Hasil untuk "Meteorology. Climatology"

Ines Montoya-Espinagosa, Antonio Agudo

Due to the rise in the use of renewable energies as an alternative to traditional ones, and especially solar energy, there is increasing interest in studying how to address photovoltaic forecasting in the face of the challenge of variability in photovoltaic energy production, using different methodologies. This work develops a hybrid approach for short and long-term forecasting based on two studies with the same purpose. A multimodal approach that combines images of the sky and photovoltaic energy history with meteorological data is proposed. The main goal is to improve the accuracy of ramp event prediction, increase the robustness of forecasts in cloudy conditions, and extend capabilities beyond nowcasting, to support more efficient operation of the power grid and better management of solar variability. Deep neural models are used for both nowcasting and forecasting solutions, incorporating individual and multiple meteorological variables, as well as an analytical solar position. The results demonstrate that the inclusion of meteorological data, particularly the surface long-wave, radiation downwards, and the combination of wind and solar position, significantly improves current predictions in both nowcasting and forecasting tasks, especially on cloudy days. This study highlights the importance of integrating diverse data sources to improve the reliability and interpretability of solar energy prediction models.

Kan Li, Chongyu Zhang, Wei Zhang et al.

To address the challenge of rapidly selecting optimal numerical model products for weather forecasting in critical applications such as aviation route planning, this study proposes an enhanced object-based methodology comprising individual object scoring matching and a regional overall forecast selection scheme, building upon previous research. The method focuses on radar reflectivity forecasts within critical areas along air routes. Individual thunderstorm cells are evaluated using weighted scores for multiple parameters, including the Threat Score (TS), center-of-mass position, maximum radar reflectivity intensity, and shape forecasting accuracy. The regional overall score is then calculated by applying different weights to each convective cell within the area. After examining case studies of various convection types and bulk tests from June to September of 2024 and 2025, the results demonstrate that this method effectively selects the optimal convective forecasts from among the numerical models initiated at different times. The methodology shows promising applications in aviation weather forecasting. Different optimal selection schemes yield varying results: for large-scale convective weather, various test schemes generally align with TS score selection; for small-scale convective weather, schemes emphasizing radar reflectivity intensity show better performance; for scattered convection, schemes prioritizing center-of-mass position forecasting demonstrate superior results. These findings provide valuable insights for precision weather forecasting in both aviation and the agricultural–ecological sectors, in which accurate convective weather prediction is crucial for operational safety and resource management.

Junwoo Song, Pyo-woong Son

Unlike GNSS, which is vulnerable to jamming and spoofing due to its inherently weak received power, eLoran exhibits robustness owing to its high field strength. Therefore, the eLoran system can maintain reliable operation even in scenarios where GNSS becomes unavailable. However, since eLoran signals propagate through ground waves, the propagation delay is susceptible to changes in surface conditions, including both terrain and meteorological variations. This study aims to analyze the correlation between the temporal variations in eLoran signal propagation delay and meteorological factors at various points along the signal path.

Generich H. Capuli

The environments of severe thunderstorms that produced hail were examined using 171 proximity soundings (2005-2024) archived in the 3rd Data Release of Project SWAP. These soundings were categorized based on their geographical occurrence into three hail-prone environments across Luzon, Philippines. Key parameters describing instability, vertical wind shear, and moisture were calculated to assess the environmental conditions for hail production. The probability of hail occurrence, expressed as a function of W$_{\text{MAX}}$ ($\sqrt{2 \times \text{CAPE}}$) and 0-6 km bulk shear (DLS), revealed patterns distinct from those reported in other regions. Hail events in Luzon were most likely under high CAPE conditions, where boundary-layer moisture was sufficient, mid- and low-level lapse rates were steep, and lifting condensation levels were high. Surprisingly, weak DLS was common across Luzon hail environments, diverging from existing severe weather climatologies, yet large DCAPE indicated environments conducive to damaging wind events. When DLS was replaced with the shear magnitude between the cloud base and equilibrium level, the probability of hail occurrence increased, better aligning with global severe weather climatologies. This finding is supported by hodograph analyses, which show largely unidirectional wind profiles: strong speed shear aloft but weak directional shear in the low-levels. Parameters such as W$_{\text{MAX}}$SHEAR, W$_{\text{MAX}}$SHEAR$_{\text{LCL-EL}}$, and BWD$_{\text{LCL-EL}}$ emerge as potential discriminators between non-severe and severe thunderstorms capable of producing hail, and as useful metrics for assessing convective storm severity in Luzon and possibly countrywide. Finally, two recurring severe setups conducive to hail were identified: (1) an easterly regime associated with trade winds, and (2) a westerly regime linked to the Asian summer monsoon.

