Hasil untuk "Meteorology. Climatology"

Yijie Li, Hewei Wang, Shaofan Wang et al.

Recent advancements in meteorology involve the use of ground-based sky cameras for cloud observation. Analyzing images from these cameras helps in calculating cloud coverage and understanding atmospheric phenomena. Traditionally, cloud image segmentation relied on conventional computer vision techniques. However, with the advent of deep learning, convolutional neural networks (CNNs) are increasingly applied for this purpose. Despite their effectiveness, CNNs often require many epochs to converge, posing challenges for real-time processing in sky camera systems. In this paper, we introduce a residual U-Net with deep supervision for cloud segmentation which provides better accuracy than previous approaches, and with less training consumption. By utilizing residual connection in encoders of UCloudNet, the feature extraction ability is further improved.

Enrico Camporeale, Thomas E. Berger

The NOAA Space Weather Prediction Center (SWPC) issues the official U.S. government forecast for M-class and X-class solar flares, yet the skill of these forecasts has never been comprehensively verified. In this study, we evaluate the SWPC probabilistic flare forecasts over a 26-year period (1998-2024), comparing them to several zero-cost and statistical baselines including persistence, climatology, Naive Bayes, and logistic regression. We find that the SWPC model does not outperform these baselines across key classification and probabilistic metrics and exhibits severe calibration issues and high false alarm rates, especially in high-stakes scenarios such as detecting the first flare after extended quiet periods. These findings demonstrate the need for more accurate and reliable eruption forecasting models which we suggest should be based on modern data-driven methods. The findings also provide a standard against which any proposed eruption prediction system should be compared. We suggest that space weather forecasters regularly update and publish analyses like the one demonstrated here to provide up-to-date standards of accuracy and reliability against which to compare new methods.

Carlo Gaetan, Philippe Naveau

In fields such as hydrology and climatology, modelling the entire distribution of positive data is essential, as stakeholders require insights into the full range of values, from low to extreme. Traditional approaches often segment the distribution into separate regions, which introduces subjectivity and limits coherence. This is especially true when dealing with multivariate data. In line with multivariate extreme value theory, this paper presents a unified, threshold-free framework for modelling marginal behaviours and dependence structures based on an extended generalized Pareto distribution (EGPD). We propose decomposing multivariate data into radial and angular components. The radial component is modelled using a semi-parametric EGPD and the angular distribution is permitted to vary conditionally. This approach allows for sufficiently flexible dependence modelling. The hierarchical structure of the model facilitates the inference process. First, we combine classical maximum likelihood estimation (MLE) methods with semi-parametric approaches based on Bernstein polynomials to estimate the distribution of the radial component. Then, we use multivariate regression techniques to estimate the angular component's parameters. The model is evaluated through synthetic simulations and applied to hydrological datasets to exemplify its capacity to capture heavy-tailed marginals and complex multivariate dependencies without threshold specification.

Thomas J. Maullin-Sapey, Fabian J. E. Telschow

In scientific disciplines such as neuroimaging, climatology, and cosmology it is useful to study the uncertainty of excursion sets of imaging data. While the case of imaging data obtained from a single study condition has already been intensively studied, confidence statements about the intersection, or union, of the excursion sets derived from different subject conditions have only been introduced recently. Such methods aim to model the images from different study conditions as asymptotically Gaussian random processes with differentiable sample paths. In this work, we remove the restricting condition of differentiability and only require continuity of the sample paths. This allows for a wider range of applications including many settings which cannot be treated with the existing theory. To achieve this, we introduce a novel notion of convergence on piecewise continuous functions over finite partitions. This notion is of interest in its own right, as it implies convergence results for maxima of sequences of piecewise continuous functions over sequences of sets. Generalizing well-known results such as the extended continuous mapping theorem, this novel convergence notion also allows us to construct for the first time confidence regions for mathematically challenging examples such as symmetric differences of excursion sets.

Qihua Wang, Yuande Yang, Jiayu Hu et al.

