We develop a trajectory-based Koopman method for sea surface temperature (SST) that lifts annual SST segments using a signature kernel -- a reproducing kernel Hilbert space (RKHS) kernel that compares paths via iterated-integral features -- and learns the one-year shift operator. By operating on annual trajectory segments rather than instantaneous fields, the method encodes finite-time history, which helps capture memory effects in SST-only evolution. The resulting operator improves out-of-sample multi-year forecast skill relative to a climatology baseline and reveals coherent spectral modes. We implement the approach via kernel extended dynamic mode decomposition (EDMD) on signature-kernel Gram matrices, yielding a single pipeline for forecasting and spectral diagnostics of high-dimensional SST dynamics.
Randall Jones, Joel A. Thornton, Chris J. Wright
et al.
Lightning plays a crucial role in the Earth's climate system, yet existing parameterizations for use in forecasting and earth system models show room for improvement in capturing spatial and temporal variations in its frequency. This study develops deep learning-based parameterizations of lightning stroke density using meteorological variables from the ERA and IMERG datasets. Convolutional neural networks (CNNs) with U-Net architectures are trained using World Wide Lightning Location Network (WWLLN) data from 2010 to 2021 and evaluated on WWLLN lightning observations from 2022 and 2023. The CNNs reduce the average domain mean bias by an order of magnitude and produce significantly higher Fractions Skill Score (FSS) values across all lightning regimes compared to the multiplicative product of CAPE and precipitation. The CNNs show skill relative to previously published parameterizations over the oceans especially, with r2 values as high as 0.93 achieved between the best performing modeled and observed lightning stroke density climatologies. The CNNs are also able to accurately capture the 12-hourly evolution of lightning spatial patterns on an event-scale with high skill. These results show the potential for deep learning to improve on lightning parameterizations in weather and earth system models.
This paper explores the versatility and depth of Bayesian modeling by presenting a comprehensive range of applications and methods, combining Markov chain Monte Carlo (MCMC) techniques and variational approximations. Covering topics such as hierarchical modeling, spatial modeling, higher-order Markov chains, and Bayesian nonparametrics, the study emphasizes practical implementations across diverse fields, including oceanography, climatology, epidemiology, astronomy, and financial analysis. The aim is to bridge theoretical underpinnings with real-world applications, illustrating the formulation of Bayesian models, elicitation of priors, computational strategies, and posterior and predictive analyses. By leveraging different computational methods, this paper provides insights into model fitting, goodness-of-fit evaluation, and predictive accuracy, addressing computational efficiency and methodological challenges across various datasets and domains.
We investigate the problem of density estimation on the unit circle and the unit sphere from a computational perspective. Our primary goal is to develop new density estimators that are both rate-optimal and computationally efficient for direct implementation. After establishing these estimators, we derive closed-form expressions for probability estimates over regions of the circle and the sphere. Then, the proposed theories are supported by extensive simulation studies. The considered settings naturally arise when analyzing phenomena on the Earth's surface or in the sky (sphere), as well as directional or periodic phenomena (circle). The proposed approaches are broadly applicable, and we illustrate their usefulness through case studies in zoology, climatology, geophysics, and astronomy, which may be of independent interest. The methodologies developed here can be readily applied across a wide range of scientific domains.
Machine learning (ML) is becoming increasingly popular in meteorological decision-making. Although the literature on explainable artificial intelligence (XAI) is growing steadily, user-centered XAI studies have not extend to this domain yet. This study defines three requirements for explanations of black-box models in meteorology through user studies: statistical model performance for different rainfall scenarios to identify model bias, model reasoning, and the confidence of model outputs. Appropriate XAI methods are mapped to each requirement, and the generated explanations are tested quantitatively and qualitatively. An XAI interface system is designed based on user feedback. The results indicate that the explanations increase decision utility and user trust. Users prefer intuitive explanations over those based on XAI algorithms even for potentially easy-to-recognize examples. These findings can provide evidence for future research on user-centered XAI algorithms, as well as a basis to improve the usability of AI systems in practice.
