Hasil untuk "Meteorology. Climatology"

Sundararaman Gopalakrishnan, Krishna K. Osuri, Dev Niyogi et al.

ABSTRACT Over the last decade, tropical cyclone (TC) track and intensity predictions have improved by nearly 50% in the Atlantic and Northern Indian Ocean, driven by advancements in ocean‐coupled numerical models, data assimilation techniques, and an expanding network of observations. However, the prediction of severe weather events driven by convection, particularly those associated with heavy precipitation over land, has not kept pace with these improvements in TC forecasting. While 1–2 km horizontal resolutions are crucial for capturing convection over land and ocean, seamless prediction across scales demands an accurate representation of the coupled evolution of ocean, land, and atmospheric states. To address the complex problem of severe weather across a spectrum of atmospheric motions—including TCs over the ocean and severe convective systems over coastal and inland regions—we have developed the Indian Ocean–Land–Atmosphere (IOLA) Coupled Mesoscale Prediction Framework. This Framework integrates the well‐tested nonhydrostatic model (NMM) dynamical core with advanced nesting techniques from the hurricane weather research and forecast (HWRF) system. It further incorporates ocean coupling from HWRF and physics packages adopted from the WRF community model. This represents the first‐ever coupled modeling system explicitly designed to tackle extreme weather events across multiple domains and scales. Extensive testing of this novel modeling framework demonstrates that a high‐resolution (1–2 km) “all‐purpose” severe weather prediction system can effectively address the challenges of forecasting extreme weather over the Indian region. One of the key focuses of this work is the application of 1‐km horizontal resolution moving nests over the monsoon region, where synoptic‐scale interactions play a critical role in modulating severe weather and heavy precipitation events. With this configuration, the model provides a high equitable threat score (ETS) > 0.18 for heavy to extreme rainfall events for 48 h and above lead times. This framework enables a unified approach to simulating severe weather phenomena accurately and flexibly. Also, it sets a new benchmark for seamless prediction of extreme weather, paving the way for improved resilience against coastal hazards and inland severe weather events.

Kaiyu Wu, Pucheng Han, Hualong Zhang et al.

While Vision Language Models (VLMs) show advancing reasoning capabilities, their application in meteorology is constrained by a domain gap and a reasoning faithfulness gap. Specifically, mainstream Reinforcement Fine-Tuning (RFT) can induce Self-Contradictory Reasoning (Self-Contra), where the model's reasoning contradicts its final answer, which is unacceptable in such a high-stakes domain. To address these challenges, we construct WeatherQA, a novel multimodal reasoning benchmark in meteorology. We also propose Logically Consistent Reinforcement Fine-Tuning (LoCo-RFT), which resolves Self-Contra by introducing a logical consistency reward. Furthermore, we introduce Weather-R1, the first reasoning VLM with logical faithfulness in meteorology, to the best of our knowledge. Experiments demonstrate that Weather-R1 improves performance on WeatherQA by 9.8 percentage points over the baseline, outperforming Supervised Fine-Tuning and RFT, and even surpassing the original Qwen2.5-VL-32B. These results highlight the effectiveness of our LoCo-RFT and the superiority of Weather-R1. Our benchmark and code are available at https://github.com/Marcowky/Weather-R1.

Shijie Zhang

Text-to-time-series generation is particularly important in meteorology, where natural language offers intuitive control over complex, multi-scale atmospheric dynamics. Existing approaches are constrained by the lack of large-scale, physically grounded multimodal datasets and by architectures that overlook the spectral-temporal structure of weather signals. We address these challenges with a unified framework for text-guided meteorological time-series generation. First, we introduce MeteoCap-3B, a billion-scale weather dataset paired with expert-level captions constructed via a Multi-agent Collaborative Captioning (MACC) pipeline, yielding information-dense and physically consistent annotations. Building on this dataset, we propose MTransformer, a diffusion-based model that enables precise semantic control by mapping textual descriptions into multi-band spectral priors through a Spectral Prompt Generator, which guides generation via frequency-aware attention. Extensive experiments on real-world benchmarks demonstrate state-of-the-art generation quality, accurate cross-modal alignment, strong semantic controllability, and substantial gains in downstream forecasting under data-sparse and zero-shot settings. Additional results on general time-series benchmarks indicate that the proposed framework generalizes beyond meteorology.

