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Atmospheric phase is the main factor affecting the accuracy of ground-based synthetic aperture radar. The atmospheric phase screen (APS) may be very complicated, due to the drastic changes in atmospheric conditions, and the conventional correction methods based on regression models cannot fit and correct it effectively. Partition correction is a feasible path to improve atmospheric phase correction (APC) accuracy for complicated APS, but the overfitting problem cannot be ignored. In this article, we propose a clustering partition method, based on the normal vector of APS, which can partition the complicated APS more reasonably, and then perform APC based on the partition results. APC, and simulation experiments on measurement data, suggests that the proposed method achieves higher accuracy than the conventional model-based methods for complicated APS and avoids severe overfitting, realizing the balance between accuracy and credibility. This article verifies the feasibility and effectiveness of using APS distribution information to guide the partition and conduct APC.

Comprehensive consideration of multiple meteorological elements for the objective identification and division of seasonal changes is important in the field of climate monitoring and diagnostic analysis. The development of relevant identification and classification methods will help us better understand the new characteristics of seasonal transitions against the background of climate change. Based on reanalysis data from the National Centers for Environmental Prediction from 1950 to 2015, we used the multi-element atmospheric condition similarity method to seasonally divide the average climatological conditions at 850 hPa over the South China Sea, and analyzed the annual mean results of the onset of seasons and annual mean seasonal variations of meteorological elements in this region. The results show the following. First, the seasonal division results based on multi-element atmospheric condition similarity coincide with the seasonal variation times of each meteorological element with atmospheric conditions comprising five meteorological elements. When the four seasons change, the meteorological elements at 850 hPa in the South China Sea have obvious seasonal variation, and atmospheric circulation and surface upward long-wave radiation change conspicuously with seasonal transformation. This confirms the validity of the method as applied to seasonal divisions in the South China Sea. Second, when the climate system translates from winter to summer in the South China Sea, thermal elements change greatly and rapidly. Changes to thermal elements in spring provide climate conditions for the onset of summer and the onset of the summer monsoon in the South China Sea. Third, when the climate system shifts from summer to winter in the South China Sea, changes to wind elements are obvious and rapid. In autumn, changes in thermal conditions between the Eurasian continent and the Pacific Ocean lead to major changes in atmospheric circulation and wind. In addition, seasonal divisions are no longer just nodes of time. Rather, they can be used as important indicators for further studies of atmospheric circulation changes, short-term climate predictions, and seasonal changes of other climate systems.

A joint statistical model of wind speed and wind shear is critical for height-dependent wind resource characteristic analysis. However, given the different atmospheric conditions that may be involved, the statistical distribution of the two variables may show multimodal characteristics. In this work, a finite mixture bivariate statistical model was designed to describe the statistical properties, which is composed of several components, each with a Weibull distribution and a normal distribution for wind speed and wind shear, respectively, with a Gaussian copula to describe the dependency structure between the two variables. To confirm the developed model, reanalysis data from six positions in the coastal sea areas of China were used. Our results disclosed that the developed joint statistical model can accurately capture the different multimodal structures presented in all the bivariate samples under mixed atmospheric conditions, giving acceptable predictions of the joint probability distributions. Proper consideration of wind shear coefficient variation is crucial in estimating height-dependent wind resource characteristics. Importantly, unlike traditional methods that are limited to specific hub heights, the model developed here can estimate wind energy potential across different hub heights, enhancing the economic viability assessment of wind power projects.

Ensuring the safety of researchers by protecting them from exposure to toxic gases in laboratories is of paramount importance. This study investigated the effectiveness of using high-surface-area MgO to remove HCl under atmospheric conditions. Two types of MgO were synthesized through the thermal decomposition 1-1-1, Tennodai, Tsukuba, of Mg(OH) and MgC O ·2 H O. HCl diluted with air passed through both MgO samples, and the amounts of HCl removed and morphological changes in the samples were compared. No significant differences in surface area or crystallinity were observed with the decomposition temperatures. X-ray diffraction analysis showed that the sample prepared from MgC O ·2 H O reacted with HCl immediately upon introducing HCl gas. In contrast, the sample obtained from Mg(OH) exhibited only MgO peaks, even 30 min after the introduction of HCl gas. Microscopic analysis revealed that the samples derived from Mg(OH) showed no significant changes in shape after the reaction, whereas the MgO prepared from MgC O ·2 H O exhibited substantial changes in overall shape. No correlation was observed between the surface area and the amount of HCl removed. When MgO is prepared from MgC O ·2 H O, the reaction occurs in the bulk material, whereas when MgO is prepared from Mg(OH) , the reaction hardly progresses after HCl adsorbs onto the MgO surface. The order of magnitude of HCl removal was consistent with the base catalytic activity of the decomposition of diacetone alcohol to acetone. These results suggest that, compared with MgO obtained from Mg(OH) , MgO derived from MgC O ·2 H O generates more active sites, resulting in the reaction with HCl from surface to progress into bulk.

