Atmospheric Characteristics Research Articles

Investigation of Tropical Cyclones in the North Indian Ocean and the Linkage to Extreme Weather Events over Sri Lanka

Heavy rainfall due to tropical cyclones (TCs) in the North Indian Ocean (NIO) adversely impacts nations frequently. Though extensive research has focused on TCs in the NIO, less attention has been given to the connection between TCs and extreme events in Sri Lanka. This study examined atmospheric characteristics during sixteen extreme events, focusing on linkages between TCs, the Indian Ocean Dipole (IOD), and mechanisms behind heavy rainfall associated with TCs over Sri Lanka. The results showed that in the pre-monsoon period, TCs move northward with high water vapor (WV) content accumulated in the Southern Hemisphere. This main WV flow over the equatorial Indian Ocean (EIO) is connected with TCs, causing considerable damage in the southwestern part of Sri Lanka. During negative IOD years, strong westerly winds create cyclonic circulations on either side of the equator. Conversely, during the post-monsoon period, the IOD phase has no significant effect. TCs generally followed westward tracks, supported by winds from the Northern Hemisphere, and caused heavy rainfall in the Eastern, Northern, and Northcentral provinces in Sri Lanka. These TCs are isolated from the main WV flow over EIO. Such observed common characteristics during pre-monsoon and post-monsoon seasons are key factors contributing to extreme rainfall in Sri Lanka.

CloudbandPy 1.0: an automated algorithm for the detection of tropical–extratropical cloud bands

Abstract. Persistent and organized convective cloud systems that arise in convergence zones can lead to the formation of synoptic cloud bands extending from the tropics to the extratropics. These cloud bands are responsible for heavy precipitation and are often a combination of tropical intrusions of extratropical Rossby waves and processes originating from the tropics. Detecting these cloud bands presents a valuable opportunity to enhance our understanding of the variability of these systems and the underlying processes that govern their behavior and that connect the tropics and the extratropics. This paper presents a new atmospheric cloud band detection method based on outgoing longwave radiation using computer vision techniques, which offers enhanced capabilities to identify long cloud bands across diverse gridded datasets and variables. The method is specifically designed to detect extended tropical–extratropical convective cloud bands, ensuring accurate identification and analysis of these dynamic atmospheric features in convergence zones. The code allows for easy configuration and adaptation of the algorithm to meet specific research needs. The method handles cloud band merging and splitting, which allows for an understanding of the life cycle of cloud bands and their climatology. This algorithm lays the groundwork for improving our understanding of the large-scale processes that are involved in the formation and life cycle of cloud bands and the connections between tropical and extratropical regions as well as evaluating the differences in cloud band types between different ocean basins.

The Variation in Atmospheric Turbidity over a Tropical Site in Nigeria and Its Relation to Climate Drivers

Atmospheric turbidity exhibits substantial spatial–temporal variability due to factors such as aerosol emissions, seasonal changes, meteorology, and air mass transport. Investigating atmospheric turbidity is crucial for climatology, meteorology, and atmospheric pollution. This study investigates the variation in atmospheric turbidity over a tropical location in Nigeria, utilizing the Ångström exponent (α), the turbidity coefficient (β), the Linke turbidity factor (TL), the Ångström turbidity coefficient (βEST), the Unsworth–Monteith turbidity coefficient (KAUM), and the Schüepp turbidity coefficient (SCH). These parameters were estimated from a six-month uninterrupted aerosol optical depth dataset (January–June 2016) and a one-year dataset (January–December 2016) of solar radiation and meteorological data. An inverse correlation (R = −0.77) was obtained between α and β, which indicates different turbidity regimes based on particle size. TL and βEST exhibit pronounced seasonality, with higher turbidity during the dry season (TL = 9.62 and βEST = 0.60) compared to the rainy season (TL = 0.48 and βEST = 0.20) from May to October. Backward trajectories and wind patterns reveal that high-turbidity months align with north-easterly air flows from the Sahara Desert, transporting dust aerosols, while low-turbidity months coincide with humid maritime air masses originating from the Gulf of Guinea. Meteorological drivers like relative humidity and water vapor pressure are linked to turbidity levels, with an inverse exponential relationship observed between normalized turbidity coefficients and normalized water vapor pressure. This analysis provides insights into how air mass origin, wind patterns, and local climate factors impact atmospheric haze, particle characteristics, and solar attenuation variability in a tropical location across seasons. The findings can contribute to environmental studies and assist in modelling interactions between climate, weather, and atmospheric optical properties in the region.

