHJ by its lack of strong atmospheric dynamics in the upper atmosphere. The broadening seen for Fe I (15.66 ± 0.58 km s−1) and Fe II (11.32 ± 0.52 km s−1) might indicate the presence of winds in the mid-atmosphere, however. Future studies of the impact of the flux variability caused by the host star activity might shed more light on the subject. Previous work indicated the presence of SiO cloud-precursors in the atmosphere of WASP-178 b and a lack of Mg I and Fe II. However, our results suggest that a scenario in which the planetary atmosphere is dominated by Mg I and Fe II is more likely. In light of our results, we encourage future observations to further elucidate these atmospheric properties.

Solar Wind Structures from the Gaussianity of Magnetic Magnitude

The heliosphere is permeated with highly structured solar wind originating from the Sun. One of the primary science objectives of Parker Solar Probe (PSP) is to determine the structures and dynamics of the plasma and magnetic fields at the sources of the solar wind. However, establishing the connection between in situ measurements and structures and dynamics in the solar atmosphere is challenging: most of the magnetic footpoint mapping techniques have significant uncertainties in the source localization of a plasma parcel observed in situ, and the PSP plasma measurements suffer from a limited field of view. Therefore, it lacks a universal tool to self-contextualize the in situ measurements. Here we develop a novel time series visualization method named Gaussianity Scalogram. Utilizing this method, by analyzing the magnetic magnitude data from both PSP and Ulysses, we successfully identify in situ structures that are possible remnants of solar atmospheric and magnetic structures spanning more than 7 orders of magnitude, from years to seconds, including polar and midlatitude coronal holes, as well as structures compatible with supergranulation, “jetlets” and “picoflares.” Furthermore, computer simulations of Alfvénic turbulence successfully reproduce the Gaussianization of magnetic magnitude, supporting the observed distribution. Building upon these discoveries, the Gaussianity Scalogram can help future studies to reveal the fractal-like fine structures in the solar wind time series from both PSP and a decades-old data archive.

Geophysical Observations of the 2023 September 24 OSIRIS-REx Sample Return Capsule Reentry

Sample return capsules (SRCs) entering Earth’s atmosphere at hypervelocity from interplanetary space are a valuable resource for studying meteor phenomena. The 2023 September 24 arrival of the Origins, Spectral Interpretation, Resource Identification, and Security-Regolith Explorer SRC provided an unprecedented chance for geophysical observations of a well-characterized source with known parameters, including timing and trajectory. A collaborative effort involving researchers from 16 institutions executed a carefully planned geophysical observational campaign at strategically chosen locations, deploying over 400 ground-based sensors encompassing infrasound, seismic, distributed acoustic sensing, and Global Positioning System technologies. Additionally, balloons equipped with infrasound sensors were launched to capture signals at higher altitudes. This campaign (the largest of its kind so far) yielded a wealth of invaluable data anticipated to fuel scientific inquiry for years to come. The success of the observational campaign is evidenced by the near-universal detection of signals across instruments, both proximal and distal. This paper presents a comprehensive overview of the collective scientific effort, field deployment, and preliminary findings. The early findings have the potential to inform future space missions and terrestrial campaigns, contributing to our understanding of meteoroid interactions with planetary atmospheres. Furthermore, the data set collected during this campaign will improve entry and propagation models and augment the study of atmospheric dynamics and shock phenomena generated by meteoroids and similar sources.

Examining the evolution of extreme precipitation event using reanalysis and the observed datasets along the Western Ghats

AbstractIn recent decades, India has witnessed an increase in the intensity, frequency, and spread of extreme weather events. The widespread increase in extreme precipitation over the Western Coast of India is a matter of great concern. The factors contributing to such devastating extreme precipitation remain unclear due to the variability present in meteorological and oceanic variables and associated large‐scale circulations. Using reanalysis and observed datasets, we attempted to identify a combination of dynamic, thermodynamic, and cloud microphysics processes contributing to the anomalous precipitation from August 1 to 10, 2019 against its climatology. Our key findings highlight the crucial role of warm sea surface temperatures (anomaly >1.4°C), outgoing longwave radiation (anomaly <−50 W·m−2), and atmospheric temperature (anomaly over the ocean is >1.6°C) in enhancing the moisture‐holding capacity of the atmosphere by almost 10%. This elevated moisture, propelled by intensified low‐level winds (anomalies exceeding 4 m·s−1), leads to a shift from ocean to land. Notably, we observe that vertical updrafts (anomalies >−0.4 m·s−1) contribute to increased atmospheric instability and moisture convergence. The presence of an ample amount of cloud hydrometeors, with anomalies surpassing 2.5 × 10−4 kg·kg−1, establishes conditions conducive to sustained intense precipitation. Our findings deepen our understanding of the complex relationships between ocean and atmospheric dynamics, and wind patterns, and emphasize their pivotal influence on regional weather patterns and land surface hydrology.