Nisha Patel, Britta Jänicke, René Burghardt et al.

ABSTRACT An increasing number of cities in Germany and Europe are formulating adaptation strategies to address the consequences of climate change. Nevertheless, quantifying whether these strategies contribute to alterations in urban infrastructure and promote climate‐sensitive urban development is challenging. This article aims to explore possible urban climate adaptation indicators (UCAIs) from literature suitable for assessing the implementation of heat‐ and water‐sensitive urban development measures in local municipalities, with a focus on Germany. In addition to a literature review, workshops and discussions with experts from Germany complemented and deepened the indicator selection process. As a result, we identified 27 indicators, which were grouped into 5 key areas: (1) surface and urban overheating indicators; (2) building type and structure indicators; (3) green infrastructure indicators; (4) soil‐sealing indicators; and (5) water‐sensitive urban development indicators. Only a few manage to map several adaptation measures, avoiding conflicts with other urban planning objectives, can be derived for cities at the national level and show promise for capturing small‐scale adaptation measures in the city. We concluded that, in particular, the green infrastructure and soil‐sealing indicators, such as green cover, access to greenery and green supply have a high potential to meet heat‐ and water‐sensitive urban development goals, while avoiding conflicts of objectives and trade‐offs. Overall, this review underscores the necessity for additional research and testing to formulate practical and effective indicators for capturing heat‐ and water‐sensitive aspects of urban development.

Hiroshi G. Takahashi

This paper presents a new precipitation dataset that is daily, has a spatial resolution of one degree on a quasi-global scale, and spans more than 42 years, using machine learning techniques. The ultimate goal of this dataset is to provide a homogeneous daily precipitation dataset for several decades without gaps, which is suitable for climate analysis. As a first step, 42 years of daily precipitation data was generated using machine learning techniques. The machine learning methods are supervised learning, and the reference data are estimated precipitation datasets from 2001 to 2020. The three machine learning methods are random forest, gradient-boosted decision trees, and convolutional neural networks. The input data are satellite observations and atmospheric circulations from reanalysis, which are somewhat modified based on knowledge of the climatological background. Using the trained statistical models, we predict back to 1979, when daily precipitation data was almost unavailable globally. The detailed procedures are described in this paper. The produced data have been partially evaluated. However, additional evaluations from different perspectives are needed. The advantages and disadvantages of this precipitation dataset are also discussed. Currently, this GPC/m precipitation dataset version is GPC/m-v1-2024.

Hannah L. Woodward, Andrew J. Rushby, Nathan J. Mayne

Planetary surface habitability has so far been considered, in the main, upon a global scale. The increasing number of 3D modelling studies of (exo)planetary climate has highlighted the need for a more nuanced understanding of surface habitability. Using satellite-derived data of photosynthetic life to represent the observed surface habitability of modern Earth, we validate a set of climatologically-defined metrics previously used in habitability studies. The comparison finds that the metrics defined by surface temperature alone show spatial patterns of habitability distinct to those defined by aridity or water availability, with no metric able to completely replicate the observed habitability. We build upon these results to introduce a new metric defined by the observed thermal limits of modern Earth-based life, along with surface water fluxes as an analogue for water and nutrient availability. Furthermore, we pay attention to not only the thermal bounds of macroscopic complex life, but additionally the limits of microbial life which have been vital to the generation of Earth's biosignatures, thus expanding considerations of climatic habitability out of a historically binary definition. Repeating the validation for our metric, along with another which uses a similar definition that incorporates conditions for both temperature and water availability, shows a significant improvement in capturing the broad patterns of surface habitability, laying the groundwork for more comprehensive assessments of potential life-supporting climates upon other planets.

