Local Meteorological Conditions Research Articles

A systematic analysis of energy-saving potential of spectrum-selective radiative cooling materials in buildings

Passive cooling of buildings through radiative cooling (RC) is considered an effective method for reducing the energy consumption of air conditioning. In recent years, spectrum-selective RC materials (RCMs) have received extensive attention owing to their low solar absorptivity and high thermal emissivity in the atmospheric window. The energy-saving performance of RCMs is related to their own properties and local meteorological conditions, building type, building form, and energy use patterns. Although several studies have explored the performance of RCMs, most existing studies have considered a single building type and meteorological condition, and simple building forms that differ significantly from real buildings. Hence, the application analyses of RCMs to buildings are not sufficiently comprehensive. This study focused on the energy-saving potential of RCMs for building applications, and presented a systematic framework to analyze the energy-saving potential of RCMs under different conditions. DeST, which is embedded with a long-wave radiative heat transfer calculation module based on the equivalent sky temperature, was adopted as the simulation software. It can accurately calculate the radiative heat transfer in different wavelength bands, thus making the simulation results more accurate. Based on the calculation results of large-scale cases considering four major factors, namely climate, building type, construction year, and installation location of RCMs, the energy-saving performance of RCMs in different cases was analyzed, and the return on investment is performed, and more comprehensive suggestions were provided for applying RCMs in future buildings.

Optimization of a Hybrid Solar–Wind Microgrid for Sustainable Development: A Case Study in Antofagasta, Chile

This paper introduces a genetic algorithm designed to optimize the sizing of a hybrid solar–wind microgrid connected to the main electric grid in Chile, serving a simulated town of 2000 houses. The goal is to promote sustainable development by using renewable energy sources (RES) to supply a small village. The model, considering local meteorological conditions, aggregated load, and Chilean electrical regulations, establishes the optimal number of photovoltaic modules and wind turbine generators and allows for the monitoring of the microgrid’s operation, whose operating strategy is proposed herein. Adhering to Chilean regulations, a maximum exporting power of 9 MW is analyzed, with no restrictions on importing power, which is needed when the renewable resources are not enough to meet the demand. The optimization algorithm, aimed at sizing the RES supply, identified an optimal solution composed of 5 photovoltaic modules of 500 Wp each (2.5 kWp in total) and 123 wind turbines of 100 kW each (total of 12,300 kW), meeting around 85% of the demand through renewable generation. Due to time mismatches between generation and load patterns, the remaining energy was imported. The project’s net present value is approximately EUR 25 M, with a levelized cost of energy at 37 EUR/MWh. A comparison with HOMER grid simulations validates the efficacy of the developed model.

Mapping the all-day radiative sky-cooling potential of India for a sustainable environment

Radiative cooling is one of the passive methods of cooling by exchanging radiative heat between a surface and the cold universe through an atmospheric window (8 to 13 µm). The effectiveness of radiative cooling depends on the optical properties of the surfaces and the local meteorological conditions. It is essential to determine the local cooling potential to implement radiative cooling strategies. In this research, India’s annual and seasonal average radiative cooling potential was computed for 437 locations from meteorological data and the map has been developed to meet the energy-efficient cooling requirements of the Indian Cooling Action Plan (ICAP) 2019 and to fulfil the targets of Sustainable Development Goals (SDG). The results show that most locations in India have a cooling potential between 60 to 140 W/m2. The minimum area required for radiative cooling to meet India’s cooling demand is also evaluated. These results will help disseminate radiative cooling technologies in India to achieve ICAP and SDG targets.

Feasibility analysis and modeling of a solar hybrid system for residential electric vehicle charging

The process of transforming sunshine energy into electrical power is known as solar power generation. Photovoltaic (PV) technology has recently proved its cost-effectiveness and low environmental impact in generating power. The key goals of this study are to develop a solar PV system for charging electric vehicles (EVs) while utilizing the residential apartment's current domestic power supply. This study focuses on modeling grid-interactive solar PV systems for charging EVs inside a 40-unit residential apartment complex. The Solar Pro tool is used to do the techno-economic analysis of the modeled PV system. The research investigates the installation of a rooftop solar plant devoted to delivering electricity to EV charging devices on a real-time five-story residential building. The performance of the PV plant is tested under a variety of scenarios, including EV loading, shadow mapping, and local meteorological conditions. The PV plant's size is optimized at 150 kW, taking into consideration economic aspects as well as the actual proportions of the structure. In addition, the MiPower tool is used to do a load flow study of the modeled system, which includes both the grid and the PV system. This research evaluates line losses, line loading, and voltage levels at each bus at maximum loading circumstances.

