Changes In Atmospheric Conditions Research Articles

InSAR-DEM Block Adjustment Model for Upcoming BIOMASS Mission: Considering Atmospheric Effects

The unique P-band synthetic aperture radar (SAR) instrument, BIOMASS, is scheduled for launch in 2024. This satellite will enhance the estimation of subcanopy topography, owing to its strong penetration and fully polarimetric observation capability. In order to conduct global-scale mapping of the subcanopy topography, it is crucial to calibrate systematic errors of different strips through interferometric SAR (InSAR) DEM (digital elevation model) block adjustment. Furthermore, the BIOMASS mission will operate in repeat-pass interferometric mode, facing the atmospheric delay errors introduced by changes in atmospheric conditions. However, the existing block adjustment methods aim to calibrate systematic errors in bistatic mode, which can avoid possible errors from atmospheric effects through interferometry. Therefore, there is still a lack of systematic error calibration methods under the interference of atmospheric effects. To address this issue, we propose a block adjustment model considering atmospheric effects. Our model begins by employing the sub-aperture decomposition technique to form forward-looking and backward-looking interferograms, then multi-resolution weighted correlation analysis based on sub-aperture interferograms (SA-MRWCA) is utilized to detect atmospheric delay errors. Subsequently, the block adjustment model considering atmospheric effects can be established based on the SA-MRWCA. Finally, we use robust Helmert variance component estimation (RHVCE) to build the posterior stochastic model to improve parameter estimation accuracy. Due to the lack of spaceborne P-band data, this paper utilized L-band Advanced Land Observing Satellite (ALOS)-1 PALSAR data, which is also long-wavelength, to emulate systematic error calibration of the BIOMASS mission. We chose climatically diverse inland regions of Asia and the coastal regions of South America to assess the model’s effectiveness. The results show that the proposed block adjustment model considering atmospheric effects improved accuracy by 72.2% in the inland test site, with root mean square error (RMSE) decreasing from 10.85 m to 3.02 m. Moreover, the accuracy in the coastal test site improved by 80.2%, with RMSE decreasing from 16.19 m to 3.22 m.

Computing bias and variance for Pacific sardine (Sardinops sagax) biomass estimated from aerial surveys in California nearshore waters

Pacific sardine (Sardinops sagax) stocks off the U.S. Pacific coast undergo boom and bust cycles modulated by decadal changes in oceanic and atmospheric conditions. During periods of low abundance, Pacific sardine may contract into nearshore waters, making it more challenging to be surveyed by research vessels that cannot operate in the shallowest habitats, 0 to 40.00 m. In response, a new transect-based design and methods were developed to compute bias and variance of biomass estimated from a collaborative aerial survey implemented in nearshore waters. From experimental aerial surveys conducted in 2010 and from 2018 to 2020, two professional aerial fish spotters underestimated school biomass by 10.5% and 12.6%, whereas most of the variability of aerial biomass was due to within-transect variance, with no significant contribution from the among-transect variance. Implementation of the new transect-based design to aerial surveys from 2020 to 2023 in southern and northern California showed that a substantial biomass of Pacific sardine occurred in nearshore waters, representing 69.9% of total biomass estimated from acoustic surveys in spring 2021, and 36.5% in summer 2021. Seasonal biomass estimates were variable within each region, affected by school dynamics during daytime in nearshore waters and the timing of aerial surveys as determined by weather and environmental conditions. Given that nearshore aerial biomass comprised a measurable proportion of total biomass estimates for Pacific sardine during the recent period of low abundance, it has been used to inform acoustic survey catchability, which is no longer assumed to be 1.0 in recent Pacific sardine stock assessments.