A deep-learning model for rapid spatiotemporal prediction of coastal water levels

With the increasing impact of climate change and relative sea level rise, low-lying coastal communities face growing risks from recurrent nuisance flooding and storm tides. Thus, timely and reliable predictions of coastal water levels are critical to resilience in vulnerable coastal areas. Over the past decade, there has been increasing interest in utilizing machine learning (ML) based models for emulation and prediction of coastal water levels. However, flood advisory systems still rely on running computationally demanding hydrodynamic models. To alleviate the computational burden, these physics-based models are either run at small scales with high resolution or at large scales with low resolution. While ML-based models are very fast, they face challenges in terms of ensuring reliability and ability to capture any surge levels. In this paper, we develop a deep neural network for spatiotemporal prediction of water levels in coastal areas of the Chesapeake Bay in the U.S. Our model relies on data from numerical weather prediction models as the atmospheric input and astronomical tide levels, while its outputs are time series of predicted water levels at several tide gauge locations across the Chesapeake Bay. We utilized a CNN-LSTM setting as the architecture of the model. The CNN part extracts the features from a sequence of gridded wind fields and fuses its output to several independent LSTM units. The LSTM units concatenate the atmospheric features with respective astronomical tide levels and produce water level time series. The novel contribution of the present work is in spatiotemporality and in prioritization of the physical relationships in the model to maintain a high analogy to hydrodynamic modeling, either in the network architecture or in the selection of predictors and predictands. The results show that this setting yields a strong performance in predicting coastal water levels that cause flooding from minor to major levels. We also show that the model stands up successfully to the rigorous comparison with a high-fidelity ADCIRC model, yielding mean RMSE and correlation coefficient of 14.3 cm and 0.94, respectively, in two extreme cases, versus 12.30 cm and 0.96 for the ADCIRC model. The results highlight the practical feasibility of employing fast yet inexpensive data-driven models for resilient coastal management.

Effect of lithium on the atmospheric corrosion characteristics of TZM as the first-wall material in EAST

The effect of Lithium (Li) coating on the corrosion behavior of molybdenum-titanium-zirconium (TZM) alloy was investigated in low- and high-humidity air at room temperature (RT). Additionally, the corrosion mechanism was also studied through immersion tests in deionized water and LiOH solution. The test results confirm that the corrosion of the TZM alloy depends on the presence of H2O and O2, particularly H2O. In environments with high humidity, the TZM surface without Li coating easily undergoes oxidation, resulting in the formation of dark-green MoO3·xH2O. The Li coating significantly accelerates the surface oxidation of the TZM alloy because of its high absorptive properties for H2O, O2, and CO2, thereby generating Li2O, LiOH, and Li2CO3. These processes lead to a high content of O2 and H2O near the TZM surface, facilitating the formation of MoO3·xH2O. The immersion of TZM in water also leads to oxidative corrosion, which is more severe than that in high humidity air because of the dissolution of MoO3, causing the water to become dark-green. The oxidation of TZM in LiOH solution exhibits a behavior similar to that of water immersion. However, the corrosion in the LiOH solution is more severe because of the reaction between the surface oxide MoO3 and LiOH, which forms a colorless Li2MoO4, accelerating the corrosion process. This study offers valuable insights into the effective anti-corrosion strategies for the cleaning and storage of the TZM wall material in the EAST tokamak.

