Hasil untuk "Meteorology. Climatology"

Qizhao Jin, Xianhuang Xu, Yong Cao et al.

Recently, Transformer-based architectures have advanced meteorological prediction. However, this position-centric tokenizer conflicts with the core principle of meteorological systems, where the weather phenomena undoubtedly involve synergistic interactions among multiple elements while positional information constitutes merely a component of the boundary conditions. This paper focuses primarily on the task of precipitation nowcasting and develops an efficient distribution-centric Meteorological Tokenization (MeTok) scheme, which spatially sequences to group similar meteorological features. Based on the rearrangement, realigned group learning enhances robustness across precipitation patterns, especially extreme ones. Specifically, we introduce the Hyper-Aligned Grouping Transformer (HyAGTransformer) with two key improvements: 1) The Grouping Attention (GA) mechanism uses MeTok to enable self-aligned learning of features from different precipitation patterns; 2) The Neighborhood Feed-Forward Network (N-FFN) integrates adjacent group features, aggregating contextual information to boost patch embedding discriminability. Experiments on the ERA5 dataset for 6-hour forecasts show our method improves the IoU metric by at least 8.2% in extreme precipitation prediction compared to other methods. Additionally, it gains performance with more training data and increased parameters, demonstrating scalability, stability, and superiority over traditional methods.

M. Molina, C. Gutiérrez, E. Sánchez

Understanding space–time features of wind speed is of high interest in meteorology and several applied sciences. Accurate wind speed measurements in combination with reliable gridded products, such as reanalyses, are needed to address the main characteristics of the wind field. Hourly 10 m wind speed from the European Centre for Medium‐Range Weather Forecasts (ECMWF) latest reanalysis (ERA5) is compared with HadISD wind observations from 245 stations across Europe. Averaged ERA5 hourly data is able to reproduce the annual cycle of monthly wind speed in Europe. ERA5 presents slightly larger (shorter) monthly medians in winter (summer) than observations. ERA5 is compared against observations for each station using a frequency distribution‐based score (score, from 0 to 1). Most of the stations exhibit hourly scores ranging from 0.8 to 0.9, indicating that ERA5 is able to reproduce the wind speed spectrum range, from light to strong relative frequencies, for any location over Europe. Ranges of mean values, variability, distribution function parameters and high or low wind thresholds frequencies are shown for this ensemble of European stations, allowing for an overall description of wind features. Generally, there is no clear relationship between scores and the variables analysed. The correlation and scores between ERA5 and HadISD is even further increased at longer time frequencies (6–24 hourly), together with centred root‐mean‐square error (RMSE) and standard deviation decreases. Hourly wind data from ERA5 reanalysis is, despite some shortcomings, valuable information to perform further detailed studies with a regular spatial and time wind distribution, from the climatological or renewable energy perspectives, for example.

Hang Yin, Yan-Ming Zhang, Jian Xu et al.

Air quality prediction plays a crucial role in public health and environmental protection. Accurate air quality prediction is a complex multivariate spatiotemporal problem, that involves interactions across temporal patterns, pollutant correlations, spatial station dependencies, and particularly meteorological influences that govern pollutant dispersion and chemical transformations. Existing works underestimate the critical role of atmospheric conditions in air quality prediction and neglect comprehensive meteorological data utilization, thereby impairing the modeling of dynamic interdependencies between air quality and meteorological data. To overcome this, we propose MDSTNet, an encoder-decoder framework that explicitly models air quality observations and atmospheric conditions as distinct modalities, integrating multi-pressure-level meteorological data and weather forecasts to capture atmosphere-pollution dependencies for prediction. Meantime, we construct ChinaAirNet, the first nationwide dataset combining air quality records with multi-pressure-level meteorological observations. Experimental results on ChinaAirNet demonstrate MDSTNet's superiority, substantially reducing 48-hour prediction errors by 17.54\% compared to the state-of-the-art model. The source code and dataset will be available on github.