Tracking Carbon Dioxide with Lagrangian Transport Simulations: Case Study of Canadian Forest Fires in May 2021

The large amounts of greenhouse gases, such as carbon dioxide, produced by severe forest fires not only seriously affect the ecosystems in the area where the fires occur but also cause a greenhouse effect that has a profound impact on the natural environment in other parts of the world. Numerical simulations of greenhouse gas transport processes are often affected by uncertainties in the location and timing of the emission sources and local meteorological conditions, and it is difficult to obtain accurate and credible predictions by combining remote sensing satellite data with given meteorological forecasts or reanalyses. To study the regional transport processes and impacts of greenhouse gases produced by sudden large-scale forest fires, this study applies the Lagrangian particle dispersion model Massive-Parallel Trajectory Calculations (MPTRAC) to conduct forward simulations of the CO2 transport process of greenhouse gases emitted from forest fires in the central region of Saskatchewan, Canada, during the period of 17 May to 25 May 2021. The simulation results are validated with the Orbiting Carbon Observatory-2 Goddard Earth Observing System (OCO-2 GEOS) Level 3 daily gridded CO2 product over the study area. In order to leverage the high computational costs of the numerical simulations of the model, we implement the forward simulations on the Tianhe-2 supercomputer platform and the JUWELS HPC system, which greatly improves the computational efficiency through parallel computation and makes near-real-time predictions of atmospheric transport processes feasible.

Sex-dependent resilience to extreme drought events: implications for climate change adaptation of a South American endangered tree species

BackgroundRecent changes in climatic trends are resulting in an increased frequency and intensity of extreme events, with unknown effect on ecosystem dynamics in the near future. Extreme drought episodes are recognized as disturbance factors capable of modifying forest dynamics and tree growth. Within this context, dioecious tree species may be impacted by climatic extremes, affecting male/female proportions and, consequently, reproductive processes and species persistence. Therefore, there is an urgent need for species-specific assessments of growth tolerance to extreme dry spells in dioecious tree species, to establish effective conservation strategies for these particular natural resources. Araucaria araucana (araucaria), an endangered dioecious Patagonian tree species, has recently undergone decay and mortality episodes in response to increasing dry climatic conditions. While sex-dependent tolerance to extreme drought episodes has been assessed in the species’ humid distribution range, there is still a lack of information on the gender-based resilience of trees growing in the drier environments of the species’ distribution.MethodsWe reconstructed, through dendrochronological methods, the sex-dependent response of 105 araucaria individuals (55 female and 50 male trees) to five regional extreme dry spells employing a set of different indices. Resistance, recovery period, and average growth reduction of standardized tree-ring growth were examined, analysing the effect of biotic (sex, pre-drought stem tree growth) and abiotic (local climatic conditions before, during, and after extreme climatic episodes) factors on tree resilience.ResultsSex influences only the species resistance to climatic disturbance, with male individuals showing lower tolerance to extreme drought events. Pre-drought radial growth rates and local meteorological conditions preceding, during, and following extreme dry spells strongly modulated araucaria radial growth resilience regardless of tree sex, influencing the species resistance, recovery period, and average growth reduction.ConclusionsWe provide novel and crucial information for the species conservation and management in the current climate change scenario, and contribute to the debate regarding the role of tree sex as a factor influencing woody species growth under particularly adverse climatic conditions. In the face of climate change, an increase in extreme drought events is expected in the easternmost araucaria xeric end distribution area, which will likely decrease the species resilience.