A new perspective on Saturn’s polar vortices: Coupling the thermosphere and stratosphere

We argue that the observed temperature and altitude structure at the base of Saturn’s polar thermosphere implies the presence of a deep, warm-cored, anticyclonic vortex that extends deep into the mesosphere, and possibly into the stratosphere. This would be a unique structure in the solar system: a permanent, large-scale middle atmospheric feature that is a direct consequence of magnetosphere-atmosphere coupling. We investigate the plausibility of the suggestion that this deep vortex is linked to the observed non-seasonal polar hotspots in Saturn’s stratosphere. We develop a simple model of the dynamics and thermal structure of the middle atmosphere polar vortex, assuming vertical viscous transport is dominant and that the zonal winds are close to geostrophic balance. The model requires the existence of a small poleward ageostrophic wind which causes compressional heating, producing the required pressure gradients. This scenario is supported by a re-analysis of previously published thermospheric modelling results. Using this model we estimate the extent to which the thermospheric vortex penetrates into the deeper atmosphere. The results are very sensitive to the distribution of eddy viscosity. The models which best match the thermospheric temperatures produce a polar hotspot in the stratosphere that is hotter and broader than observed, indicating that our model, while a useful starting point, is incomplete. We also point out a puzzle concerning the half-widths of the polar hotspot and the polar altitude bulge at the base of the thermosphere. We are able to explain the greater width of the hotspot provided that there is enhanced downwards vertical transport of angular momentum closer to the pole, a situation that is expected given the strong convergence and downwelling predicted in this region. Finally, we speculate that the tropospheric polar cyclone could be connected to the anticyclonic vortex in the thermosphere.

How do wavelength correlations affect transmission spectra? Application of a new fast and flexible 2D Gaussian process framework to transiting exoplanet spectroscopy

The use of Gaussian processes (GPs) is a common approach to account for correlated noise in exoplanet time series, particularly for transmission and emission spectroscopy. This analysis has typically been performed for each wavelength channel separately, with the retrieved uncertainties in the transmission spectrum assumed to be independent. However, the presence of noise correlated in wavelength could cause these uncertainties to be correlated, which could significantly affect the results of atmospheric retrievals. We present a method that uses a GP to model noise correlated in both wavelength and time simultaneously for the full spectroscopic dataset. To make this analysis computationally tractable, we introduce a new fast and flexible GP method that can analyse 2D datasets when the input points lie on a (potentially non-uniform) 2D grid - in our case a time by wavelength grid - and the kernel function has a Kronecker product structure. This simultaneously fits all light curves and enables the retrieval of the full covariance matrix of the transmission spectrum. Our new method can avoid the use of a `common-mode' correction, which is known to produce an offset to the transmission spectrum. Through testing on synthetic datasets, we demonstrate that our new approach can reliably recover atmospheric features contaminated by noise correlated in time and wavelength. In contrast, fitting each spectroscopic light curve separately performed poorly when wavelength-correlated noise was present. It frequently underestimated the uncertainty of the scattering slope and overestimated the uncertainty in the strength of sharp absorption peaks in transmission spectra. Two archival VLT/FORS2 transit observations of WASP-31b were used to compare these approaches on real observations. Our method strongly constrained the presence of wavelength-correlated noise in both datasets, and significantly different constraints on atmospheric features such as the scattering slope and strength of sodium and potassium features were recovered.

Small but mighty: High-resolution spectroscopy of ultra-hot Jupiter atmospheres with compact telescopes

When observing transmission spectra produced by the atmospheres of ultra-hot Jupiters (UHJs), large telescopes are typically the instrument of choice given the very weak signal of the planet’s atmopshere. The aim of the present study is to demonstrate that, for favourable targets, smaller telescopes are fully capable of conducting high-resolution cross-correlation spectroscopy. We apply the cross-correlation technique to data from the 2.1 m telescope at the Wendelstein Observatory, using its high-resolution spectrograph FOCES, in order to demonstrate its efficacy in resolving the atmosphere of the UHJ KELT-9 b. Using three nights of observations with the FOCES spectrograph and one with the HARPS-N spectrograph, we conduct a performance comparison between FOCES and HARPS-N. This comparison considers both single-transit and combined observations over the three nights. We then consider the potential of 2 m class telescopes by generalising our results to create a transit emulator capable of evaluating the potential of telescopes of this size. With FOCES, we detected seven species in the atmosphere of KELT-9b: Ti II, Fe I, Fe II, Na I, Mg I, Na II, Cr II, and Sc II. Although HARPS-N surpasses FOCES in performance thanks to the mirror of the TNG, our results reveal that smaller telescope classes are capable of resolving the atmospheres of UHJs given sufficient observing time. This broadens the potential scope of such studies, demonstrating that smaller telescopes can be used to investigate phenomena such as temporal variations in atmospheric signals and the atmospheric loss characteristics of these close-in planets.