Mapping seamless monthly XCO2 in East Asia: Utilizing OCO-2 data and machine learning

High spatial resolution XCO2 data is key to investigating the mechanisms of carbon sources and sinks. However, current carbon satellites have a narrow swath and uneven observation points, making it difficult to obtain seamless and full-coverage data. We propose a novel method combining extreme gradient boosting (XGBoost) with particle swarm optimization (PSO) to construct the relationship between OCO-2 XCO2 data and auxiliary data (i.e., vegetation, meteorological, anthropogenic emissions, and LST data), and to map the seamless monthly XCO2 concentration in East Asia from 2015 to 2020. Validation results based on TCCON ground station data demonstrate the high accuracy of the model with an average R2 of 0.93, Root Mean Square Error (RMSE) of 1.33 and Mean Absolute Percentage Error (MAPE) of 0.24 % in five sites. The results show that the average atmospheric XCO2 concentration in East Asia shows a continuous increasing trend from 2015 to 2020, with an average annual growth rate of 2.21 ppm/yr. This trend is accompanied by clear seasonal variations, with the highest XCO2 concentration in winter and the lowest in summer. Additionally, anthropogenic activities contributed significantly to XCO2 concentrations, which were higher in urban areas. These findings highlight the dynamics of regional XCO2 concentrations over time and their association with human activities. This study provides a detailed examination of XCO2 distribution and trends in East Asia, enhancing our comprehension of atmospheric CO2 dynamics.

Aroma volatiles and sensory quality of organically-grown apple cultivars after dynamic controlled atmosphere (DCA) storage and comparison with CA-stored fruit with and without 1-methylcyclopropene (1-MCP)

Long-term storage of many apple cultivars can be especially challenging for organic production where use of 1-methylcyclopropene (1-MCP), a crop protectant that controls ripening is disallowed. For many cultivars, controlled atmosphere (CA) storage alone, is inadequate for managing disorders and ripening. Dynamic controlled atmosphere (DCA), by determining the pO2 just above the lower oxygen limit (LOL), permits the use of lower pO2 setpoints without any negative impacts of anaerobiosis. Consequently, DCA can replace 1-MCP for maintenance of fruit quality. The goal of this study was to determine where this held true for a selection of old and new release organically-grown cultivars in the context of sensory perception. Cultivars included ‘Enterprise’ and ‘GoldRush’ harvested in 2018 (year 1), and ‘Honeycrisp’, ‘Golden Delicious’, ‘Jonagold’, and ‘Fuji’ harvested in 2020 (year 2). The LOL was monitored for DCA using chlorophyll fluorescence (CF) and compared with conventional static CA with or without 1-MCP treatment at harvest. DCA storage suppressed ripening to a similar level as 1-MCP plus CA in both years as indicated by reduced ester biosynthesis and, for some cultivars, maintenance of flesh firmness. However, ripening suppression did not impact the overall sensory preference of ‘Enterprise’, ‘Honeycrisp’, or ‘Fuji’, as all cultivars retained firmness and tartness regardless of storage regime and even negatively impacted it for ‘Goldrush’, an especially tart and firm cultivar. Sensory liking of ‘Golden Delicious’ and ‘Jonagold’ which have more typical diminishing firmness and acid profiles were most benefited by either 1-MCP treatment or DCA storage. The results highlight that use of storage technologies that suppress ripening beyond conventional CA storage are best employed on cultivars with more conventional softening and acid metabolism profiles as opposed to new cultivars that were selected primarily against these ripening phenotypes. The results also demonstrate the value of sensory evaluation compared with instrumental measurements to reveal the influence of different storage regimes on human perception.

