Hasil untuk "Meteorology. Climatology"

Alexandru-Ionut BĂNESCU, Elena GRIGORE, Florina TATU et al.

Atmospheric circulation exerts a dominant influence on the occurrence, intensity, and spatial distribution of climate-related hazards in Romania. This study investigates the relationship between major European synoptic-scale circulation patterns, such as the Icelandic Low, Azores High, Atlantic Ridge, Scandinavian High, Greenland High, Eastern European High, and Saharan High; and the frequency and characteristics of high-impact weather events affecting Romania. Using a combination of reanalysis datasets, synoptic composites, and event catalogues, we identify the circulation configurations most commonly associated with heatwaves, severe convection (hailstorms, downbursts, tornadoes), heavy precipitation episodes, cold spells, and windstorms. Results highlight several robust linkages: for example, a persistent Icelandic Low centred near the British Isles frequently promotes south-westerly advection and heatwave development over Romania, while blocking patterns such as the Scandinavian High or Greenland High often favours cold-air intrusions and enhanced cyclogenesis over the Mediterranean and Black Sea region. By integrating circulation-type analysis with reported hazard events, this study provides an updated framework for understanding Romania’s exposure to climate risks, while raising the awareness that the atmospheric processes are developing at larger scales than national boundaries, making severe weather events strongly linked to the broader circulation.

Yiwei Xu, Jie Wang, Libin Zhu et al.

A shipborne campaign was conducted in China’s southeastern coastal waters (21° N–32° N) from 14 to 31 January 2024 to investigate atmospheric CO₂ and CH₄ concentrations and their offshore gradients. Advanced instrumentation enabled high-precision measurements, validated by canister sampling with strong correlations to reference data. The voyage employed a dual-route design: a northbound baseline along the mainland coast and a southbound route with offshore excursions up to 80 nm, facilitating the first quantification of GHG gradients in the continental shelf region. Baseline concentrations from the northbound route revealed regional variability: CO₂ levels ranged from 422.75 ± 9.96 ppm (Fujian) to 445.62 ± 1.51 ppm (Zhejiang), while CH₄ levels spanned 2005.78 ± 5.89 ppb (Fujian) to 2064.59 ± 13.93 ppb (Zhejiang). Southbound analysis at 10 nm intervals showed CO₂ gradients transitioning from positive to negative at ~30 nm and back to positive at ~70 nm, whereas CH₄ exhibited complex behavior, including a positive–negative–positive transition at 30–40 nm and consistent increase beyond 50 nm. Under winter monsoon conditions, transport flux analysis identified eastward CO₂ fluxes of 3819.55–6587.77 g·m⁻²·s⁻¹ and CH₄ fluxes of 6.42–11.42 g·m⁻²·s⁻¹. Southward transport diminished along the coast, with CO₂ fluxes declining from 5741.07 to 879.76 g·m⁻²·s⁻¹ and CH₄ fluxes from 9.84 to 1.49 g·m⁻²·s⁻¹ between Zhoushan and Hong Kong. The Taiwan Strait demonstrated a funneling effect, enhancing southward transport. These findings address data gaps in ocean regions and provide insights for future GHG monitoring.

Xinyi Chen, Marta Blangiardo, Connor Gascoigne et al.

Exposure to high ambient temperatures is a significant driver of preventable mortality, with non-linear health effects and elevated risks in specific regions. To capture this complexity and account for spatial dependencies across small areas, we propose a Bayesian framework that integrates non-linear functions with the Besag, York, and Mollie (BYM2) model. Applying this framework to all-cause mortality data in Switzerland, we quantified spatial inequalities in heat-related mortality. We retrieved daily all-cause mortality at small areas (2,145 municipalities) for people older than 65 years from the Swiss Federal Office of Public Health and daily mean temperature at 1km$\times$1km grid from the Swiss Federal Office of Meteorology. By fully propagating uncertainties, we derived key epidemiological metrics, including heat-related excess mortality and minimum mortality temperature (MMT). Heat-related excess mortality rates were higher in northern Switzerland, while lower MMTs were observed in mountainous regions. Further, we explored the role of the proportion of individuals older than 85 years, green space, average temperature, deprivation, urbanicity, air pollution, and language regions in explaining these discrepancies. We found that spatial disparities in heat-related excess mortality were primarily driven by population age distribution, green space, and vulnerabilities associated with elevated temperature exposure.