Temperate glaciers in the Kangri Karpo region of the southeastern Qinghai–Tibet Plateau (QTP) have experienced significant ablation in recent decades, increasing the risk of glacier-related hazards and impacting regional water resources. However, the spatial and temporal pattern of mass loss in these glaciers remains inadequately quantified. In this study, we used ASTER L1A stereo images to construct a high-resolution elevation time series and provide a comprehensive spatial–temporal assessment of glacier elevation change from 2000 to 2024. The results indicate that almost all glaciers have experienced rapid ablation, with an average surface elevation decrease of −18.35 ± 5.13 m, corresponding to a rate of −0.76 ± 0.21 m yr⁻¹. Glaciers in the region were divided into the northern and southern basins, with average rates of −0.79 ± 0.17 m yr⁻¹ and −0.72 ± 0.13 m yr⁻¹, respectively. A notable difference in acceleration trends between the two basins was observed, with the elevation rate increasing from −0.78 ± 0.17m yr⁻¹ to −1.04 ± 0.17 m yr⁻¹ and from −0.52 ± 0.13 m yr⁻¹ to −0.92 ± 0.13 m yr⁻¹, respectively. The seasonal cycle was identified in glacier surface elevation change, with an accumulation period from November to March followed by a prolonged ablation period. The seasonal amplitude decreased with elevation, with higher elevations exhibiting longer accumulation periods and less ablation. Correlation analysis with meteorological data indicated that higher summer temperatures and increased summer rainfall intensify elevation loss, while increased spring snowfall may reduce ablation. Our analysis highlights distinct variations in glacier elevation changes across different locations, elevations, and climatic conditions in the Kangri Karpo region, providing valuable insights into glacier responses to environmental changes on the Tibetan Plateau.

Mingge Li, Yawen Kong, Meng Fan et al.

In recent years, high-altitude cities with low emissions in western China have exhibited an upward trend in surface ozone (O₃). Based on observations and reanalysis data, this study analyzed the evolutionary characteristics and pollution mechanisms of ozone in Xining and quantified the impact of stratospheric intrusion. The results indicated that an upward trend in summer O₃ was observed in Xining. A total of 29 ozone exceedance days were found. Potential exceedance days (>150 and >140 μg/m³) showed substantial increases from 2022 to 2023. Using a stratospheric intrusion to surface (SITS) event identification algorithm, 42 events were found in Xining, with an average duration of 8.4 h. Spring exhibited the highest event frequency (13 events) and longest average duration. SITS events contributed an average of 19.7% to surface ozone, significantly exacerbating local exceedance risks. A typical ozone pollution episode from 25 July to 3 August 2021 was analyzed. The peak O₃ reached 170 μg/m³. Elevated temperature, intensified radiation, and unfavorable meteorological conditions synergistically promoted local photochemical ozone production and accumulation. Notably, a SITS event was simultaneously detected, elevating surface ozone by 24%, which confirmed that stratospheric intrusion was the main cause of pollution.

Zulian Zhang, Mingquan Wang, Weiyi Mao et al.

ABSTRACT This study employed high‐resolution (1 × 1 km) multisource fusion data (HCLDAS) and observational data from 190 automatic weather stations to analyze summer temperature variations across 12 altitude levels in the Hotan area from June to August 2023. Statistical methods, including root mean square error (RMSE) and temperature accuracy rates (TT1, TT2), were applied to validate data reliability and investigate spatiotemporal patterns. Key findings include: (1) Data Validation: HCLDAS demonstrated high accuracy, with a mean RMSE of 0.42°C and temperature accuracies of 98.15% (≤ 1°C) and 99.08% (≤ 2°C), confirming its suitability for complex terrains. (2) Altitude‐Dependent Trends: High elevations (≥ 4500 m): Continuous warming from July to August (+0.37°C to +0.96°C), driven by glacier‐albedo feedback (e.g., Muztagh Ata retreat) and weakened westerlies enhancing thermal forcing, elevating the 0°C isotherm. Mid‐elevations (2000–4500 m): Sharp vertical cooling (−18.21°C total) but significant June–July warming (+1.24°C to +2.96°C). Low elevations: July–August cooling (−0.07°C to −1.05°C) due to cold air drainage and oasis effects (evaporation/dust reflection). (3) Diurnal Variability: Maximum daily temperature range (12.6°C) occurred at 1300–1500 m (arid landscapes), while the minimum (6.08°C) was observed at 4000–4500 m (rocky terrain). (4) Threshold Analysis: ≤ 0°C grids (38.51% of total) concentrated above 2500 m, while ≥ 35°C grids (55.59%) dominated below 3000 m, with cumulative hours increasing at lower altitudes. The results provide a scientific basis for high‐temperature monitoring, snowmelt flood warnings, and optimized meteorological infrastructure in arid, high‐altitude regions.