Atmospheric dynamics and fire-induced phenomena: Insights from a comprehensive analysis of the Sertã wildfire event

The Sertã wildfire started on October 15th, 2017, in a mountainous area in central Portugal, on the hottest day of an exceptionally dry month compared to the previous two decades. This dry spell was attributed to a persistent heatwave from October 1st to 16th, which impacted most of mainland Portugal. Due to the critical weather and vegetation conditions, the fire was very intense, generating a thermal plume that reached high altitudes. The main purpose of this work is to analyze and discuss the impact of the Sertã wildfire on the dynamics of atmospheric mountain circulation. To accomplish this, a numerical modeling system integrating meteorological, fire spread, and smoke dispersion models was employed. The results indicate that the interaction between the wildfire and the local mountain breeze in central Portugal, under the synoptic weather conditions in Europe, which included the Hurricane Ophelia over the Atlantic Ocean and a warm-sector polar frontal depression in northern Europe, led to a locally disturbed weather situation. The consistent, efficient, and uniform local transport of atmospheric properties at lower levels was disrupted, intensifying wind shear and developing more turbulent advection patterns. Moreover, the fire's impact contributed to the development of a baroclinic wave at 500 hPa over mass centers, which settled at the surface and enhanced cloud development over the region. This phenomenon aligns with the atmospheric circulation effects studied in the Bjerknes and Rossby theories.

Long-Term Validation of Aeolus Level-2B Winds in the Brazilian Amazon

The Atmospheric Dynamics Mission ADM-Aeolus was successfully launched in August 2018 by the European Space Agency (ESA). The Aeolus mission carried a single instrument, the first-ever Doppler wind lidar (DWL) in space, called Atmospheric LAser Doppler INstrument (ALADIN). Aeolus circled the Earth, providing vertical profiles of horizontal line-of-sight (HLOS) winds on a global scale. The Aeolus satellite’s measurements filled critical gaps in existing wind observations, particularly in remote regions such as the Brazilian Amazon. This area, characterized by dense rainforests and rich biodiversity, is essential for global climate dynamics. The weather patterns of the Amazon are influenced by atmospheric circulation driven by Hadley cells and the Intertropical Convergence Zone (ITCZ), which are crucial for the distribution of moisture and heat from the equator to the subtropics. The data provided by Aeolus can significantly enhance our understanding of these complex atmospheric processes. In this long-term validation study, we used radiosonde data collected from three stations in the Brazilian Amazon (Cruzeiro do Sul, Porto Velho, and Rio Branco) as a reference to assess the accuracy of the Level 2B (L2B) Rayleigh-clear and Mie-cloudy wind products. Statistical validation was conducted by comparing Aeolus L2B wind products and radiosonde data covering the period from October 2018 to March 2023 for Cruzeiro do Sul and Porto Velho, and from October 2018 to December 2022 for Rio Branco. Considering all available collocated winds, including all stations, a Pearson’s coefficient (r) of 0.73 was observed in Rayleigh-clear and 0.85 in Mie-cloudy wind products, revealing a strong correlation between Aeolus and radiosonde winds, suggesting that Aeolus wind products are reliable for capturing wind profiles in the studied region. The observed biases were −0.14 m/s for Rayleigh-clear and −0.40 m/s for Mie-cloudy, fulfilling the mission requirement of having absolute biases below 0.7 m/s. However, when analyzed annually, in 2022, the bias for Rayleigh-clear was −0.95 m/s, which did not meet the mission requirements.

Advancing Climate Modeling through High-Performance Computing: Towards More Accurate and Efficient Simulations

A crucial branch of science called climate modeling uses mathematical equations and computer simulations to study and forecast the Earth's climate sys- tem. The main elements of climate modeling, such as general circulation models (GCMs), data assimilation methods, and numerical formulations, are outlined in this paper. GCMs, which include grid point and spectral models, are effective instruments for examining the behavior of the climate. Four-Dimensional Data Assimilation (4D-Var) is one example of a data assimilation technique that in- corporates observational data into models to improve their correctness. Numeri cal methods, ocean dynamics, heat transport, radiative transfer, and atmospheric dynamics are all included in numerical formulations. The simulation of different climate processes is possible because to these mathematical representations. Fur thermore, the detection of precipitation patterns within climate modeling is using machine learning techniques like Random Forest more frequently. This paper highlights the importance of high-performance computing (HPC) in climate modeling, boosting efficiency and simulations, in the context of research technique. Advanced data assimilation and validation techniques are also examined, as well as the influence of high-resolution modeling on small-scale climatic processes. On HPC platforms, accessibility to climate modeling is addressed. It is shown how climate modeling crosses physics, mathematics, computer science, and engineering to be interdisciplinary. A comprehensive understanding of the Earth's intricate climate system gains from the integration of all its parts, from atmospheric dynamics to data assimilation. We explore the consequences of these research approaches, their contribution to enhancing climate prediction models, and the influence of various factors on climatic variables in the debate. Climate modeling becomes an essential tool for studying precipitation patterns and climate change, ultimately improving our comprehension of the complex cli- mate system on Earth.