Amina El Bernoussi, Mohamed El Arrouchi

The paper introduces a new regression model designed for situations where both the response and covariates are non-stationary extremes. This method is specifically designed for situations where both the response variable and covariates are represented as block maxima, as the limiting distribution of suitably standardized componentwise maxima follows an extreme value copula. The framework focuses on the regression manifold, which consists of a collection of regression lines aligned with the asymptotic result. A Logistic-normal prior is applied to the space of spectral densities to gain insights into the model based on the data, resulting in an induced prior on the regression manifolds. Numerical studies demonstrate the effectiveness of the proposed method, and an analysis of real meteorological data provides intriguing insights into the relationships between extreme losses in precipitation and temperature.

Zili Liu, Hao Chen, Lei Bai et al.

Obtaining accurate weather forecasts at station locations is a critical challenge due to systematic biases arising from the mismatch between multi-scale, continuous atmospheric characteristic and their discrete, gridded representations. Previous works have primarily focused on modeling gridded meteorological data, inherently neglecting the off-grid, continuous nature of atmospheric states and leaving such biases unresolved. To address this, we propose the Kolmogorov Arnold Neural Interpolator (KANI), a novel framework that redefines meteorological field representation as continuous neural functions derived from discretized grids. Grounded in the Kolmogorov Arnold theorem, KANI captures the inherent continuity of atmospheric states and leverages sparse in-situ observations to correct these biases systematically. Furthermore, KANI introduces an innovative zero-shot downscaling capability, guided by high-resolution topographic textures without requiring high-resolution meteorological fields for supervision. Experimental results across three sub-regions of the continental United States indicate that KANI achieves an accuracy improvement of 40.28% for temperature and 67.41% for wind speed, highlighting its significant improvement over traditional interpolation methods. This enables continuous neural representation of meteorological variables through neural networks, transcending the limitations of conventional grid-based representations.

Ashok Kumar, Abbinav Sankar Kailasam, Anish Rai et al.

Climate is an evolving complex system with dynamic interactions and non-linear feedback mechanisms, shaping environmental and socio-economic outcomes. Crop production is highly sensitive to climatic fluctuations (and many other environmental, social and governance factors). This paper studies the price volatility of agricultural crops as influenced by meteorological variables, which is critical for agricultural planning, sustainable finance and policy-making. As case studies, we choose the two Indian states: Madhya Pradesh (for Soybean) and Odisha (for Brinjal/Eggplant). We employ an Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) model to estimate the conditional volatility of the log returns from 2012 to 2024. We further explore the cross-correlations between price volatility and the meteorological variables followed by a Granger-causal test to analyze the causal effect of meteorological variables on the volatility. The Seasonal Auto-Regressive Integrated Moving Average with Exogenous Regressors (SARIMAX) and Long Short-Term Memory (LSTM) models are implemented as simple machine learning models of price volatility with meteorological factors as exogenous variables. Finally, to capture spatial dependencies in volatility across districts, we extend the analysis using a Conditional Autoregressive (CAR) model to construct monthly volatility surfaces that reflect both local price risk as well as geographic dependence. We believe, this paper will illustrate the usefulness of simple machine learning models in agricultural finance, and help the farmers to make informed decisions by considering climate patterns and making beneficial decisions with regard to crop rotation or allocations. In general, incorporating meteorological factors to assess agricultural performance could help to understand and reduce price volatility and possibly lead to economic stability.

Philipp Ertz, Petra Friederichs

The aim of this study is to provide a probabilistic gust analysis for the region of Germany that is calibrated with station observations and with an interpolation to unobserved locations. To this end, we develop a spatial Bayesian hierarchical model (BHM) for the post-processing of surface maximum wind gusts from the COSMO-REA6 reanalysis. Our approach uses a non-stationary extreme value distribution for the gust observations, with parameters that vary according to a linear model using COSMO-REA6 predictor variables. To capture spatial patterns in surface wind gust behavior, the regression coefficients are modeled as 2-dimensional Gaussian random fields with a constant mean and an isotropic covariance function that depends on the distance between locations. In addition, we include an elevation offset in the distance metric for the covariance function to account for the topography. This allows us to include data from mountaintop stations in the training process. The training of the BHM is carried out with an independent data set from which the data at the station to be predicted are excluded. We evaluate the spatial prediction performance at the withheld station using Brier score and quantile score, including their decomposition, and compare the performance of our BHM to climatological forecasts and a non-hierarchical, spatially constant baseline model. This is done for 109 weather stations in Germany. Compared to the spatially constant baseline model, the spatial BHM significantly improves the estimation of local gust parameters. It shows up to 5 % higher skill for prediction quantiles and provides a particularly improved skill for extreme wind gusts. In addition, the BHM improves the prediction of threshold levels at most of the stations. Although a spatially constant approach already provides high skill, our BHM further improves predictions and improves spatial consistency.