Puja Das, Auroop R. Ganguly

Abstract Earth system models inform water policy and interventions, but knowledge gaps in hydrologic representations limit the credibility of projections and impacts assessments. The literature does not provide conclusive evidence that incorporating higher resolutions, comprehensive process models, and latest parameterization schemes, will result in improvements. We compare hydroclimate representations and runoff projections across two generations of Coupled Modeling Intercomparison Project (CMIP) models, specifically, CMIP5 and CMIP6, with gridded runoff from Global Runoff Reconstruction (GRUN) and ECMWF Reanalysis V5 (ERA5) as benchmarks. Our results show that systematic embedding of the best available process models and parameterizations, together with finer resolutions, improve runoff projections with uncertainty characterizations in 30 of the largest rivers worldwide in a mechanistically explainable manner. The more skillful CMIP6 models suggest that, following the mid-range SSP370 emissions scenario, 40% of the rivers will exhibit decreased runoff by 2100, impacting 850 million people.

Morgan R. Steckler, Kevin R. Birdwell, Haowen Xu et al.

Access to continuous, quality assessed meteorological data is critical for understanding the climatology and atmospheric dynamics of a region. Research facilities like Oak Ridge National Laboratory (ORNL) rely on such data to assess site-specific climatology, model potential emissions, establish safety baselines, and prepare for emergency scenarios. To meet these needs, on-site towers at ORNL collect meteorological data at 15-minute and hourly intervals. However, data measurements from meteorological towers are affected by sensor sensitivity, degradation, lightning strikes, power fluctuations, glitching, and sensor failures, all of which can affect data quality. To address these challenges, we conducted a comprehensive quality assessment and processing of five years of meteorological data collected from ORNL at 15-minute intervals, including measurements of temperature, pressure, humidity, wind, and solar radiation. The time series of each variable was pre-processed and gap-filled using established meteorological data collection and cleaning techniques, i.e., the time series were subjected to structural standardization, data integrity testing, automated and manual outlier detection, and gap-filling. The data product and highly generalizable processing workflow developed in Python Jupyter notebooks are publicly accessible online. As a key contribution of this study, the evaluated 5-year data will be used to train atmospheric dispersion models that simulate dispersion dynamics across the complex ridge-and-valley topography of the Oak Ridge Reservation in East Tennessee.

Caren Marzban, Yikun Zhang, Nicholas Bond et al.

Whether a variable is the cause of another, or simply associated with it, is often an important scientific question. Causal Inference is the name associated with the body of techniques for addressing that question in a statistical setting. Although assessing causality is relatively straightforward in the presence of temporal information, outside of that setting - the situation considered here - it is more difficult to assess causal effects. The development of the field of causal inference has involved concepts from a wide range of topics, thereby limiting its adoption across some fields, including meteorology. However, at its core, the requisite knowledge for causal inference involves little more than basic probability theory and regression, topics familiar to most meteorologists. By focusing on these core areas, this and a companion article provide a steppingstone for the meteorology community into the field of (non-temporal) causal inference. Although some theoretical foundations are presented, the main goal is the application of a specific method, called matching, to a problem in meteorology. The data for the application are in public domain, and R code is provided as well, forming an easy path for meteorology students and researchers to enter the field.

Chanh Kieu, Thanh T. N. Nguyen, Duc-Trong Le et al.

A reliable tropical cyclone (TC) climatology is the key to assessing historical and future changes in TC activities. While global TC records have been systematically maintained since the early 1940s, substantial uncertainties remain for the pre-satellite era during which TC observations relied mostly on scattered aircraft reconnaissance and sporadic ship reports. This study presents a deep learning (DL) approach to reconstruct historical TC activity in the western North Pacific (WNP) basin, with a main focus on the pre-satellite era. Using data feature enrichment tailored for tropical cyclogenesis (TCG), we demonstrate that DL can effectively capture the main characteristics and changes in TCG climatology during the post-satellite era. With additional cross-validations, the reconstruction of TCG climatology is then extended to a pre-satellite period (1940-1960) during which TC base-track datasets are most uncertain. Our DL reconstruction reveals a significant missing of TCG in the current best-track data between September and November during the pre-satellite era. Such a TCG undercount in the best track data occurs mainly around 10-15$^\circ$N in the central WNP, while coastal regions show better consistency with DL reconstruction. These findings not only highlight the potential of DL for improving historical assessments of TC activity, but also advance our understanding of TCG processes by identifying key environmental conditions conducive to TC formation. The DL approach presented herein can be applied to other ocean basins, climate proxies, or reanalysis datasets for future TC climate studies.

