High-resolution Temperature Research Articles

Harnessing Multi-Source Data and Deep Learning for High-Resolution Land Surface Temperature Gap-Filling Supporting Climate Change Adaptation Activities

Addressing global warming and adapting to the impacts of climate change is a primary focus of climate change adaptation strategies at both European and national levels. Land surface temperature (LST) is a widely used proxy for investigating climate-change-induced phenomena, providing insights into the surface radiative properties of different land cover types and the impact of urbanization on local climate characteristics. Accurate and continuous estimation across large spatial regions is crucial for the implementation of LST as an essential parameter in climate change mitigation strategies. Here, we propose a deep-learning-based methodology for LST estimation using multi-source data including Sentinel-2 imagery, land cover, and meteorological data. Our approach addresses common challenges in satellite-derived LST data, such as gaps caused by cloud cover, image border limitations, grid-pattern sensor artifacts, and temporal discontinuities due to infrequent sensor overpasses. We develop a regression-based convolutional neural network model, trained on ECOSTRESS (ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station) mission data, which performs pixelwise LST predictions using 5 × 5 image patches, capturing contextual information around each pixel. This method not only preserves ECOSTRESS’s native resolution but also fills data gaps and enhances spatial and temporal coverage. In non-gap areas validated against ground truth ECOSTRESS data, the model achieves LST predictions with at least 80% of all pixel errors falling within a ±3 °C range. Unlike traditional satellite-based techniques, our model leverages high-temporal-resolution meteorological data to capture diurnal variations, allowing for more robust LST predictions across different regions and time periods. The model’s performance demonstrates the potential for integrating LST into urban planning, climate resilience strategies, and near-real-time heat stress monitoring, providing a valuable resource to assess and visualize the impact of urban development and land use and land cover changes.

The Seasonal Correlation Between El Niño and Southern Oscillation Events and Sea Surface Temperature Anomalies in the South China Sea from 1958 to 2024

This study utilizes high-resolution sea surface temperature (SST) reanalysis data (0.25° × 0.25°) to investigate the relationship between SST anomalies in the South China Sea and ENSO events. The main findings are as follows: First, there is a delayed correlation between ENSO and SST anomalies in the South China Sea, with the correlation being stronger during El Niño years than during La Niña years. Second, the correlation with the peak values of the Oceanic Niño Index (ONI) is strongest for El Niño events with a 9-month lead, while for La Niña events, it is strongest with a 2-month lead. Seasonally, during El Niño events, the strongest correlations are observed in summer with a 3-month lead and in winter with a 1-month lag. For La Niña events, the strongest correlations are seen in summer with an 8-month lag and in winter with a 9-month lag. Finally, an analysis of atmospheric anomalies and shear kinetic energy anomalies relative to SST anomalies reveals a significant seasonal SST response, particularly during the summer of El Niño years and the winter of La Niña years. Overall, these results enhance our understanding of ENSO’s influence on the South China Sea and provide valuable insights for climate prediction and ecosystem protection in the region.

Photon-Assisted Synchronization of Molecular Electrochemical Oscillators

Abstract Formation of hydronium and carbonate ions from carbon dioxide in the aqueous phase is a reversible process and can both produce and consume ions. These equilibrium reactions represent molecular electrochemical oscillators in pure water. Reversible switching of ionic dynamics is a chaotic process, which is influenced by the CO2 level, temperature and other factors. As demonstrated in previous works, weak variations of magnetic fields induce different electrochemical reactivity and generate ionic waves caused by mutual synchronization of molecular oscillators. Such waves correlate between transparent electrochemical cells, suggesting macroscopic mechanisms for their interaction. This work explores these observations by monitoring the high-resolution ionic dynamics and temperature of independent fluidic cells with electrochemical impedance spectroscopy. Synchronization effects are shown to occur primarily in the CO2 dissolving scenario on the 3-10 minute scale. Without CO2 access, mutual synchronization is either non-existent or negligible. Maximal correlations with r>0.9 are achieved between 4-6 cells. The number of synchronization events is about five times lower when cells are separated between non-transparent containers. To explain such results, we consider the hypothesis of molecular quantum networks that operate with spin conversion of water isomers, investigation of these mechanisms can lead to new quantum sensing technologies.

