Atmospheric Radiation Research Articles

Issues in radiation dose measurement using electronic personal dosimeters during disaster relief activities.

In this study, we measured the radiation exposure of the medical relief team of the Japanese Red Cross Society (JRCS) members during the Noto Peninsula earthquake using electronic personal dosimeters (EPDs) and investigated the frequency of electromagnetic interference (EMI) events that caused abnormally high dose readings. Six JRCS medical relief team members (two physicians, three nurses, and one logistics officer) involved in the Noto Peninsula earthquake disaster relief activities were provided with EPDs to measure their radiation exposure during the activity period. A background radiation dosimeter was also installed on-site to record ambient radiation levels. Over 2.5days of disaster relief activities, the background radiation dose was 3.0 ± 3.0μSv. However, the highest recorded dose among the team members was 2075.0 ± 207.5 μSv for a nurse, while the average dose for the other members was 40.0 ± 39.5 μSv. A significant radiation dose was observed despite no radioactive material dispersion. Among the four individuals who exhibited abnormally high dose readings, three were operating digital devices at the time of measurement, suggesting a strong likelihood that electromagnetic interference was the cause. The effective management of radiation doses using EPDs during nuclear disasters requires the implementation of countermeasures against EMI from digital devices.

Neutron Resilience of Flexible Perovskite Solar Cells Using PTAA‐Derived Hole Transport Layers

Flexible perovskite solar cells hold promise of being an enabling technology for space missions: by reducing the encumbrance and weight of the payload's power system, launch costs can be minimized. The increased interest, however, must be accompanied by thorough testing under a broad range of space‐related sources of degradation and investigation of their effects on the layer stack. In this work, the resilience against atmospheric neutron radiation (<800 MeV) of flexible devices is studied, using two different hole transporting materials: a commercial PTAA and a PTAA‐based in‐house synthesized polymer. After 5 109 particles/cm2 irradiation, 380 times higher than the yearly fast neutron fluence in low Earth orbit, the devices show good stability, with efficiency losses below 20%. Further investigation by light intensity‐dependent JV scans, hyperspectral photoluminescence microscopy, X‐ray diffraction, X‐ray reflectivity, and atomic force microscopy reveals that the neutron radiation mainly affects the perovskite/hole transport layer interface, to a different extent depending on the material. This work confirms that accelerated stress testing is an important tool to determine the feasibility of this technology for space applications and provides insights on the damages caused by atmospheric neutrons which will help inform future decisions for the fabrication of space‐resilient flexible perovskite solar cells.

Parameterized models for estimating atmospheric longwave radiation and surface net radiation in the Brazilian semi-arid region

Parameterized models for estimating atmospheric longwave radiation and surface net radiation in the Brazilian semi-arid region

Dual decoding generative adversarial networks for infrared image enhancement

Infrared imaging technology is vital for security monitoring, industrial detection, and medical diagnosis. However, atmospheric thermal radiation degrades its quality, causing contrast reduction, texture blurring, and non-uniform noise. To address these challenges, this paper introduces a novel infrared image enhancement method using a dual decoding generative adversarial network (2D-GAN). First, internal and external skip connections are designed to enhance high-frequency detail transmission and mitigate gradient vanishing in deep networks, with local details being preserved as a result. Second, a cross-layer attention mechanism is proposed to adaptively adjust feature map weights spatially and across channels, with information loss during encoding-decoding being minimized and texture clarity and structural coherence being improved. Finally, a joint loss function is designed to integrate pixel-level accuracy, semantic consistency, and global structural coherence, with image realism and perceptual quality being enhanced consequently. Experiments demonstrate superior performance over existing methods in comparative and ablation studies on public datasets, confirming excellent enhancement capabilities and generalization.