Marcin Chrust, Anthony T. Weaver, Philip Browne et al.

An Ensemble of Data Assimilations (EDA) can provide valuable information on the analysis and short-range forecast uncertainties. The present ECMWF operational ocean analysis and reanalysis system, called ORAS5, produces an ensemble but does not exploit it for the specification of the background-error covariance matrix $\mathbf{B}$, a key component of the data assimilation system. In this article, we describe EDA developments for the ocean, which take advantage of the short-range forecast ensemble for specifying, in two distinct ways, parameters of a covariance model representation of $\mathbf{B}$. First, we generate a climatological ensemble over an extended period to produce seasonally varying climatological estimates of background-error variances and horizontal correlation length-scales. Second, on each assimilation cycle, we diagnose flow-dependent variances from the ensemble and blend them with the climatological estimates to form hybrid variances. We also use the ensemble to diagnose flow-dependent vertical correlation length-scales. We demonstrate for the Argo-rich period that this new, hybrid formulation of $\mathbf{B}$ results in a significant reduction of background errors compared to the parameterized formulation of $\mathbf{B}$ used in ORAS5. The new ocean EDA system will be employed in ORAS6, ECMWF's next generation ocean reanalysis system.

Zhanxiang Hua, Yutong He, Chengqian Ma et al.

Weather forecasting remains a crucial yet challenging domain, where recently developed models based on deep learning (DL) have approached the performance of traditional numerical weather prediction (NWP) models. However, these DL models, often complex and resource-intensive, face limitations in flexibility post-training and in incorporating NWP predictions, leading to reliability concerns due to potential unphysical predictions. In response, we introduce a novel method that applies diffusion models (DM) for weather forecasting. In particular, our method can achieve both direct and iterative forecasting with the same modeling framework. Our model is not only capable of generating forecasts independently but also uniquely allows for the integration of NWP predictions, even with varying lead times, during its sampling process. The flexibility and controllability of our model empowers a more trustworthy DL system for the general weather community. Additionally, incorporating persistence and climatology data further enhances our model's long-term forecasting stability. Our empirical findings demonstrate the feasibility and generalizability of this approach, suggesting a promising direction for future, more sophisticated diffusion models without the need for retraining.

Marcio Cataldi, Vitor Luiz Victalino Galves, Leandro Alcoforado Sphaier et al.

