This paper compares two similar diffusion equations that appear in meteorology. One is the quasi-geostrophic equation, and the other is the convection-diffusion equation. Both are two-dimensional bilinear equations, and the order of differentiation is the same. Naturally, their scales also coincide. However, the direction in which the nonlinear effects act differs: one acts along the isothermal surface, while the other acts along the temperature gradient in a specified direction. The main assertion quantifies this difference through the large-time behavior of their solutions. In particular, the nonlinear distortions in the asymptotic profiles of both equations are compared. In this context, the spatial symmetry of the first approximation plays a crucial role, but the solutions require no symmetry. As an appendix, the mixed problem of those models are studied.
Ilaria Cazzaniga, Ana I. Dogliotti, Susanne Kratzer
et al.
The use of high-resolution data in aquatic applications increased significantly in the last decade with the launch of decametre-scale optical sensors. More recently, commercial very-high resolution (VHR) sensors, offering finer spatial and temporal resolutions, have shown the potential of complementing data from high-resolution missions. Planet SuperDove (SD), with a band-setting similar to the Copernicus Sentinel-2 MultiSpectral Instrument (S2-MSI), a 3-m spatial resolution and quasi-daily revisiting time, show the potential for widening water monitoring applications to smaller water basins, and finer-scale phenomena. However, the uncertainties in SD products need to be quantified, to assess their fitness-for-purpose for these applications. This work aims to provide uncertainty estimates for SD-derived aquatic remote sensing reflectance (RRS) in different water types, benefitting from the radiometric measurements of the AERONET-OC network. RRS was derived from both Surface Reflectance (SR) products, distributed by Planet, or from data processed with ACOLITE. The comparability between SD and S2-MSI products was also assessed comparing RRS and Rayleigh-corrected reflectance (RRC) from S2-MSI and SD. The results indicate generally low performance across all bands for both SD RRS products, except in the most turbid waters, and highlight the lack of a publicly available robust atmospheric correction processor for SD data for most optical water types. The comparison to S2-MSI shows promising results only when comparing RRC values, but differences still suggest issues associated with calibration and radiometry of the SD sensors. The results also highlight the need for a harmonization strategy to ensure consistent integration of these datasets within multi-source monitoring systems.
Peter B. Gibson, Neelesh Rampal, Felix W. Goddard
et al.
Abstract Global climate models project that the South Pacific will be a hotspot for some of the largest atmospheric river (AR) changes. Thus, there is an urgent need to review both historical trends and updated high-resolution climate projections tailored to this region. Here we show that significant trends in AR frequency from reanalysis are mostly still constrained to the ocean (~45–60°S). For landfalling ARs, trends in synoptic-scale features are not yet considered robust, whereas percentile-based moisture transports show stronger increases over parts of southern New Zealand and Tasmania. Furthermore, high-resolution downscaled climate projections indicate that landfalling AR trends should become much more widespread and robustly detectable (5 of 6 models) in the next 10–20 years, first appearing across regions of southern New Zealand during spring and winter. Even under a moderate emissions scenario, projections indicate that the frequency of extreme landfalling ARs could double before mid-century, carrying significant societal impacts.