Roberto G. Aragón, M. Eugenia Cornejo, Jesús Medina et al.

A fundamental issue about installation of photovoltaic solar power stations is the optimization of the energy generation and the fault detection, for which different techniques and methodologies have already been developed considering meteorological conditions. This fact implies the use of unstable and difficult predictable variables which may give rise to a possible problem for the plausibility of the proposed techniques and methodologies in particular conditions. In this line, our goal is to provide a decision support system for photovoltaic fault detection avoiding meteorological conditions. This paper has developed a mathematical mechanism based on fuzzy sets in order to optimize the energy production in the photovoltaic facilities, detecting anomalous behaviors in the energy generated by the facilities over time. Specifically, the incorrect and correct behaviors of the photovoltaic facilities have been modeled through the use of different membership mappings. From these mappings, a decision support system based on OWA operators informs of the performances of the facilities per day, by using natural language. Moreover, a state machine is also designed to determine the stage of each facility based on the stages and the performances from previous days. The main advantage of the designed system is that it solves the problem of "constant loss of energy production", without the consideration of meteorological conditions and being able to be more profitable. Moreover, the system is also scalable and portable, and complements previous works in energy production optimization. Finally, the proposed mechanism has been tested with real data, provided by Grupo Energético de Puerto Real S.A. which is an enterprise in charge of the management of six photovoltaic facilities in Puerto Real, Cádiz, Spain, and good results have been obtained for faulting detection.

Yutong Xiong, Xun Zhu, Ming Wu et al.

Sparse meteorological forecasting is indispensable for fine-grained weather forecasting and deserves extensive attention. Recent studies have highlighted the potential of spatio-temporal graph convolutional networks (ST-GCNs) in predicting numerical data from ground weather stations. However, as one of the highest fidelity and lowest latency data, the application of the vision data from satellites in ST-GCNs remains unexplored. There are few studies to demonstrate the effectiveness of combining the above multi-modal data for sparse meteorological forecasting. Towards this objective, we introduce Vision-Numerical Fusion Graph Convolutional Network (VN-Net), which mainly utilizes: 1) Numerical-GCN (N-GCN) to adaptively model the static and dynamic patterns of spatio-temporal numerical data; 2) Vision-LSTM Network (V-LSTM) to capture multi-scale joint channel and spatial features from time series satellite images; 4) a GCN-based decoder to generate hourly predictions of specified meteorological factors. As far as we know, VN-Net is the first attempt to introduce GCN method to utilize multi-modal data for better handling sparse spatio-temporal meteorological forecasting. Our experiments on Weather2k dataset show VN-Net outperforms state-of-the-art by a significant margin on mean absolute error (MAE) and root mean square error (RMSE) for temperature, relative humidity, and visibility forecasting. Furthermore, we conduct interpretation analysis and design quantitative evaluation metrics to assess the impact of incorporating vision data.

Luis Angel Espinosa, Maria Manuela Portela, Nikte Ocampo-Guerrero

This study examines the trends in heatwave characteristics across mainland Portugal from 1980/1981 to 2022/2023, utilising ERA5-Land reanalysis data. To achieve this, the study applies the Heatwave Magnitude Index (HWMI) to identify heatwave days for minimum (T_min) and maximum (T_max) temperatures across 15 grid-points representing Portugal’s diverse geography and climate. Three key annual parameters are analysed: the number of heatwave days (ANDH), the average temperature during heatwaves (AATW), and the intensity of heatwave events (AIHD). Results reveal a consistent increase in heatwave persistence throughout mainland Portugal, with more pronounced trends observed for T_max compared to T_min. ANDH T_min shows upward trends across all grid-points, with increases ranging from 0.8 to 4.2 days per decade. ANDH T_max exhibits even more significant increases, with 11 out of 15 grid-points showing statistically significant rises, ranging from 2.2 to 4.4 days per decade. Coastal areas, particularly in the south, demonstrate the most substantial increases in heatwave persistence. The intensity of heatwaves, as measured by AIHD, also shows positive trends across all grid-points for both T_min and T_max, with southern locations experiencing the most significant increases. The study also discusses decadal trends in annual averages of T_min and T_max, as well as extreme measures such as annual minimum (AMIN) and annual maximum (AMAX), daily temperatures spatially represented across mainland Portugal. These analyses reveal widespread warming trends, with more pronounced increases in T_max compared to T_min. The AMIN and AMAX trends further corroborate the overall warming pattern from the heatwave analyses, with notable spatial variations observed. The findings indicate a substantial worsening in the occurrence, duration, and intensity of heatwave events. This increased persistence of heatwaves, especially evident from the early 2000s onwards, suggests a potential climate regime shift in mainland Portugal. The results underscore the need for adaptive strategies to address the growing challenges posed by more frequent and intense heatwaves in the region.