Identifying sensitive precursors of ozone formation using photochemical indicators and transition values comprised by continuous automatic monitoring data

The O3 photochemical indicator is a tool used to identify the sensitive precursors of O3 formation. Establishing a reliable photochemical indicator and its transition value composed of continuous automatic monitoring of chemical components can not only analyze sensitive precursors of O3 formation in real-time but also analyze the time and space distribution characteristics of sensitive precursors of O3 formation at a relatively lower cost. Among the photochemical indicators that are often mentioned at present, the ratio of ozone to nitrogen oxides (O3/NOx, O3/NOZ, and O3/NOY) should have the greatest potential for continuous monitoring. This study used the Community Multiscale Air Quality Modeling System to evaluate whether these three indicators are applicable to environmental conditions in Taiwan. Research results show that the indicator O3/NOZ was less suitable for Taiwan, while the indicator O3/NOY (transition value = 5.3) and the indicator O3/NOx (transition value = 29.3) could more accurately evaluate the ozone sensitivity of Taiwan's overall grid, and had no obvious bias in judging the ozone sensitivity of rural and urban grids. This study also found that it is not appropriate to directly use the photochemical indicator transition values from the literature. Usually, because the value is too high or too low, there is an obvious bias in judging the ozone sensitivity of rural and urban grids. This result also demonstrates the need to consider local meteorological and emission conditions when estimating transition values of photochemical indicators. In addition, when using the photochemical indicator O3/NOY, attention should be paid to the calculation of NOY species composition. The more complete the NOY composition, the better its ability to correctly judge ozone sensitivity.

Seasonal Performance Characterization of a Gen3 Particle-Based Concentrating Solar Plant With a Spatially Resolved Transient Thermal Storage Model

Particle-based Gen3 Concentrating Solar Power (CSP) can be paired with high-temperature power cycles (>700 °C) and can have built-in long duration (≥10 hours) thermal energy storage if the working particles are stored properly in thermal energy storage bins. Although high-temperature and long-duration thermal energy storage can mitigate daily intermittencies in solar irradiation, seasonal variability in local meteorological conditions can still have a significant impact on the overall performance of CSP systems. Aside from daily cloud coverage affecting incident solar radiation, wind speed and ambient temperature are also significant variables regarding system heat attenuation, component efficiency, and overall solar conversion efficiency. In this work, we present simulation results for a Gen3 CSP prototype system in operation over four weeks throughout the year in Albuquerque, NM. The meteorological conditions are taken directly from the TMY3 data at the Albuquerque International Sunport, where the hourly Direct Normal Irradiation (DNI), wind speed, and ambient temperature are of particular interest. An investigation of the sensitivity of individual components like storage and ducting to the local meteorological conditions is provided and extended to the overall performance of the CSP system. The results from this study show that the particle inlet temperature at the particle-to-sCO2 heat exchanger can change as fast as 30 °C/min under standard operation in a passive mode.

Extreme events of snow grain size increase in East Antarctica and their relationship with meteorological conditions

Abstract. This study explores the seasonal variations in snow grain size on the East Antarctic Plateau, where dry metamorphism occurs, by using microwave radiometer observations from 2000 to 2022. Local meteorological conditions and large-scale atmospheric phenomena have been considered in order to explain some peculiar changes in the snow grains. We find that the highest ice divide is the region with the largest grain size in the summer, mainly because the wind speed is low. Moreover, some extreme grain size values with respect to the average (over +3σ) were identified. In these cases, the ERA5 reanalysis revealed a high-pressure blocking close to the onsets of the summer increase in the grain size. It channels moisture intrusions from the mid-latitudes, through atmospheric rivers that cause major snowfall events over the plateau. If conditions of weak wind and low temperature occur during the following weeks, dry snow metamorphism is facilitated, leading to grain growth. This determines anomalous high maximums of the snow grain size at the end of summer. These phenomena confirm the importance of moisture intrusion events in East Antarctica and their impact on the physical properties of the ice sheet surface, with a co-occurrence of atmospheric rivers and seasonal changes in the grain size with a significance of over 95 %.

