Hasil untuk "Meteorology. Climatology"

Giuliana Pallotta, Shiheng Duan, Céline Bonfils et al.

In recent years, Deep-Learning Earth System Models (DL-ESMs) have emerged as promising and computationally efficient alternatives to traditional ESMs. Here, we present an evaluation framework for testing DL-ESMs from a traditional model development perspective, utilizing the PCMDI Metrics Package (PMP) standardized diagnostics. This methodology allows DL-ESMs, such as Ai2's ACE2 and Google's NeuralGCM, to be rigorously tested via multiple metrics to access their ability to simulate climatology and key modes of variability in observational reference datasets. By evaluating DL-ESMs as traditional models, we extend their application into uncharted territory and find encouraging results. This evaluation represents a critical step toward establishing trust in DL-ESMs within the scientific community, thus enhancing confidence in their potential to accelerate Earth System modeling, and guiding future model development. Our analysis sheds light on the fit-for-purpose of DL-ESMs offering insights for a wide range of Earth System science applications.

Jiaqi Yao, Zimeng Zhao, Ming Pan et al.

Abstract Floods are destructive, yet quantifying 3D dynamics remains challenging for traditional satellites. The SWOT mission provides Water Surface Elevation (WSE) measurements for flood monitoring. We assessed the catastrophic Miyun flood (July 2025) where rainfall was extreme. We developed the SWOT-FVE framework to reconstruct hydrographs and volumes, revealing level rises up to 3.2 m and volume increases of 1.54 billion m³. These findings demonstrate SWOT’s unique capability to resolve 3D flood responses, advancing flood assessment and resilience planning.

Vahe Atoyan, Vahe Atoyan, Thomas Bawin et al.

Hyperspectral imaging (HSI) captures rich spectral data across hundreds of contiguous bands for diverse applications. Dimension reduction (DR) techniques are commonly used to map the first three reduced dimensions to the red, green, and blue channels for RGB visualization of HSI data. In this study, we propose a novel approach, HSBDR-H, which defines pixel colors by first mapping the two reduced dimensions to hue and saturation gradients and then calculating per-pixel brightness based on band entropy so that pixels with high intensities in informative bands appear brighter. HSBDR-H can be applied on top of any DR technique, improving image visualization while preserving low computational cost and ease of implementation. Across all tested methods, HSBDR-H consistently outperformed standard RGB mappings in image contrast, structural detail, and informativeness, especially on highly detailed urban datasets. These results suggest that HSBDR-H can complement existing DR-based visualization techniques and enhance the interpretation of complex hyperspectral data in practical applications. Tested in remote sensing applications involving urban and agricultural datasets, the method shows potential for broader use in other disciplines requiring high-dimensional data visualization.

John R. Lanzante

Abstract The heat index (HI), based on Steadman’s model of thermoregulation, has found wide applicability for estimating human heat stress. It has proven useful in research endeavors aimed at estimating future changes associated with global climate change as well as operationally for the issuance of heat advisories by the U.S. National Weather Service (NWS). The actual computation of the HI based on ambient temperature and humidity has been streamlined by the use of a polynomial fit by Rothfusz to Steadman’s model output. Recently, Steadman’s model has been updated by Lu and Romps to provide applicability to temperature and humidity values that were “out of range” of Steadman’s model. The authors of this extended HI (EHI) have provided computer code to enable application. However, because of the complexity of the model, execution of the code would be relatively costly for use in applications involving dense grids and multimodel ensembles covering decades such as those appropriate for assessing the range of possible changes in human heat stress. Here, shortcuts are provided in the form of a “lookup table” and polynomials that provide computational savings of several orders of magnitude. Error analyses provide estimates of accuracy. Significance Statement Over 40 years ago, an index was developed to estimate the heat stress on the human body due to the combined effects of temperature and humidity. It has been used widely both to issue public heat advisories and in studies aimed at assessing possible future changes in heat stress due to climate change. Recently, that index has been updated and extended to handle higher temperatures that are becoming more common due to climate change. This work provides efficient and accurate shortcuts to compute the new index which require much less computing time and can be beneficial for more demanding tasks such as estimating the range of possibilities in the future.

Chiu-Wai Yan, Shi Quan Foo, Van Hoan Trinh et al.