Multi-scale remote sensing retrieval model for land surface emissivity and its impact on land surface temperature estimation

ABSTRACT As a fundamental parameter of the surface radiation process, land surface emissivity (LSE) is the most direct factor affecting the land surface temperature (LST) retrieval accuracy. Based on the measured data from 2017 to 2023, the characteristics of LSE changes were analysed, different timescale LSE retrieval models were developed, and the impact of LSE on pixel scale observational LST acquisition was evaluated by using unmanned aerial vehicle (UAV) remote sensing images. The results show that with changes in atmospheric environmental conditions, the LSE changes are relatively less pronounced at 3.99 µm and 4.02 µm in the mid-infrared band and at 10.8 µm and 11.8 µm in the thermal infrared band than in the other bands. At the timescale above day, the LSE is significant correlation with the normalized differential vegetation index (NDVI). Vegetation is the main parameter that dominates the LSE change under heterogeneous underlying surfaces. On a daily timescale, LSE exhibits typical diurnal variation characteristics, and the amplitude of diurnal variation is influenced mainly by shallow soil water and heat conditions. The developed LSE model based on Fengyun-4A (FY-4A) satellite remote-sensing data can effectively reproduce the daily variation characteristics of LST and avoid the ‘jumping’ phenomenon that may occur in the NDVI threshold method. The root-mean-square error (RMSE) between the retrieved LST and in situ data is reduced to 3.78°C, and the retrieval accuracy is better than that of the published LST product. When using UAVs to obtain pixel-scale LSTs, dry bare soil is more sensitive to LSE. When the LSE increases from 0.95 to 1.0, the average LST obtained by the UAV deviates by approximately 1.0°C. This study preliminarily provides some spatiotemporal variation patterns of LSE and lays a foundation for the later-modified LST retrieval algorithms.

Shape-Changing Wood Joints in Crafts and Industry and Their Potential for Building Construction and Wood Culture

Abstract Timber has regained popularity in construction in recent years due to its ecological benefits. The connection methods used in this study play a vital role in the sustainability of structures and materials. Monomaterial timber connections are sustainable alternatives to metal fasteners and adhesives commonly used in construction. Wood is an anisotropic material with dimensional changes resulting from changes in atmospheric conditions. Understanding and accounting for this property are crucial for the longevity and functionality of wooden structures. The cumulative knowledge of wood´s material characteristics and its use in design, construction, and human culture can be defined as wood culture developed through artists’ and craftsmen’s experiences, science, and industry. The development of various techniques by artisans to leverage the dimensional change in wood to join timber elements is a major contribution to wood culture. In contrast, until now, the timber industry has mainly focused on limiting or controlling these changes in standardized production and has neglected their use for joining timber elements. However, technological advances have changed dramatically. The digital manufacturing and analysis of wood structures have the potential to guide machine tools and may allow the integration of dimensional changes, especially in the design and construction of timber joints. This study explores the state-of-the-art utilization of dimensional changes in timber to join elements in craft, material science, and industrial production. The potential of techniques utilizing this behavior for innovation in modern design and construction and their implications for wood culture were examined. Research gaps and avenues for further research are identified.

Himalayas as a global hot spot of springtime stratospheric intrusions: Insight from isotopic signatures in sulfate aerosols

Downward transport of stratospheric air into the troposphere (identified as stratospheric intrusions) could potentially modify the radiation budget and chemical of the Earth's surface atmosphere. As the highest and largest plateau on earth, the Tibetan Plateau including the Himalayas couples to global climate, and has attracted widespread attention due to rapid warming and cryospheric shrinking. Previous studies recognized strong stratospheric intrusions in the Himalayas but are poorly understood due to limited direct evidences and the complexity of the meteorological dynamics of the third pole. Cosmogenic 35S is a radioactive isotope predominately produced in the lower stratosphere and has been demonstrated as a sensitive chemical tracer to detect stratospherically sourced air mass in the planetary boundary layer. Here, we report 6-month (April–September 2018) observation of 35S in atmospheric sulfate aerosols (35SO42−) collected from a remote site in the Himalayas to reveal the stratospheric intrusion phenomenon as well as its potential impacts in this region. Throughout the sampling campaign, the 35SO42− concentrations show an average of 1,070 ± 980 atoms/m3. In springtime, the average is 1,620 ± 730 atoms/m3, significantly higher than the global existing data measured so far. The significant enrichments of 35SO42− measured in this study verified the hypothesis that the Himalayas is a global hot spot of stratospheric intrusions, especially during the springtime as a consequence of its unique geology and atmospheric couplings. In combined with the ancillary evidences, e.g., oxygen-17 anomaly in sulfate and modeling results, we found that the stratospheric intrusions have a profound impact on the surface ozone concentrations over the study region, and potentially have the ability to constrain how the mechanisms of sulfate oxidation are affected by a change in plateau atmospheric properties and conditions. This study provides new observational constraints on stratospheric intrusions in the Himalayas, which would further provide additional information for a deeper understanding on the environment and climatic changes over the Tibetan Plateau.