Huiling Zhou, Kaixuan Tang, Longfang Huang et al.

Cultivating tourists’ environmental responsibility behavior is an effective way to relieve the pressure of ecological environment in tourist destinations. Based on the value-attitude-behavior theory, this paper constructs a relationship model of values, ecotourism attitude, social responsibility awareness and environmental responsibility behavior, and explores the mechanism of tourists’ values influence on environmental responsibility behavior. Taking three scenic areas (Mao’er Mountain, Yulong River and Xingping Ancient Town) in the Lijiang River Basin of Guilin, a world-famous tourist destination in China, as a case study, the structural equation model is used to test the theoretical hypotheses of tourists’ environmental responsibility behavior. The results of the three studies show that values have a significant positive impact on tourists’ environmental responsibility behavior, while ecotourism attitude cannot directly affect tourists’ environmental responsibility behavior, but values can indirectly affect tourists’ environmental responsibility behavior through the chain intermediary of ecotourism attitude and social responsibility awareness. The findings of the study can effectively guide tourists’ environmental responsibility behavior, which has far-reaching significance for the sustainable development of tourist destinations.

Yann Georg Büchau, Hasan Mashni, Matteo Bramati et al.

PARMESAN (the Python Atmospheric Research Package for MEteorological TimeSeries and Turbulence ANalysis) is a Python package providing common functionality for atmospheric scientists doing time series or turbulence analysis. Several meteorological quantities such as potential temperature, various humidity measures, gas concentrations, wind speed and direction, turbulence and stability parameters can be calculated. Furthermore, signal processing functionality such as properly normed variance spectra for frequency analysis is available. In contrast to existing packages with similar goals, its routines for physical quantities are derived from symbolic mathematical expressions, enabling inspection, automatic rearrangement, reuse and recombination of the underlying equations. Building on this, PARMESAN's functions as well as their comprehensive parameter documentation are mostly auto-generated, minimizing human error and effort. In addition, sensitivity/error propagation analysis is possible as mathematical operations like derivations can be applied to the underlying equations. Physical consistency in terms of units and value domains are transparently ensured for PARMESAN functions. PARMESAN's approach can be reused to simplify implementation of robust routines in other fields of physics.

Takanori Watanabe, Hiroshi Matsuyama, Irina Kuzhevskaia et al.

Siberia, which experienced disastrous heat waves in 2010 and 2012, is one of the regions in which extreme climate events have occurred recently. To compare the long-term trends of extreme climate events in the southern part of Siberia with those of surrounding regions, we calculated 11 extreme climate indexes from observational data for 1950–2019 and analyzed the trends in Siberia and other parts of Russia using statistical techniques, i.e., Welch’s <i>t</i>-test, the Mann–Kendall test, Sen’s slope estimator, and a cluster analysis. We clarified that high-temperature events in March are more frequent in Siberia than in the surrounding areas. However, the increasing trends of high temperatures in Siberia were lower than those in northwestern China and Central Asia. The intensity of heavy precipitation is increasing in Siberia, as it is in the surrounding areas. Compared to the surrounding areas analyzed in previous studies, the trend of heavy precipitation in Siberia has not increased much. In particular, Siberia shows a more remarkable decreasing trend in heavy precipitation during the summer than other regions. The dry trends in the summer, however, do not occur in Siberia as a whole, and the opposite trend of summer precipitation was observed in some areas of Siberia.

