Atmosphere

The effects of different modeling and solving approaches on the simulation of a steam ejector have been investigated with the computational fluid dynamics (CFD) technique. The four most frequently used and recommended turbulence models (standard k-ε, RNG k-ε, realizable k-ε and SST k-ω), two near-wall treatments (standard wall function and enhanced wall treatment), two solvers (pressure- and density-based solvers) and two spatial discretization schemes ( the second-order upwind scheme and the quadratic upstream interpolation for convective kinematics (QUICK) of the convection term have been tested and compared for a supersonic steam ejector under the same conditions as experimental data. In total, more than 185 cases of 17 different modeling and solving approaches have been carried out in this work. The simulation results from the pressure-based solver (PBS) are slightly closer to the experimental data than those from the density-based solver (DBS) and are thus utilized in the subsequent simulations. When a high-density mesh with y⁺ < 1 is used, the SST k-ω model can obtain the best predictions of the maximum entrainment ratio (ER) and an adequate prediction of the critical back pressure (CBP), while the realizable k-ε model with the enhanced wall treatment can obtain the best prediction of the CBP and an adequate prediction of the ER. When the standard wall function is used with the three k-ε models, the realizable k-ε model can obtain the best predictions of the maximum ER, and the three k-ε models can gain the same CBP value. For a steam ejector with recirculation inside the diffuser, the realizable k-ε model or the enhanced wall treatment is recommended for adoption in the modeling approach. When the spatial discretization scheme of the convection term changes from a second-order upwind scheme to a QUICK scheme, the effect can be ignored for the maximum ER calculation, while only the CBP value from the standard k-ε model with the standard wall function is reduced by 2.13%. The calculation deviation of the ER between the two schemes increases with the back pressure at the unchoked flow region, especially when the standard k-ε model is adopted. The realizable k-ε model with the two wall treatments and the SST k-ω model is recommended, while the standard k-ε is more sensitive to the near-wall treatment and the spatial discretization scheme and is not recommended for an ejector simulation. Full article

Precipitation measurement with high spatial and temporal resolution over highly elevated and complex terrain in the eastern part of Turkey is an essential task to manage the water structures in an optimum manner. The objective of this study is to evaluate the consistency and hydrologic utility of 13 Gridded Precipitation Datasets (GPDs) (CPCv1, MSWEPv2.8, ERA5, CHIRPSv2.0, CHIRPv2.0, IMERGHHFv06, IMERGHHEv06, IMERGHHLv06, TMPA-3B42v7, TMPA-3B42RTv7, PERSIANN-CDR, PERSIANN-CCS, and PERSIANN) over a mountainous test basin (Karasu) at a daily time step. The Kling-Gupta Efficiency (KGE), including its three components (correlation, bias, and variability ratio), and the Nash-Sutcliffe Efficiency (NSE) are used for GPD evaluation. Moreover, the Hanssen-Kuiper (HK) score is considered to evaluate the detectability strength of selected GPDs for different precipitation events. Precipitation frequencies are evaluated considering the Probability Density Function (PDF). Daily precipitation data from 23 meteorological stations are provided as a reference for the period of 2015–2019. The TUW model is used for hydrological simulations regarding observed discharge located at the outlet of the basin. The model is calibrated in two ways, with observed precipitation only and by each GPD individually. Overall, CPCv1 shows the highest performance (median KGE; 0.46) over time and space. MSWEPv2.8 and CHIRPSv2.0 deliver the best performance among multi-source merging datasets, followed by CHIRPv2.0, whereas IMERGHHFv06, PERSIANN-CDR, and TMPA-3B42v7 show poor performance. IMERGHHLv06 is able to present the best performance (median KGE; 0.17) compared to other satellite-based GPDs (PERSIANN-CCS, PERSIANN, IMERGHHEv06, and TMPA-3B42RTv7). ERA5 performs well both in spatial and temporal validation compared to satellite-based GPDs, though it shows low performance in producing a streamflow simulation. Overall, all gridded precipitation datasets show better performance in generating streamflow when the model is calibrated by each GPD separately. Full article