Different wildfire types promoted two-step terrestrial plant community change across the Triassic-Jurassic transition

Frequent wildfires associated with emplacement of the Central Atlantic Magmatic Province (CAMP) are thought to have been important drivers of two significant changes in terrestrial plant communities and diversity during the Triassic-Jurassic Mass Extinction (TJME, ca. 201.51 Ma). ​However, it remains to be investigated whether these two changes are potentially related to different wildfire types. To better understand this relationship, we used a new method to reanalyze fossil pollen and spores across the Triassic-Jurassic transition in the Jiyuan Basin from the North China Plate. Results show that two peaks in wildfire frequency experienced different types of wildfires, with each linked to significant changes in plant communities and diversity losses. In the first wildfire peak, canopy fires dominated and are accompanied by significant losses of canopy forming plants, while in the second wildfire peak, ground cover fires dominated accompanied by significant losses of ground cover plants. ​Changes in atmospheric humidity conditions were an important control on the two different wildfire peaks. Relatively humid climatic conditions corresponded to the prevalence of canopy fires and hindered the spread and development of ground cover fires in wet surface conditions. Conversely, relatively arid climatic conditions corresponded with the prevalence of ground cover fires in dry surface environments. Our results provide a potential relationship between terrestrial plant communities and wildfire types, which is important to further understanding of terrestrial environmental and floral changes driven by Large Igneous Provinces.

A Novel Dynamic and Self-Adaptive InCre Technique Based on PI Control and EO Optimization for Hybrid PV-TEG Conversion Systems

This work presents an improvement of the incremental conductance technique by adding PI controllers optimized by the recent equilibrium optimization algorithm. It addresses the main weaknesses of the incremental conductance algorithm consisting of the choice of step-size and the elimination of oscillations around the MPP. The proposed approach makes the MPPT control more dynamic and self-adaptive against the atmospheric conditions change. It has been applied for a photovoltaic generator, the thermoelectric generator and for the hybridization of the two generators. The simulation results, and the comparative study with the optimized step-size P&O and Incremental conductance methods, prove the efficiency and superiority of the proposed technique in terms of energy extraction and speed. The introduced work is developed in the MATLAB/Simulink environment.

The Online Identification of the Behaviour of Pollutants inside the Tunnel Tube

A tunnel tube is a relatively small space that allows for the accumulation of gaseous and liquid substances containing harmful substances. Given this fact, a ventilation system is the most critical component of a tunnel’s technological equipment, greatly influencing its reliability and safe operation. The dynamic behaviour of pollutants in the tunnel tube is characterized by a significant stochastic component and changing parameters over time due to pressure, airflow, and atmospheric condition changes. This work addresses the issue of modelling individual parts of the tunnel tube for optimal tunnel ventilation control. It is necessary to create a model of a controlled system that is used for predicting process variables to calculate optimal control action. By using recursive identification methods in conjunction with a predictive controller, the proposed concept can be applied to numerous similar applications.

Analysis and Design of Modified Incremental Conductance-Based MPPT Algorithm for Photovoltaic System

This study discusses the design of the Maximum Power Point Tracking (MPPT) technique for photovoltaic (PV) systems employing a modified incremental conductance (IncCond) algorithm to extract maximum power from a PV module. A PV module, a DC-DC converter, and a resistive load constitute the PV system. In the scientific literature, it is well-documented that typical MPPT algorithms have significant drawbacks, such as fluctuations around the MPP and poor tracking during a sudden change in atmospheric conditions. To solve the deficiencies of conventional methodology, a novel modified IncCond method is proposed in this study. The simulation results demonstrate that the updated IncCond algorithm presented allows for less oscillation around the maximum power point (MPP), a rapid dynamic response, and superior performance.