Vincent Canwat

ABSTRACT Although trees are viewed as providers of several beneficial services, their roles in windstorm mitigation are not always positive. This study assessed the paradoxical roles of trees in windstorm mitigation by analyzing how the characteristics and management practices of trees affect their damage to physical infrastructure. Using primary data collected from Gulu City in Uganda, descriptive statistics and an ordered probit model were generated. The analysis revealed three key findings. First, decreasing the distance between trees and buildings, parked vehicles, roads, and power lines by 1 m significantly increases the likelihood of damage by trees. Second, buildings with greater tree cover are less likely to experience damage from windstorms compared to those with minimal or no tree cover. Third, buildings, parked vehicles, roads, and power lines with low sensitivity are less likely to be damaged by windstorms and windstorm‐induced tree falls than those with high sensitivity. The exposure and sensitivity of physical infrastructure to windstorms and windstorm‐induced tree fall reveal inadequate tree management and ineffective regulation enforcement, which are driven by low adaptive capacity, notably limited knowledge of tree farmers on proper tree management, weak capacity of extension staff, and financial constraints. Poor tree management results from a lack of awareness of improved tree management practices, which stems from limited access to forestry extension support. Additionally, the limited capacity of extension personnel hampers the delivery of forestry extension services. Financial challenges also hinder both the provision of forestry extension services and the enforcement of stricter building regulations and their ongoing maintenance. The findings have implications for improving urban tree monitoring and management, financing and provision of forestry extension services, and enforcement of building and land use regulations.

Luo Xu, Ning Lin, Dazhi Xi et al.

Blackouts in recent decades show an increasing prevalence of power outages due to extreme weather events such as hurricanes. Precisely assessing the spatiotemporal outages in distribution networks, the most vulnerable part of power systems, is critical to enhance power system resilience. The Sequential Monte Carlo (SMC) simulation method is widely used for spatiotemporal risk analysis of power systems during extreme weather hazards. However, it is found here that the SMC method can lead to large errors by directly applying the fragility function or failure probability of system components in time-sequential analysis, particularly overestimating damages under evolving hazards with high-frequency sampling. To address this issue, a novel hazard resistance-based spatiotemporal risk analysis (HRSRA) method is proposed. This method converts the time-varying failure probability of a component into a hazard resistance as a time-invariant value during the simulation of evolving hazards. The proposed HRSRA provides an adaptive framework for incorporating high-spatiotemporal-resolution meteorology models into power outage simulations. By leveraging the geographic information system data of the power system and a physics-based hurricane wind field model, the superiority of the proposed method is validated using real-world time-series power outage data from Puerto Rico during Hurricane Fiona 2022.

Benli Liu, Wanyue Peng, Yunhua Zhang

Arid areas are sensitive and vulnerable to climate change and may face more climate risks in the future under the background of global warming. The adaptability of society to future climate change impacts relies heavily on the awareness and perception of local populations. This study focuses on the middle and lower reaches of the Heihe River, which is the second-largest inland river in China, and examine the temperature and precipitation changes from 1981 to 2020, employing the Sen + Mann-Kendall trend analysis method. The local farmers and herdsmen were interviewed, and their variations in awareness and perception regarding climate change were assessed. The results show that local residents are highly sensitive to climate warming but not to precipitation increases, indicating that the communities faces substantial constraints imposed by limited water resources. Residents of the downstream desert area feel a wetter climate than those of the mountain and oasis areas in the middle reach, suggesting a greater water scarcity pressure in the latter. The increased allocation of ecological water to the downstream portion of the Heihe river, as implemented by the “97″ water distribution plan in 2000, may be a contributing factor to this phenomenon. The disparities in the fact and residents’ awareness and perception of climate change are different among the mountainous, oasis, and desert regions, which are influenced by regional differences in climate change, agricultural production conditions, and water policies. The government should consider these factors when formulating water policies to ensure successful and balanced development.