Atmospheric CO2 Ice in the Martian Polar Regions: Physical and Spectral Properties From Mars Climate Sounder Observations

Abstract ice clouds are important for polar energy balance and the carbon dioxide cycle on Mars. However, uncertainties remain regarding their physical and radiative properties, which control how polar clouds interact with the global Martian climate. Here, we use Mars Climate Sounder (MCS) observations of atmospheric radiance to estimate these physical and radiative properties. We find that Martian clouds are typically composed of large particles from a narrow size distribution with an effective radius of 46 m and an effective variance of in the southern hemisphere, and an effective radius of 42 m and an effective variance of in the north. The similarity in sizes of ice particles in both hemispheres may be due to the fact that clouds tend to form near the same pressure level in each hemisphere, despite the higher surface pressures in the north. We use a simplified convective cooling model to show that the small effective variance we derive may be a consequence of the fact that is also the dominant atmospheric constituent on Mars, which allows ice particles to reach sizes upwards of 10 m within seconds. At the same time, the fact that the Martian atmosphere is so thin means that large particles fall rapidly to the surface, reducing the range of particle sizes that can remain in the atmosphere for any extended period of time. This study is part of ongoing work to add ice opacity profiles to the MCS retrieval pipeline.

Demonstrating Hierarchical System Development With the Common Community Physics Package Single‐Column Model: A Case Study Over the Southern Great Plains

ABSTRACTThis study demonstrates a specific application of the hierarchical system development (HSD) approach to investigate, analyze, and attribute model issues within the Unified Forecast System (UFS), with a focus on process isolation. By evaluating a non‐precipitating, shallow cumulus case at the Atmospheric Radiation Measurement Southern Great Plains site in the UFS global forecast against the observation, the investigation identifies a warmer and deeper daytime convective planetary boundary layer (PBL) and misrepresented nocturnal PBL transition. Hypothesis testing, which employs the Common Community Physics Package (CCPP) single‐column model (SCM) and uses the same physics as the UFS global model, confirms that these issues are attributed to the model physics and initialization. Specifically, misrepresented PBL processes are linked to problematic surface condition and a lack of cloud formation, which may stem from deficiencies in PBL and cloud microphysics parameterizations and their interactions. The UFS initial condition contributes to an earlier, excessively collapsed daytime convective boundary layer and a lack of decoupling between the stable boundary layer and residual layer late in the afternoon. This work introduces an avenue for the community to engage with the application of HSD, along with the CCPP and CCPP SCM, to understand the interplay of model physics, disentangle the roles of model components, as well as facilitate model and forecast improvement.

Particle-free thin film deposition from acetylene and silane precursors using atmospheric plasma vacuum ultraviolet radiation

Abstract We present a study on photon-initiated thin film deposition by separating vacuum ultraviolet (VUV) radiation produced by atmospheric helium plasma from a precursor gas using a windowless setup. Acetylene (C2H2) and silane (SiH4) were selected as high electron affinity precursors with a strong tendency to form particles during plasma-based deposition. The VUV radiation from the plasma enabled dust-free deposition of homogeneous thin films from both precursors. Films were analyzed by Fourier-transform infrared spectroscopy, while the strong ionic contribution to film formation has been studied by positive ion mass spectrometry. We found profoundly different trends for film absorbance and relative abundances of ionic species identified as growth precursors with admixture for the two precursors and a strong influence of solvent and impurity species.