Assessment of the global applicability of three leading indices that characterize the spectral distribution of solar irradiance impacting various photovoltaic technologies

At each instant, the actual solar irradiance has a specific spectral distribution that most generally differs substantially from the standardized reference conditions under which photovoltaic (PV) cells are rated. To help reduce the dimensionality of the calculations needed to quantify the spectral effects on PV cells, various spectral indices have been introduced in the literature to simply characterize the incident spectral distribution with a single number. In previous studies, diverging conclusions have been reached about whether or not a single number can effectively represent a specific spectral distribution. This disagreement needs to be resolved before the usage of spectral indices can be considered best practice. To that effect, this study proposes a novel modeling approach. Here, three spectral indices (Average Photon Energy, APE; Blue Fraction, BF; and Useful Fraction, UF) are critically evaluated and compared using the SMARTS radiative transfer model and a large database of atmospheric inputs that are representative of the vast diversity of possible conditions worldwide, thus avoiding the rarity and limitations of costly experimental measurements. A total of 28,260 pairs of direct normal and global horizontal spectra are obtained, from which the three indices are calculated over six different wavebands that characterize different PV technologies. The key variables that have a significant impact on the indices are found to be: air mass, aerosol optical depth, Ångström exponent, and precipitable water. Using a variety of tests to evaluate the behavior of the three indices relatively to the reference direct and global spectra, results show that UF is prone to anomalous variations under a variety of circumstances. APE and BF behave similarly and reliably, but APE is found more responsive to even small changes in atmospheric conditions, and is thus preferable. A critical finding is that none of the indices can be considered bijective because compensations between red-shifting and blue-shifting atmospheric processes often result in different spectral distributions having the same index value.

Effect of Land Cover Change and Deforestation Around Maninjau Lake on Rainfall and Surface Temperature

Fish deaths in Maninjau Lake have been frequently reported. Apart from fishery activities, this can also be caused by changes in atmospheric conditions around Lake Maninjau due to changes in land cover around Lake Maninjau. Therefore, this study analyzes land cover changes and deforestation in 2001 and 2020 and their relationships with surface temperature and rainfall. Rainfall data and extreme temperatures were obtained from the Integrated Multi-satellite Retrievals for GPM (IMERG) and ECMWF v5 (ERA5), respectively. Land cover data based on the Landsat satellite is used to see land changes. The results showed a decrease in the amount of vegetated land every year and an increase in residential and building land and agricultural land, which correlates with an increase in the surface temperature in the Maninjau area. Furthermore, the results also show a coherent relationship between rainfall trends and areas of deforestation.

A multi-sensor array for detecting and analyzing nocturnal avian migration.

Avian migration has fascinated humans for centuries. Insights into the lives of migrant birds are often elusive; however, recent, standalone technological innovations have revolutionized our understanding of this complex biological phenomenon. A future challenge for following these highly mobile animals is the necessity of bringing multiple technologies together to capture a more complete understanding of their movements. Here, we designed a proof-of-concept multi-sensor array consisting of two weather surveillance radars (WSRs), one local and one regional, an autonomous moon-watching sensor capable of detecting birds flying in front of the moon, and an autonomous recording unit (ARU) capable of recording avian nocturnal flight calls. We deployed this array at a field site in central Oklahoma on select nights in March, April, and May of 2021 and integrated data from this array with wind data corresponding to this site to examine the influence of wind on the movements of spring migrants aloft across these spring nights. We found that regional avian migration intensity is statistically significantly negatively correlated with wind velocity, in line with previous research. Furthermore, we found evidence suggesting that when faced with strong, southerly winds, migrants take advantage of these conditions by adjusting their flight direction by drifting. Importantly, we found that most of the migration intensities detected by the sensors were intercorrelated, except when this correlation could not be ascertained because we lacked the sample size to do so. This study demonstrates the potential for multi-sensor arrays to reveal the detailed ways in which avian migrants move in response to changing atmospheric conditions while in flight.

Projections of Changes in Atmospheric Conditions Leading to Storm Surges along the Coast of Santos, Brazil

This study aims to assess the changes in the atmospheric conditions favorable to storm surges over the Santos Coast in Southeast Brazil. Storm surges can favor high sea level rises and coastal erosion, affecting people and strategic structures in coastal areas. The assessment of the atmospheric conditions was based on the downscaling of climate simulations of the Brazilian Earth System Model by the Eta regional climate model at higher spatial resolution. The detection scheme used by the model was able to reproduce the three observed atmospheric patterns favorable to storm surges found by recent studies: Pattern 1 is characterized by a cyclone on the synoptic scale over the ocean; Pattern 2 presents an intense wind fetch from the southeast; Pattern 3 is characterized by winds parallel to the coast. The simulations underestimated the number of cases in Patterns 1 and 2. However, it overestimated the number of days in Pattern 3. The model presented more intense winds in the three patterns. The storm surges characterized by Pattern 1 will become more intense. However, it will be equal to or less frequent. In Pattern 2, the number of events will decrease. Nevertheless, these episodes will be associated with more precipitation along the coastline. Pattern 3 will have a similar number of storm surges.