Anastasiia Rabokon, Yuliia Bilonozhko, Anastasiia Postovoitova et al.

This work analyses intron length polymorphism of β-tubulin genes in populations of Antarctic pearlwort (Colobanthus quitensis) from the relatively compact region of the Argentine Islands-Kyiv Peninsula (the maritime Antarctic). Analysis of the length polymorphism of the two introns of β-tubulin genes in natural populations of C. quitensis revealed a generally low level of genetic polymorphism. Investigation of the first intron length polymorphism revealed two groups of populations. The population of the largest of Berthelot Islands has representatives of both groups. The second intron length polymorphism of β-tubulin genes identified individual genotypes in 7 of the 11 studied populations of C. quitensis. We speculate that this might be due to the spread of plants from different locations or a combination of changes under different environmental conditions.

Namkha Norbu, Xiaolei Sheng, Qiang Liu et al.

Excessive particle concentrations during heating periods, which greatly affect people’s physical and mental health and their normal lives, continue to be a concern. It is more practical to understand and analyze the relationship between the fractal dimension and particle size concentration distribution of atmospheric particulate matter before and after adjusting heating energy consumption types. The data discussed and analyzed in this paper were collected by monitoring stations and measured from 2016 to 2018 in Xi’an. The data include fractal dimension and particle size concentration changes in the atmospheric particulate matter before and after adjusting the heating energy consumption types. The results indicate that adjusting the heating energy consumption types has a significant impact on particulate matter. The average concentration of PM_2.5 decreased by 26.4 μg/m³. The average concentration of PM₁₀ decreased by 31.8 μg/m³. At the same time, the different particle sizes showed a downward trend. The particles ranging from 0.265 to 0.475 μm demonstrated the maximum decrease, which was 8.80%. The heating period in Xi’an mainly involves particles ranging from 0 to 0.475 μm. The fractal dimensions of the atmospheric particulate matter before and after adjusting the heating energy consumption types were 4.809 and 3.397, respectively. After adjusting the heating energy consumption types, the fractal dimension decreased by 1.412. At that time, the proportions of particle sizes that were less than 1.0 μm, 2.0 μm, and 2.5 μm decreased by 1.467%, 0.604%, and 0.424%, respectively. This paper provides new methods and a reference value for the distribution and effective control of atmospheric particulate matter by adjusting heating energy consumption types.

Charalampos Kolyvas, Spyros Missiakoulis, Flora Gofa

Natalia Kotsianou, Elissavet Feloni

Joshin John Bejoy, G. Ambika

We present a study on the spatio-temporal pattern underlying the climate dynamics in various locations spread over India, including the Himalayan region, coastal region, central and northeastern parts of India. We try to capture the variations in the complexity of their dynamics derived from temperature and relative humidity data from 1948-2022. By estimating the recurrence-based measures from the reconstructed phase space dynamics using a sliding window analysis on the data sets, we study the climate variability in different spatial locations. The study brings out the variations in the complexity of the underlying dynamics as well as their heterogeneity across the locations in India. We find almost all locations indicate shifts to more irregular and stochastic dynamics for temperature data around 1972-79 and shifts back to more regular dynamics beyond 2000. These patterns correlate with reported shifts in the climate and Indian Summer Monsoon related to strong and moderate ENSO events and confirm their associated regional variability.

James D. Johnston, Scott C. Collingwood, James D. LeCheminant et al.