Andrew R Kampfschulte, Rebecca K Miller

Community-wide wildfire mitigation can effectively protect homes from structure ignition. The Firewise USA program provides a framework for grassroots wildfire preparedness. Here, we examine the 500 Firewise USA sites in California to understand participation and demographic trends. We find important regional differences regarding the influence of underlying fire hazard, fire history, and other Firewise sites on new site formation. Sites in the Bay Area and Sierras respond strongly to fire history and proximity to other Firewise sites, while Northern and Southern California have few Firewise sites despite underlying hazardous conditions and large fire history. Firewise sites are often whiter, older, and more well-educated than California’s median population, potentially leaving out many communities that do not meet this demographic profile but face severe risks from wildfires. These findings offer important insights into the factors motivating communities to pursue wildfire protection, particularly important given recent severe and destructive wildfire seasons.

Abel Centella-Artola, Arnoldo Bezanilla-Morlot, Roberto Serrano-Notivoli et al.

The paper presents a high-resolution (-3km) gridded dataset for daily precipitation across Cuba for 1961-2008, called CubaPrec1. The dataset was built using the information from the data series of 630 stations from the network operated by the National Institute of Water Resources. The original station data series were quality controlled using a spatial coherence process of the data, and the missing values were estimated on each day and location independently. Using the filled data series, a grid of 3 × 3 km spatial resolution was constructed by estimating daily precipitation and their corresponding uncertainties at each grid box. This new product represents a precise spatiotemporal distribution of precipitation in Cuba and provides a useful baseline for future studies in hydrology, climatology, and meteorology. The data collection described is available on zenodo: https://doi.org/10.5281/zenodo.7847844

V. K. Oikonomou, Pyotr Tsyba, Olga Razina

In this short review, we discuss how Earth's climatological and geological history and also how the shadows of galactic black holes might reveal our Universe's past evolution. Specifically we point out that a pressure singularity that occurred in our Universe's past might have left its imprint on Earth's geological and climatological history and on the shadows of cosmological black holes. Our approach is based on the fact that the $H_0$ tension problem may be resolved if some sort of abrupt physics change occurred in our Universe $70-150\,$Myrs ago, an abrupt change that deeply affected the Cepheid parameters. We review how such an abrupt physics change might have been caused in our Universe by a smooth passage of it through a pressure finite-time singularity. Such finite-time singularities might occur in modified gravity and specifically in $F(R)$ gravity, so we show how modified gravity might drive this type of evolution, without resorting to peculiar cosmic fluids or scalar fields. The presence of such a pressure singularity can distort the elliptic trajectories of bound objects in the Universe, causing possible geological and climatological changes on Earth, if its elliptic trajectory around the Sun might have changed. Also, such a pressure singularity affects directly the circular photon orbits around supermassive galactic black holes existing at cosmological redshift distances, thus the shadows of some cosmological black holes at redshifts $z\leq 0.01$, might look different in shape, compared with the SgrA* and M87* supermassive black holes. This feature however can be checked experimentally in the very far future.

Christina Pacher, Irene Schicker, Rosmarie deWit et al.

Both clustering and outlier detection play an important role for meteorological measurements. We present the AWT algorithm, a clustering algorithm for time series data that also performs implicit outlier detection during the clustering. AWT integrates ideas of several well-known K-Means clustering algorithms. It chooses the number of clusters automatically based on a user-defined threshold parameter, and it can be used for heterogeneous meteorological input data as well as for data sets that exceed the available memory size. We apply AWT to crowd sourced 2-m temperature data with an hourly resolution from the city of Vienna to detect outliers and to investigate if the final clusters show general similarities and similarities with urban land-use characteristics. It is shown that both the outlier detection and the implicit mapping to land-use characteristic is possible with AWT which opens new possible fields of application, specifically in the rapidly evolving field of urban climate and urban weather.