The primary goal of this study is to introduce the initial phase of developing an impact-based forecasting system for extreme heatwaves, utilizing a novel multivariate index which, at this early stage, already employs a combination of a statistical approach and physical principles related to human body water loss. This system also incorporates a mitigation plan with hydration-focused measures. Since 1990, heatwaves have become increasingly frequent and intense across many regions worldwide, particularly in Europe and Asia. The main health impacts of heatwaves include organ strain and damage, exacerbation of cardiovascular and kidney diseases, and adverse reproductive effects. These consequences are most pronounced in individuals aged 65 and older. Many national meteorological services have established metrics to assess the frequency and severity of heatwaves within their borders. These metrics typically rely on specific threshold values or ranges of near-surface (2 m) air temperature, often derived from historical extreme temperature records. However, to our knowledge, only a few of these metrics consider the persistence of heatwave events, and even fewer account for relative humidity. In response, this study aims to develop a globally applicable normalized index that can be used across various temporal scales and regions. This index incorporates the potential health risks associated with relative humidity, accounts for the duration of extreme heatwave events, and is exponentially sensitive to exposure to extreme heat conditions above critical thresholds of temperature. This novel index could be more suitable/adapted to guide national meteorological services when emitting warnings during extreme heatwave events about the health risks on the population. The index was computed under two scenarios: first, in forecasting heatwave episodes over a specific temporal horizon using the WRF model; second, in evaluating the relationship between the index, mortality data, and maximum temperature anomalies during the 2003 summer heatwave in Spain. Moreover, the study assessed the annual trend of increasing extreme heatwaves in Spain using ERA5 data on a climatic scale. The results show that this index has considerable potential as a decision-support and health risk assessment tool. It demonstrates greater sensitivity to extreme risk episodes compared to linear evaluations of extreme temperatures. Furthermore, its formulation aligns with the physical mechanisms of water loss in the human body, while also factoring in the effects of relative humidity.

Efthymia Koliokosta

Shikha Verma, Kuldeep Srivastava, Akhilesh Tiwari et al.

Extreme weather events pose significant challenges, thereby demanding techniques for accurate analysis and precise forecasting to mitigate its impact. In recent years, deep learning techniques have emerged as a promising approach for weather forecasting and understanding the dynamics of extreme weather events. This review aims to provide a comprehensive overview of the state-of-the-art deep learning in the field. We explore the utilization of deep learning architectures, across various aspects of weather prediction such as thunderstorm, lightning, precipitation, drought, heatwave, cold waves and tropical cyclones. We highlight the potential of deep learning, such as its ability to capture complex patterns and non-linear relationships. Additionally, we discuss the limitations of current approaches and highlight future directions for advancements in the field of meteorology. The insights gained from this systematic review are crucial for the scientific community to make informed decisions and mitigate the impacts of extreme weather events.

Julia Garagnon, Marc Luetscher, Éric Weber

Cave climatology and its impact on contemporary biogeochemical cycles are still poorly documented. Ventilation in karst environment plays a fundamental role in these two fields and its understanding could bring elements to study them. However, only a few cavers have tried to understand and describe it, very often in a qualitative way or by theoretical approaches. The aim of this study is to test physical concepts with empirical data. For this purpose, a ventilation model has been built and compared with field temperature and air velocity measurements in the Milandre Cave Laboratory (Switzerland). The model explains about 95% of the measured airflow thus confirming the major role of temperature on the air dynamics. However, these first results also reveal that the measured winter air flow is lower than predicted by the model and that the air flow reversal occurs at a lower temperature than anticipated. Combined with a forced ventilation experiment these results underline the influence of the atmospheric composition (particularly the water vapor and concentration in CO$_2$ and O$_2$), waterflow rates and network geometry on the air flow. This work paves the way for a better quantification of heat and mass fluxes in relation to underground ventilation.

Soo-Hyun Kim, Jung-Hoon Kim, Hye-Yeong Chun et al.

Abstract Atmospheric turbulence at commercial aircraft cruising altitudes is a main threat to aviation safety worldwide. As the air transport industry expands and is continuously growing, investigating global response of aviation turbulence under climate change scenarios is required for preparing optimal and safe flying plans for the future. This study examines future frequencies of moderate-or-greater-intensity turbulence generated from various sources, viz., clear-air turbulence and mountain-wave turbulence that are concentrated in midlatitudes, and near-cloud turbulence that is concentrated in tropics and subtropics, using long-term climate model data of high-emissions scenario and historical condition. Here, we show that turbulence generated from all three sources is intensified with higher occurrences globally in changed climate compared to the historical period. Although previous studies have reported intensification of clear-air turbulence in changing climate, implying bumpier flights in the future, we show that intensification of mountain-wave turbulence and near-cloud turbulence can also be expected with changing climate.