Deep learning approaches have been widely adopted for precipitation nowcasting in recent years. Previous studies mainly focus on proposing new model architectures to improve pixel-wise metrics. However, they frequently result in blurry predictions which provide limited utility to forecasting operations. In this work, we propose a new Fourier Amplitude and Correlation Loss (FACL) which consists of two novel loss terms: Fourier Amplitude Loss (FAL) and Fourier Correlation Loss (FCL). FAL regularizes the Fourier amplitude of the model prediction and FCL complements the missing phase information. The two loss terms work together to replace the traditional $L_2$ losses such as MSE and weighted MSE for the spatiotemporal prediction problem on signal-based data. Our method is generic, parameter-free and efficient. Extensive experiments using one synthetic dataset and three radar echo datasets demonstrate that our method improves perceptual metrics and meteorology skill scores, with a small trade-off to pixel-wise accuracy and structural similarity. Moreover, to improve the error margin in meteorological skill scores such as Critical Success Index (CSI) and Fractions Skill Score (FSS), we propose and adopt the Regional Histogram Divergence (RHD), a distance metric that considers the patch-wise similarity between signal-based imagery patterns with tolerance to local transforms. Code is available at https://github.com/argenycw/FACL

Filippo Bigi, Sanggyu Chong, Michele Ceriotti et al.

Regression methods are fundamental for scientific and technological applications. However, fitted models can be highly unreliable outside of their training domain, and hence the quantification of their uncertainty is crucial in many of their applications. Based on the solution of a constrained optimization problem, we propose "prediction rigidities" as a method to obtain uncertainties of arbitrary pre-trained regressors. We establish a strong connection between our framework and Bayesian inference, and we develop a last-layer approximation that allows the new method to be applied to neural networks. This extension affords cheap uncertainties without any modification to the neural network itself or its training procedure. We show the effectiveness of our method on a wide range of regression tasks, ranging from simple toy models to applications in chemistry and meteorology.

Tennessee Leeuwenburg, Nicholas Loveday, Elizabeth E. Ebert et al.

`scores` is a Python package containing mathematical functions for the verification, evaluation and optimisation of forecasts, predictions or models. It supports labelled n-dimensional (multidimensional) data, which is used in many scientific fields and in machine learning. At present, `scores` primarily supports the geoscience communities; in particular, the meteorological, climatological and oceanographic communities. `scores` not only includes common scores (e.g., Mean Absolute Error), it also includes novel scores not commonly found elsewhere (e.g., FIxed Risk Multicategorical (FIRM) score, Flip-Flop Index), complex scores (e.g., threshold-weighted continuous ranked probability score), and statistical tests (such as the Diebold Mariano test). It also contains isotonic regression which is becoming an increasingly important tool in forecast verification and can be used to generate stable reliability diagrams. Additionally, it provides pre-processing tools for preparing data for scores in a variety of formats including cumulative distribution functions (CDF). At the time of writing, `scores` includes over 50 metrics, statistical techniques and data processing tools. All of the scores and statistical techniques in this package have undergone a thorough scientific and software review. Every score has a companion Jupyter Notebook tutorial that demonstrates its use in practice. `scores` supports `xarray` datatypes, allowing it to work with Earth system data in a range of formats including NetCDF4, HDF5, Zarr and GRIB among others. `scores` uses Dask for scaling and performance. Support for `pandas` is being introduced. The `scores` software repository can be found at https://github.com/nci/scores/

Nathaniel Cresswell-Clay, Bowen Liu, Dale Durran et al.

A key challenge for computationally intensive state-of-the-art Earth System models is to distinguish global warming signals from interannual variability. Here we introduce DLESyM, a parsimonious deep learning model that accurately simulates the Earth's current climate over 1000-year periods with no smoothing or drift. DLESyM simulations equal or exceed key metrics of seasonal and interannual variability--such as tropical cyclogenesis over the range of observed intensities, the cycle of the Indian Summer monsoon, and the climatology of mid-latitude blocking events--when compared to historical simulations from four leading models from the 6th Climate Model Intercomparison Project. DLESyM, trained on both historical reanalysis data and satellite observations, is an accurate, highly efficient model of the coupled Earth system, empowering long-range sub-seasonal and seasonal forecasts while using a fraction of the energy and computational time required by traditional models.

Brian Jackson, Lori Fenton, Travis Brown et al.