Machine learning (ML) models are successful with weather forecasting and have shown progress in climate simulations, yet leveraging them for useful climate predictions needs exploration. Here we show this feasibility using Neural General Circulation Model (NeuralGCM), a hybrid ML-physics atmospheric model developed by Google, for seasonal predictions of large-scale atmospheric variability and Northern Hemisphere tropical cyclone (TC) activity. Inspired by physical model studies, we simplify boundary conditions, assuming sea surface temperature (SST) and sea ice follow their climatological cycle but persist anomalies present at the initialization time. With such forcings, NeuralGCM can generate 100 simulation days in ~8 minutes with a single Graphics Processing Unit (GPU), while simulating realistic atmospheric circulation and TC climatology patterns. This configuration yields useful seasonal predictions (July to November) for the tropical atmosphere and various TC activity metrics. Notably, the predicted and observed TC frequency in the North Atlantic and East Pacific basins are significantly correlated during 1990 to 2023 (r=~0.7), suggesting prediction skill comparable to existing physical GCMs. Despite challenges associated with model resolution and simplified boundary forcings, the model-predicted interannual variations demonstrate significant correlations with the observation, including the sub-basin TC tracks (p<0.1) and basin-wide accumulated cyclone energy (p<0.01) of the North Atlantic and North Pacific basins. These findings highlight the promise of leveraging ML models with physical insights to model TC risks and deliver seamless weather-climate predictions.
Abstract Future change in precipitation driven by anthropogenic influences on the Earth’s radiative balance will further affect ecosystems, water resources, agriculture, economies, lives and livelihoods. Increased clarity on anthropogenically forced precipitation change can assist adaptation in some contexts. Climate scientists typically quantify precipitation change in models using the average value of the percentage change evident in many different models, i.e., $$\% \Delta {P}^{j}=100\left(\,\frac{{{P}_{2}}^{j}-{{P}_{1}}^{j}}{{{P}_{1}}^{j}}\right)$$ % Δ P j = 100 P 2 j − P 1 j P 1 j , where $${P}_{i}^{j}$$ P i j is the average value of precipitation over Period $$i$$ i in model $$j$$ j . Here we use theory and results from CMIP6 climate models under preindustrial, historical and future forcing to assess the accuracy of this approach. We show that this standard approach inaccurately estimates precipitation change evident in models, even in infinitely large ensembles. Under a wide variety of circumstances, the discrepancy is approximated by $$100({\mu }_{2}/{\mu }_{1})/(m/{{{\rm {CoV}}}}^{2}-1)$$ 100 ( μ 2 / μ 1 ) / ( m / CoV 2 − 1 ) , where $${\mu }_{i}$$ μ i is the population mean for Period $$i$$ i , $$m$$ m is the number of years in the reference period, and CoV is the Coefficient of Variation (i.e., the standard deviation of precipitation variability divided by the mean). The discrepancy is therefore greater for shorter reference periods and is greatest where the $${CoV}$$ CoV is large (which tends to occur in dry regions) and anthropogenic forcing increases precipitation. The discrepancy using climate model output under SSP370 forcing has an average value of 5.7% over the tropics in December–January–February, with far greater values in many subregions. Alternative approaches to quantifying precipitation change are described.
Abstract The intensity of storm‐time disturbance in the ground magnetic field varies significantly at different longitudes due to the magnetic local time (MLT) dependent contributions from different magnetospheric and ionospheric currents. Local geomagnetic field disturbances at low‐to‐mid latitudes often deviate considerably from the global depression represented by symmetric geomagnetic storm indices (such as Dst/SymH/SMR). In this study, we quantitatively investigated the geomagnetic horizontal field depressions (ΔH) at different local time sectors, compared to the longitudinally averaged SuperMAG Ring current (SMR), at eleven low‐latitude stations during a large number (665) of geomagnetic storms that occurred from 1996 to 2024. The relative disturbances (i.e., ΔH‐SMR) exhibit significant asymmetry with respect to MLT, which further varies with storm evolution, intensity, and phase. The MLT asymmetry of ΔH grows rapidly in the early main phase and then grows gradually with storm intensity. Further, the MLT sector of weakest/strongest ΔH depression shifts from post‐dawn/post‐dusk to pre‐dawn/pre‐dusk periods as storm intensity increases. Finally, an empirical model is derived that can quantitatively represent the MLT variations in the low‐latitude ΔH disturbances during geomagnetic storms. This model is very useful in estimating the low‐latitude geomagnetic field disturbances at different longitudes/MLT sectors from the global SMR index and can have significant applications in space weather studies.