Size resolved particle contribution to vehicle induced ultrafine particle number concentration in a metropolitan curbside region

The concentrations and behavior of nano particles (10–1000 nm) in Delhi, a densely populated megacity, in different seasons (winter, spring, summer, monsoon, and autumn) are examined, for the first time. The concentration of particles is classified into four different sizes as Nnuc (10–30 nm, nucleation), Nsatk (30–50 nm, small Aitken), Nlatk (50–100 nm, large Aitken), and Nacc (100–1000 nm, accumulation mode), and the total (10–1000 nm) particle number concentration (PNC) as Ntotal. PNC ranges between 104 cm−3 and 106 cm−3 over Delhi during the year, and the highest concentration occurs in winter. Winter concentration is 2, 1.6 and 1.3 times higher than monsoon, summer, autumn and spring concentrations, respectively. Nnuc, Nsatk, Nlatk and Nacc and their respective contributions to total PNC exhibit significant seasonal variations. During winter Nlatk and Nacc contribute more to total due to coagulation, with Nacc alone contributing >40% to total PNC. Nnuc, Nsatk, and Nlatk are higher in spring and summer during mid-day due to nucleation and/or ultrafine particle burst events. The direct primary emissions from engine exhaust produce a prominent double hump structure during morning and evening peak hours in winter and autumn. PNC and their contributions exhibit day-night variations as they are influenced by variations in emission sources and meteorological parameters (wind speed, relative humidity, temperature, solar radiation and boundary layer height) between day and night. Carbon monoxide correlates positively with Nacc in all seasons (R2 ≥ 0.5) as fossil fuel emission is a predominant source for gases and particles in the study environment. These quantitative results on seasonal variations of nano particles and air pollutants together with the knowledge on seasonal variations in meteorological parameters and atmospheric dynamics provide a foundation which can positively contribute better to the urban planning and devising mitigation measures aimed at improving air quality and public health.

Relations between cyclones and ozone changes in the Arctic using data from satellite instruments and the MOSAiC ship campaign

Abstract. Large-scale meteorological events (e.g. cyclones), referred to as synoptic events, strongly influence weather predictability but still cannot be fully characterised in the Arctic region because of the sparse coverage of measurements. Due to the fact that atmospheric dynamics in the lower stratosphere and troposphere influence the ozone field, one approach to analyse these events further is the use of space-borne measurements of ozone vertical distributions and total columns in addition to conventional parameters such as pressure or wind speed. In this study we investigate the link between cyclones and changes in stratospheric ozone by using a combination of unique measurements during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) ship expedition, ozone profile and total column observations by satellite instruments (OMPS-LP, TROPOMI), and ERA5 reanalysis data. The final goal of the study is to assess whether the satellite ozone data can be used to obtain information about cyclones and provide herewith an additional value in the assimilation by numerical weather prediction models. Three special cases during the MOSAiC expedition were selected and classified for the analysis. They are one “normal” cyclone, where a low surface pressure coincides with a minimum in tropopause height, and two “untypical” cyclones, where this is not observed. The influence of cyclone events on ozone in the upper-troposphere lower-stratosphere (UTLS) region was investigated, using the fact that both are correlated with tropopause height changes. The negative correlation between tropopause height from ERA5 and ozone columns was investigated in the Arctic region for the 3-month period from June to August 2020. This was done using total ozone columns and sub-columns from TROPOMI, OMPS-LP, and MOSAiC ozonesonde data. The greatest influence of tropopause height changes on ozone contour levels occurs at an altitude between 10 and 20 km. Moreover, the lowering of the 250 ppb ozonopause (at about 11 km altitude) below 9 km was used to detect cyclones using OMPS-LP ozone observations. The potential of this approach was demonstrated in two case studies where the boundaries of cyclones could be determined using ozone observations. The results of this study can help improve our understanding of the relationship between cyclones, tropopause height, and ozone in the Arctic and demonstrate the usability of satellite ozone data in addition to the conventional parameters for investigating cyclones in the Arctic.