Sulfur dioxide (SO₂) is a serious air pollutant emitted from different sources in many developing regions worldwide, where the contribution of different potential influencing factors remains unclear. Using Shandong, a typical industrial province in China as an example, we studied the spatial distribution of SO₂ and used geographical detectors to explore its influencing factors. Based on the daily average concentration in Shandong Province from 2014 to 2019, we explored the influence of the diurnal temperature range, secondary production, precipitation, wind speed, soot emission, sunshine duration, and urbanization rate on the SO₂ concentration. The results showed that the diurnal temperature range had the largest impact on SO₂, with q values of 0.69, followed by secondary production (0.51), precipitation (0.46), and wind speed (0.42). There was no significant difference in the SO₂ distribution between pairs of sunshine durations, soot emissions, and urbanization rates. The meteorological factors of precipitation, wind speed, and diurnal temperature range were sensitive to seasonal changes. There were nonlinear enhancement relationships among those meteorological factors to the SO₂ pollution. There were obvious geographical differences in the human activity factors of soot emissions, secondary production, and urbanization rates. The amount of SO₂ emissions should be adjusted in different seasons considering the varied effect of meteorological factors. Full article

Many studies have shown that air pollutants have complex impacts on urban precipitation. Meteorological weather station and satellite Aerosol Optical Depth (AOD) product data from the last 20 years, combined with simulation results from the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), this paper focuses on the effects of air pollutants on summer precipitation in different regions of Beijing. These results showed that air pollution intensity during the summer affected the precipitation contribution rate (PCR) of plains and mountainous regions in the Beijing area, especially in the plains. Over the past 20 years, plains PCR increased by ~10% when the AOD augmented by 0.15, whereas it decreased with lower pollution levels. In contrast, PCR in mountainous areas decreased with higher pollution levels and increased with lower pollution levels. Our analysis from model results indicated that aerosol increases reduce the effective particle size of cloud droplets and raindrops. Smaller cloud raindrops more readily transport to high air layers and participate in the generation of ice-phase substances in the clouds, increasing the total amount of cloud water in the air in a certain time, which ultimately enhanced precipitation intensity on the plains. The removal of pollutants caused by increased precipitation in the plains decreased rainfall levels in mountainous areas. Full article

Globally, the climate is changing, and this has implications for livestock. Climate affects livestock growth rates, milk and egg production, reproductive performance, morbidity, and mortality, along with feed supply. Simultaneously, livestock is a climate change driver, generating 14.5% of total anthropogenic Greenhouse Gas (GHG) emissions. Herein, we review the literature addressing climate change and livestock, covering impacts, emissions, adaptation possibilities, and mitigation strategies. While the existing literature principally focuses on ruminants, we extended the scope to include non-ruminants. We found that livestock are affected by climate change and do enhance climate change through emissions but that there are adaptation and mitigation actions that can limit the effects of climate change. We also suggest some research directions and especially find the need for work in developing country settings. In the context of climate change, adaptation measures are pivotal to sustaining the growing demand for livestock products, but often their relevance depends on local conditions. Furthermore, mitigation is key to limiting the future extent of climate change and there are a number of possible strategies. Full article

The atmosphere over Lake Baikal covers a vast area (31,500 square meters) and has more significant differences in the composition and variability of gaseous and aerosol components in atmospheric air than in coastal continental areas and is still a poorly studied object. In recent years, the anthropogenic impact on the ecosystem of Lake Baikal has been increasing due to the development of industry in the region, the expansion of tourist infrastructure and recreational areas of the coastal zone of the lake. In addition, one of the significant sources of atmospheric pollution in the Baikal region is the emissions of smoke aerosol and trace gases from forest fires, the number of which is increasing in the region. This article presents the results of experimental studies of the dispersed composition of aerosols and gas impurities, such as ozone, sulfur dioxide, and nitrogen oxides during route ship measurements in the water area of Lake Baikal in the summer of 2020. Full article