Prior studies suggest brick workers in Nepal may be chronically exposed to hazardous levels of fine particulate matter (PM_2.5) from ambient, occupational, and household sources. However, findings from these studies were based on stationary monitoring data, and thus may not reflect a worker’s individual exposures. In this study, we used RTI International’s MicroPEMs to collect 24 h PM_2.5 personal breathing zone (PBZ) samples among brick workers (<i>n</i> = 48) to estimate daily exposures from ambient, occupational, and household air pollution sources. Participants were sampled from five job categories at one kiln. The geometric mean (GM) PM_2.5 exposure across all participants was 116 µg/m³ (95% confidence interval [CI]: 94.03, 143.42). Job category was significantly (<i>p</i> < 0.001) associated with PBZ PM_2.5 concentrations. There were significant pairwise differences in geometric mean (GM) PBZ PM_2.5 concentrations among workers in administration (GM: 47.92, 95% CI: 29.81, 77.03 µg/m³) vs. firemen (GM: 163.46, 95 CI: 108.36, 246.58 µg/m³, <i>p</i> = 0.003), administration vs. green brick hand molder (GM: 163.35, 95% CI: 122.15, 218.46 µg/m³, <i>p</i> < 0.001), administration vs. top loader (GM: 158.94, 95% CI: 102.42, 246.66 µg/m³, <i>p</i> = 0.005), firemen vs. green brick machine molder (GM: 73.18, 95% CI: 51.54, 103.90 µg/m³, <i>p</i> = 0.03), and green brick hand molder vs. green brick machine molder (<i>p</i> = 0.008). Temporal exposure trends suggested workers had chronic exposure to hazardous levels of PM_2.5 with little to no recovery period during non-working hours. Multi-faceted interventions should focus on the control of ambient and household air pollution and tailored job-specific exposure controls.

Xianghui Fang, Nan Chen

An information-theoretic framework is developed to assess the predictability of ENSO complexity, which is a central problem in contemporary meteorology with large societal impacts. The information theory advances a unique way to quantify the forecast uncertainty and allows to distinguish the predictability limit of different ENSO events. One key step in applying the framework to compute the information gain representing the predictability is to build a statistically accurate dynamical model. To this end, a recently developed multiscale stochastic model, which succeeds in capturing both the large-scale dynamics and many crucial statistical properties of the observed ENSO complexity, is incorporated into the information-theoretic framework. It is shown that different ENSO events possess very distinct predictability limits. In addition to the ensemble mean, the ensemble spread also has remarkable contributions to the predictability. While the information theory indicates that predicting the onset of the eastern Pacific El Niños is challenging, it reveals a universal tendency to convert strong predictability to skillful forecast for predicting many central Pacific El Niños about two years in advance. In addition, strong predictability is found for the La Niña events, corresponding to the effective discharge process. In the climate change scenario with the strengthening of the background Walker circulation, the predictability of sea surface temperature in central Pacific has a significant response with a notable increase in summer and fall. Finally, the Gaussian approximation is shown to be accurate in computing the information gain, which facilitates the use of more sophisticated models to study the ENSO predictability.

M. Reznychenko, O. Bogomaz, D. Kotov et al.

The paper presents the results of ionospheric observations performed over the Ukrainian Antarctic Akademik Vernadsky station and Millstone Hill (USA). Ionospheric parameters such as peak electron density and height (hmF2 and NmF2) in October 2021 are shown and discussed. The results of the comparative analysis between observations and redictions of the International Reference Ionosphere 2016 (IRI-2016) model are presented. The main objectives of this work are an investigation of the ionosphere response to space weather effects in the Northern and Southern hemispheres in the American longitudinal sector using ionosondes located at the Vernadsky station and near the magnetically conjugate region – Millstone Hill, and a comparison of observations with the model. The F2-layer peak height was calculated from ionograms obtained by ionosonde using subsequent electron density profile inversion. Diurnal variations of hmF2 and NmF2 were calculated using a set of sub-models of the IRI-2016 model for comparison with experimental results. A strong negative response of the ionosphere to the moderate geomagnetic storm on October 12, 2021 was revealed over the Vernadsky station and Millstone Hill. During October 21–31, 2021, the gradual night-to-night increase in NmF2 (by a factor of ~2) was observed over the Vernadsky station. It was found that the IRI hmF2 sub-models (SHU-2015 and AMTB-2013) provide a relatively good agreement with the observed variations of hmF2 in the daytime and nighttime for almost the entire investigated period over both the Vernadsky station and Millstone Hill. The largest deviations for both IRI hmF2 sub-models occurred during the nighttime of geomagnetically disturbed periods. The IRI NmF2 submodels (URSI and CCIR) generally agree with the observations. However, observations and model predictions differ somewhat in the geomagnetically disturbed periods. According to the results of the standard deviation calculations, it cannot be concluded that any of the IRI-2016 sub-models is better than the others. The hypotheses on the possible reasons for the differences in the modeled and observed variations of hmF2 and NmF2 are proposed and discussed in the frame of well-known ionospheric storms’ mechanisms. The results obtained in this paper demonstrate the peculiarities of the ionosphere in different hemispheres of the American longitude sector under geomagnetically quiet and disturbed conditions and provide one more validation of the modern empirical international reference models of the ionosphere.