Analysing the impact of day-night road traffic variation on ultrafine particle number size distribution and concentration at an urban site in the megacity Delhi

Air quality is the biggest threat in many urban cities globally. This study measured and analyzed aerosol number size distribution between 10 to 1090 nm in the megacity Delhi during 2022. The study examined the particle number concentration in the ultrafine (<100 nm) and accumulation (100–1000 nm) ranges during day and night in terms of source's intensity, emission patterns and local meteorological conditions. Nucleation mode particles (Nnuc, 10–30 nm) contributed ∼50 % during daytime in summer periods due to increased fresh traffic emissions. In the daytime, particle concentration was dominated by smaller sizes, such as Nnuc and Aitken mode particles (Natk, 30–100 nm), while in the nighttime, the particles were found in Aitken and Accumulation (Nacc, 100–1000 nm) modes. Nnuc and Natk were reduced by ∼45 % and 4 % at night, while Nacc increased by ∼34 %. The mean normalized aerosol number size distribution confirmed these findings, showing clearly that Nnuc dominated during the daytime, especially during peak rush hours, and during the night particles concentration was increased up to the sizes of 300 nm particles. The aerosol number size distribution and the changes experienced in the percentage of the concentration of the different particle size ranges highlight the role of engine exhaust emissions in the atmospheric aerosol population in urban areas. The findings of the study help to identify the different source's intensity and behavior during day and nighttime, which can be used for policy formulation and decision-making to mitigate air quality and its associated health impacts.

Estimation of PM2.5 Concentration across China Based on Multi-Source Remote Sensing Data and Machine Learning Methods

Long-term exposure to high concentrations of fine particles can cause irreversible damage to people’s health. Therefore, it is of extreme significance to conduct large-scale continuous spatial fine particulate matter (PM2.5) concentration prediction for air pollution prevention and control in China. The distribution of PM2.5 ground monitoring stations in China is uneven with a larger number of stations in southeastern China, while the number of ground monitoring sites is also insufficient for air quality control. Remote sensing technology can obtain information quickly and macroscopically. Therefore, it is possible to predict PM2.5 concentration based on multi-source remote sensing data. Our study took China as the research area, using the Pearson correlation coefficient and GeoDetector to select auxiliary variables. In addition, a long short-term memory neural network and random forest regression model were established for PM2.5 concentration estimation. We finally selected the random forest regression model (R2 = 0.93, RMSE = 4.59 μg m−3) as our prediction model by the model evaluation index. The PM2.5 concentration distribution across China in 2021 was estimated, and then the influence factors of high-value regions were explored. It is clear that PM2.5 concentration is not only related to the local geographical and meteorological conditions, but also closely related to economic and social development.

An Interpretable Deep Learning Approach for Detecting Marine Heatwaves Patterns

Marine heatwaves (MHWs) refer to a phenomenon where the sea surface temperature is significantly higher than the historical average for that region over a period, which is typically a result of the combined effects of climate change and local meteorological conditions, thereby potentially leading to alterations in marine ecosystems and an increased incidence of extreme weather events. MHWs have significant impacts on the marine environment, ecosystems, and economic livelihoods. In recent years, global warming has intensified MHWs, and research on MHWs has rapidly developed into an important research frontier. With the development of deep learning models, they have demonstrated remarkable performance in predicting sea surface temperature, which is instrumental in identifying and anticipating marine heatwaves (MHWs). However, the complexity of deep learning models makes it difficult for users to understand how the models make predictions, posing a challenge for scientists and decision-makers who rely on interpretable results to manage the risks associated with MHWs. In this study, we propose an interpretable model for discovering MHWs. We first input variables that are relevant to the occurrence of MHWs into an LSTM model and use a posteriori explanation method called Expected Gradients to represent the degree to which different variables affect the prediction results. Additionally, we decompose the LSTM model to examine the information flow within the model. Our method can be used to understand which features the deep learning model focuses on and how these features affect the model’s predictions. From the experimental results, this study provides a new perspective for understanding the causes of MHWs and demonstrates the prospect of future artificial intelligence-assisted scientific discovery.

Improving model representation of rapid ozone deposition over soil in the central Tibetan Plateau

Ozone deposition velocity with a daily mean of 0.49 cm s−1 was observed in the Tibetan Plateau and rationalized by local meteorological and soil conditions. Our research suggested widespread ozone deposition velocity over soil of 0.1–0.7 cm s−1.