Yanmo Weng, Avantika Gori

This study presents a comprehensive climatological benchmarking of tropical cyclones (TCs) generated by AI-based global weather prediction models. Using all TC events from the North Atlantic and Western Pacific basins between 2020 and 2025, we assess the ability of two AI models (Pangu-Weather and Aurora) to reproduce observed TC track density, climatology of storm characteristics, and physical consistency with TC theory. By comparing AI-simulated TCs with ERA5 reanalysis, we benchmark the distributions of intensity, size, forward speed, and evaluate the model's ability to credibly simulate extratropical transition. Results show that both Pangu and Aurora perform well in reproducing storm track density, forward speed distribution, and outer size distribution. Aurora shows an improved performance in simulating storm intensity compared to Pangu, with less bias in the distribution of minimum central pressure and maximum wind speed. However, both models overestimate the distribution of storm inner size (radius of maximum winds), especially for extreme events. AI models capture the relative frequency and temporal evolution of extratropical transition patterns with reasonable accuracy. The AI-simulated TCs are also less likely to conform to gradient wind balance compared to ERA5, indicating that the AI TCs may not be physically realistic in many cases. This benchmarking identifies systematic biases that can guide future corrections and support extended applications of AI models for TC hazard and risk assessment. Our work establishes a foundation for future studies using AI weather models in the context of TC climatological and hazard research.

Céline Cunen, Thea Roksvåg, Claudio Heinrich-Mertsching et al.

Combining forecasts from multiple numerical weather prediction (NWP) models have shown substantial benefit over the use of individual forecast products. Although combination, in a broad sense, is widely used in meteorological forecasting, systematic studies of combination methodology in meteorology are scarce. In this article, we study several combination methods, both state-of-the-art and of our own making, with a particular emphasis on situations where one seeks to predict when a particular event of interest will occur. Such time-to-event forecasts require particular methodology and care. We conduct a careful comparison of the different combination methods through an extensive simulation study, where we investigate the conditions under which the combined forecast will outperform the individual forecasting products. Further, we investigate the performance of the methods in a case-study modelling the time to first hard freeze in Norway and parts of Fennoscandia.

V. Benes, M. Svitek, A. Michalikova et al.

Air pollution in cities and the possibilities of reducing this pollution represents one of the most important factors that today's society has to deal with. This paper focuses on a systemic approach to traffic emissions with their relation to meteorological conditions, analyzing the effect of weather on the quantity and dispersion of traffic emissions in a city. Using fuzzy inference systems (FIS) the model for prediction of changes in emissions depending on various conditions is developed. The proposed model is based on traffic, meteorology and emission data measured in Prague, Czech Republic. The main objective of the work is to provide insight into how urban planners and policymakers can plan and manage urban transport more effectively with environmental protection in mind.

Carlo Giupponi, Giuliana Barbato, Veronica Leoni et al.

Recent advancements in spatial risk assessment methodologies, particularly those incorporating GIS and economic evaluations, have significantly enhanced our ability to assess and manage risks associated with natural disasters. Entrepreneurs, investors, and public administrations need information about climate change risks for effective planning and decision making. To move from generic global or national projections about climate change scenarios, towards more actionable information on climate risks for socioeconomic agents, the three dimensions of risk (Hazard, Exposure and Vulnerability) must be quantified and mapped with the involvement of stakeholders. In this study, spatial indicators, tailored to the social and ecological systems of interest and co-designed with the key stakeholders are aggregated into sectoral risk indexes quantified in economic terms. Climate risk indexes were calculated and mapped for the four key economic sectors of the study area of the Belluno Province (Italian Alps): summer tourism, winter sports and events, eyewear industry, and electricity supply. Stakeholders were involved during the assessment to share knowledge, data and needs and to provide expert judgments on intermediate and final results. Outputs include a series of maps and statistical summaries, highlighting future trends of climate related risks, their spatial variability within the area and the estimated levels of uncertainty. Estimates on expected changes of future damages with constant Exposure and Vulnerability, provided socioeconomic agents with simple and clear messages about how their activities could suffer or benefit from climate change in the future.

Tra Mai Ngo, Van Hung Hoang, Huu Tap Van et al.