Extreme meteorological disasters such as droughts, floods, heat stress, and low-temperature frost damage are increasing in frequency, spatial extent and severity in the context of global climate change. Additionally, shifts in modern agricultural production systems and the emergence of new technologies such as artificial intelligence and big data present novel opportunities for the development of agricultural meteorological services. Convenient and accurate agricultural meteorological services can provide critical support for safeguarding food security and enhancing disaster prevention and mitigation efforts. To further enhance the application capability of national agricultural meteorological services, the new version of China Agricultural Meteorological Service System (CAgMSS3.0) is under development based on the existing CAgMSS2.0 framework and is integrated with Meteorological Big Data Cloud Platform (Tianqing) of China Meteorological Administration. CAgMSS3.0 utilizes Tianqing cloud servers for the deployment of its basic data and algorithms. Compared with CAgMSS2.0, several new modules are introduced, such as crop meteorological suitability index, annual agroclimatic evaluation and prediction, all-weather crop growth condition monitoring and analysis via optical and microwave remote sensing, agricultural meteorological disaster index, and grid-based agricultural meteorological disaster monitoring and prediction. Furthermore, CAgMSS3.0 has improved soil moisture monitoring and evaluation by integrating machine learning with multi-source data fusion. It also incorporates advanced meteorological forecasting technology for the occurrence and development of agricultural pest and disease, an interactive national-provincial agricultural weather prediction framework, and refined methods for agricultural climate zoning as well as agricultural meteorological disaster risk zoning. This system significantly enhances the operational capacity of national agricultural meteorological services through its application. Nevertheless, CAgMSS3.0 has some limitations. First, functional modules currently lack integration of global agricultural meteorological monitoring and forecasting components. Second, emerging domains such as climate quality monitoring and forecasting for agricultural products, as well as agricultural meteorological financial and insurance services require further development in the system. Third, the application of cutting-edge technology, especially AI-driven decision support in agricultural meteorology, remains undeveloped. Future iterations of agricultural meteorological service system are expected to be incorporated into a new-generation weather business integration platform structured around an "intelligent core". Meanwhile, a large-scale model based on "AI + mechanism model" will be developed for crop growth simulation and intelligent agricultural meteorological services. These improvements are anticipated to facilitate more efficient, accurate, and intelligent agricultural meteorological services.
Sandra Andersson, Maria Norman, Tomas Landelius
et al.
Abstract. SMHIGridClim, the Swedish Meteorological and Hydrological Institute Gridded Climatology, covers Fennoscandia at 2.5 km horizontal resolution for the period 1961–2018. It includes two-meter temperature and two-meter relative humidity at 1-, 3-, or 6-hour temporal resolution (which varies over the covered period), as well as daily minimum and maximum temperatures, daily precipitation, and daily snow depth. The gridding is performed using optimal interpolation with the open-source software gridpp from the Norwegian Meteorological Institute. Observations used in the analysis are provided by the Swedish, Finnish, and Norwegian meteorological institutes, as well as the European Centre for Medium-Range Weather Forecasts (ECMWF). Quality control of the observations is conducted using the open-source software TITAN, also developed at the Norwegian Meteorological Institute. The first guess for the optimal interpolation is obtained from the UERRA-HARMONIE reanalysis at 11 km horizontal resolution, which is statistically downscaled to fit a subset of the operational MEPS numerical prediction system at 2.5 km horizontal resolution, with daily and yearly variations in the downscaling parameters. The quality of the analysis varies over time and depends on both the accuracy of forecasts and the quality and density of available observations. In terms of annual mean root mean square error (RMSE), the quality of SMHIGridClim is comparable to similar gridded datasets covering the Nordic countries. SMHIGridClim is available at https://doi.org/10.7910/DVN/ZFZL6K (Andersson et al., 2025).
In climate science, the tuning of climate models is a computationally intensive problem due to the combination of the high-dimensionality of the system state and long integration times. Here we demonstrate the potential of a parameter estimation algorithm which makes use of synchronization to tune a global atmospheric model at modest computational costs. We first use it to directly optimize internal model parameters. We then apply the algorithm to the weights of each member of a supermodel ensemble to optimize the overall predictions. In both cases, the algorithm is able to find parameters which result in reduced errors in the climatology of the model. Finally, we introduce a novel approach which combines both methods called adaptive supermodeling, where the internal parameters of the members of a supermodel are tuned simultaneously with the model weights such that the supermodel predictions are optimized. For a case designed to challenge the two previous methods, adaptive supermodeling achieves a performance similar to a perfect model.