Influence of atmospheric circulation on the stable isotope of precipitation in the monsoon margin region

Precipitation stable isotope is a good medium for studying the water cycle and atmospheric water vapor transport process. To better understand the influence and control mechanism of atmospheric circulation on the stable isotope of precipitation in the East Asian monsoon margin region. We set up observation points in the Shiyang River basin in the East Asian monsoon marginal region to observe precipitation isotopes and analyze the mechanism and process of the control of atmospheric circulation on precipitation isotopes in the monsoon marginal region. The results show that: The results show that the stable isotope of precipitation in the monsoon margin region is greatly affected by the local meteorological conditions, and there are obvious temperature effects. The main water vapor affecting the monsoon margin region throughout the year are west wind water vapor, southeast monsoon water vapor, plateau wind water vapor and local circulation water vapor. In July and August, 25.33% and 65% of the southeast monsoon water vapor reached the monsoon margin region, respectively. The variation of stable isotope of precipitation in the monsoon margin region is mainly controlled by the East Asian Monsoon and the Northwest Pacific Monsoon Trough, but is less dependent on the Westerly Wind and Plateau Wind.

No Response of Surface-Level Atmospheric Electrical Parameters in Israel to Severe Space Weather Events

We report ground-based measurements of the atmospheric electric field (Ez = −potential gradient (PG)) and current density (Jz) that were conducted at two locations in Israel. One is at the Emilio Segre cosmic ray station located on Mt. Hermon (34.45° N, 2020 m AMSL) in northern Israel near the Syrian-Lebanon border, and the other is at the Wise astronomical observatory in the Negev desert highland plateau of southern Israel (31.18° N, 870 m AMSL). We searched for possible effects of strong, short-term solar events on the potential gradient and the vertical current density, as disruptions to the global electric circuit are often observed following strong solar events. The first case study (St. Patrick’s Day, 17 March 2015) was classified as the strongest event of 2015. The second case study (8 September 2017) was categorized as the strongest event of 2017 and one of the twenty strongest events on record to date. The results show that the electrical parameters measured at ground level at both stations were not affected during the two massive proton events and the ensuing geomagnetic storms. The magnetospheric shielding in lower latitudes is strong enough to shield against the flux of energetic particles from solar events, obscuring any impact that may be noticeable above the local daily variations induced by local meteorological conditions (aerosol concentrations, clouds, high humidity, and wind speed), which were investigated as well.

Urban WRF-Chem evaluation over a high-altitude tropical city

Morphology and grid resolution are important aspects that need to be considered in urban modeling applications, since together with buildings they induce a direct effect on wind and dispersion of pollutants over urban areas. In this study, we evaluate high-resolution simulations of a multi-layer urban canopy model (UCM) based on a local climate zone (LCZ) classification coupled to the Weather Research and Forecasting model with Chemistry (WRF-Chem), in the local meteorological conditions and air quality pollutants of a highly urbanized megacity. This modeling system, known as Building Effect Parameterization (BEP) considers the effects of buildings’ vertical and horizontal surfaces on the momentum that considerably impacts the lower part of the urban boundary layer (UBL). Simulations of the urbanized model (WRFu) were compared against a Noah land surface model (Noah LSM) with no urban physics (WRF) for the same period. It was observed that the LCZ classification and urban parameterization coupled to the model have a direct influence in meteorological parameters and pollutant concentrations. Urban simulations of temperature and wind speed showed higher sensitivity to initial and boundary conditions, increasing the correlation with observations and reducing the bias error. An important observation is that emissions drive air quality concentrations despite the improvements in local meteorology.

Data‐ and Model‐Based Urban O3 Responses to NOx Changes in China and the United States

AbstractUrban air pollution continues to pose a significant health threat, despite regulations to control emissions. Here we present a comparative analysis of urban ozone (O3) responses to nitrogen oxide (NOx) changes in China and the United States (US) over 2015–2020 by integrating various data‐ and model‐based methods. The data‐based deep learning (DL) model exhibited good performance in simulating urban air quality: the correlation coefficients (R) of O3 daily variabilities with respect to independent O3 observations are 0.88 and 0.79 over N. China, 0.87 and 0.90 over S. China, and 0.87 and 0.49 over E. United States by the DL and GEOS‐Chem chemical transport models, respectively. Furthermore, the data‐based methods suggest volatile organic compound (VOC)‐limited regimes in urban areas over northern inland China and transitional regimes over eastern US urban areas; in contrast, GEOS‐Chem model suggests strong NOx‐limited regimes. Sensitivity analysis indicates that the inconsistent O3 responses are partially caused by the inaccurate representation of O3 precursor concentrations at the locations of urban air quality stations in the simulations, while the data‐based methods are driven by the variabilities in local O3 precursor concentrations and meteorological conditions. The O3 responses to NOx changes reported here provide a better understanding of urban O3 pollution; for example, reductions in NOx emissions are suggested to have resulted in an increase in surface O3 by approximately 7 ppb in the Sichuan Basin in 2014–2020.