The meteorological contrast index in the context of climate change and public health

In the context of climate change, extreme weather events and sudden shifts in weather patterns are becoming increasingly frequent. The atmosphere is considered a source of meteorological and climatic risks for human beings and living organisms. Numerous studies have examined the correlation between meteorological variables and human morbidity and mortality. However, only a few authors have investigated the impact of environmental changes on human health and, to our knowledge, there are no meteorological indices proposing a methodology for assessing changes in atmospheric conditions. Under the hypothesis that meteorological disruptions have an impact on human health, this article proposes a method to calculate a new index, the Meteorological Contrast Index (MCI), based on weather changes. This index takes into account three variables: i) categorization based on the type of atmospheric process, ii) changes in these categories over a specific time period, and iii) the level of stress associated with these changes, considering the severity of the transition from one category to another. If the predictive value of this index is proven for a specific meteorological variable and disease, it could be valuable in defining biometeorological early warning systems for the prevention and management of healthcare resources.•The Meteorological Contrast Index is the first index that proposes a method to assess changes in atmospheric conditions.•Atmospheric changes are a significant source of biometeorological distress, which can be quantitatively defined using the Meteorological Contrast Index.•Certain diseases are sensitive to the weather, and their incidence may increase under specific sequences of weather types.

Atmospheric Periodic Oscillations

The North Atlantic Oscillation, Quasi Biennial Oscillation, their negative and positive phases are discussed in presented article An El Niño event is a prolonged period of abnormally high sea-surface temperatures (SST) in the tropical Pacific Ocean. It goes hand in hand with changes in atmospheric conditions and can have strong repercussions on global weather patterns. El Niño can also significantly affect the global average temperature The ECMWF seasonal forecast system has been operational for more than five years and will soon be replaced by an upgraded system SEAS5.

Thermo-hydraulic numerical modelling of in-soil conditions in reinforced soil walls

The role of temperature and relative humidity in long-term mechanical and chemical degradation of polyester fibres due to hydrolysis and creep is well documented. This study presents the results of a thermo-hydraulic 2D finite-element model used to estimate the magnitude and distribution of in situ temperature, relative humidity, and degree of saturation in the backfill of reinforced soil walls (RSWs) due to changes in atmospheric boundary conditions. Boundary conditions for in-air temperature, relative humidity and daily precipitation were taken from weather databases for continental, Mediterranean, desert, and tropical climates. Scenarios with different water tables, and permeable or impermeable zones around the reinforced soil zone were analyzed. Numerical outcomes show that mean in-soil temperature values can be related to the mean annual atmospheric value for each geographical location, with relevant fluctuations limited to the first 3 m of distance from the vertical and horizontal boundaries. In-soil relative humidity values depended on the climate dataset and the permeability of the zones adjacent to the reinforced soil. The results of this study and lessons learned are a valuable precursor for future studies of coupled thermo-hydro-mechanical modelling of polyester geosynthetic RSWs under in situ operational conditions.

Odour Perception Using a Sniffing Team at a Municipal Solid Waste Treatment Plant: A Case Study

The monitoring of odour intensity, generated by a landfill area, is a difficult activity since it is a multi-source problem with discontinuous odour emissions. A modified sniffing team method is described here and applied to determine the main odour sources in a landfill located in Pomerania, Poland. Four consecutive test sessions were performed during the following months: August, December, April, and June. It was found that the main odour sources are as follows: a closed-chamber composting facility for leach storage; the site wherein technological operations associated with compost turn-over during open-air aeration processes are performed; and the landfill site. The results of the sniffing team method present the indicative values of sensory testing. The application of the presented method was limited by disturbances due to changing atmospheric conditions. The calculated odour intensities and concentrations correspond with real sensitive perceptions of the tested environment.