ABSTRACT This dataset comprises vertical arrays of air temperature measurements collected on Livingston and Deception Islands, Antarctica, between 2006 and early 2024. The arrays, part of the PERMATHERMAL network integrated into the Global Terrestrial Network for Permafrost (GTN‐P) database, were designed to support studies on permafrost thermal regimes and snow cover dynamics. Standard configurations included temperature sensors placed at heights of 2.5, 5, 10, 20, 40, 80, and 160 cm above the ground, mounted on wooden masts to minimise thermal interference. Higher‐resolution configuration with up to 15 vertical measurements (between 2.5 and 160 cm above the ground surface) and miniature configuration with 8 sensors (between 2.5 and 40 cm above the ground surface) were also occasionally deployed for spatial‐specific studies. Data were mainly recorded using iButton DS1921G (Miniature configuration) and DS1922L (standard and high‐resolution configurations) temperature loggers (Maxim Integrated). Despite occasional gaps due to energy depletion or device damage, the dataset provides reliable long‐term monitoring in a region where such measurements are logistically challenging. Originally acquired to estimate seasonal snow thickness through the analysis of vertical thermal gradients, the dataset has broader applications. These include investigating snowpack thermophysical properties, ground‐atmosphere energy exchanges, snow hydrology, ecological processes, and remote sensing calibration. Raw data in American Standard Code for Information Interchange (ASCII) format, without filtering or preprocessing, are made available to ensure flexibility for diverse research needs, allowing users to apply tailored cleaning and analysis protocols. The dataset is particularly valuable for addressing the scarcity of observational air temperature data in Antarctica. It provides a ground‐based complement to satellite measurements and supports studies on snow‐atmosphere interactions, soil thermal regimes, and the micrometeorology of polar environments. This unique resource facilitates multidisciplinary research across cryospheric science, hydrology, ecology, and remote sensing, offering insights into processes in extreme environments. The contribution of these long‐term measurements highlights the importance of accessible, high‐resolution datasets to advance understanding of Antarctica's complex environmental systems.
Anja Rutishauser, Signe H. Larsen, Nanna B. Karlsson
et al.
Greenland’s peripheral glaciers and ice caps contribute disproportionately to sea-level rise relative to their small area. Winter snow accumulation directly influences glacier mass balance and downstream hydrology, but spatially extensive observations of this important mass balance component remain sparse. In this study, we present a unique multi-year (2008–2024) dataset of winter snow accumulation over A.P. Olsen Ice Cap, Northeast Greenland, from ground-penetrating radar surveys covering an average of 47 km per survey year. Our results reveal strong spatial heterogeneity that is likely influenced by wind redistribution and local topography, especially in the ablation zone. We compare our findings with automatic weather station data from three sites and outputs from the Copernicus Arctic Regional Reanalysis (CARRA). Governed by the high spatial variability, the automatic weather station point-based observations significantly underestimate regional accumulation by 40–45%. Despite the high spatial variability, the CARRA accumulated precipitation variable provides a reasonable overall mean winter snow accumulation (RMSE of 0.07 m w.e.); however, it fails to reproduce the complex non-linear relationship between snow depth and elevation observed in the radar data. Our findings emphasize the need for high-resolution, spatially extensive measurements to better understand snow accumulation on ice caps and glaciers and improve reanalysis assessments.
Abstract This study presents the feasibility of forecasting fire emission fluxes within an operational modeling framework. The aim is to propose a methodology that combines the intensity of the fire detected, the air temperature, wind, and precipitation where the fire is located, to estimate the value of its intensity for the following days. Over the summer of 2022 in Europe, these estimates are compared with fire emission finally observed after the forecast. This is done using two different simulations with horizontal resolutions of 15 and 50 km. This enables us to discuss the best approach for minimizing forecast error and its sensitivity to the model resolution.
Chalachew Muluken Liyew, Elvira Di Nardo, Rosa Meo
et al.
This paper presents a statistical analysis of air temperature data from 32 stations in Italy and the UK up to 2000 m above sea level, from 2002 to 2021. The data came from both highland and lowland areas, in order to evaluate both the differences due to location, and elevation. The analysis focused on detecting trends at annual and monthly time scales, employing both ordinary least squares, robust S-estimator regression, and Mann-Kendall and Sen's slope methods. Then hierarchical clustering using Dynamic Time Warping (DTW) was applied to the monthly data to analyze the intra-annual pattern similarity of trends within and across the groups. Two different regions of Europe were chosen because of the different climate and temperature trends, namely the Northern UK (smaller trends) and the North-West Italian Alps (larger trends). The main novelty of the work is to show that stations having similar locations and altitudes have similar monthly slopes by quantifying them using DTW and clustering. These results reveal the nonrandomness of different trends along the year and among different parts of Europe, with a modest influence of altitude in wintertime. The findings revealed that group average trends were close to the NOAA values for the areas in Italy and the UK, confirming the validity of analyzing a small number of stations. More interestingly, intra-annual patterns were detected commonly at the stations of each of the groups, and clearly different between them Hierarchical clustering in combination with DTW showed consistent similarity between monthly patterns of means and trends within the group of stations and inconsistent similarity between patterns across groups. Distance correlation matrices also contributes to what is the main result of the paper, which is to clearly show the different temporal patterns in relation to location and (in some months) altitude.