Large power transformers (LPTs) are critical yet vulnerable components of the power grid. More frequent and intense heat waves or high temperatures can degrade their operational lifetime and increase the risk of premature failure. Without adequate preparedness, a widespread situation could ultimately lead to prolonged grid disruption and incur excessive economic costs. Here, we investigate the potential impact of climate warming and corresponding shifts in summertime “hot days” on a selected LPT located in the Northeast United States. We apply an analogue method, which detects the occurrence of hot days based on the salient, associated large-scale atmospheric conditions, to assess the risk of future change in their occurrence. Compared with the more conventional approach that relies on climate model-simulated daily maximum temperature, the analogue method produces model medians of late twentieth century hot day frequency that are more consistent with observation and have stronger inter-model consensus. Under the climate warming scenarios, multi-model medians of both model daily maximum temperature and the analogue method indicate strong decadal increases in hot day frequency by the late twenty-first century, but the analogue method improves model consensus considerably. The decrease of transformer lifetime with temperature increase is further assessed. The improved inter-model consensus of the analogue method is viewed as a promising step toward providing actionable information for a more stable, reliable, and environmentally responsible national grid.

Atmospheric parameters play a vital role in the dispersion of air pollutants. Benzene is a confirmed human carcinogen. It is also a neurotoxin and an irritant compound. The objective of this study was to examine the CFD simulation by Fluent16 software to simulate and analyze the effect of atmospheric conditions on the dispersion of benzene in eight different scenarios in a petroleum refinery. According to the results of this study, the highest and lowest impacts of atmospheric parameters occurred on spring days and autumn nights, respectively. Wind direction did not have a significant effect on the benzene distribution due to the artificial ceiling of piping installations in the computational domain. However, the wind speed had a critical role in the benzene dispersion. The maximum concentration occurred at 36- to 37-m distance from the inlet boundary for all scenarios except winter nights. On winter nights, this distance increased to 38 m. Benzene concentrations were the highest at their sources of release. They decreased after the artificial ceiling of the pipelines was at 5.5- to 7-m height where the air displacement was not sufficient, and therefore, leading to a gradual reduction in concentration. The accumulation of benzene concentration in the small domain was noticeable compared to the benzene concentration distributed in the total computational domain, and the authors recommended control measures in this domain. This study demonstrated CFD simulation methodology could enable the investigators to predict the benzene concentration dispersion in the atmosphere of a petroleum refinery plant. These findings can be used by occupational health engineers for health risk assessment of refinery personnel involved with maintenance operations and engineering control systems.

Background In-flight conditions are hypothesized to influence the timing and success of long-distance migration. Wind assistance and thermal uplift are thought to reduce the energetic costs of flight, humidity, air pressure and temperature may affect the migrants’ water balance, and clouds may impede navigation. Recent advances in animal-borne long-distance tracking enable evaluating the importance of these factors in determining animals’ flight altitude. Methods Here we determine the effects of wind, humidity, temperature, cloud cover, and altitude (as proxy for climbing costs and air pressure) on flight altitude selection of two long-distance migratory shorebirds, far eastern curlew ( Numenius madagascariensis ) and whimbrel ( Numenius phaeopus ). To reveal the predominant drivers of flight altitude selection during migration we compared the atmospheric conditions at the altitude the birds were found flying with conditions elsewhere in the air column using conditional logistic mixed effect models. Results Our results demonstrate that despite occasional high-altitude migrations (up to 5550 m above ground level), our study species typically forego flying at high altitudes, limiting climbing costs and potentially alleviating water loss and facilitating navigation. While mainly preferring migrating at low altitude, notably in combination with low air temperature, the birds also preferred flying with wind support to likely reduce flight costs. They avoided clouds, perhaps to help navigation or to reduce the risks from adverse weather. Conclusions We conclude that the primary determinant of avian migrant’s flight altitude selection is a preference for low altitude, with wind support as an important secondary factor. Our approach and findings can assist in predicting climate change effects on migration and in mitigating bird strikes with air traffic, wind farms, power lines, and other human-made structures.