РОЗПОДІЛЕННЯ ЕЛЕКТРОСТАТИЧНОГО ПОЛЯ У НЕЕКРАНОВАНИХ СИЛОВИХ КАБЕЛЯХ З РІЗНОЮ КОНФІГУРАЦІЄЮ СТРУМОПРОВІДНИХ ЖИЛ

Introduction. Nuclear energy is an undeniable component of green energy and makes a significant contribution to achieving sustainable development goals and increasing the country's energy security. It is a reliable and predictable source of electricity for up to 60 years with an average cost of energy produced of 0.4 euros/kWh. Problem. Nuclear power plants consist of different zones that differ in ambient temperature, radiation level, and requirements for testing resistance to accidents. Cables for nuclear power plants are constantly exposed to difficult conditions throughout their entire service life and must provide reliable power supply, meet environmental safety and economic efficiency requirements. The introduction of modern innovative polymer compositions requires a comprehensive analysis of the influence of electrical insulation on the electrical parameters of cables of various designs at the stage of their design and production to ensure a high level of operational functionality at nuclear power plants. The purpose of the work is to analyze the distribution of the electrostatic field around insulated conductive cores of various configurations with the determination of their electrical capacitance in unshielded power cables of local power supply systems of the turbine compartment of nuclear power plants. The methodology is based on determining the electric charge density by the secondary source method based on the Fredholm integral equations of the first and second kind in models of unshielded power cables with sector and round conductive cores. Scientific novelty. The distribution of the plane-parallel electrostatic field in unshielded power cables has been determined depending on the scheme of applying an electric potential (zero and non-zero, equal to 1000 V) to conductive cores of various configurations. It has been proven that in a power cable with cores of a uniform configuration, under the condition of creating a dipole spatial distribution of the electrostatic field, the electric capacitances between the cores have the largest values. The correctness of the obtained theoretical provisions has been experimentally confirmed, which is confirmed by the coincidence of the calculated values ​​of the electric capacitance with the experimental values ​​with a difference of 8.5%. Practical significance. The electric field strength on the surface of the electrical insulation of the cores has been determined depending on the inspection scheme when applying the working and increased test voltage. It is proposed to fill the interphase space with a dielectric material with a dielectric permittivity ε1=2.0 to reduce the electrostatic field strength by 2 times in the air gaps at the technological stage of cable manufacturing. The presented methodology for determining the spatial distribution of the electrostatic field around insulated conductive cores of various configurations can be used to estimate the values ​​of the electrical capacitance of insulating gaps as reference for controlling cables at the technological stage of manufacturing during acceptance tests and in operational conditions. References 27, figures. 7.

Causal Directions Matter: How Environmental Factors Drive Convective Cloud Detrainment Heights

Abstract This study investigates how environmental factors influence the level of maximum detrainment (LMD) in deep convective clouds. Through a novel application of the Linear Non‐Gaussian Acyclic Model (LiNGAM), we discover causal structures between environmental variables and LMD, observed at six tropical sites operated by the Atmospheric Radiation Measurement (ARM) user facility. LiNGAM effectively identifies causal directions among variables of interest, revealing robust relationships such as those among the lifting condensation level (LCL), level of free convection (LFC), and convective inhibition (CIN), aligning with prior knowledge. Relative humidity is shown to directly influence LMD; however, this relationship exhibits strong nonlinearity and becomes difficult to detect when the contrast between oceanic and continental environments is excluded from the analysis. This study highlights the importance of establishing causal relationships before performing statistical inference.

Exposing Process‐Level Biases in a Global Cloud Permitting Model With ARM Observations

Abstract The emergence of global convective‐permitting models (GCPMs) represents a significant advancement in climate modeling, offering improved representation of deep convection and complex precipitation patterns. In this study, we evaluate the performance of the Simple Cloud‐Resolving E3SM Atmosphere Model (SCREAM) using its doubly periodic configuration (DP‐SCREAM) against large eddy simulations and modern observational data sets from the Atmospheric Radiation Measurement program. We introduce several new transitional cloud regime cases, such as the transition from shallow to deep convection and from stratocumulus to cumulus, as well as cold‐air outbreak scenarios. The results reveal both strengths and limitations of SCREAM, particularly in the accurate simulation of cloud transitions and midlevel convection, with varying degrees of sensitivity to horizontal and vertical resolution. Despite improvements at higher resolutions, key biases remain, including the abrupt transition from shallow to deep convection and the lack of congestus clouds. These findings underscore the need for further refinement in turbulence parameterizations and vertical grid resolution in GCPMs.