Reliably measuring the collinearity of bivariate data is crucial in statistics, particularly for time-series analysis or ongoing studies in which incoming observations can significantly impact current collinearity estimates. Leveraging identities from Welford's online algorithm for sample variance, we develop a rigorous theoretical framework for analyzing the maximal change to the Pearson correlation coefficient and its p-value that can be induced by additional data. Further, we show that the resulting optimization problems yield elegant closed-form solutions that can be accurately computed by linear- and constant-time algorithms. Our work not only creates new theoretical avenues for robust correlation measures, but also has broad practical implications for disciplines that span econometrics, operations research, clinical trials, climatology, differential privacy, and bioinformatics. Software implementations of our algorithms in Cython-wrapped C are made available at https://github.com/marc-harary/sensitivity for reproducibility, practical deployment, and future theoretical development.
This article presents the Sorting Composite Quantile Regression Neural Network (SCQRNN), an advanced quantile regression model designed to prevent quantile crossing and enhance computational efficiency. Integrating ad hoc sorting in training, the SCQRNN ensures non-intersecting quantiles, boosting model reliability and interpretability. We demonstrate that the SCQRNN not only prevents quantile crossing and reduces computational complexity but also achieves faster convergence than traditional models. This advancement meets the requirements of high-performance computing for sustainable, accurate computation. In organic computing, the SCQRNN enhances self-aware systems with predictive uncertainties, enriching applications across finance, meteorology, climate science, and engineering.
Checking the radiometric calibration of satellite hyper-spectral sensors such as the PACE Ocean Color Instrument (OCI) while they operate in orbit and evaluating remote sensing reflectance, the basic variable from which a variety of optical and biogeochemical ocean properties can be derived, requires measuring upwelling radiance just above the surface (Lw) and downwelling planar irradiance reaching the surface (Es). For this, the current HyperNav systems measure Lw at about 2 nm spectral resolution in the ultraviolet to near infrared, but Es in only four 10 nm wide spectral bands centered on 412, 489, 555, and 705 nm. In this study, the Es data acquired in these spectral bands in clear sky conditions are used to reconstruct via a multi-linear regression model the hyper-spectral Es signal at 0.5 nm resolution from 315 to 900 nm, the OCI spectral range, allowing an estimate of Es at the HyperNav, OCI, and other sensors’ resolutions. After correction of gaseous absorption and normalization by the top-of-atmosphere incident solar flux, the atmospheric diffuse transmittance is expressed as a linear combination of Es measured in those 4 spectral bands. Based on simulations for Sun zenith angles from 0 to 75° and a wide range of (i.e., expected) atmospheric, surface, and water conditions, the Es spectrum is reconstructed with a bias of less than 0.4% in magnitude and an RMS error (RMSE) ranging from 0% to 2.5%, depending on wavelength. The largest errors occur in spectral regions with strong gaseous absorption. In the presence of typical noise on Es measurements and uncertainties on the ancillary variables, the bias and RMSE become −2.5% and 7.0%, respectively. Using a General Additive Model with coefficients depending on Sun zenith angle and aerosol optical thickness at 550 nm improves statistical performance in the absence of noise, especially in the ultraviolet, but provides similar performance on noisy data, indicating more sensitivity to noise. Adding spectral bands in the ultraviolet, e.g., centered on 325, 340, and 380 nm, yields marginally more accurate results in the ultraviolet, due to uncertainties in the gaseous transmittance. Comparisons between the measured and reconstructed Es spectra acquired by the MOBY spectroradiometer show agreement within predicted uncertainties, i.e., biases less than 2% in magnitude and RMS differences less than 5%. Reconstruction can also be achieved accurately with other sets of spectral bands and extended to cloudy conditions since cloud optical properties, like aerosol properties, tend to vary regularly with wavelength. These results indicate that it is sufficient, for many scientific applications involving hyper-spectral Es, to measure Es in a few coarse spectral bands in the ultraviolet to near infrared and reconstruct the hyperspectral signal using the proposed multivariate linear modeling.