Extreme wildfire events in recent years are shaking our established knowledge of how fire regimes respond to climate variables and how societies need to react to fire impacts. Albeit fires are stochastic and extreme in nature, the speed, intensity, and extension of new extreme fires that have occurred during the last years are unprecedented. Here, we identify common features of these emerging novel extreme wildfire events characterized by very high fire intensity and rapid rates of spread, and we review the major mechanisms behind their occurrence. We then point to the major challenges that extreme wildfire events pose to science and societies worldwide, both today and in the future. Climate change and other factors are contributing to more flammable landscapes and the promotion of unstable atmospheric conditions that ultimately promote wildfire development. Anticipating these novel conditions is a key scientific challenge with paramount implications for present and future fire management, ecosystems, and human well-being.

The Tibetan Plateau (TP) plays a critical role in influencing regional and global climate, via both thermal and dynamical mechanisms. Meanwhile, as the largest high-elevation part of the cryosphere outside the polar regions, with vast areas of mountain glaciers, permafrost and seasonally frozen ground, the TP is characterized as an area sensitive to global climate change. However, meteorological stations are biased and sparsely distributed over the TP, owing to the harsh environmental conditions, high elevations, complex topography and heterogeneous surfaces. Moreover, due to the weak representation of the stations, atmospheric conditions and the local land–atmosphere coupled system over the TP as well as its effects on surrounding regions are poorly quantified. This paper presents a long-term (2005–2016) in situ observational dataset of hourly land–atmosphere interaction observations from an integrated high-elevation and cold-region observation network, composed of six field stations on the TP. These in situ observations contain both meteorological and micrometeorological measurements including gradient meteorology, surface radiation, eddy covariance (EC), soil temperature and soil water content profiles. Meteorological data were monitored by automatic weather stations (AWSs) or planetary boundary layer (PBL) observation systems. Multilayer soil temperature and moisture were recorded to capture vertical hydrothermal variations and the soil freeze–thaw process. In addition, an EC system consisting of an ultrasonic anemometer and an infrared gas analyzer was installed at each station to capture the high-frequency vertical exchanges of energy, momentum, water vapor and carbon dioxide within the atmospheric boundary layer. The release of these continuous and long-term datasets with hourly resolution represents a leap forward in scientific data sharing across the TP, and it has been partially used in the past to assist in understanding key land surface processes. This dataset is described here comprehensively for facilitating a broader multidisciplinary community by enabling the evaluation and development of existing or new remote sensing algorithms as well as geophysical models for climate research and forecasting. The whole datasets are freely available at the Science Data Bank (https://doi.org/10.11922/sciencedb.00103; Ma et al., 2020) and additionally at the National Tibetan Plateau Data Center (https://doi.org/10.11888/Meteoro.tpdc.270910, Ma 2020).

High levels of ultrafine particles (UFPs; diameter of less than 50 nm) are frequently produced from new particle formation under urban conditions, with profound implications on human health, weather, and climate. However, the fundamental mechanisms of new particle formation remain elusive, and few experimental studies have realistically replicated the relevant atmospheric conditions. Previous experimental studies simulated oxidation of one compound or a mixture of a few compounds, and extrapolation of the laboratory results to chemically complex air was uncertain. Here, we show striking formation of UFPs in urban air from combining ambient and chamber measurements. By capturing the ambient conditions (i.e., temperature, relative humidity, sunlight, and the types and abundances of chemical species), we elucidate the roles of existing particles, photochemistry, and synergy of multipollutants in new particle formation. Aerosol nucleation in urban air is limited by existing particles but negligibly by nitrogen oxides. Photooxidation of vehicular exhaust yields abundant precursors, and organics, rather than sulfuric acid or base species, dominate formation of UFPs under urban conditions. Recognition of this source of UFPs is essential to assessing their impacts and developing mitigation policies. Our results imply that reduction of primary particles or removal of existing particles without simultaneously limiting organics from automobile emissions is ineffective and can even exacerbate this problem.