Quantifying the influence of transmission path characteristics on urban railway noise.

Every ambient noise study employs the source-path-receiver structure to explore the overall behaviour of sound. Noise levels are affected by changes in distance, intervening barriers, and atmospheric conditions along the transmission path between the source and the receiver. The objective of this study is to quantify the influence of transmission path characteristics for a realistic time-varying moving line source. In this context, railway noise was considered to explore the variance of noise in an urban setting over a variety of measuring distances, including 25, 50, 100, and 200 m, with variables such as air temperature, humidity, and wind condition. The data corresponding to 106 trains was collected for analysis, and it was observed that the effect of the wind was more significant for larger distances between the source and the receiver. When the sound levels were measured in two opposite wind directions, a considerable noise level difference was observed. For every 1 m/s increase in wind speed, within a distance of 50 m, the average sound attenuation induced by the upwind phenomena was 0.2 dBA. The impact of air temperature changes on received sound level from a moving source was insignificant within the range of temperatures considered in the study. The effect of humidity is less at shorter distances but at larger distances, increasingly attenuates noise levels. Analysis of variance was performed on the selected variables to determine whether the means of each group were significantly different from each other and found that train speed had a more significant impact on railway noise compared to other parameters.

National-scale imperviousness mapping and detection of urban land changes

With high land take for urbanization a serious concern, the European Union and Member States have set quantitative targets for limiting land take in Europe. Consistent and reliable information on land take is crucial for measuring progress in achieving these targets. In the big data era, Earth observation data offers opportunities to regularly and consistently monitor land use change dynamics on a national scale. However, often-used methods such as image classification do not solve the mixed pixels problem common to urban areas with high heterogeneity. Moreover, the urban land use changes detected by these methods are often affected by random noise caused by non-relevant changes in atmospheric conditions, sun angle, vegetation phenology, etc. Therefore, the resulting products are often judged as being of insufficient quality for operational application.In this study, we present an approach to measuring annual land take using imperviousness mapping and change detection. Based on the Google Earth cloud computing platform, we first applied a data mining approach on annual Sentinel-2 image scenes to extract temporal and spectral information at Germany wide scale. Then spectral unmixing algorithm was used to estimate sub-pixel fractions of imperviousness. We end by using imperviousness changes to detect newly built surfaces. The change detection products were validated using high-resolution historical aerial photos and images. The results achieved an accuracy of 87% for detecting new built-up and 98% for detecting changes. The difference between the two numbers is caused by the areas with changes on the surface but not for urban use.We found that the integration of temporal information improves the performance of spectral unmixing than using spectral information alone, because it can better distinguish imperviousness from other land covers with similar spectral signatures but different temporal characteristics. We also found that, due to the sub-pixel analysis approach, spectral-unmixing-derived imperviousness is sensitive to urban features even with a size of just a few pixels. Moreover, the imperviousness layer represents urban land with a continuous index, meaning that random noise, represented as a low-range change of fractions, can be filtered out. Our study demonstrated that remote sensing products can be tailored to fulfil the needs of specific applications supporting sustainable land-use policy. This opens up more possibilities for the diverse and growing user communities of urban land change products.

Proving “new physics” by measuring cosmic ray fluxes

Radiation on Earth’s surface has been measured for more than a century using different particle detectors. These detectors have evolved from electroscopes to sophisticated, thousands-ton LHC detectors. Recently, with the availability of cheap particle detectors and simple data acquisition systems, publications have attempted to link changes in detector count rate to various astrophysical phenomena. However, measurement errors, meteorological conditions, and disturbances of electrical and geomagnetic fields can significantly impact cosmic ray fluxes. Some authors overlook these factors and publish “unique” correlations between their detector count rates and events such as solar and lunar eclipses, lightning strokes, Venus’s transit over the Sun, and others.When searching for the causes of cosmic ray enhancements, carefully distinguishing the atmospheric, instrumental, and astrophysical effects is essential. This paper aims to demonstrate how analyzing different species of cosmic ray flux can provide valuable insights into the underlying physical processes. We will explain how to verify that measurements are not due to abrupt changes in atmospheric conditions or equipment malfunctions but rather evidence of a novel physical phenomenon. Our goal is to provide a clear path from measurement to physical inference.