Youman Wei, Siyan Wang, Hongwei Dang et al.

Under the impact of COVID-19 and the needs for urban expansion, a large number of outpatient buildings have been rapidly constructed, but the problem of high energy consumption has always been ignored. There is a lack of research on the adaptability of building shape in different climate zones. Many studies have shown that a reasonable shape in the early stage of design can significantly reduce the energy consumption of buildings. Therefore, it helps if architects quickly select a reasonable shape that can effectively reduce energy consumption. This study summarized a number of outpatient building cases in China and proposed three typical building shapes: centralized-type (Shape-1), corridor-type (Shape-2), and courtyard-type (Shape-3). The Design Builder tool was used to simulate and analyze the typical building energy consumption in different climate zones. The simulation results show that Shape-2 (angle: 0°) should be chosen in severe cold zone; Shape-1 (angle: 90°) should be chosen in cold zone; Shape-1 (angle: 0°) should be chosen in hot summer and cold winter zone; Shape-1 (angle: 60°) should be chosen in hot summer and warm winter zone; and Shape-1 or Shape-2 can be chosen in warm zone. The results of this study can provide suggestions for the energy saving design of outpatient buildings in China and other areas with similar conditions. The result can help architects make rapid shape selection in the early stage of design.

Jinyuan Xin, Daen Bao, Yining Ma et al.

Wind power, as one of the primary clean energies, is an important way to achieve the goals of carbon peak and carbon neutrality. Therefore, high-resolution measurement and accurate forecasting of wind speed are very important in the organization and dispatching of the wind farm. In this study, several methodologies, including the mesoscale WRF (Weather Research and Forecasting(WRF) model, mathematical statistics algorithms, and machine learning algorithms, were adopted to systematically explore the predictability and optimization of wind speed of a Gobi grassland wind farm located in western Inner Mongolia. Results show that the rear-row turbines were significantly affected by upwind turbine wakes. The output power of upwind-group turbines was 591 KW with an average wind speed of 7.66 m/s, followed by 532 KW and 7.02 m/s in the middle group and 519 KW and 6.92 m/s in the downwind group. The higher the wind speed was, the more significantly the wake effect was presented. Intercomparison between observations and WRF simulations showed an average deviation of 3.73 m/s. Two postprocessing methods of bilinear interpolation and nearest replacement could effectively reduce the errors by 34.85% and 36.19%, respectively, with average deviations of 2.43 m/s and 2.38 m/s. A cycle correction algorithm named Average Variance–Trend (AVT) can further optimize the errors to 2.14 m/s and 2.13 m/s. In another aspect, the categorical boosting (CatBoost) artificial intelligence algorithm also showed a great performance in improving the accuracy of WRF outputs, and the four-day average deviation of 26–29 September decreased from 3.21 m/s to around 2.50 m/s. However, because of the influence of large-scale circulations, there still exist large errors in the results of various correction algorithms. It is therefore suggested through the investigation that data assimilation of the northwest and Mongolian plateau, boundary layer parameterization scheme optimization, and embedding of high-resolution topographic data could have great potential for obtaining more accurate forecasting products.