Stratospheric Aerosol Intervention experiment for the Chemistry–Climate Model Initiative

Abstract. A new Stratospheric Aerosol Intervention (SAI) experiment has been designed for the Chemistry–Climate Model Initiative (CCMI-2022) to assess the impacts of SAI on stratospheric chemistry and dynamical responses and inter-model differences using a constrained setup with a prescribed stratospheric aerosol distribution and fixed sea surface temperatures and sea ice. This paper serves a dual purpose: first, it describes the details of the experimental setup and the prescribed aerosol distribution and demonstrates the suitability of the simplified setup to study SAI impacts in the stratosphere in a multi-model framework. The experiment allows attributing inter-model differences to the resulting impacts on atmospheric chemistry, radiation, and dynamics rather than the model uncertainty arising from differences in aerosol forcing and feedbacks from the ocean and sea ice under SAI. Second, we use the Whole Atmosphere Community Climate Model (WACCM6) to compare the interactive stratospheric aerosol configuration with coupling to land, ocean, and sea ice used to produce the stratospheric aerosol distribution with the results of the constrained SAI experiment. With this, we identify and isolate the stratosphere-controlled SAI-induced impacts from those influenced by the coupling with the ocean. Overall, this comparison facilitates an advanced process-level understanding of the drivers of SAI-induced atmospheric responses. For example, we confirm earlier suggestions that the SAI-induced positive phase of the North Atlantic Oscillation in winter, with the corresponding winter warming over Eurasia and related changes, is driven by stratosphere–troposphere coupling. Future multi-model comparisons will thus provide an important contribution to upcoming scientific assessments of ozone depletion.

Impact of assimilating thermodynamic and kinematic profiles on a convection initiation forecast

Abstract The first field campaign of a network of water vapor lidars, called the MicroPulse Differential absorption lidar (MPD) Network Demonstration Project, combined the observational capabilities of the water vapor MPD, the Atmospheric Emitted Radiance Interferometer (AERI), and the Doppler wind lidar (DWL) at five sites at the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) field observatory from 22 April – 19 July 2019. During the field campaign, water vapor profiles from the MPD were collected to complement the ARM/SGP temperature and water vapor profiles from the AERI, wind profiles from the DWL and supplementary radiosondes, along with operational weather radar and surface station data. The impacts of assimilating AERI, MPD, and DWL are evaluated for a mesoscale convection initiation (CI) and precipitation event on 14 June 2019. Short-term forecasts of CI and precipitation are improved by assimilating all MPD and AERI profiling data in comparison to assimilating conventional observations and DWL. While AERI shows marginal impact on the forecasts of CI near the sites, assimilating the MPD water vapor profiles contributes the most to improving forecast skill over almost all forecast times and reflectivity thresholds. The data assimilation (DA) experiments shows the development of moist absolutely unstable layers near the observing sites and MPD DA improved the vertical moisture profile, leading to an improvement of the southern CI forecasts for the MPD DA experiment. Furthermore, MPD DA additionally leads to improved CI forecasts north of the sites due to enhanced thermodynamic instability and modified wind field leading to convergence in the lower atmosphere.

A Rapid Deterioration of the Transmissive Atmospheric Radiative Regime in the Western Arctic

Abstract The tendency for the atmosphere to reside in one of two radiative states (“transmissive” or “opaque”) is unique to the high latitudes. This phenomenon makes the Arctic climate particularly sensitive to change if the conditions that support one of these states vanish. This study examines 25 years of in‐situ data from the North Slope of Alaska to investigate how these two states have changed over time. While November once had nearly equal occurrences of both states, the transmissive state has almost completely disappeared, resulting in an increase of over 30 in surface downwelling longwave radiation since the turn of the century. This dramatic shift highlights a crucial climate feedback that any region prone to sea ice loss may experience—reducing the transmissive state enhances atmospheric warming and moistening, further promoting the opaque state. This feedback accelerates surface energy imbalances and could amplify Arctic change beyond current projections.