F. A. Zaman, L. W. Townsend, W. C. deWet et al.

Abstract Radiation transport codes have been an increasingly important tool for studying the space radiation environment, which includes high‐energy and high‐nuclear‐charge particles. The unique advantage of transport models lies in covering a wider range of particles, energies, and angles than would be attainable in a laboratory or measurable by an instrument. However, since there are several transport codes developed by different teams that have contributed heavily to the literature, differences are expected between such codes. In this work, we use five such radiation transport codes (FLUctuating KAscade, GEometry ANd Tracking, High‐Energy Transport Code‐Human Exploration and Development in Space, Monte Carlo N‐Particle, and Particle and Heavy Ion Transport code System) to study the radiation environment near the Moon, specifically the lunar “albedo” radiation, which is the radiation emitted by the lunar surface through interactions with incident galactic cosmic rays and solar energetic particles. The primary goal of this paper is to provide a general characterization of the lunar albedo radiation and to find the areas where the modeled transport codes agree and disagree by using almost identical input parameters. The results of this work show overall good agreement between the codes. However, some areas of discrepancies exist that are reported herein. Thus, this paper equips the space weather and radiation biology communities with a comparison between popular radiation transport models applied to lunar albedo radiation. The overall agreement and, in some cases, discrepancies between these transport codes provide fundamental insight necessary for assessing code reliability and identifying where further study and improvements are needed to advance our understanding of lunar albedo radiation.

Ziqi LIU, Yao LU, Yan LI

The interdecadal characteristics and atmospheric circulation causes of the extensive and persistent extreme cold events （EPECE） in China are analyzed in this paper based on NCEP/NCAR and the dataset CN05.1 of daily mean temperature from 1961 to 2018.The results show that there is an overall fluctuating decreasing trend characteristic of the occurrence days of EPECE from 1961 to 2018， but the trend turns around 1995， and the occurrence days of the events before 1995 have a significant decreasing trend， and the events are mainly of the countrywide type； after 1995， the trend becomes increasing， and the main types are the northwestern/Jiangnan type and mid-eastern type.By analyzing the atmospheric circulation field with the Siberia high index， blocking high index， and potential vorticity （PV） anomaly characteristics， the atmospheric circulation causes of the northwestern/Jiangnan type and mid-eastern type of events after 1995 were comparatively studied.The results further showed that the strong Eurasian polar vortex became weak during the EPECEs from 1961 to 1995， the frequency of blocking high in Ural was low at 21.8%， the maximum anomalies of the Siberia high index was 8.9 hPa， and the anomalous low PV circulation in Ural was strong with a northeast-southwest axis， which was a typical anticyclonic Rossby wave breaking， it allowed cold air to move southward to affect most parts of the country.During the EPECEs from 1996 to 2018， the Eurasian polar vortex was continuously replenished and maintained in the process of weakening， the frequency of Ural blocking high reached 34.6% and the range was east-north， the anomalies of Siberia high reached 11.5 hPa， and the high vortex air located in the Baikal region was relatively weak， and the anomalously low PV circulation was able to move eastward and northward from Ural to north of Lake Baikal， thus affecting the Ural blocking high can be extended to central Eurasia， making it possible for cold air to reach more southern regions and maintain it for a long time when it affected China during 1996 -2018.

Baoping Xu, Yuekang Liu, Yanzhe Dou et al.

Material emission and ventilation are two aspects influencing indoor air quality. In this study, a model predictive control (MPC) strategy is proposed for intermittent ventilation system in office buildings, to achieve a healthy indoor environment. The strategy is based on a dynamic model for predicting emissions of volatile organic compounds (VOCs) from materials. The key parameters of formaldehyde from panel furniture in the model are obtained by an improved C-history method and large-scale chamber experiments. The effectiveness of the determined key parameters is validated, which are then used to predict the formaldehyde concentration variation and the pre-ventilation time in a typical office room. In addition, the influence of some main factors (i.e., vacant time, loading ratio, air change rate) on the pre-ventilation time is analyzed. Results indicate that the pre-ventilation time of the intermittent ventilation system ranges from several minutes to several hours. The pre-ventilation time decreases exponentially with the increase in the vacant time, the air change rate, and with the decrease in the loading ratio. When the loading ratio of the furniture is 0.30 m²/m³ and the vacant time is 100 days, the required pre-ventilation time approaches zero. Results further reveal that an air change rate of 2 h⁻¹ is the most effective means for rapid removal of indoor formaldehyde for the cases studied. The proposed strategy should be helpful for achieving effective indoor pollution control.