This study examines the fly ash from Soc Son municipal waste power plant (SMPP) and suggests ways to repurpose it to reduce its environmental impact. Fly ash from the Soc Son waste power plant has a gray color, spherical particles with a 5–103 μ m diameter, and a high carbon and heavy metal content. Bermorite crystals can absorb and release heavy metals, making monitoring secondary pollutants during incineration crucial. The EDX analysis of fly ash from the Soc Son waste power plant revealed that it was predominantly contaminated with metal elements, with the highest percentage of calcium. The EDX was able to detect heavy metals in incinerator fly ash. The concentration of Zn in the fly ash exceeded QCVN 07:2009/BTNMT standards, indicating the high amounts of some elements that may be hazardous to the environment and human health. Using the SEM/EDX and XRF, the fly ash from the Soc Son landfill power plant was analyzed and discovered that it exceeds permissible limits for dangerous heavy elements. The most common inorganic elements are Ca, followed by Zn, Pb, Cd, and Ag. Fly ash is classed as hazardous waste due to its high concentration of heavy metals, which results from the combustion of municipal solid waste that has not been separated. Vietnam fights municipal solid waste incinerator fly ash production. Some nations stabilize fly ash to remove harmful components and use it in buildings. Stabilized fly ash makes unfired construction bricks and cement manufacturing components and combining fly ash with inorganic trash protects the environment.

Jurgen Vanhamel, Marc Berwaerts, Stefano Speretta et al.

Current monitoring systems to detect sporadic E use ground-based setups, ionosondes, and the network of GNSS satellites in order to assess the phenomenon of sporadic E. This paper aims to monitor sporadic E using a miniature space-based platform in an atypical way. The setup consists of multiple radio-amateur beacon systems aboard satellites, each having a specific modulation and transmission scheme. This Radio Amateur Beacon System for the Investigation of the Ionosphere (RABSII) is coupled to a GNSS receiver, revealing the location of the platform. Multiple beacon data streams are sequentially sent from a satellite platform towards the Earth. By receiving and comparing the Signal-to-Noise ratios of these streams using a dedicated ground-based radio-amateur network of receiving stations, the presence of sporadic E can be determined, and a location-based model can be built. The advantage of this miniaturized, low-power, low-cost instrument is its ability to be put on any satellite platform in the future in order to map sporadic E.

Maximilian Pierzyna, Sukanta Basu, Rudolf Saathof

This study introduces OTCliM (Optical Turbulence Climatology using Machine learning), a novel approach for deriving comprehensive climatologies of atmospheric optical turbulence strength ($C_n^2$) using gradient boosting machines. OTCliM addresses the challenge of efficiently obtaining reliable site-specific $C_n^2$ climatologies near the surface, crucial for ground-based astronomy and free-space optical communication. Using gradient boosting machines and global reanalysis data, OTCliM extrapolates one year of measured $C_n^2$ into a multi-year time series. We assess OTCliM's performance using $C_n^2$ data from 17 diverse stations in New York State, evaluating temporal extrapolation capabilities and geographical generalization. Our results demonstrate accurate predictions of four held-out years of $C_n^2$ across various sites, including complex urban environments, outperforming traditional analytical models. Non-urban models also show good geographical generalization compared to urban models, which capture non-general site-specific dependencies. A feature importance analysis confirms the physical consistency of the trained models. It also indicates the potential to uncover new insights into the physical processes governing $C_n^2$ from data. OTCliM's ability to derive reliable $C_n^2$ climatologies from just one year of observations can potentially reduce resources required for future site surveys or enable studies for additional sites with the same resources.

Mohammad Talafha, Sora Kim, Kyung-Suk Suh

Numerical simulations of atmospheric dispersion and dose assessment were performed for the Jordan Research and Training Reactor (JRTR) to evaluate its radiological effects on surrounding population and the environment. A three-dimensional atmospheric dispersion model was applied to investigate the behavior of the radionuclides released into the air, and a dose assessment model was used to estimate the radiological impact on the population residing in nearby cities around the JRTR. Considering full core meltdown an accidental scenario, most of the source term was assumed to be released from the JRTR. Simulations were performed to calculate the air and deposition concentrations of radioactive materials for July 2013 and January 2014. The monthly averaged values of concentrations, depositions, and dose rates were analyzed to identify the most harmful effects in each month. The results showed that relatively harmful effects occurred in January 2014, and the total annual dose rate was estimated to be approximately 1 mSv outside the 10 km radius from JRTR. However, the impact of a nuclear accident is not as severe as it might seem, as the affected area is not highly populated, and appropriate protective measures can significantly reduce the radiation exposure. This study provides useful information for emergency preparedness and response planning to mitigate the radiological consequences of a nuclear accident at the JRTR.

S. Sarker

The study of climatology serves as a foundation for students who wish to specialize in water resources, hydrology, or environmental engineering. Climatology is the study of long-term average weather patterns. It is a distinct field of study from meteorology and is subdivided into a number of subfields. In order to predict the future hydrologic and hydraulic scenarios, knowledge of climatology is essential. In other words, climatology allows us to determine the likelihood of snowfall and hail, the amount of solar thermal radiation that can reach a specific location, etc. Climatology focuses frequently on how the climate has changed over time and how these changes have affected people and events. The primary objective of this technical note is to acquaint and encourage engineers with the basics of the climate and its processes so that they can understand the climatic impact on water resource systems as beginners.