Eunhwa Jang, Seongwoo Choi, Eunchul Yoo et al.

Abstract Various shipping emissions controls have recently been implemented at both local and national scales. However, it is difficult to track the effect of these on PM2.5 levels, owing to the non-linear relationship that exists between changes in precursor emissions and PM components. Positive Matrix Factorisation (PMF) identifies that a switch to cleaner fuels since January 2020 results in considerable reductions in shipping-source-related PM2.5, especially sulphate aerosols and metals (V and Ni), not only at a port site but also at an urban background site. CMAQ sensitivity analysis reveals that the reduction of secondary inorganic aerosols (SIA) further extends to inland areas downwind from ports. In addition, mitigation of secondary organic aerosols (SOA) in coastal urban areas can be anticipated either from the results of receptor modelling or from CMAQ simulations. The results in this study show the possibility of obtaining human health benefits in coastal cities through shipping emission controls.

Vitali Müller, Markus Hauk, Malte Misfeldt et al.

The GRACE Follow-On satellite mission measures distance variations between the two satellites in order to derive monthly gravity field maps, indicating mass variability on Earth on a few 100 km scale due to hydrology, seismology, climatology and others. This mission hosts two ranging instruments, a conventional microwave system based on K(a)-band ranging (KBR) and a novel laser ranging instrument (LRI), both relying on interferometric phase readout. In this paper we show how the phase measurements can be converted into range data using a time-dependent carrier frequency (or wavelength) that takes potential intraday variability in the microwave or laser frequency into account. Moreover, we analyze the KBR-LRI residuals and discuss which error and noise contributors limit the residuals at high and low Fourier frequencies. It turns out that the agreement between KBR and LRI biased range observations can be slightly improved by considering intraday carrier frequency variations in the processing. Although the effect is probably small enough to have little relevance for gravity field determination at the current precision level, the analysis is of relevance for detailed instrument characterization and potentially for future more precise missions.

Bo Li, Junmin Li, Wuyang Chen et al.

Combined wind and wave power generation has advantages such as energy synergy and complementarity and will play a leading role in the integrated development of offshore renewable energy. From the perspective of joint energy development, this study focuses on the meteo-climatic wind and wave conditions in Dongluo Island, Hainan, in the South China Sea. Based on the concurrent measurement from in situ monitoring system, hourly data from June 2020 to September 2021 are used to reveal typical climate characteristics associated with the weak (inverse) correlation between wind and wave. The energy flux density of wind and wave are also assessed to describe the energy pattern. Principal component analysis (PCA) shows the wind parameters contribute a larger variance to the matrix of the wind–wave dataset than the waves, suggesting a lower stability of the wind climate. The first three components via PCA are then classified into five clusters to represent different climatic characteristics. Among them, the dominating cluster symbolizes a climatic circumstance with weaker winds and waves below normal. This cluster, evenly distributed in different seasons, shows the lowest wave–wind correlation, suggesting a favorable condition of the synergy of the two energies throughout the year. The clusters with the second and third largest sample sizes are mainly dominated in spring and winter, respectively. The magnitudes of the wind and wave parameters in these two clusters yield to a relation of “as one falls, another rises”, implying a high interest in complementarity between the two resources to a certain extent. The energy features inferred by meteo-climatic clusters are further verified by direct assessment of energy density. There are generally consistent variations between wind–wave climate and energy, both in magnitude and in seasonality. Based on these results, differentiated exploitation schemes considering the complementarity or synergy of wind and wave according to different seasons are recommended.