Lot Ram, Chanchal Singh, Diptiranjan Rout et al.

The Mars ambient space environment evolves with the varying solar activity. Understanding the Martian space environment, particularly the topside ionosphere across different phases of Solar Cycles (SC) 24 \& 25 remains a key research gap in planetary ionospheric science. In this study, we utilized the NASA Mars Atmosphere and Volatile EvolutioN (MAVEN) mission data (150-500 km) from Martian years 32-38 (2015-2024) during solar quiet-time. This study investigated the behavior of topside ionosphere (e-, CO2+, O2+, NO+, OH+, O+, N+ \& C+) across different phases of SC over the northern hemisphere. A significant variation in ionosphere is observed over low-latitude (0-30°N) with higher densities compared to mid-latitude (31-60°N) across SC. Additionally, we found that the Martian northern ionospheric densities were highest during solar maximum phase on both dayside and nightside compared to low active phases. The dayside densities were approximately 1-2 orders higher compared to those on the nightside. The electron and molecular ions densities increased by factors of 1-5 and 1-13, respectively. While O+ ion density was enhanced by nearly 2-2.5 times, along with an upliftment of 40-50 km in the peak height. The enhanced dayside densities are attributed to the elevated solar irradiance (1.4-2 times) and varying solar wind flux. Furthermore, the enhanced day-to-night plasma transport and elevated solar electron flux during maxima, higher by 33-66\% than during low-activity, can contribute to the increased nightside ionization. This work, for the first time, uses long-term MAVEN datasets across the descending-to-maxima phases of SC to reveal climatology of Martian topside ionosphere.

S. Widiyantoro, S. Widiyantoro, E. Gunawan et al.

Relocation of earthquakes recorded by the agency for meteorology, climatology and geophysics (BMKG) in Indonesia and inversions of global positioning system (GPS) data reveal clear seismic gaps to the south of the island of Java. These gaps may be related to potential sources of future megathrust earthquakes in the region. To assess the expected inundation hazard, tsunami modeling was conducted based on several scenarios involving large tsunamigenic earthquakes generated by ruptures along segments of the megathrust south of Java. The worst-case scenario, in which the two megathrust segments spanning Java rupture simultaneously, shows that tsunami heights can reach ~ 20 m and ~ 12 m on the south coast of West and East Java, respectively, with an average maximum height of 4.5 m along the entire south coast of Java. These results support recent calls for a strengthening of the existing Indonesian Tsunami Early Warning System (InaTEWS), especially in Java, the most densely populated island in Indonesia.

Javier Vaquero-Martinez, Manuel Anton

After 30 years since the beginning of the Global Positioning System (GPS), or, more generally, Global Navigation Satellite System (GNSS) meteorology, this technique has proven to be a reliable method for retrieving atmospheric water vapor; it is low-cost, weather independent, with high temporal resolution and is highly accurate and precise. GNSS ground-based networks are becoming denser, and the first stations installed have now quite long time-series that allow the study of the temporal features of water vapor and its relevant role inside the climate system. In this review, the different GNSS methodologies to retrieve atmospheric water vapor content re-examined, such as tomography, conversion of GNSS tropospheric delay to water vapor estimates, analyses of errors, and combinations of GNSS with other sources to enhance water vapor information. Moreover, the use of these data in different kinds of studies is discussed. For instance, the GNSS technique is commonly used as a reference tool for validating other water vapor products (e.g., radiosounding, radiometers onboard satellite platforms or ground-based instruments). Additionally, GNSS retrievals are largely used in order to determine the high spatio-temporal variability and long-term trends of atmospheric water vapor or in models with the goal of determining its notable influence on the climate system (e.g., assimilation in numerical prediction, as input to radiative transfer models, study of circulation patterns, etc.