Short-term exposure to various ambient air pollutants and emergency department visits for cause-stable ischemic heart disease: a time-series study in Shanghai, China

Studying the impact of local meteorological conditions and air pollution on cardiovascular disease is crucial for reducing the burden of cardiovascular disease. However, there have been few studies on the acute effects of various air pollutants on stable ischemic heart disease (SIHD), and the effects of these factors are not well defined and require further investigation. We performed a time-series study aimed at exploring the association between short-term exposure to various air pollutants and emergency department (ED) visits for SIHD during 2013–2020 in Baoshan District Renhe Hospital of Shanghai, China. The associations between air pollution (NO2, PM2.5, PM10, SO2 O3-8 h and CO) and ED visits were analyzed using quasi-Poisson regression. Subgroup and sensitivity analyses were conducted. From 2013 to 2020, a total of 18,241 ED visits for SIHD were recorded. Elevated PM2.5, PM10, NO2, SO2 and CO were significantly associated with increased ED visits for SIHD at lag (0, 5), lag 0, lag (0-4, 01-03), lag (0-3, 5, 01-03) and lag (3-5). When the concentration of O3-8 h was lower than the threshold recommended by the WHO, exposure to O3-8 h was associated with a slightly decreased risk of SIHD. Moreover, the relationship between different types of air pollution and the frequency of ED visits exhibited variations based on gender, age, and seasonality. This study suggests that short-term exposure to PM2.5, PM10, NO2, SO2 and CO might induce SIHD, especially in old females. Air pollution control measures should be encouraged to prevent the occurrence and development of SIHD.

An infiltration model for inclined covers with consideration of capillary barrier effect

Covers with capillary barrier effect (CCBEs) are widely used as surface cover systems in landfills, mine tailings and water-sensitive soil slopes to prevent rainwater from infiltrating into the protected materials below. In the engineering design, the water-prevention performance of the CCBEs must be quantificationally evaluated according to the local meteorological conditions. In this study, according to the moisture distribution during infiltration, the fine-grained soil layer was divided into saturated, transition and initial zones. Capillary barrier effects were analyzed by considering water migration and redistribution at the interface between fine and coarse-grained layers. Then, the total time, including the time to reach the interface (tHf), the time for overcoming the capillary barrier effect (tc) and the time for passing through the coarse-grained layer (tg), was determined to quantificationally evaluate the water-prevention capacity of the inclined CCBEs under different rainfall conditions. For the case studies selected from literature, the calculated results by the proposed model agree well with the measured data. Finally, some parameters and the factors influencing the capillary barrier effect were further analyzed.

The right tree for the right street canyons: An approach of tree species selection for mitigating air pollution

Trees planted in street canyons affect the dispersion and deposition of air pollutants primarily through their aerodynamic and deposition effects to change the air quality. However, the potential of trees to improve air quality is uncertain as it depends on a combination of tree forms (species), street canyon geometry and local meteorological conditions. We developed a method to select tree forms with appropriate morphology in specific street canyons based on Computational Fluid Dynamics (CFD) simulation, aiming to promote the dispersion and concentration reduction of pollutants at pedestrian height. In this method, we combined the leaf area density (LAD), crown diameter (CD), tree height (H), and trunk height (TH) as representative metrics for various tree forms. The effects of street canyon aspect ratio, wind direction, and wind speed on the potential of trees to regulate pollutant concentrations were comprehensively considered. In a case study in Wuhan, China, 15 tree forms and corresponding 36 tree species adapted to different street canyons were identified using the validated ENVI-met model. Our research offers practical recommendations and effective tools for professionals and policymakers to select tree species that can enhance the air quality in street canyons.