Multi-Criteria Assessment of Urban Thermal Hotspots: A GIS-Based Remote Sensing Approach in a Mediterranean Climate City

One of the most significant urban challenges focuses on addressing the effects of urban overheating as a consequence of climate change. Several methods have been developed to characterize urban heat islands (UHIs); however, the most widely used involve complex planning, huge time consumption, and substantial human and technical resources on field monitoring campaigns. Therefore, this study aims to provide an easily accessible and affordable remote sensing method for locating urban hotspots and addresses a multi-criteria assessment of urban heat-related parameters, allowing for a comprehensive city-wide evaluation. The novelty is based on leveraging the potential of the last Landsat 9 satellite, the application of kernel spatial interpolation, and GIS open access data, providing very high-resolution land surface temperature images over urban spaces. Within GIS workflow, the city is divided into LCZs, thermal hotspots are detected, and finally, it is analyzed to understand how urban factors, such as urban boundaries, building density, and vegetation, affect urban scale LST, all using graphical and analytical cross-assessment. The methodology has been tested in Seville, a representative warm Mediterranean city, where variations of up to 10 °C have been found between homogeneous residential areas. Thermal hotspots have been located, representing 11% of the total residential fabric, while results indicate a clear connection between the urban factors studied and overheating. The conclusions support the possibility of generating a powerful affordable tool for future research and the design of public policy renewal actions in vulnerable areas.

GaN Monolithic Optopairs for Rapid and High-Resolution Temperature Measurements

GaN Monolithic Optopairs for Rapid and High-Resolution Temperature Measurements

High-Resolution Air Temperature Forecasts in Urban Areas: A Meteorological Perspective on Their Added Value

High-Resolution Air Temperature Forecasts in Urban Areas: A Meteorological Perspective on Their Added Value

Glacier velocity and surge detection in the Karakoram region, Pakistan: using remotely sensed data with cross-correlation feature tracking

ABSTRACT This research addresses the knowledge gap regarding glacier surging detection (GSD) and their potential downstream implications for Glacier Lake Outburst Floods (GLOFs) risk. This study investigates GSD across the Karakoram region, Pakistan, focusing on 30 glaciers from 2013 to 2023. The increasing occurrence of irregular and rapid glacial surges poses significant threats to downstream communities, the potential formation of ice dam lakes, and subsequent outburst floods. High-resolution satellite imagery, DEM, temperature, and precipitation data quantified glacier velocity rate (GVR) using feature-tracking, surge detection, and correlations. Findings indicate substantial movement ≥42 m/y during 2015–19 and 2021–22, peaking at 56.98 m/y in 2018–19; with significant Z-Scores, surges were recorded in KG-01, KG-06, KG-07, KG-19, KG-29, and KG-30. The region witnessed significant elevation changes, with a positive change at the terminus of KG-01, KG-06, KG-07, and KG-29 and a negative at the glacier’s accumulation zones. Furthermore, climatic factors, including rising average temperatures (17°C to 20°C) and precipitation (14 to 30 mm/y), significantly influenced glacier velocity, affecting glaciers by 20-43% and 38-59%, respectively. These findings underscore the pivotal role of surge velocities in shaping glacier outlets and influencing lake drainage, potentially leading to GLOFs.

High-Resolution Temperature Fiber Ring Laser Sensor Based on Relative Intensity Noise Measurement

High-Resolution Temperature Fiber Ring Laser Sensor Based on Relative Intensity Noise Measurement

Comparative optical thermometry analysis using Na2SrP2O7:Er3+/Yb3+ phosphors: evaluation of LIRTCL and LIRNTCL methods for high-resolution temperature sensing.

Optical thermometry is a valuable non-contact technique for temperature measurement, especially in environments where traditional methods are impractical. Despite its advantages, enhancing the precision of optical thermometers remains a significant challenge. In this study, we explored the thermometric properties of Na2SrP2O7 phosphors co-doped with Er3+/Yb3+, synthesized via a solid-state reaction method, for temperature sensing within the 200-440 K range under 980 nm excitation. Upconversion (UC) luminescence, observed in the visible spectrum, was analyzed using the fluorescence intensity ratio (FIR) method, focusing on both thermally coupled (TCLs) and non-thermally coupled (NTCLs) energy levels of Er3+/Yb3+. Specifically, the transitions 2H11/2 → 4I15/2, 4S3/2 → 4I15/2, and 4F9/2 → 4I15/2 were examined to calculate thermometric parameters. The maximum absolute sensitivity (S A) and relative sensitivity (S R) for the 2H11/2 → 4I15/2 to 4S3/2 → 4I15/2 transition were 0.0009 K-1 and 0.6% K-1, respectively, while for the 2H11/2 → 4I15/2 to 4F9/2 → 4I15/2 transition, S A was 0.004 K-1, with a maximum S R of 1.14% K-1. Furthermore, by employing a luminescence intensity ratio technique based on TCLs (LIRTCL), the minimum temperature uncertainty (δT) was found to be 1.31 K at 320 K. In contrast, the luminescence intensity ratio method based on NTCLs (LIRNTCL) yielded a much lower minimum δT value of 0.34 K at 200 K, indicating superior performance in terms of temperature resolution. These findings demonstrate that the LIRNTCL technique provides more sensitive and accurate temperature measurement compared to LIRTCL. The excellent temperature resolution and sensitivity of Na2SrP2O7:Er3+/Yb3+ phosphors highlight their potential for highly accurate optical thermometry applications in scientific and industrial contexts.

The Loss of Beneficial Thermal Priming on Global Coral Reefs.