Hao Yang, Xiaomeng Zhu, Zhiyuan Fang et al.

The mechanism of aerosol pollution transport remains highly elusive owing to the myriad of influential factors. In this study, ground station data, satellite data, ground-based LiDAR remote sensing data, sounding data, ERA5 reanalysis and a backward trajectory model were combined to investigate the formation process and optical properties of winter aerosol pollution in Beijing and surrounding areas. The analysis of ground station data shows that compared to 2019 and 2021, the pandemic lockdown policy resulted in a decrease in the total number of pollution days and a decrease in the average concentration of particulate matter in the Beijing area in 2020. The terrain characteristics of the Beijing-Tianjin-Hebei (BTH) made it prone to northeast and southwest winds. The highest incidence of aerosol pollution in Beijing occurs in February and March during the spring and winter seasons. Analysis of a typical heavy aerosol pollution process in the Beijing area from 28 February to 5 March 2019 shows that dust and fine particulate matter contributed to the primary pollution; surface air temperature inversion and an average wind speed of less than 3 m/s were conducive to the continuous accumulation of pollutants, which was accompanied by the oxidation reaction of NO₂ and O₃, forming photochemical pollution. The heavy aerosol pollution was transmitted and diffused towards the southeast, gradually eliminating the pollution. Our results provide relevant research support for the prevention and control of aerosol pollution.

Dani Khoury, Maurice Millet, Yasmine Jabali et al.

Sixty-four phenols grouped as nitrated, bromo, amino, methyl, chloro-phenols, and cresols, and thirty-eight organic acids grouped as mono-carboxylic and dicarboxylic are analyzed in forty-two fog samples collected in the Alsace region between 2015 and 2021 to check their atmospheric behavior. Fogwater samples are collected using the Caltech Active Strand Cloudwater Collector (CASCC2), extracted using liquid–liquid extraction (LLE) on a solid cartridge (XTR Chromabond), and then analyzed using gas chromatography coupled with mass spectrometry (GC-MS). The results show the high capability of phenols and acids to be scavenged by fogwater due to their high solubility. Nitro-phenols and mono-carboxylic acids have the highest contributions to the total phenolic and acidic concentrations, respectively. 2,5-dinitrophenol, 3-methyl-4-nitrophenol, 4-nitrophenol, and 3,4-dinitrophenol have the highest concentration, originating mainly from vehicular emissions and some photochemical reactions. The top three mono-carboxylic acids are hexadecenoic acid (C16), eicosanoic acid (C18), and dodecanoic acid (C12), whereas succinic acid, suberic acid, sebacic acid, and oxalic acid are the most concentrated dicarboxylic acids, originated either from atmospheric oxidation (mainly secondary organic aerosols (SOAs)) or vehicular transport. Pearson’s correlations show positive correlations between organic acids and previously analyzed metals (<i>p</i> < 0.05), between mono- and dicarboxylic acids (<i>p</i> < 0.001), and between the analyzed acidic compounds (<i>p</i> < 0.001), whereas no correlations are observed with previously analyzed inorganic ions. Total phenolic and acidic fractions are found to be much higher than those observed for pesticides, polycyclic aromatic hydrocarbons (PAHs), and polychlorinated biphenyls (PCBs) measured at the same region due to their higher scavenging by fogwater.

Jinhe Li, Yubin Li, Jie Tang

This study analyzes the Northwestern Pacific tropical cyclone (TC) size difference between the China Meteorological Administration (CMA) dataset and the Joint Typhoon Warning Center (JTWC) dataset. The TC size is defined by the near-surface 34-knot wind radius (R34). Although there is a high correlation (correlation coefficient of 0.71) between CMA and JTWC R34 values, significant discrepancies are still found between them. The JTWC tends to report larger R34 values than the CMA for large-sized TCs, while the trend is reversed for compact TCs. Despite spatial distribution discrepancies, both datasets exhibit significant similarity (spatial correlation coefficient of 0.61), particularly in latitudinal distribution; higher R34 values are observed near 25° N. An investigation of key parameters affecting R34 estimations shows that the discrepancies in R34 values between the two agencies’ estimates of TC size are primarily influenced by the size itself and latitude. There is a high correlation between R34 difference and R34 values, with a high correlation of up to 0.58 with the JTWC’s R34 values. There is also a significant correlation between R34 difference and latitude, with a correlation coefficient of 0.26 in both the CMA and JTWC datasets. Case studies of Typhoons “Danas” and “Maysak” confirm distinct characteristics in R34 estimations during different development stages, with the JTWC capturing TC intensification better, while the CMA underestimates TC size during rapid growth phases. During the weakening stage of the TC, both agencies accurately estimate the R34 values. These findings contribute valuable insights into the discrepancies and characteristics of R34 datasets, informing the selection and utilization of data for typhoon research and forecasting.