Transiting exoplanets with the Mid-InfraRed Instrument on board JWST: From simulations to observations

Context. The James Webb Space Telescope (JWST) has now started its exploration of exoplanetary worlds. In particular, the Mid-InfraRed Instrument (MIRI) with its Low-Resolution Spectrometer (LRS) carries out transit, eclipse, and phase-curve spectroscopy of exoplanetary atmospheres with an unprecedented precision in a so far almost uncharted wavelength range. Aims. The precision and significance in the detection of molecules in exoplanetary atmospheres relies on a thorough understanding of the instrument itself and on accurate data reduction methods. This paper aims to provide a clear description of the instrumental systematics that affect observations of transiting exoplanets through the use of simulations. Methods. We carried out realistic simulations of transiting-exoplanet observations with the MIRI LRS instrument that included the model of the exoplanet system, the optical path of the telescope, the MIRI detector performances, and instrumental systematics and drifts that could alter the atmospheric features we are meant to detect in the data. After we introduce our pipeline, we show its performance on the transit of L 168-9b, a super-Earth-sized exoplanet observed during the commissioning of the MIRI instrument. Results. This paper provides a better understanding of the data themselves and of the best practices in terms of reduction and analysis through comparisons between simulations and real data. We show that simulations validate the current data-analysis methods. Simulations also highlight instrumental effects that impact the accuracy of our current spectral extraction techniques. These simulations are proven to be essential in the preparation of JWST observation programs and help us to assess the detectability of various atmospheric and surface scenarios.

The Rossiter-McLaughlin effect and exoplanet transits: A delicate association at medium and low spectral resolution

The characterization of exoplanetary atmospheres via transit spectroscopy is based on the comparison between the stellar spectrum filtered through the atmosphere and the unadulterated spectrum from the occulted stellar region. The disk-integrated spectrum is often used as a proxy for the occulted spectrum, yet they differ along the transit chord depending on stellar type and rotational velocity. This is referred to as the Rossiter-McLaughlin (RM) effect, which is known to bias transmission spectra at high spectral resolution when calculated with the disk-integrated stellar spectrum. Recently, it was shown that the first claimed atmospheric signal from an exoplanet cannot arise from absorption in the core of the sodium doublet, because the features observed at high resolution are well reproduced by the RM effect. However, it remains unclear as to whether the detection made at medium spectral resolution with the HST arises from the smoothed RM signature or from the wings of the planetary absorption line. More generally, the impact of the RM effect at medium and low spectral resolution remains poorly explored. To address this question, we simulated realistic transmission spectra in a variety of systems using the EVaporating Exoplanets code. We find that the Rm effect should not bias broadband atmospheric features, such as hazes or molecular absorption, measured with the JWST/NIRSPEC (prism mode) at low resolution. However, absorption signatures from metastable helium or sodium measured at medium resolution with the JWST/NIRSPEC (G140H mode) or HST/STIS can be biased, especially for planets on misaligned orbits across fast rotators. In contrast, we show that the Na signature originally reported in HD 209458b, an aligned system, cannot be explained by the RM effect, supporting a planetary origin. Contamination by the RM effect should therefore be accounted for when interpreting high- and medium-resolution transmission spectra of exoplanets.