We used attitude data from the Mars Ingenuity helicopter with a simple steady-state model to estimate windspeeds and directions at altitudes of 3 meters up to 24 meters, the first time winds at such altitudes have been probed on Mars. We compared our estimates to concurrent wind data at 1.5 m height from the meteorology package MEDA onboard the Mars 2020 Perseverance rover and to predictions from meteorological models. Wind directions inferred from the Ingenuity data agreed to within uncertainties with the directions measured by MEDA, when the latter were available, but deviated from model-predicted directions by as much as 180 deg in some cases. Also, the inferred windspeeds are often much higher than expected. For example, meteorological predictions tailored to the time and location of Ingenuity's 59th flight suggest Ingenuity should not have seen windspeeds above about 15 m/s, but we inferred speeds reaching nearly 25 m/s. By contrast, the 61st flight was at a similar time and season and showed weaker winds then the 59th flight, suggesting winds shaped by transient phenomena. For flights during which we have MEDA data to compare to, inferred windspeeds imply friction velocities exceeding 1 m/s and roughness lengths of more than 10 cm based on a boundary layer model that incorporates convective instability, which seem implausibly large. These results suggest Ingenuity was probing winds sensitive to aerodynamic conditions hundreds of meters upwind instead of the conditions very near Mars 2020, but they may also reflect a need for updated boundary layer wind models. An improved model for Ingenuity's aerodynamic response that includes the effects of transient winds may also modify our results. In any case, the work here provides a foundation for exploration of planetary boundary layers using drones and suggests important future avenues for research and development.

Seol-Hee Oh, Yoo-Geun Ham

Abstract To address the challenge of limited training samples, this study employs the model-agnostic meta-learning (MAML) algorithm along with domain-knowledge-based data augmentation to predict winter temperatures on the Korean Peninsula. While data augmentation has been achieved by using global climate model simulations, the proposed augmentation is purely based on the observed data by defining the labels using large-scale climate variabilities associated with the Korean winter temperatures. The MAML-applied convolutional neural network (CNN) (referred to as the MAML model) demonstrates superior correlation skills for Korean temperature anomalies compared to a reference model (i.e., the CNN without MAML) and state-of-the-art dynamical forecast models across all target lead months during the boreal winter seasons. Sensitivity experiments show that the domain-knowledge-based data augmentation enhances the forecast skill of the MAML model. Moreover, occlusion sensitivity results reveal that the MAML model better captures the physical precursors that influence Korean winter temperatures, resulting in more accurate predictions.

Serhat Yüksel, Serkan Eti, Hasan Dinçer et al.

Ensuring energy performance management is important in many ways, such improvement of energy efficiency and decrease of energy costs are reduced. There are various indicators of the effectiveness of energy performance management of buildings. Due to this situation, businesses need to make the necessary improvements for the development of these factors. Nonetheless, these actions cause an increase in the costs of the companies. Hence, among these actions, the more important ones need to be identified. Owing to this issue, businesses can use their limited budgets for more priority indicators. The purpose of this study is to evaluate the main indicators of energy performance management systems. In this way, a new model is proposed to make a priority analysis for the hospitals. Firstly, five indicators of energy performance management systems are selected by considering ISO 50006 standards. Furthermore, these indicators are weighted by using Spherical fuzzy CRITIC. Secondly, G7 countries are examined with fuzzy RATGOS technique. Identification of the most significant indicators of the energy performance systems is an important novelty of this study. The most significant methodological novelty of this study is proposing a new technique to the literature named RATGOS. It is understood that energy efficiency is the most crucial indicator of energy performance management. Furthermore, it is also identified that France is the most successful G7 economy with respect to the energy performance management. Japan and United States have also high performance in this respect. It is recommended that necessary actions should be taken to increase energy efficiency. By conducting an energy audit, energy consumption data is analyzed so that energy losses and inefficiencies can be detected. This assessment provides opportunities for energy efficiency and helps identify improvement strategies.

Babak Ghazi, Rajmund Przybylak, Piotr Oliński et al.

Study region: Poland, with the regions of Baltic Coast and Pomerania, Masuria-Podlasie, Greater Poland, Masovia, Silesia, and Lesser Poland located in the basins of the Baltic Coast rivers, the Vistula River and the Oder River. Study focus: This study focused on completing the documentation of historical floods in Poland before the 19th century and providing a valuable source for historical hydrology studies in Europe. To this end, a comprehensive database of all floods for the 17th–18th centuries was used, that was based on documentary evidence from 293 sources and 978 weather notes describing all flood occurrences. New hydrological insights for the region: The finding of this study revealed the occurrences of 678 floods, including 37 new cases that have been discovered and documented only in this research. Spatial analysis of the results revealed that most of the floods occurred in the Vistula River basin. The number of floods by season was greatest for summer (JJA) (47 %) and smallest for autumn (7 %). Investigation of the origin of floods indicated that rain was the main factor contributing to occurrences of floods in Poland (38 %). The estimation of the intensity of floods showed that most of the floods were “smaller, regional floods” (257 cases) based on the Brázdil et al. (2006b) classification and “extraordinary” (501 cases) in the Barriendos & Coeur (2004) classification.