The Indian summer monsoon is a highly complex and critical weather system that directly affects the livelihoods of over a billion people across the Indian subcontinent. Accurate short-term forecasting remains a major scientific challenge due to the monsoon's intrinsic nonlinearity and its sensitivity to multi-scale drivers, including local land-atmosphere interactions and large-scale ocean-atmosphere phenomena. In this study, we address the problem of forecasting daily rainfall across India during the summer months, focusing on both one-day and three-day lead times. We use Autoformers - deep learning transformer-based architectures designed for time series forecasting. These are trained on historical gridded precipitation data from the Indian Meteorological Department (1901--2023) at spatial resolutions of $0.25^\circ \times 0.25^\circ$, as well as $1^\circ \times 1^\circ$. The models also incorporate auxiliary meteorological variables from ECMWFs reanalysis datasets, namely, cloud cover, humidity, temperature, soil moisture, vorticity, and wind speed. Forecasts at $0.25^\circ \times 0.25^\circ$ are benchmarked against ECMWFs High-Resolution Ensemble System (HRES), widely regarded as the most accurate numerical weather predictor, and at $1^\circ \times 1^\circ $ with those from National Centre for Environmental Prediction (NCEP). We conduct both nationwide evaluations and localized analyses for major Indian cities. Our results indicate that transformer-based deep learning models consistently outperform both HRES and NCEP, as well as other climatological baselines. Specifically, compared to our model, forecasts from HRES and NCEP model have about 22\% and 43\% higher error, respectively, for a single day prediction, and over 27\% and 66\% higher error respectively, for a three day prediction.
Davi Lazzari, Amália Garcez, Nicole Poltozi
et al.
An important consequence of human induced climate change is the increase in extreme weather events. This study contributes to the understanding of Brazil's climate change by examining historical temperature and precipitation patterns. Extreme events of temperature and precipitation are identified using data from the Brazilian Institute of Meteorology, which includes records from 634 meteorological stations operating intermittently since 1961. Using the first 30 years (1961 to 1990) as the reference period, our results show a significant increase in warm days and a corresponding decrease in cold days over the last 30 years (1991 to 2020), in agreement with previous works. In terms of precipitation, it indicates a trend toward drier conditions in the Northeast region of Brazil, whereas the South is experiencing wetter conditions, with an increase in the number of heavy precipitation days in South and in the extremely dry periods in the Northeast. These results have been verified for consistency with several extreme climate indices measured in this study. Additionally, data from S2iD is analyzed, an official database that records natural disasters in Brazil, to estimate their impact in terms of human losses and financial costs over the past decade. Our findings indicate that drought events are the most economically costly, with multiple instances causing damages exceeding a billion USD, whereas storms have the greatest impact on people. Although it is not possible to directly attribute the natural disasters recorded in the S2iD database to the extreme weather events identified through meteorological data, discussion is done on potential implications of these events in the frequency and location of the disasters.
Daniel R Chavas, Suzana J Camargo, Michael K Tippett
Genesis potential indices (GPIs) are widely used to understand the climatology of tropical cyclones (TCs). However, the sign of projected future changes depends on how they incorporate environmental moisture. Recent theory combines potential intensity and mid-tropospheric moisture into a single quantity called the ventilated potential intensity, which removes this ambiguity. This work proposes a new GPI ($GPI_v$) that is proportional to the product of the ventilated potential intensity and the absolute vorticity raised to a power. This power is estimated to be approximately 5 by fitting observed tropical cyclone best-track and ECMWF Reanalysis v5 (ERA5) data. Fitting the model with separate exponents yields nearly identical values, indicating that their product likely constitutes a single joint parameter. Likewise, results are nearly identical for a Poisson model as for the power law. $GPI_v$ performs comparably well to existing indices in reproducing the climatological distribution of tropical cyclone genesis and its covariability with El Niño-Southern Oscillation, while only requiring a single fitting exponent. When applied to Coupled Model Intercomparison Project Phase 6 (CMIP6) projections, $GPI_v$ predicts that environments globally will become gradually more favorable for TC genesis with warming, consistent with prior work based on the normalized entropy deficit, though significant changes emerge only at higher latitudes under relatively strong warming. $GPI_v$ helps resolve the debate over the treatment of the moisture term and its implication for changes in TC genesis favorability with warming, and its clearer physical interpretation may offer a step forward towards a theory for genesis across climate states.
Russell L. Elsberry, Joel W. Feldmeier, Hway-Jen Chen
et al.