This study examined whether varying moisture availability and roughness length for the land surface under a simulated Tropical Cyclone (TC) could affect its production of precipitation. The TC moved over the heterogeneous land surface of the southeastern U.S. in the control simulation, while the other simulations featured homogeneous land surfaces that were wet rough, wet smooth, dry rough, and dry smooth. Results suggest that the near-surface atmosphere was modified by the changes to the land surface, where the wet cases have higher latent and lower sensible heat flux values, and rough cases exhibit higher values of friction velocity. The analysis of areal-averaged rain rates and the area receiving low and high rain rates shows that simulations having a moist land surface produce higher rain rates and larger areas of low rain rates in the TC’s inner core. The dry and rough land surfaces produced a higher coverage of high rain rates in the outer regions. Key differences among the simulations happened as the TC core moved over land, while the outer rainbands produced more rain when moving over the coastline. These findings support the assertion that the modifications of the land surface can influence precipitation production within a landfalling TC. Full article

Thirty-year periods are treated in climatology as spans with relatively representative and stable climatic patterns, which can be used for calculating climate normals. Annual and seasonal series of circulation types were used to compare two 30-year sub-periods, 1961–1990 and 1991–2020, the second one being strongly influenced by recent global warming. This analysis was conducted according to the objective classification of circulation types and the climatic characteristics of sunshine duration, temperature, humidity, precipitation, and wind speed as calculated for the territory of the Czech Republic during the 1961–2020 period. For both sub-periods, their statistical characteristics were calculated, and the statistical significance of differences between them was evaluated. There was a statistically significant increase in the annual frequencies of anticyclonic circulation types and a significant decrease in cyclonic circulation types during 1991–2020 compared with 1961–1990. Generally, in both 30-year periods, significant differences in means, variability, characteristics of distribution, density functions, and linear trends appear for all climatic variables analysed except precipitation. This indicates that the recent 30-year “normal” period of 1991–2020, known to be influenced more by recent climate change, is by its climatic characteristics unrepresentative of the stable climatic patterns of previous 30-year periods. Full article

A growing number of physical objects with embedded sensors with typically high volume and frequently updated data sets has accentuated the need to develop methodologies to extract useful information from big data for supporting decision making. This study applies a suite of data analytics and core principles of data science to characterize near real-time meteorological data with a focus on extreme weather events. To highlight the applicability of this work and make it more accessible from a risk management perspective, a foundation for a software platform with an intuitive Graphical User Interface (GUI) was developed to access and analyze data from a decommissioned nuclear production complex operated by the U.S. Department of Energy (DOE, Richland, USA). Exploratory data analysis (EDA), involving classical non-parametric statistics, and machine learning (ML) techniques, were used to develop statistical summaries and learn characteristic features of key weather patterns and signatures. The new approach and GUI provide key insights into using big data and ML to assist site operation related to safety management strategies for extreme weather events. Specifically, this work offers a practical guide to analyzing long-term meteorological data and highlights the integration of ML and classical statistics to applied risk and decision science. Full article

Natural resources degradation poses multiple challenges particularly to environmental and economic processes. It is usually difficult to identify the degree of degradation and the critical vulnerability values in the affected systems. Thus, among other tools, indices (composite indicators) may also describe these complex systems or phenomena. In this approach, the Water and Land Resources Degradation Index was applied to the fifth largest Mediterranean island, Crete, for the 1999–2014 period. The Water and Land Resources Degradation Index uses 11 water and soil resources related indicators: Aridity Index, Water Demand, Drought Impacts, Drought Resistance Water Resources Infrastructure, Land Use Intensity, Soil Parent Material, Plant Cover, Rainfall, Slope, and Soil Texture. The aim is to identify the sensitive areas to degradation due to anthropogenic interventions and natural processes, as well as their vulnerability status. The results for Crete Island indicate that prolonged water resources shortages due to low average precipitation values or high water demand (especially in the agricultural sector), may significantly affect Water and Land degradation processes. Hence, Water and Land Resources Degradation Index could serve as an extra tool to assist policymakers to improve their decisions to combat Natural Resources degradation. Full article