Comparing the atmospheric and ocean characteristics associated with two distinctly intensified pre‐monsoon tropical cyclones over the Bay of Bengal

AbstractTropical cyclone (TC) Amphan is analyzed in terms of the various factors that governed the intensification process associated with it and compared with Fani. Furthermore, the TC radial characteristics and ocean productivity are examined. Notably, both TCs formed in the Bay of Bengal during the pre‐monsoon seasons of 2020 and 2019, respectively. For this study, both ocean and atmospheric parameters from various sources including global analyses, satellite observations, and outputs from Model for Prediction Across Scales‐Atmosphere (MPAS‐A) and Advanced Research Weather Research and Forecasting (WRF‐ARW), are considered. The results indicate a gradual decrease in vertical wind shear during Fani. In the case of Amphan, the increase in mid‐tropospheric relative humidity values is found to be substantial. The sea surface cooling after the passage of Amphan was higher than in the case of Fani. The higher sea surface temperature in the Amphan case corresponds to the lower aerosol loading (partly because of lockdown measures) than that of Fani in the pre‐cyclone phase. And the decrease (increase) in aerosol loading coincides with an increase (decrease) in the direct radiative forcing at the ocean surface. The Madden–Julian Oscillation played a greater role in the cyclogenesis of Fani, but Kelvin waves offered a major support in the case of Amphan. The warmer sea surface, higher tropical cyclone heat potential, and conducive ocean and atmospheric setting together supported the further intensification of Amphan to the supercyclone stage. The difference in chlorophyll concentration showed a significant variation, with higher positive values seen in the case of Amphan implicating greater vertical mixing. The numerical modeling effort indicated superior performance of MPAS‐A compared to WRF‐ARW in simulating the radial parameters of the TCs.

Sources and paths of extreme cold surges over China and their associated atmospheric circulations

AbstractThe extreme cold surges (ECSs) are among the extreme weather events that affect China. Here, a total of 31 ECSs that occurred from 1962 to 2018 in China were identified using observed temperature data. In addition, we used the hybrid single‐particle Lagrangian integrated trajectory (HYSPLIT) model to trace their sources. And the atmospheric circulation of ECSs with different paths were investigated. The ECSs were classified to K‐type, B‐type and S‐type ECSs (which was subdivided into the S1‐type, S2‐type and S3‐type) according to their origins and entry locations into China. The S‐type ECSs occurred the most, accounting for 58.06%. The duration and temperature drop of the K‐type ECSs were higher than those of others. And K‐type and S1‐type ECSs mainly affected Northeast China, S2‐type ECSs mainly affected the regions east of 105° E. For decadal variation, Since the 1980s, ECSs frequency has decreased (especially only one ECS occurred in the 1980s), and K‐type and S2‐type ECSs disappeared. Different types of cold surges have different atmospheric circulation characteristics. The “positive–negative–positive” wave train structures of geopotential height anomalies at 500 hPa were very conducive to the formations of K‐type, B‐type and S3‐type ECSs. For K‐type and S1‐type ECSs, the cold high gradually moved south to Siberia, and combined with the cyclone anomalies, causing the cold air to enter China. Before the B‐type ECSs occurred, cold high appeared in the west of Xinjiang, which made the southeast cold air invade China. Regarding S1‐type and S2‐type ECSs, the negative anomalies over Eurasia from day −6 to day −4 and the subsequent cyclone anomalies at 850 hPa accompanied by the southeastward extension of cold high led to ECSs.

The Lower Atmospheric Characteristics of Dust Storms Using Ground-Based Sensor Data: A Comparative Analysis of Two Cases in Jinan, China