Four-dimensional COAMPS Dynamic Initialization (FCDI) analyses that include high-temporal- and high-spatial-resolution GOES-16 Atmospheric Motion Vector (AMV) datasets are utilized to understand and predict why pre-Bonnie (2022), designated as a Potential Tropical Cyclone (PTC 2), did not undergo rapid intensification (RI) while passing along the coast of Venezuela during late June 2022. A tropical cyclone lifecycle-prediction model based on the ECMWF ensemble indicated that no RI should be expected for the trifurcation southern cluster of tracks along the coast, similar to PTC 2, but would likely occur for two other track clusters farther offshore. Displaying the GOES-16 mesodomain AMVs in 50 mb layers illustrates the outflow burst domes associated with the PTC 2 circulation well. The FCDI analyses forced by thousands of AMVs every 15 min document the 13,910 m wind-mass field responses and the subsequent 540 m wind field adjustments in the PTC 2 circulation. The long-lasting outflow burst domes on both 28 June and 29 June were mainly to the north of PTC 2, and the 13,910 m FCDI analyses document conditions over the PTC 2 which were not favorable for an RI event. The 540 m FCDI analyses demonstrated that the intensity was likely less than 35 kt because of the PTC 2 interactions with land. The FCDI analyses and two model forecasts initialized from the FCDI analyses document how the PTC 2 moved offshore to become Tropical Storm Bonnie; however, they reveal another cyclonic circulation farther west along the Venezuelan coast that has some of the characteristics of a Caribbean False Alarm event.
This study introduces an innovative analytical methodology for examining the interconnections among the atmosphere, ocean, and society. The primary area of interest pertains to the North Atlantic Oscillation (NAO), a notable phenomenon characterised by daily to decadal fluctuations in atmospheric conditions over the Northern Hemisphere. The NAO has a prominent impact on winter weather patterns in North America, Europe, and to some extent, Asia. This impact has significant ramifications for civilization, as well as for marine, freshwater, and terrestrial ecosystems, and food chains. Accurate predictions of the surface NAO hold significant importance for society in terms of energy consumption planning and adaptation to severe winter conditions, such as winter wind and snowstorms, which can result in property damage and disruptions to transportation networks. Moreover, it is crucial to improve climate forecasts in order to bolster the resilience of food systems. This would enable producers to quickly respond to expected changes and make the required modifications, such as adjusting their food output or expanding their product range, in order to reduce potential hazards. The forecast centres prioritise and actively research the predictability and variability of the NAO. Nevertheless, it is increasingly evident that conventional analytical methods and prediction models that rely solely on scientific methodologies are inadequate in comprehensively addressing the transdisciplinary dimension of NAO variability. This includes a comprehensive view of research, forecasting, and social ramifications. This study introduces a new framework that combines sophisticated Big Data analytic techniques and forecasting tools using a generalised additive model to investigate the fluctuations of the NAO and the interplay between the ocean and atmosphere. Additionally, it explores innovative approaches to analyze the socio-economic response associated with these phenomena using text mining tools, specifically modern deep learning techniques. The analysis is conducted on an extensive corpora of free text information sourced from media outlets, public companies, government reports, and newspapers. Overall, the result shows that the NAO index has been reproduced well by the Deep-NAO model with a correlation coefficient of 0.74.
Sahar Derakhshan, David P. Eisenman, Rupa Basu
et al.
Introduction. Several frameworks exist to measure vulnerability to extreme heat events using a health equity approach, but little evidence validates these measures and their applications. We investigated the degree to which social vulnerability measures and their constituent elements correlate with excess emergency room visits as an outcome measure. Methods. The relationship between six commonly used social vulnerability indicators and measured excess emergency room visit rates (processed by including heat-related illnesses and all-internal causes diagnosis, with considerations for age and heat days) was tested through geospatial analytics and statistical regressions, for both California and Los Angeles County. Results. The vulnerability indicators and the outcome measure were significantly positively associated at the census tract-level but weaker (∼0.2 rs) at the scale of California and stronger (∼0.6 rs) at the scale of Los Angeles County. Hazard-specific vulnerability indicators showed stronger relationships with outcome measures regardless of scale. A Poisson regression model showed a significant inter-county variation, indicating the importance of localized assessments for equitable environmental policies. Conclusion. The findings identify communities that are overburdened by heat and pollution and highlight the need for use of both social vulnerability and indicators of adverse outcomes from excessive heat. Patterns are found across all measures that suggest that populations facing accessibility barriers may be less likely to visit emergency rooms. This suggestion needs to be tested in other environmental settings to draw broader conclusions but has direct implications for environmental scientists and mitigation planners who use these methods.