Patricio Pacheco, Héctor Ulloa, Eduardo Mera

Through chaos theory, experimental data of hourly time series are analyzed. These time series consist of Radon concentration levels and meteorological variables of temperature, pressure, and relative humidity within the boundary layer and very close to the ground. Results were obtained in two urban dwellings for family use and for two different periods of time, of the order of one month and one month plus one week, respectively. Each time series was subjected to a chaotic analysis showing the existence of the characteristic chaotic parameters in the appropriate ranges: Lyapunov coefficient (λ), correlation dimension (Dc), Kolmogorov entropy (SK), Lempel-Ziv complexity (LZ), Hurst coefficient (H), maximum predictability time (τ), lost information (<ΔI>) and fractal dimension (D). The studied processes show to be irreversible. From the chaotic parameters, it is shown that the ratio between the entropy of each meteorological variable and the radon concentration is very sensitive to relative humidity. Likewise, the meteorological variables that most affect the concentration of Radon are relative humidity and temperature. The concordance between the results obtained and those delivered by analyzes carried out through other methodologies in longer periods is verified.

Xiaomin Hu, Jing Zhang, Wenhao Xue et al.

With the intensification of global warming and economic development in China, the near-surface ozone (O₃) concentration has been increasing recently, especially in the Beijing-Tianjin-Hebei (BTH) region, which is the political and economic center of China. However, O₃ has been measured in real time only over the past few years, and the observational records are discontinuous. Therefore, we propose a new method (WRFC-XGB) to establish a near-surface O₃ concentration dataset in the BTH region by integrating the Weather Research and Forecasting with Chemistry (WRF-Chem) model with the extreme gradient boosting (XGBoost) algorithm. Based on this method, the 8-h maximum daily average (MDA8) O₃ concentrations are obtained with full spatiotemporal coverage at a spatial resolution of 0.1° × 0.1° across the BTH region in 2018. Two evaluation methods, sample- and station-based 10-fold cross-validation (10-CV), are used to assess our method. The sample-based (station-based) 10-CV evaluation results indicate that WRFC-XGB can achieve excellent accuracy with a high coefficient of determination (R²) of 0.95 (0.91), low root mean square error (RMSE) of 13.50 (17.70) µg m⁻³, and mean absolute error (MAE) of 9.60 (12.89) µg m⁻³. In addition, superb spatiotemporal consistencies are confirmed for this model, including the estimation of high O₃ concentrations, and our WRFC-XGB model outperforms traditional models and previous studies in data mining. In addition, the proposed model can be applied to estimate the O₃ concentration when it has not been measured. Furthermore, the spatial distribution analysis of the MDA8 O₃ in 2018 reveals that O₃ pollution in the BTH region exhibits significant seasonality. Heavy O₃ pollution episodes mainly occur in summer, and the high O₃ loading is distributed mainly in the southern BTH areas, which will pose challenges to atmospheric environmental governance for local governments.

Bibi Nariefa Abrahim, Nicolas James Cullen, Jonathan Paul Conway

AbstractHigh‐altitude observations of mountain meteorology remain extremely rare in the Southern Alps despite their importance for detecting changes in seasonal snow and glacier extent. To address this, we present a unique in situ analysis of surface meteorology, including radiative forcing from clouds, using automatic weather station data obtained near the terminus of Brewster Glacier in the Southern Alps of New Zealand over the period 2010–2020. The average air temperature is 2.1°C at an altitude of 1,650 m above sea level, and seasonal cycles of surface temperature and albedo indicate the automatic weather station (AWS) site is typically snow‐covered between June and November. Snowfall can occur anytime of the year, but rainfall accounts for more than half of the total precipitation (56%). In the absence of strong gradient airflow, a katabatic wind often develops on the glacier, which is strongest at night time and early morning. Partly cloudy conditions are most frequent (43%) at a daily time scale, followed by overcast (34%) and clear‐sky conditions (23%). A distinct diurnal and seasonal cycle of cloud cover is observed, with clouds most frequent in the afternoon during spring. Daily clear‐sky conditions are most common during winter, in particular in June, while the atmospheric transmission of shortwave radiation is lowest in summer. For 4 months of the year (May–August), the increase in incoming longwave radiation by clouds exceeds the decrease in incoming shortwave radiation. Interannual extremes in melt on Brewster Glacier cannot be fully attributed to summer air temperature anomalies and future work should seek to examine the influence of clouds and atmospheric moisture fluxes on seasonal mass balance variations.