Potential of mm‐Wave Doppler‐Polarimetric Profiler Observations for Quality Assessment of Hydrometeor Classification Schemes

Abstract Dual‐polarization weather radars have improved the accuracy of precipitation estimates. However, challenges persist in evaluating hydrometeor classification (HMC) algorithms, thereby impacting the accuracy of precipitation estimates. This study proposes to use full Doppler spectra in both polarizations from a Ka‐ and W‐band Doppler‐polarimetric profiler with a 45° elevation angle to provide insights into hydrometeor characteristics. A novel methodology was developed to link the observed spectra with the output of an HMC scheme. We applied the wradlib HMC scheme using C‐band weather radar data from the Netherlands for six cases (2021–2022). The HMC scheme output is used to calculate mixing ratios that are combined with the corresponding scattering properties using the Atmospheric Radiative Transfer Simulator microwave single scattering properties database (frozen hydrometeors) and T‐matrix calculations (liquid hydrometeors) to simulate Doppler spectra of polarimetric variables that would be measured by the profiler. Comparing these simulations with actual profiler measurements enables a quality assessment. The method works in stratiform cases, but convective cases reveal the influence of turbulence and wind variability. Uncertainty arises from the selection of specific parameterizations for the particle size distribution and the relationship between hydrometeor size and terminal fall velocity as well as from the derived mixing ratios. Additionally, the 45° angle complicates separating horizontal wind from hydrometeor fall velocities, although the Mie notch in the dual‐wavelength ratio can be effectively used to remove the radial wind component. Our results underline limitations that must be addressed but also show that integrating spectral and dual‐frequency observations could yield valuable insights into hydrometeor characteristics.

Research on Gas Detection Algorithm Based on Reconstruction of Background Infrared Radiation

In response to the pressing need for long-range, non-contact detection in hazardous gas leakage monitoring within chemical industrial parks, this study proposes a gas detection algorithm based on an infrared radiation physical model that utilizes dual-band infrared radiation background reconstruction. The proposed method addresses the issues of the existing detection methods’ lack of physical model support. First, appropriate filter wavelength ranges are selected based on the absorption spectral characteristics of the target gas. Subsequently, a physical model incorporating atmospheric attenuation, background radiation, and gas absorption properties is established based on gas radiative transfer theory. The non-absorption band data are then employed to reconstruct the theoretical background radiation of the absorption band. Furthermore, leveraging the synergistic observation advantages of a dual-band infrared imaging system, gas morphology identification is achieved by inverting the difference between the theoretical background and the actual measured values in the absorption band. Experimental results demonstrate that this method enables gas morphology detection through background reconstruction without requiring pre-collected gas-free background images. By implementing dual-band infrared radiation background reconstruction, this study achieves effective gas detection, providing a reliable technical approach for real-time monitoring and early warning of industrial gas leaks. The proposed algorithm enhances detection capabilities, offering significant potential for applications in industrial safety and environmental monitoring.

Harmonized aerosol size distribution, cloud condensation nuclei, chemistry and optical properties at 10 sites

A harmonized data set of aerosol particle number size distributions and cloud condensation nuclei (CCN) concentrations at 10 US Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) sites is presented. Additional complementary data including total particle number concentration, aerosol chemical composition and aerosol optical properties are included where available. Calculated parameters related to particle activation, (e.g., activated fraction, hygroscopicity parameter and critical diameter) are provided. DOE/ARM data are acquired and processed following DOE/ARM protocols which are based on international recommendations. Here, the data have undergone additional stages of quality assessment including various closure tests. This data set may be useful for studying aerosol processes and, with other data, assessing aerosol-cloud- interactions. High-resolution (5-min) data is provided along with 1-h averaged data, making the data set suitable for exploring the importance of new particle formation (NPF) events on CCN concentrations. Further applications include model and satellite evaluation of aerosol size distributions and CCN concentrations, and exploring relationships among different aerosol properties which may be useful for improving model parametrizations.

Prevalence and determinants of profound vitamin D deficiency (25-hydroxyvitamin D

Prevalence and determinants of profound vitamin D deficiency (25-hydroxyvitamin D <10 nmol/L) in the UK Biobank and potential implications for disease association studies.