JWST-TST DREAMS: Nonuniform Dayside Emission for WASP-17b from MIRI/LRS

We present the first spectroscopic characterization of the dayside atmosphere of WASP-17b in the mid-infrared using a single JWST MIRI/LRS eclipse observation. From forward-model fits to the 5–12 μm emission spectrum, we tightly constrain the heat redistribution factor of WASP-17b to be 0.92 ± 0.02 at the pressures probed by this data, indicative of inefficient global heat redistribution. We also marginally detect a supersolar abundance of water, consistent with previous findings for WASP-17b, but note our weak constraints on this parameter. These results reflect the thermodynamically rich but chemically poor information content of MIRI/LRS emission data for high-temperature hot Jupiters. Using the eclipse mapping method, which utilizes the signals that the spatial emission profile of an exoplanet imprints on the eclipse light curve during ingress/egress due to its partial occultation by the host star, we also construct the first eclipse map of WASP-17b, allowing us to diagnose its multidimensional atmospheric dynamics for the first time. We find a day–night temperature contrast of order 1000 K at the pressures probed by this data, consistent with our derived heat redistribution factor, along with an eastward longitudinal hotspot offset of 18.7−3.8+11.1◦ , indicative of the presence of an equatorial jet induced by day–night thermal forcing being the dominant redistributor of heat from the substellar point. These dynamics are consistent with general circulation model predictions for WASP-17b. This work is part of a series of studies by the JWST Telescope Scientist Team, in which we use Guaranteed Time Observations to perform Deep Reconnaissance of Exoplanet Atmospheres through Multi-instrument Spectroscopy.

Unravelling the atmospheric dynamics involved in flash drought development over Spain

AbstractFlash droughts (FDs) are distinguished by a rapid development associated with strong precipitation deficits and/or increases in atmospheric evaporative demand in the short‐term, but little is known about the atmospheric conditions underlying these events. In this study, we analyse for the first time the atmospheric dynamics involved in the development of FDs in Spain over the period 1961–2018. FDs are related to large‐scale atmospheric circulation patterns affecting the region, in particular with the positive phase of the North Atlantic Oscillation (NAO). The NAO is the main atmospheric driver of FDs in winter and autumn, and it is essential in explaining FD development in spring. We also found that FDs are typically linked to strong positive anomalies in 500 hPa geopotential height and sea level pressure over the region during the weeks prior to the onset. At the synoptic scale, the most common weather types (WTs) recorded during the development of FDs are Anticyclonic Western (ANT_W_AD), East (E_AD) and Northeast (NE_AD) advection, and Anticyclonic (ANTICYC). In particular, ANTICYC WT is the main atmospheric driver of FDs in summer. Ridging conditions occur frequently during FDs in all seasons, being the most important factor controlling FD development in spring. Likewise, we noted that some of the FDs recorded in summer are related to and/or exacerbated by Saharan air intrusions associated with pronounced ridges. The results of this research have important implications for the understanding, monitoring and prediction of FDs in Spain, providing a detailed assessment of the main atmospheric dynamics involved in FD triggering at different spatial scales.

Research on Impact Mechanism of Organizational Resilience on Sustainable Competitive Advantage of Enterprises

This study is the first to comprehensively investigate the impact mechanism of organizational resilience on the sustainable competitive advantage of enterprises based on dynamic capability theory, social network theory, and resource dependence theory. It systematically analyzes the multidimensional effects of organizational resilience and reveals the moderating effect of the network digital atmosphere and environmental dynamism on strategic capabilities. Herein, six basic hypotheses and 19 sub-hypotheses are proposed. Through the analyses of 386 valid questionnaires in the Yangtze River Delta and Pearl River Delta regions, the results show that organizational resilience has a positive impact on the sustainable competitive advantage of enterprises, and strategic capability plays a mediating role through two dimensions, which are strategic formulation capability and resource integration capability. In addition, the network digital atmosphere and environmental dynamics regulate the relationship between organizational resilience and strategic capability in different dimensions. The research results provide empirical support for strengthening corporate strategic capabilities, responding to environmental changes, maintaining competitive advantages, and achieving the sustainable development of enterprises.