Two severe dust storm (DS) events (15–17 March and 28–29 March) hit northern China in 2021 consecutively. The lower atmospheric vertical dynamic and thermal structures during the two cases were compared using the ground-based sensor data from the microwave radiometer and radar wind profiler, combined with the environmental and meteorological observations data in Jinan, China. It was found that both cases occurred under the background of cold vortexes over northeastern China. The dust was transported through the cold air on the northwest route. During the dust period, 2–3 km was the west or northwest airflow, and below 2 km was the northeast wind. The variation in the dynamic structure determined the duration of the DS. During the DS maintenance phase, the vertical wind shear (VWS) below 3 km measured approximately 10 m∙(s∙km)−1. The increased VWS during the dust intrusion period facilitated the transportation of dust. In contrast, the more significant VWS was not conducive to the maintenance of DS, and the shift to south wind control in the upper middle layer indicated the weakening of DS. In both cases, we observed a cliff-like decrease in relative humidity as a prominent indicator of dust outbreaks, occurring approximately 2–5 h beforehand. The diurnal difference between the vertical temperature and relative humidity during the dust maintenance period was found to be insignificant.

Atmospheric environment characteristic of severe dust storms and its impact on sulfate formation in downstream city

This study comprehensively investigated the impact of dust storms (DSs) on downstream cities, by selecting representative DS events. In this paper, we discussed the characteristics of meteorological conditions, air pollutants, PM2.5 components, and their influence on sulfate formation mechanisms. During DSs, strong winds, reaching speeds of up to 10 m/s, led to significant increases in PM10 and PM2.5, with maximum concentrations of 2684.5 and 429 μg/m3, respectively. Primary gaseous pollutants experienced substantial reductions, with decline rates of 48.1, 34.9, 36.8, and 9.0 % for SO2, NO2, NH3, and CO, respectively. Despite a notable increase in PM2.5 concentrations, only 7.6 % of the total mass of PM2.5 was attributed to ionic and carbonaceous components, a much lower value than observed before the DSs (77.3 %). Concentrations of Fe, Ti, and Mn exhibited increases by factors of 6.5–14.1, 10.4–17.0, and 1.6–4.7, respectively. In contrast to the significant decrease of >76.2 % in nitrogen oxidation ratio (NOR), sulfur oxidation ratio (SOR) remained at a relatively high level, displaying a strong positive correlation with high concentrations of Fe, Mn, and Ti. Quantitative analysis revealed an average increase of 0.187 and 0.045 μg/m3 in sulfate from natural sources and heterogeneous generation, respectively. The heterogeneous reaction on mineral dust was closely linked to atmospheric humidity, radiation intensity, the form of metal existence, and concentrations of it. High concentrations of titanium dioxide and iron‑manganese oxides in mineral dust promoted heterogeneous oxidation of SO2 through photocatalysis during the daytime and metal ion catalysis during the nighttime. This study establishes that the metal components in mineral dust promote heterogeneous sulfate formation, quantifies the yield of sulfate generated as a result, and provides possible mechanisms for heterogeneous sulfate formation.

The architecture of visual narrative: Can text-to-image algorithms enhance the power of stylistic narrative for architecture

Architecture has always been a means of communicating stories through its design, with its structures and spaces serving as visual narratives. However, recent advancements in technology have created opportunities for architects to enhance their storytelling capabilities through the use of text-to-image algorithms. These algorithms have the potential to improve visual narratives by enabling architects to translate written descriptions into tangible visual representations. This article explores the architecture of visual narrative and how text-to-image algorithms can enhance it in diverse styles. This inquiry aims to help architectural epistemology understand and foresee the potential impact of this technology on the field of architecture. To understand the limits of AI in generating styles to enhance architectural narrative, six distinct styles were chosen for experimentation. The styles were selected based on their unique features, including an architect’s style, movement, or era. These styles include Zaha Hadid, Brutalist, modern-minimalistic, Peter Zumthor, Gothic, and Gaudi. The narrative was kept the same for each style while observing the changes in AI-generated visuals. The results were evaluated by comparing AI’s interpretations in terms of stylistic, environmental, material, form-based, and atmospheric features. While the results showed promise in terms of variations in each category, AI was not successful in implementing all stylistic features while keeping the narrative stable. In particular, after the second environment layer, the modern-minimalistic, Zumthor, and Brutalist styles lost their distinct features, while Gothic and Gaudi-inspired visuals were hardly generated even in the second environment layer. As a result, AI performed well in generating detailed environmental features without any given narrative and creating an atmospheric environment with enlightening the environment for the last layer.