Haze is a majorly disastrous type of weather in China, especially central and eastern of China. The development of haze is mainly caused by highly concentrated fine particles (PM_2.5) on a regional scale. Here, we present the results from an autumn and winter study conducted from 2013 to 2020 in seven highly polluted areas (27 representative stations) in central and eastern China to analyze the growth mechanism of PM_2.5. At the same time, taking Beijing Station as an example, the characteristics of aerosol composition and particle size in the growth phase are analyzed. Taking into account the regional and inter-annual differences of fine particles (PM_2.5) distribution, the local average PM_2.5 growth value of the year is used as the boundary value for dividing slow, rapid, and explosive growth (only focuses on the hourly growth rate greater than 0). The average value of PM_2.5 in the autumn and winter of each regional representative station shows a decreasing trend as a whole, especially after 2017, whereby the decreasing trend was significant. The distribution value of +ΔPM_2.5 (PM_2.5 hourly growth rate) in the north of the Huai River is lower than that in the south of the Huai River, and both of the +ΔPM_2.5 after 2017 showed a significant decreasing trend. The average PM_2.5 threshold before the explosive growth is 70.8 µg m⁻³, and the threshold that is extremely prone to explosive growth is 156 µg m⁻³ to 277 µg m⁻³ in north of the Huai River. For the area south of the Huai River, the threshold for PM_2.5 explosive growth is relatively low, as a more stringent threshold also puts forward stricter requirements on atmospheric environmental governance. For example, in Beijing, the peak diameters gradually shift to larger sizes when the growth rate increases. The number concentration increasing mainly distributed in Aitken mode (AIM) and Accumulation mode (ACM) during explosive growth. Among the various components of submicron particulate matter (PM₁), organic aerosol (OA), especially primary OA (POA), have become one of the most critical components for the PM_2.5 explosive growth in Beijing. During the growth period, the contribution of secondary particulate matter (SPM) to the accumulated pollutants is significantly higher than that of primary particulate matter (PPM). However, the proportion of SPM gradually decreases when the growth rate increases. The contribution of the PPM can reach 48% in explosive growth. Compared to slow and rapid growth, explosive growth mainly occurs in the stable atmosphere of higher humidity, lower pressure, lower temperature, small winds, and low mixed layers. Full article

Wind disasters are responsible for significant physical destruction, injury, loss of life, and economic damage. This study examined the extreme wind triggering mechanism over a typical mountain area with complex terrain, i.e., Dali city in Yunnan Province on the Yunnan-Guizhou Plateau in China. Using the observation data, we first optimized the Weather Research and Forecasting (WRF) model configuration and parametrization schemes for better simulating the wind in this area using a 1-month simulation. Then, the triggering mechanism of extreme wind was investigated by performing a series of sensitive experiments based on a typical extreme wind case. The results indicate that terrain uplift is critical for triggering the local 8–9-scale (the wind velocity between 17.2 and 24.4 m/s) extreme winds over high topography regions. When a large-scale atmospheric circulation is passing, accompanied with regional terrain lifting, the instantaneous wind velocity can reach 9- to 10-scale (the mean wind velocity between 20.8 and 28.4 m/s), causing broken power lines. These results suggest that it is essential to avoid sites where these factors can affect the operation of power transmission lines, or to establish warning systems in the existing systems. Full article

A comparison was made of two eddy dissipation rate (EDR) estimates based on flight data recorded by commercial flights. The EDR estimates from real-time data using the National Center for Atmospheric Research (NCAR) Algorithm were compared with the EDR estimates derived using the Netherlands Aerospace Centre (NLR) Algorithm using quick assess recorder (QAR) data. The estimates were found to be in good agreement in general, although subtle differences were found. The agreement between the two algorithms was better when the flight was above 10,000 ft. The EDR estimates from the two algorithms were also compared with the vertical acceleration experienced by the aircraft. Both EDR estimates showed good correlation with the vertical acceleration and would effectively capture the turbulence subjectively experienced by pilots. Full article