Jamie Mullineaux, Takoua Jendoubi, Baptiste Leurent

The spread of COVID-19 has brought challenges to health, social and economic systems around the world. With little to no prior immunity in the global population transmission has been driven primarily by human interaction. However, as with common respiratory illnesses such as the flu it's suggested that COVID-19 may become seasonal as immunity grows. Yet the effects of meteorological conditions on the spread of COVID-19 are poorly understood with previous studies producing contrasting results, due at least in part to limited and inconsistent study designs. This study investigates the effect of meteorological conditions on COVID-19 infections in England using a spatio-temporal model applied to case counts during the initial England lockdown. By modelling spatial and temporal effects to account for the nature of a human transmissible virus the model isolates meteorological effects. Inference based on 95% highest posterior density intervals shows humidity is negatively associated with COVID-19 spread. The lack of evidence for other weather factors affecting COVID-19 transmission shows care should be taken with respect to seasonality when designing COVID-19 policies and public communications.

Isabell Stucke, Deborah Morgenstern, Thorsten Simon et al.

Upward lightning (UL) has become an increasingly important threat to wind turbines as ever more of them are being installed for renewably producing electricity. The taller the wind turbine the higher the risk that the type of lightning striking the man-made structure is UL. UL can be much more destructive than downward lightning due to its long lasting initial continuous current leading to a large charge transfer within the lightning discharge process. Current standards for the risk assessment of lightning at wind turbines mainly take the summer lightning activity into account, which is inferred from LLS. Ground truth lightning current measurements reveal that less than 50% of UL might be detected by lightning location systems (LLS). This leads to a large underestimation of the proportion of LLS-non-detectable UL at wind turbines, which is the dominant lightning type in the cold season. This study aims to assess the risk of LLS-detectable and LLS-non-detectable UL at wind turbines using direct UL measurements at the Gaisberg Tower (Austria) and Säntis Tower (Switzerland). Direct UL observations are linked to meteorological reanalysis data and joined by random forests, a powerful machine learning technique. The meteorological drivers for the non-/occurrence of LLS-detectable and LLS-non-detectable UL, respectively, are found from the random forest models trained at the towers and have large predictive skill on independent data. In a second step the results from the tower-trained models are extended to a larger study domain (Central and Northern Germany). The tower-trained models for LLS-detectable lightning is independently verified at wind turbine locations in that domain and found to reliably diagnose that type of UL. Risk maps based on case study events show that high diagnosed probabilities in the study domain coincide with actual UL events.

Daniel Braun, Kerstin Ebell, Vera Schemann et al.

This paper presents a novel color scheme designed to address the challenge of visualizing data series with large value ranges, where scale transformation provides limited support. We focus on meteorological data, where the presence of large value ranges is common. We apply our approach to meteorological scatterplots, as one of the most common plots used in this domain area. Our approach leverages the numerical representation of mantissa and exponent of the values to guide the design of novel "nested" color schemes, able to emphasize differences between magnitudes. Our user study evaluates the new designs, the state of the art color scales and representative color schemes used in the analysis of meteorological data: ColorCrafter, Viridis, and Rainbow. We assess accuracy, time and confidence in the context of discrimination (comparison) and interpretation (reading) tasks. Our proposed color scheme significantly outperforms the others in interpretation tasks, while showing comparable performances in discrimination tasks.

Fang Huang, Wencong Cheng, PanFeng Wang et al.