Public aspects of medicine, Meteorology. Climatology
Abstract Atmospheric nitrous acid (HONO) chemistry is of critical importance to air quality during polluted haze events, especially in China. However, current air quality models (such as WRF-CHEM, WRF-CMAQ, Box-MCM) generally underestimate the concentration of HONO, leading to a lack of fundamental understanding of haze pollution. Here, by combining field observations during haze events in Beijing and modeling results, we developed the new parameterization scheme for heterogeneous nitrogen dioxide (NO2) reaction on aerosol surfaces with the synergistic effects of relative humidity and ammonia, which has not been considered in existing air quality models. Including NO2 heterogeneous reactions into modeling significantly improves the estimation accuracy of HONO and OH levels, with the contribution reaching up to 91% and 78% during pollution episodes. The OH derived by HONO can partly explain high concentrations of particulate matter. Together, our work provides a new approach to illustrate the formation of HONO, OH, and haze with the consideration of heterogeneous NO2 → HONO chemistry.
Microclimatic monitoring (air <i>T</i>, air pressure, CO<sub>2</sub>, ventilation, humidity, methane, and radon) in selected show caves in Slovenia has been a continuous process for more than 10 years, a process that aims to supervise the use of the caves for tourism in the sense of sustainable environmental management. After the cataclysmic eruption of the Hunga Tonga–Hunga Ha’apai (HTHH) volcano on 15 January 2022, global propagation of ionospheric disturbances was reported worldwide as barometric pressure changes and seismic noise events. Weather stations in Slovenia reported 2–4 hPa changes in atmospheric pressure 16 h after the eruption at 20:30 CET (19:30 UTC). Changes in atmospheric pressure were also detected at 15 air monitoring sites in 3 different caves (20–120 m below the surface), at 8 water monitoring sites in 4 different caves (1–10 m below the water surface), and on the surface (4 air and 2 water monitoring sites), where we identified a small but significant increase in atmospheric pressure of <1 hPa, with the highest signal at 21:00 CET (20:00 UTC). At some cave monitoring locations, air <i>T</i> fell during this global event induced by a far-field volcanic eruption. Cave CO<sub>2</sub>, methane, and radon measurements did not show significant changes related to the eruption. This is the first evidence of atmospheric pressure changes due to the HTHH volcano eruption in karst caves and waters.
David E. Rupp, Christopher Daly, Matthew K. Doggett
et al.
Abstract The exponential growth in solar radiation measuring stations across the conterminous United States permits the generation of gridded solar irradiance data that capture the spatiotemporal variability of solar irradiance far more accurately than was previously possible from ground-based observations. Taking advantage of these observations, we generated a 30-yr climatology (1991–2020) of mean monthly global irradiance at a resolution of 30 arc s (∼800 m) on both a horizontal surface and a sloped ground surface. This paper describes the methods used to generate the gridded data, which include extensive quality control of station data, spatial interpolation of effective cloud transmittance using the “PRISM” method, and simulation of the effects of elevation, shading, and reflection from nearby terrain on solar irradiance. A comparison of the new dataset with several other solar radiation products reveals some spatial features in solar radiation that are either lacking or underresolved in some or all of the other datasets. Examples of these features include strong gradients near foggy coastlines and along mountain ranges where there is persistent orographically driven cloud formation. The workflow developed to create the long-term means will be used as a template for generating time series of monthly and daily solar radiation grids up to the present.
Fiber-based multi-wavelength lasers have a variety of important applications in telecommunication and meteorology. We experimentally study a fiber loop laser with an integrated Erbium doped fiber (EDF). The output optical spectrum is measured as a function of the EDF temperature. We find that below a critical temperature of about $10\unit{% K}$ the measured optical spectrum exhibits a sequence of narrow and unequally-spaced peaks. An intriguing connection between the peaks' wavelengths and the sequence of prime numbers is discussed. An hypothesis, which attributes the comb formation to intermode coupling, is explored.
This study attempts to investigate the effects of global climate change (via temperature and rainfall) on cereal production in Sichuan over the 1978–2018 period, whether agricultural credit combining with technical progress (i.e., mechanical farming rate) mitigate the effect of climate change. The present study empirically analyzed the short-term and long-term interrelation among all the considered variables by using the autoregressive distributed lag (ARDL) model. The results of the ARDL bounds testing revealed that there is a long-term cointegration relationship between the variables. The findings showed that temperature significantly negatively affected cereal production, while rainfall significantly contributed to cereal production in the context of Sichuan province, China. Agricultural credit, especially in the long run, significantly improved cereal production, implying that agricultural credit is used to invest in climate mitigation technologies in cereal production. Findings further indicated that the mechanical farming rate significantly enhanced cereal production, indicating that technical progress has been playing a vital role. This study suggests that the policymakers should formulate more comprehensive agricultural policies to meet the financial needs of the agricultural sector and increase support for production technology.