This study aimed at analysis of the general-index change for the mean annual and seasonal precipitation in six stations in Latakia Governorate (Syria). The data of precipitation were collected for 40 consecutive years (1970–2010) in order to figure out the extent of the changes and variability in precipitation rates and the impact of this change on changes in the potential density that might cause extremely high or low precipitation rates according to Gumbel distribution of the extreme precipitation rates. Results revealed a decrease of the annual precipitation rates in all stations, the reduction in precipitation ranged from 46 to 210 mm during the whole period of the study. Spring, however, recorded the highest and statistically significant reduction, which reached 46–210 mm, while winter precipitation increased by 21–82 mm. Spring also has witnessed a decrease of about 3–9% of the total annual precipitation as compared to winter precipitation which increased by 5–8% of the total. The potential density of extremely high winter precipitation rates increased in all stations as indicated from Gumbel distribution in winter, and a greater increase took place in the probabilities of occurrence of the extremely low spring precipitation rates. This shows significant probability of occurrence of drought during spring season. By contrast, probabilities of winter precipitation rates increased more, thus winter is relatively more humid than before and spring is relatively drier than before. Full article

Based on the China Meteorological Administration (CMA) best-track data, the ERA5 reanalysis, and the Global Precipitation Measurement (GPM) precipitation data, this paper analyzes the reasons for the heavy rainfall event of Super Typhoon Rammasun in 2014, and the results are as follows: (1) Rammasun was blocked by the western Pacific subtropical high (WPSH), the continental high, and the mid-latitude westerly trough. Such a stable circulation pattern maintained the vortex circulation of Rammasun. (2) During the period of landfall, the southwest summer monsoon surge was reinforced due to the dramatic increase of the zonal wind and the cross-equatorial flow near 108° E. The results of the dynamic monsoon surge index (DMSI) and boundary water vapor budget (BWVB) show that the monsoon surge kept providing abundant water vapor for Rammasun, which led to the enhanced rainfall. (3) The East Asian monsoon manifested an obvious low-frequency oscillation with a main period of 20–40 days in the summer of 2014, which propagated northward significantly. When the low-frequency oscillation reached the extremely active phase, the monsoon surge hit the maximum and influenced the circulation of Rammasun. Meanwhile, the convergence and water vapor flux associated with the low-frequency oscillation significantly contributed to the heavy rainfall. Full article

Niaodao, a lakeside wetland, was used as the focus of this study to investigate the effect of rainfall changes on the greenhouse gas fluxes of wetland ecosystems. Wetland plots with different moisture characteristics (+25%, −25%, +75%, and −75% rainfall treatments and the control treatment (CK)) were constructed to observe in situ field greenhouse gas emissions at 11:00 and 15:00 (when the daily mean values were similar) in the growing season from May to August 2020 by static chamber–gas chromatography and to investigate the responses of wetland greenhouse gases to different rainfall treatments. The results showed the following: (1) The carbon dioxide (CO₂) flux ranged from −49.409 to 374.548 mg·m⁻²·h⁻¹. The mean CO₂ emission flux was greater at 11:00 than at 15:00, and the +25% and +75% treatments exhibited substantially higher CO₂ emissions. In addition, the CO₂ flux showed a small peak at the beginning of the growing season when the temperature first started to rise. All treatments showed the effect of the CO₂ source, and their effects were significantly different. (2) The methane (CH₄) flux ranged from −213.839 to 330.976 µg·m⁻²·h⁻¹ and exhibited an absorption state at 11:00 and an emission state at 15:00. The CH₄ emission flux in August (the peak growing season) differed greatly between treatments and was significantly negatively correlated with the rainfall amount (p < 0.05). (3) The nitrous oxide (N₂O) flux ranged from −10.457 to 16.878 µg·m⁻²·h⁻¹ and exhibited a weak source effect throughout the growing season, but it was not significantly correlated with soil moisture; it was, however, negatively correlated with soil temperature. (4) The different treatments resulted in significant differences in soil physical and chemical properties (electrical conductivity, pH, total soil carbon, and total soil nitrogen). The rainfall enhancement treatments significantly improved soil physical and chemical properties. Full article