Meteorological satellite imagery is critical for meteorologists. The data have played an important role in monitoring and analyzing weather and climate changes. However, satellite imagery is a kind of observation data and exists a significant time delay when transmitting the data back to Earth. It is important to make accurate predictions for meteorological satellite images, especially the nowcasting prediction up to 2 hours ahead. In recent years, there has been growing interest in the research of nowcasting prediction applications of weather radar images based on deep learning. Compared to the weather radar images prediction problem, the main challenge for meteorological satellite images prediction is the large-scale observation areas and therefore the large sizes of the observation products. Here we present a deep multi-scales extrapolation fusion method, to address the challenge of the meteorological satellite images nowcasting prediction. First, we downsample the original satellite images dataset with large size to several images datasets with smaller resolutions, then we use a deep spatiotemporal sequences prediction method to generate the multi-scales prediction images with different resolutions separately. Second, we fuse the multi-scales prediction results to the targeting prediction images with the original size by a conditional generative adversarial network. The experiments based on the FY-4A meteorological satellite data show that the proposed method can generate realistic prediction images that effectively capture the evolutions of the weather systems in detail. We believe that the general idea of this work can be potentially applied to other spatiotemporal sequence prediction tasks with a large size.

Pierre Durand

At the surface of the earth, the available radiative energy <i>R_n</i> is distributed between the ground heat flux and the sensible and latent heat fluxes according to the surface energy balance (SEB) equation. In the past decades, most attempts to measure the individual terms of this equation have revealed a non-closure problem, regardless of the site of observation or period of the year. Today, no definitive answer has been provided to this question. In general, it is suspected that the sensible and latent heat fluxes (<i>H</i> and <i>L_vE</i>, respectively) that are calculated with the eddy-covariance technique are underestimated. This paper suggests two additional terms that should be considered in the SEB equation, which are based on thermodynamic considerations. They are directly related to <i>H</i> and <i>L_vE</i> and appear to be interesting candidates for explaining (at least in part) the non-closure of the SEB. The distribution of the correction between <i>H</i> and <i>L_vE</i> varies as a function of the Bowen ratio <i>B</i>. The correction relative to <i>H</i> is dominant for values of <i>B</i> that are greater than 0.2 and represents more than 80% of the total correction for values greater than unity. The impact of these corrections on the SEB closure was tested on a large set of observations from 24 FLUXNET sites around the world with different vegetation types. The closure defect, which is about 17% in the original dataset, is reduced to about 3% with the proposed corrections.

Ana Žaknić-Ćatović, William A. Gough

Canadian high-frequency temperature time series exhibit physical heterogeneity in the coexistence of radiative and advective populations in the total air temperature sample. This work examines forty-five Canadian hourly air temperature records to study seasonal characteristics and variability of radiative and advective population counts and their corresponding temperature biases and trends. The Linear Pattern Discrimination algorithm, conceptualized in a previous study, was adjusted to seasonal analysis on the equinox-to-equinox time scale. Count analysis of radiative and advective days supports the existence of two distinct thermal regimes, Spring–Summer and Fall–Winter. Further, seasonal advective counts for the majority of examined stations typically decrease in numbers. The consistently warmer winter radiative temperature extrema points to the critical role of the advective population in control of the overall temperature magnitude. Canadian northwest warming trends are found to be the highest, indicating the amplifying effect of decreasing advective counts with rapidly increasing temperatures that weaken the advective population’s moderating ability to control the magnitude of the total temperature population.

E. Tarnavsky, David Grimes, R. Maidment et al.

AbstractTropical Applications of Meteorology Using Satellite Data and Ground-Based Observations (TAMSAT) rainfall monitoring products have been extended to provide spatially contiguous rainfall estimates across Africa. This has been achieved through a new, climatology-based calibration, which varies in both space and time. As a result, cumulative estimates of rainfall are now issued at the end of each 10-day period (dekad) at 4-km spatial resolution with pan-African coverage. The utility of the products for decision making is improved by the routine provision of validation reports, for which the 10-day (dekadal) TAMSAT rainfall estimates are compared with independent gauge observations. This paper describes the methodology by which the TAMSAT method has been applied to generate the pan-African rainfall monitoring products. It is demonstrated through comparison with gauge measurements that the method provides skillful estimates, although with a systematic dry bias. This study illustrates TAMSAT’s value as ...