Efthymia Koliokosta

Dionysia Kotta

Yuya Baba

The merits of ocean prediction for heavy rainfall prediction were examined using hindcast experiments for three summer heavy rainfall events in 2010, 2016, and 2021 in Japan. In these events, the rainfall stemmed from Baiu and stationary fronts. The hindcast experiments were conducted using regional atmospheric and coupled models (RUN-ATM and RUN-CPL). The results show that RUN-CPL predicted more accurate rainfall properties than RUN-ATM. RUN-ATM underestimated the accumulated rainfall compared with RUN-CPL, and the underestimation became more significant as the lead time increased. This was due to decreased horizontal vapor transport in the ocean southwest of Japan. Pressure patterns that dominated the vapor transport were different in each case. When an atmospheric model was used, the sea level pressure difference between the Pacific high and Japan was weakened, contributing to weaker vapor transport from the southwest because of the weakened anticyclonic and cyclonic circulations at the region of Pacific high and over Japan. The degraded pressure patterns generated by RUN-ATM stemmed from incorrect latent heat flux response to the sea surface temperature. When air-sea was decoupled in the atmospheric model, the decrease of sea surface temperature by latent heat flux did not occur, so the latent heat flux was overestimated. Also, this caused the decrease in the pressure difference between Pacific high and Japan areas, leading to a weaker moisture transport from the ocean southwest of Japan. The heat budget analysis in the ocean mixed layer suggests that ocean dynamics, especially vertical mixing, contributes to suppress the overestimation of latent heat flux around the Pacific high. It is concluded that heavy rainfall prediction that incorporates appropriate air-sea coupling and ocean prediction provides better results than atmosphere-only model prediction for front-derived heavy rainfall events.

Theodoros Grigoratos, Athanasios Mamakos, RaviTeja Vedula et al.

Vehicles’ exhaust particulate matter (PM) emissions have significantly decreased over the years. On the other hand, non-exhaust emissions, i.e., particle emissions from brakes and tires, have increased due to the increase in the vehicle fleet, traffic congestion, and the distance traveled. As a result, regulatory bodies are investigating the possibility of mitigating non-exhaust emissions. The Euro 7 proposal introduces specific emission limits for both brakes and tires for the first time in a regulation worldwide. The methodology for brake particle emissions sampling and measurement builds on the work of the Particle Measurement Programme (PMP) informal working group of the United Nations Economic Commission for Europe (UNECE). The recently adopted Global Technical Regulation (GTR) on brakes from light-duty vehicles up to 3.5 t prescribes the technical details. In this paper, we present the technical specifications for the measurements of PM. We also evaluate the penetrations for two cases with two setups for minimum and maximum particle losses. This study, using aerosol engineering calculations, estimates the maximum expected differences between the two setups, both of which are compliant with the GTR. This study also discusses the mass ratios of PM_2.5 and PM₁₀ as a function of the mass median diameters.

Trishna Bhattacharyya, Abhijit Chatterjee, Sanat K. Das et al.