C. J. Tomlinson, L. Chapman, J. Thornes et al.

T. Zhang, Y. Ebihara

Abstract An increase in geomagnetically induced currents (GICs) is an inevitable result of geomagnetic field disturbances, and is harmful to the power grid, in particular, at high latitudes. At mid and low latitudes, the amplitude of the GICs is, in general, small, but large‐amplitude GICs are often observed during magnetic storms. It is of importance to understand major characteristics and extreme values of GICs at mid and low latitudes. For the geoelectric field disturbances ΔE observed at Kakioka (27.8° geomagnetic latitude) in Japan in 1996–2004, we performed superposed epoch analyses with respect to three types of geomagnetic disturbances: (a) storm sudden commencements (SSCs)/sudden impulses (SIs), (b) main phase of magnetic storms, and (c) bay disturbances. It is shown that the SSCs/SIs and the main phase of the magnetic storms are equally important for causing large‐amplitude disturbances of ΔE at Kakioka. GICs are thought to be amplified when the SIs and/or the bay disturbances occur during the magnetic storms. The maximum value of ΔE tends to be correlated with the maximum value of ΔH during the three types of events, where ΔH is the horizontal component of the geomagnetic field. Assuming that a quasi‐linear relationship between the maximum ΔE and the maximum ΔH is valid, we estimated GICs at three substations in Japan for an extreme SSCs/SIs, and the extreme magnetic storms. This scheme could be applicable to estimate roughly the GICs against extreme events, and to forecast the maximum GICs in a real‐time manner.

Randy J. Chase, David R. Harrison, Amanda Burke et al.

Recently, the use of machine learning in meteorology has increased greatly. While many machine learning methods are not new, university classes on machine learning are largely unavailable to meteorology students and are not required to become a meteorologist. The lack of formal instruction has contributed to perception that machine learning methods are 'black boxes' and thus end-users are hesitant to apply the machine learning methods in their every day workflow. To reduce the opaqueness of machine learning methods and lower hesitancy towards machine learning in meteorology, this paper provides a survey of some of the most common machine learning methods. A familiar meteorological example is used to contextualize the machine learning methods while also discussing machine learning topics using plain language. The following machine learning methods are demonstrated: linear regression; logistic regression; decision trees; random forest; gradient boosted decision trees; naive Bayes; and support vector machines. Beyond discussing the different methods, the paper also contains discussions on the general machine learning process as well as best practices to enable readers to apply machine learning to their own datasets. Furthermore, all code (in the form of Jupyter notebooks and Google Colaboratory notebooks) used to make the examples in the paper is provided in an effort to catalyse the use of machine learning in meteorology.

Guillaume Sérazin, Anne Marie Tréguier, Clément de Boyer Montégut

Climatologies of the mixed layer depth (MLD) have been provided using several definitions based on temperature/density thresholds or hybrid approaches. The upper ocean pycnocline (UOP) that sits below the mixed layer base remains poorly characterised, though this transition layer is an ubiquitous feature of the ocean surface layer. Available hydrographic profiles provide near-global coverage of the world's ocean and are used to build a seasonal climatology of UOP properties -- intensity, depth, thickness -- to characterise the spatial and seasonal variations of upper ocean stratification. The largest stratification values $\mathcal{O}(10^{-3}\,s^{-2})$ are found in the intertropical band, where seasonal variations of the UOP are also very small. The deepest ($>$ 200 m) and least stratified $\mathcal{O}(10^{-6}\,s^{-2})$ UOPs are found in winter along the Antarctic Circumpolar Current (ACC) and at high latitudes of the North Atlantic. The UOP thickness has a median value of 23 m with limited seasonal and spatial variations; only a few regions have UOP thicknesses exceeding 35 m. The UOP properties allow the characterisation of the upper ocean restratification that generally occurs in early spring and is generally associated with large variability. Depending on the region, this restratification may happen gradually as around the Rockall plateau or abruptly as in the Kuroshio Extension. The UOP is also likely to merge intermittently with the permanent pycnocline in winter. The upper limit of the UOP is eventually consistent with MLD estimates, except in a few notable regions such as in the Pacific Warm Pool where barrier layers are important, and during wintertime at high latitudes of the North Pacific.