Multiple studies have investigated the occurrence of severe convective winds and have increased our understanding of the forces driving severe winds and their spatial and temporal patterns. Some of the data used in studies have come from airport stations maintained by the National Weather Service. Their standardization across the United States makes them ideal for research, but they are limited in their distribution. This study aims to create a climatology of severe convective winds in West Texas using a mesoscale network (“mesonet”). Like their ASOS (Automated Surface Observing System) counterparts, these stations are standardized and well maintained. This study provides a 15-year climatology of severe convective wind gusts measured by the West Texas Mesonet (WTM). After extracting and manually verifying the measured gusts from over 30 WTM stations, both spatial and temporal distributions are presented. While temporal patterns in the gust distribution generally matched previous research, the high spatial resolution of the mesonet elucidated differences across a regional escarpment known as the Caprock. Comparison with regional AWOS / ASOS stations also revealed potential effects of a larger urban area. In addition to gust data, thermodynamic characteristics and rainfall accumulations associated with each gust were also investigated. In doing so, a substantial contribution from dry thunderstorm outflow winds and heat bursts to the production of regional severe wind was documented.
Zachary J. Suriano, Gina R. Henderson, Julia Arthur
et al.
Abstract Extreme snow ablation can greatly impact regional hydrology, affecting streamflow, soil moisture, and groundwater supplies. Relatively little is known about the climatology of extreme ablation events in the eastern United States, and the causal atmospheric forcing mechanisms behind such events. Studying the Susquehanna River basin over a 50-yr period, here we evaluate the variability of extreme ablation and river discharge events in conjunction with a synoptic classification and global-scale teleconnection pattern analysis. Results indicate that an average of 4.2 extreme ablation events occurred within the basin per year, where some 88% of those events resulted in an increase in river discharge when evaluated at a 3-day lag. Both extreme ablation and extreme discharge events occurred most frequently during instances of southerly synoptic-scale flow, accounting for 35.7% and 35.8% of events, respectively. However, extreme ablation was also regularly observed during high pressure overhead and rain-on-snow synoptic weather types. The largest magnitude of snow ablation per extreme event occurred during occasions of rain-on-snow, where a basinwide, areal-weighted 5.7 cm of snow depth was lost, approximately 23% larger than the average extreme event. Interannually, southerly flow synoptic weather types were more frequent during winter seasons when the Arctic and North Atlantic Oscillations were positively phased. Approximately 30% of the variance in rain-on-snow weather type frequency was explained by the Pacific–North American pattern. Evaluating the pathway of physical forcing mechanisms from regional events up through global patterns allows for improved understanding of the processes resulting in extreme ablation and discharge across the Susquehanna basin. Significance Statement The purpose of this study is to better understand how certain weather patterns are related to extreme snowmelt and streamflow events and what causes those weather patterns to vary with time. This is valuable information for informing hazard preparation and resource management within the basin. We found that weather patterns with southerly winds were the most frequent patterns responsible for extreme melt and streamflow, and those patterns occurred more often when the Arctic and North Atlantic Oscillations were in their “positive” configuration. Future work should consider the potential for these patterns, and related impacts, to change over time.
Time series data, spanning applications ranging from climatology to finance to healthcare, presents significant challenges in data mining due to its size and complexity. One open issue lies in time series clustering, which is crucial for processing large volumes of unlabeled time series data and unlocking valuable insights. Traditional and modern analysis methods, however, often struggle with these complexities. To address these limitations, we introduce R-Clustering, a novel method that utilizes convolutional architectures with randomly selected parameters. Through extensive evaluations, R-Clustering demonstrates superior performance over existing methods in terms of clustering accuracy, computational efficiency and scalability. Empirical results obtained using the UCR archive demonstrate the effectiveness of our approach across diverse time series datasets. The findings highlight the significance of R-Clustering in various domains and applications, contributing to the advancement of time series data mining.