Profiles of aerosols and cloud layers have been investigated over a high-altitude urban atmosphere in the eastern Himalayas in India, for the first time, using a Raman LIDAR. The study was conducted post-monsoon season over Darjeeling (latitude 27°01<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mo>′</mo></msup></semantics></math></inline-formula> N longitude 88°36<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mo>′</mo></msup></semantics></math></inline-formula> E, 2200 masl), a tourist destination in north-eastern India. In addition to the aerosols and cloud characterization and atmospheric boundary layer detection, the profile of the water vapor mixing ratio has also been analyzed. Effects of atmospheric dynamics have been studied using the vertical profiles of the normalized standard deviation of RCS along with the water vapor mixing ratio. The aerosol optical characteristics below and above the Atmospheric Boundary Layer (ABL) region were studied separately, along with the interrelation of their optical and microphysical properties with synoptic meteorological parameters. The backscatter coefficient and the extinction coefficient were found in the range from <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>7.15</mn><mo>×</mo><msup><mn>10</mn><mrow><mo>−</mo><mn>10</mn></mrow></msup></mrow></semantics></math></inline-formula> m<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mrow><mo>−</mo><mn>1</mn></mrow></msup></semantics></math></inline-formula> sr<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mrow><mo>−</mo><mn>1</mn></mrow></msup></semantics></math></inline-formula> to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>3.01</mn><mo>×</mo><msup><mn>10</mn><mrow><mo>−</mo><mn>5</mn></mrow></msup></mrow></semantics></math></inline-formula> m<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mrow><mo>−</mo><mn>1</mn></mrow></msup></semantics></math></inline-formula> sr<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mrow><mo>−</mo><mn>1</mn></mrow></msup></semantics></math></inline-formula> and from <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>1.02</mn><mo>×</mo><msup><mn>10</mn><mrow><mo>−</mo><mn>5</mn></mrow></msup></mrow></semantics></math></inline-formula> m<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mrow><mo>−</mo><mn>1</mn></mrow></msup></semantics></math></inline-formula> to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>2.28</mn><mo>×</mo><msup><mn>10</mn><mrow><mo>−</mo><mn>3</mn></mrow></msup></mrow></semantics></math></inline-formula> m<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mrow><mo>−</mo><mn>1</mn></mrow></msup></semantics></math></inline-formula>, respectively. The LIDAR ratio varies between 3.9 to 78.39 sr over all altitudes. The variation of the linear depolarization ratio from 0.19 to 0.32 indicates the dominance, of non-spherical particles. The periodicity observed in different parameters may be indicative of atmospheric wave phenomena. Cloud parameters, such as scattering coefficients, top and bottom height, and optical depth for different cloud phases, have been evaluated. A co-located Micro Rain Radar has been used with LIDAR for cloud life cycle study.

Wadie Skaf, Arzu Tosayeva, Dániel T. Várkonyi

Time-Series Forecasting is a powerful data modeling discipline that analyzes historical observations to predict future values of a time-series. It has been utilized in numerous applications, including but not limited to economics, meteorology, and health. In this paper, we use time-series forecasting techniques to model and predict the future incidence of chickenpox. To achieve this, we implement and simulate multiple models and data preprocessing techniques on a Hungary-collected dataset. We demonstrate that the LSTM model outperforms all other models in the vast majority of the experiments in terms of county-level forecasting, whereas the SARIMAX model performs best at the national level. We also demonstrate that the performance of the traditional data preprocessing method is inferior to that of the data preprocessing method that we have proposed.

Pascal Pernot

Validation of prediction uncertainty (PU) is becoming an essential task for modern computational chemistry. Designed to quantify the reliability of predictions in meteorology, the calibration-sharpness (CS) framework is now widely used to optimize and validate uncertainty-aware machine learning (ML) methods. However, its application is not limited to ML and it can serve as a principled framework for any PU validation. The present article is intended as a step-by-step introduction to the concepts and techniques of PU validation in the CS framework, adapted to the specifics of computational chemistry. The presented methods range from elementary graphical checks to more sophisticated ones based on local calibration statistics. The concept of tightness, is introduced. The methods are illustrated on synthetic datasets and applied to uncertainty quantification data extracted from the computational chemistry literature.

Ivan Lukyanenko, Mikhail Mozikov, Yury Maximov et al.

Hail risk assessment is necessary to estimate and reduce damage to crops, orchards, and infrastructure. Also, it helps to estimate and reduce consequent losses for businesses and, particularly, insurance companies. But hail forecasting is challenging. Data used for designing models for this purpose are tree-dimensional geospatial time series. Hail is a very local event with respect to the resolution of available datasets. Also, hail events are rare - only 1% of targets in observations are marked as "hail". Models for nowcasting and short-term hail forecasts are improving. Introducing machine learning models to the meteorology field is not new. There are also various climate models reflecting possible scenarios of climate change in the future. But there are no machine learning models for data-driven forecasting of changes in hail frequency for a given area. The first possible approach for the latter task is to ignore spatial and temporal structure and develop a model capable of classifying a given vertical profile of meteorological variables as favorable to hail formation or not. Although such an approach certainly neglects important information, it is very light weighted and easily scalable because it treats observations as independent from each other. The more advanced approach is to design a neural network capable to process geospatial data. Our idea here is to combine convolutional layers responsible for the processing of spatial data with recurrent neural network blocks capable to work with temporal structure. This study compares two approaches and introduces a model suitable for the task of forecasting changes in hail frequency for ongoing decades.