Adaptive Reuse: Atmospherics in Buildings Repurposed as Coffee Shops

Opening a business in an existing building incurs lower energy and material consumption than constructing a new building. However, implementing this strategy in certain domains, such as retail and hospitality, requires operational changes. Despite an increasing focus on sustainability in these sectors, the primary objective remains creating appealing spaces for consumers, with companies frequently stipulating numerous requirements for their stores’ new buildings. To promote sustainability, scholars have suggested that organizations, designers, and constructors find new uses for existing spaces. This study highlights how adapting buildings not built for commercial use can both promote sustainability and benefit new users. It explores a trend in European cities where existing buildings, often outside the usual commercial districts, are repurposed as cafés. Many of these projects provide attractive user destinations without requiring extensive renovations. Specifically, we investigate coffee shops in Copenhagen and the atmospheric characteristics that enhance their appeal. Based on observations and interviews, new and old atmospheric components and the atmospheres they jointly create are identified and divided into themes: capacious and accommodating environments, uniqueness, synergy between old and new, and appealing neighborhoods. Finally, user responses to these themes—such as lingering, revisiting, sharing narratives, influencing neighborhood development, and building communities—are described.

A modification to the extended Rytov theory

This paper proposes a heuristic modification to the extended Rytov theory of strong scintillation [Andrews L, Phillips R. Laser beam propagation through random media. 2nd ed., Washington, USA: SPIE Press; 2005, Andrews LC, Al-Habash MA, Hopen CY, et al. Theory of optical scintillation: Gaussian-beam wave model. Waves Random Media. 2001;11(3):271–291]. This is done by dividing the large and small scales in the extended Rytov theory into further sub-scales. Expressions for the induced irradiance variance, and log-irradiance, due to each sub-scale are presented. The modified theory is used to derive and justify recent results in irradiance fluctuation modeling and hence answer a question initially proposed in [Shishter YM, Young RC, Ali FH. Irradiance probability density function for turbulence induced fading in free space optics. JOSA A. 2022;39(7):1267–1274, Shishter YM, Young RC, Ali FH. General statistical model of irradiance fluctuations in free space optics. JOSA A. 2023;40(1):53–63]. Plots of the irradiance pdf under the modified theory are in support of the theory. The validity of the theory presented would result in modifications to the classical scintillation analysis of waves in random media, with applications to the design of optical systems. Moreover, it is observed that the mathematical framework developed also follows from the assumption of variations in the turbulence along a horizontal path. The work presented in this paper suggests the need of further theoretical and experimental investigations into the effect of variations in atmospheric turbulence characteristics along horizontal paths in the received signal irradiance distribution.

Performance and process-based evaluation of the BARPA-R Australasian regional climate model version 1

Abstract. Anthropogenic climate change is changing the Earth system processes that control the characteristics of natural hazards both globally and across Australia. Model projections of hazards under future climate change are necessary for effective adaptation. This paper presents BARPA-R (the Bureau of Meteorology Atmospheric Regional Projections for Australia), a regional climate model designed to downscale climate projections over the Australasian region with the purpose of investigating future hazards. BARPA-R, a limited-area model, has a 17 km horizontal grid spacing and makes use of the Met Office Unified Model (MetUM) atmospheric model and the Joint UK Land Environment Simulator (JULES) land surface model. To establish credibility and in compliance with the Coordinated Regional Climate Downscaling Experiment (CORDEX) experiment design, the BARPA-R framework has been used to downscale ERA5 reanalysis. Here, an assessment of this evaluation experiment is provided. Performance-based evaluation results are benchmarked against ERA5, with comparable performance between the free-running BARPA-R simulations and observationally constrained reanalysis interpreted as a good result. First, an examination of BARPA-R's representation of Australia's surface air temperature, precipitation, and 10 m winds finds good performance overall, with biases including a 1 ∘C cold bias in daily maximum temperatures, reduced diurnal temperature range, and wet biases up to 25 mm per month in inland Australia. Recent trends in daily maximum temperatures are consistent with observational products, while trends in minimum temperatures show overestimated warming and trends in precipitation show underestimated wetting in northern Australia. Precipitation and temperature teleconnections are effectively represented in BARPA-R when present in the driving boundary conditions, while 10 m winds are improved over ERA5 in six out of eight of the Australian regions considered. Secondly, the paper considers the representation of large-scale atmospheric circulation features and weather systems. While generally well represented, convection-related features such as tropical cyclones, the South Pacific Convergence Zone (SPCZ), the Northwest Cloudband, and the monsoon westerlies show more divergence from observations and internal interannual variability than mid-latitude phenomena such as the westerly jets and extratropical cyclones. Having simulated a realistic Australasian climate, the BARPA-R framework will be used to downscale two climate change scenarios from seven CMIP6 global climate models (GCMs).