Xiuqin Xu, Hanqiu Peng, Ying Chen

Modern time series data often display complex nonlinear dependencies along with irregular regime-switching behaviors. These features present technical challenges in modeling, inference, and in offering insightful understanding into the underlying stochastic phenomena. To tackle these challenges, we introduce a novel modeling framework known as the Deep Switching State Space Model (DS$^3$M). This framework is engineered to make accurate forecasts for such time series while adeptly identifying the irregular regimes hidden within the dynamics. These identifications not only have significant economic ramifications but also contribute to a deeper understanding of the underlying phenomena. In DS$^3$M, the architecture employs discrete latent variables to represent regimes and continuous latent variables to account for random driving factors. By melding a Recurrent Neural Network (RNN) with a nonlinear Switching State Space Model (SSSM), we manage to capture the nonlinear dependencies and irregular regime-switching behaviors, governed by a Markov chain and parameterized using multilayer perceptrons. We validate the effectiveness and regime identification capabilities of DS$^3$M through short- and long-term forecasting tests on a wide array of simulated and real-world datasets, spanning sectors such as healthcare, economics, traffic, meteorology, and energy. Experimental results reveal that DS$^3$M outperforms several state-of-the-art models in terms of forecasting accuracy, while providing meaningful regime identifications.

Weizheng Fu, Nicholas Ssessanga, Tatsuhiro Yokoyama et al.

Abstract A novel two‐step three‐dimensional (3‐D) computerized ionospheric tomography (CIT) technique has been developed to image the structure of daytime midlatitude sporadic‐E (Es). The CIT relies on total electron content (TEC) from a dense ground‐based Global Navigation Satellite System (GNSS) receiver network over the Japan area. First, on a coarse grid, the TEC data and a multiplicative algebraic reconstruction technique (MART) are used to reconstruct the F region from a smooth background. Then, on a fine grid and using singular value decomposition (SVD), the residues after deducting the F region contribution to TEC are utilized in reconstructing the E region, extending 80–180 km in altitude. To vertically constrain the E region solution, we introduced a family of subsets of time‐dependent empirical orthogonal functions (EOFs) from a Chapman model function tuned to manually scaled ionosonde observations. We analyzed three event days to validate the results. East‐West (E‐W) aligned frontal structures, spanning several hundred kilometers, migrating northward in the morning and southward in the afternoon, were observed. The new technique effectively tracks the Es‐height variation over time, which had proved difficult to reproduce in earlier tempts at 3‐D Es reconstructions.

Yamila Barrera, Leonardo Boechi, Matthieu Jonckheere et al.

The Reseau de Transport d'Electricité (RTE) is the French main electricity network operational manager and dedicates large number of resources and efforts towards understanding climate time series data. We discuss here the problem and the methodology of grouping and selecting representatives of possible climate scenarios among a large number of climate simulations provided by RTE. The data used is composed of temperature times series for 200 different possible scenarios on a grid of geographical locations in France. These should be clustered in order to detect common patterns regarding temperatures curves and help to choose representative scenarios for network simulations, which in turn can be used for energy optimisation. We first show that the choice of the distance used for the clustering has a strong impact on the meaning of the results: depending on the type of distance used, either spatial or temporal patterns prevail. Then we discuss the difficulty of fine-tuning the distance choice (combined with a dimension reduction procedure) and we propose a methodology based on a carefully designed index.

Lei Zhang, Yu Shi, Chaozhu Hu et al.

The Cotter-Holm Slice Model (CHSM) was introduced to study the behavior of whether and specifically the formulation of atmospheric fronts, whose prediction is fundamental in meteorology. Considered herein is the influence of stochastic forcing on the Incompressible Slice Model (ISM) in a smooth 2D bounded domain, which can be derived by adapting the Lagrangian function in Hamilton's principle for CHSM to the Euler-Boussinesq Eady incompressible case. First, we establish the existence and uniqueness of local pathwise solution (probability strong solution) to the ISM perturbed by nonlinear multiplicative stochastic forcing in Banach spaces $W^{k,p}(D)$ with $k>1+1/p$ and $p\geq 2$. The solution is obtained by introducing suitable cut-off operators applied to the $W^{1,\infty}$-norm of the velocity and temperature fields, using the stochastic compactness method and the Yamada-Watanabe type argument based on the Gyöngy-Krylov characterization of convergence in probability. Then, when the ISM is perturbed by linear multiplicative stochastic forcing and the potential temperature does not vary linearly on the $y$-direction, we prove that the associated Cauchy problem admits a unique global-in-time pathwise solution with high probability, provided that the initial data is sufficiently small or the diffusion parameter is large enough. The results partially answer the problems left open in Alonso-Or{á}n et al. (Physica D 392:99--118, 2019, pp. 117).