A bivariate simultaneous pollutant forecasting approach by Unified Spectro-Spatial Graph Neural Network (USSGNN) and its application in prediction of O3 and NO2 for New Delhi, India

Declining urban air quality affects socioeconomic stability, public health, and ecosystems and is demanding attention of the administration to address environmental sustainability goals. Given the effects of ozone, a greenhouse gas, on local climate and health, this study introduces a Unified Spectro-Spatial Graph Neural Network (USS-GNN) designed for simultaneous 24-hour forecasting of ozone and its precursor, nitrogen-dioxide, while addressing their chemical interactions and spatiotemporal dynamics. This model exploits the graph structure of atmospheric dynamics and mines high-level spatial, spectral, and physical features from atmospheric data through a Dot Product Edge Attention mechanism and a location-aware graph feature rewiring technique. The proposed model is developed for Indian capital city New Delhi, utilizes hourly observations for the years 2021 and 2022 and achieved R2 values of 0.650 and 0.618, RMSE of 13.950 and 16.120 μg/m3, MAE of 10.730 and 12.930 μg/m3 for ozone and nitrogen-dioxide respectively, outperforming state-of-the-art models. The model’s forecast analysis identified error-prone areas, effects of local meteorology, and pollutant interdependencies. An ablation study further detailed the impacts of graph operations on forecasts. Moreover, this study promotes the utility of bivariate modeling frameworks in improving urban pollution monitoring and supporting sustainable city management through data-driven policy implementations.

Affective authoritarianism as joyful ‘oeuvre?’ Godly Subjects and Suburban Gladiators

This paper presents the spatial experience of authoritarianism as a dynamic atmosphere with the seductive potential for radical joy. For Lefebvre (1996), joyful co-creation of space – an ‘oeuvre’ – was a vital facet of everyday urban life. As such, the paper challenges normative portrayals of authoritarianism as an inherently joyless, bleak and oppressive operation of power, and delves deeper into its variegated, contextually contingent and complex P/political spatialisations, conflicts and contradictions. Affect emerges as a productive lens through which to explore the complexities, range of emotions, identities and world-makings of authoritarian space, and its paradoxical capacity for joy. The paper draws from ethnographic reflections and spatial vignettes from three comparative experiences in different parts of the world: a mega-church service in suburban Singapore, and visits to two suburban hardcore gyms (in Northern England and Southeastern United States). These experiences are thematically framed as spaces of ‘Godly Subjects’ and ‘Gladiators’. They are spiritual, exhilarating spaces; spaces of radical possibilities. They are also inherently hierarchal, patriarchal, disciplined and oppressive. Thus, conjunctural subjectivities are affectively embodied in these spaces resulting in conflictual experiences. Though joyful, these are spaces of ‘cruel optimism’, where desire and aspiration are caught in a loop of un-achievability. Such is the contradictory nature of authoritarian power/space. However, whilst critical, the paper avoids an easy taxonomy of authoritarianism into clear binaries, framing it as both an open and closed socio-cultural-political space, and highlighting how and why more affective and spatialised readings of authoritarianism are needed across contextual cultural geographies at this moment.

Airborne measurements of mesoscale divergence at high latitudes during HALO-(AC)3

Abstract Boundary layer cloud transformations at high latitudes play a key role for the Arctic climate and are partially controlled by large-scale dynamics such as subsidence. While measuring large- and mesoscale divergence on spatial scales on the order of 100 km has proven notoriously difficult, recent airborne campaigns in the subtropics have successfully applied measurement techniques using multiple dropsonde releases in circular flight patterns. In this paper it is shown that this method can also be effectively applied at high latitudes, in spite of the considerable differences in atmospheric dynamics compared to the subtropics. To show the applicability, data collected during the airborne HALO-(AC)3 field campaign near Svalbard in Spring 2022 were analysed, where several flight patterns involving multiple dropsonde launches were realized by two aircraft. This study presents a first overview of the results. We find that the method indeed yields reliable estimates of mesoscale gradients in the Arctic, producing robust vertical profiles of horizontal divergence and consequently, subsidence. Sensitivity to aspects of the method is investigated, including dependence on sampling area and the divergence calculation.