Atmospheric turbulence observed during a fuel-bed-scale low-intensity surface fire

Abstract. The ambient atmospheric environment affects the growth and spread of wildland fires, whereas heat and moisture released from the fires and the reduction of the surface drag in the burned areas can significantly alter local atmospheric conditions. Observational studies on fire–atmosphere interactions have used instrumented towers to collect data during prescribed fires, but a few towers in an operational-scale burn plot (usually > 103 m2) have made it extremely challenging to capture the myriad of factors controlling fire–atmosphere interactions, many of which exhibit strong spatial variability. Here, we present analyses of atmospheric turbulence data collected using a 4 × 4 array of fast-response sonic anemometers during a fire experiment on a 10 m × 10 m burn plot. In addition to confirming some of the previous findings on atmospheric turbulence associated with low-intensity surface fires, our results revealed substantial heterogeneity in turbulent intensity and heat and momentum fluxes just above the combustion zone. Despite the small plot (100 m2), fire-induced atmospheric turbulence exhibited strong dependence on the downwind distance from the initial line fire and the relative position specific to the fire front as the surface fire spread through the burn plot. This result highlights the necessity for coupled atmosphere–fire behavior models to have 1–2 m grid spacing to resolve heterogeneities in fire–atmosphere interactions that operate on spatiotemporal scales relevant to atmospheric turbulence. The findings here have important implications for modeling smoke dispersion, as atmospheric dispersion characteristics in the vicinity of a wildland fire are directly affected by fire-induced turbulence.

Unraveling the influence of varied airflow patterns on atmospheric instabilities, rain microphysics, and cloud features over Delhi, the capital city of India

This present investigation unveils a novel insight into the dominant airmass flows originating from both continental and maritime sources, shaping the characteristics of the raindrop size distribution (DSD), atmospheric instabilities, and cloud effective radius over Delhi (28.62°N, 77.17°E), the capital city of India. The investigation employs the Hybrid Single-Particle Lagrangian Integrated Trajectory model to analyze airmass back trajectories corresponding to days featuring rain events within the study area. An observable pattern emerges: days with rainfall driven by continental (maritime) activities experience higher (lower) atmospheric instabilities, which is validated by radiosonde measurements. This enhanced atmospheric instability leads to the prevalence of larger raindrops during continental-influenced rain events, in contrast to days marked by maritime airflow, as estimated from surface-based disdrometer measurements. Furthermore, observations from the INSAT 3D/3DR satellite indicate a tendency towards a smaller cloud effective radius associated with continental airmass inflow, as opposed to maritime inflow. Notably, a category of rain events characterized by ambiguous airmass back trajectories, referred to as mixed types, is identified. These mixed events showcase intermediary variations in the investigated atmospheric parameters, positioning them between the continental and maritime scenarios. The discernible differences in DSD characteristics between rain events influenced by continental and maritime activities yield fluctuations in the radar reflectivity-rain rate (Z-R) power law relations, which bear significance for the application of rain radar remote sensing. Hence, this investigation underscores the critical implication of incorporating airmass source information while characterizing rain features and related atmospheric parameters over a capital metropolis like Delhi.