Atmospheric emission of heavy metals from different anthropogenic sources is a great concern to human beings due to their toxicities. In order to disclose the emission levels and the distribution patterns of zinc (Zn) in the modern cement industry with respect to its low boiling point (~900 °C) comparing to the high-temperature (1450 °C) clinker production process, solid samples representing the input and output flow of Zn during the entire production process in two preheater–precalciner cement plants (CPs) were collected and analyzed. For the first time, it was found that the behaviour of Zn inside different precalciner CPs was similar despite a huge difference in the Zn inputs to the CPs; namely, almost all the Zn input was output in clinker, which was then mixed with different additives and retarder to make cement products. The high-temperature clinkerisation process would incorporate Zn into the aluminosilicate of clinker. As a result, there was no enrichment of Zn during clinker production and the atmospheric emission factor was relatively low at 0.002%, or 1.28–9.39 mg Zn·t⁻¹ clinker. Our result for the atmospheric Zn emissions from CPs was much lower than most previous reports, implying the CPs were not a crucial Zn emission source. However, the higher load of Zn in some raw/alternative materials—like nonferrous smelting slag with a Zn content of ~2%—could greatly increase the content of Zn in clinker and cement products. Therefore, further investigation on the environmental stability of Zn in such Zn-laden cement and concrete should be carried out. Full article

The combination of techniques that incorporate observational data may improve numerical weather prediction forecasts; thus, in this study, the methodology and potential value of one such combination were investigated. A series of experiments on a single case day was used to explore a 3DVAR-based technique (the variational version of the Local Analysis and Prediction System; vLAPS) in combination with Newtonian relaxation (observation and analysis nudging) for simulating moist convection in the Advanced Research version of the Weather Research and Forecasting model. Experiments were carried out with various combinations of vLAPS and nudging for a series of forecast start times. A limited subjective analysis of reflectivity suggested all experiments generally performed similarly in reproducing the overall convective structures. Objective verification indicated that applying vLAPS analyses without nudging performs best during the 0–2 h forecast in terms of placement of moist convection but worst in the 3–5 h forecast and quickly develops the most substantial overforecast bias. The analyses used for analysis nudging were at much finer temporal and spatial scales than usually used in pre-forecast analysis nudging, and the results suggest that further research is needed on how to best apply analysis nudging of analyses at these scales. Full article

The HAFS model is an effort under the NGGPS and UFS initiatives to create the next generation of hurricane prediction and analysis system based on FV3-GFS. It has been validated extensively using traditional verification indicators such as tracker error and biases, intensity error and biases, and the radii of gale, damaging and hurricane strength winds. While satellite images have been used to verify hurricane model forecasts, they have not been used on HAFS. The community radiative transfer model CRTM is used to generate model synthetic satellite images from HAFS model forecast state variables. The 24 forecast snapshots in the mature stage of hurricane Dorian in 2019 are used to generate a composite model synthetic GOES-R infrared brightness image. The composite synthetic image is compared to the corresponding composite image generated from the observed GOES-R data, to evaluate the model forecast TC vortex intensity, size, and asymmetric structure. Results show that the HAFS forecast TC Dorian agrees reasonably well with the observation, but the forecast intensity is weaker, its overall vortex size smaller, and the radii of its eye and maximum winds larger than the observed. The evaluation results can be used to further improve the model. While these results are consistent with those obtained by traditional verification methods, evaluations based on composite satellite images provide an additional benefit with richer information because they have near-real-times spatially and temporally continuous high-resolution data with global coverage. Composite satellite infrared images could be used routinely to supplement traditional verification methods in the HAFS and other hurricane model evaluations. Note since this study only evaluated one hurricane, the above conclusions are only applicable to the model behavior of the mature stage of hurricane Dorian in 2019, and caution is needed to extend these conclusions to expect model biases in predicting other TCs. Nevertheless, the consistency between the evaluation using composite satellite images and the traditional metrics, of hurricane Dorian, shows that this method has the potential to be applied to other storms in future studies. Full article