Measuring Urban Heat Island Indicators from Surface Temperature Using Spatial Techniques

Abstract Using remote sensing technology and modeling methodologies to monitor changes in land surface temperature (LST) and urban heat islands (UHI) has become an essential reference for making decisions on sustainable land use. This study estimates LST and UHI in Salah al-din Province to contribute to land management, Urban planning, or climate resilience in the region; as a result of environmental changes in recent years, LANDSAT Satellite Imagery from 2014- 2024 was implemented to estimate the LST and UHI indexes in Salah al-din Province, ArcGIS 10.7 was use to calculate the indices, and The normalized mean vegetation index (NDVI) was calculated as it is closely related to extracting (LST &amp; UHI) indices. Results confirmed that extracting the vegetation index, atmospheric radiation, brightness temperature of the satellite, and earth’s surface emissivity from (Landsat-8) bands and processing them on ArcGIS facilitates the estimation of each of (LST and UHI) indexes. Results showed that (UHI) forms a complete correlation with (LST). The correlation coefficient in 2014 and 2024.

Experimental Study to Investigate the Effect of Metallic Wire Mesh on the Performance of Flat Plate Solar Water Heater Collector

Abstract Researchers and engineers are repeatedly innovating in solar energy technology to improve the effectiveness, cost-effectiveness, and environmental sustainability of solar collectors. In this study, a domestically produced solar flat plate collector (FPC) was designed, including a copper coil and a galvanized base plate insulated with glass wool, a transparent glass cover, and a wooden frame. Experimental tests were carried out in March 2024 in Karbala, Iraq (32°N, 44°E). The study examined the effects of variables such as air speed, ambient temperature, solar irradiance, and the presence of metallic wire mesh, using an open-loop system with a constant inlet water temperature. The inlet water flow rate varied across 2, 3, 4, and 6 L/min. The outlet water temperature was recorded hourly from 9 a.m. to 4 p.m. each day, over an 8-day period in March 2024, under both conditions (with and without wire mesh). Although there was minimal fluctuation in ambient temperature and solar radiation across the test days during the same hours, the results indicate a 22% increase in outlet water temperature when the FPC was equipped with wire mesh. The highest temperature differential between inlet and outlet water was noted at 12 p.m., with a flow rate of 2 L/min.

Characterizing Uncertainty in Deep Convection Triggering Using Explainable Machine Learning

Abstract Realistically representing deep atmospheric convection is important for accurate numerical weather and climate simulations. However, parameterizing where and when deep convection occurs (“triggering”) is a well-known source of model uncertainty. Most triggers parameterize convection deterministically, without considering the uncertainty in the convective state as a stochastic process. In this study, we develop a machine learning model, a random forest, that predicts the probability of deep convection, and then apply clustering of Shapley additive explanations (SHAP) values, an explainable machine learning method, to characterize the uncertainty of convective events. The model uses observed large-scale atmospheric variables from the Atmospheric Radiation Measurement constrained variational analysis dataset over the Southern Great Plains, United States. The analysis of feature importance shows which mechanisms driving convection are most important, with large-scale vertical velocity providing the highest predictive power for more certain, or easier to predict, convective events, followed by the dynamic generation rate of dilute convective available potential energy. Predictions of uncertain, or harder to predict, convective events instead rely more on other features such as precipitable water or low-level temperature. The model outperforms conventional convective triggers. This suggests that probabilistic machine learning models can be used as stochastic parameterizations to improve the occurrence of convection in weather and climate models in the future. Significance Statement Convective storms, which produce clouds and precipitation, are difficult to represent in models since they occur at scales smaller than a model grid box. The purpose of this study is to better understand why convection is sometimes easier or harder to predict with certainty. This is important because predicting where and when convection occurs in atmospheric models affects the energy, moisture, and momentum processes in these models, which is known to lead to errors in weather forecasts and climate projections. This work highlights the importance of representing uncertainty in processes like convection.