Warm-season marine heatwaves (MHWs) have greatly increased in frequency, severity, and extent over the last few decades, driving more frequent and severe coral bleaching episodes. Given the grave near-term threat to coral reefs imposed by MHWs, it is important to assess the mechanisms by which corals may acquire higher thermal tolerance. Recent field and laboratory studies have demonstrated that exposure to sublethal heat stress, known as "priming," can reduce bleaching susceptibility during a subsequent MHW. Little is known, however, about how often priming conditions occur, and how effective those conditions may be at protecting coral reefs. We employed a global historical coral bleaching database and a high-resolution sea surface temperature dataset to assess the frequency of priming and examine its effect on coral bleaching sensitivity on a global scale. The analysis showed that coral reefs in parts of the western to central tropical Pacific experienced priming on average over twice a decade and had a higher likelihood of priming protection. Mixed-effects regression models indicated that priming conditions could mitigate coral bleaching response by up to 12% in advance of a moderate MHW. However, the protective effect of priming decreased, and even became harmful, with more severe MHWs. We detected spatial variations in priming frequency that could provide insight for conservation planning and explain some variations in bleaching sensitivity to MHWs. Even so, our findings suggest that thermal priming will not be sufficient to protect most coral reefs from MHWs in the future, without substantial efforts to mitigate climate change.

Fast generation of high-dimensional spatial extremes

Widespread extreme climate events cause many fatalities, economic losses and have a huge impact on critical infrastructure. It is therefore of utmost importance to estimate the frequency and associated consequences of spatially concurrent extremes. Impact studies of climate extremes are severely hampered by the lack of extreme observations, and even large ensembles of climate simulations often do not include enough extreme or record-breaking climate events for robust analysis. On the other hand, weather generators specifically fitted to extreme observations can quickly generate many physically or statistically plausible extreme events, even with intensities that have never been observed before. We propose a Fourier-based algorithm for generating high-resolution synthetic datasets of rare events, using essential concepts of classical modelling of (spatial) extremes. Here, the key feature is that the stochastically generated datasets have the same spatial dependence as the observed extreme events. Using high-resolution gridded precipitation and temperature datasets, we show that the new algorithm produces realistic spatial patterns, and is particularly attractive compared to other existing methods for spatial extremes. It is exceptionally fast, easy to implement, scalable to high dimensions and, in principle, applicable for any spatial resolution. We generated datasets with 10000 gridpoints, a number that can be increased without difficulty. Since current impact models often require high-resolution climate inputs, the new algorithm is particularly useful for improved impact and vulnerability assessment.

Machine learning based high-resolution air temperature modelling from landsat-8, MODIS, and In-Situ measurements with ERA-5 inter-comparison in the data sparse regions of Himachal Pradesh

Machine learning based high-resolution air temperature modelling from landsat-8, MODIS, and In-Situ measurements with ERA-5 inter-comparison in the data sparse regions of Himachal Pradesh

High-Resolution Nearshore Sea Surface Temperature from Calibrated Landsat Brightness Data

Understanding and monitoring nearshore environments is essential, given that these fine-scaled ecosystems are integral to human well-being. While satellites offer an opportunity to gain synchronous and spatially extensive data of coastal areas, off-the-shelf calibrated satellite sea surface temperature (SST) measurements have only been available at coarse resolutions of 1 km or larger. In this study, we develop a novel methodology to create a simple linear equation to calibrate fine-scale Landsat thermal infrared radiation brightness temperatures (calibrated for land sensing) to derive SST at a resolution of 100 m. The constants of this equation are derived from correlations of coincident MODIS SST and Landsat data, which we filter to find optimal pairs. Validation against in situ sensor data at varying distances from the shore in Northern California shows that our SST estimates are more accurate than prior off-the-shelf Landsat data calibrated for land surfaces. These fine-scale SST estimates also demonstrate superior accuracy compared with coincident MODIS SST estimates. The root mean square error for our minimally filtered dataset (n = 557 images) ranges from 0.76 to 1.20 °C with correlation coefficients from r = 0.73 to 0.92, and for our optimal dataset (n = 229 images), the error is from 0.62 to 0.98 °C with correlations from r = 0.83 to 0.92. Potential error sources related to stratification and seasonality are examined and we conclude that Landsat data represent skin temperatures with an error between 0.62 and 0.73 °C. We discuss the utility of our methodology for enhancing coastal monitoring efforts and capturing previously unseen spatial complexity. Testing the calibration methodology on Landsat images before and after the temporal bounds of accurate MODIS SST measurements shows successful calibration with lower errors than the off-the-shelf, land-calibrated Landsat product, extending the applicability of our approach. This new approach for obtaining high-resolution SST data in nearshore waters may be applied to other upwelling regions globally, contributing to improved coastal monitoring, management, and research.

Multiple timescale variations in fronts in the Seto Inland Sea, Japan

Abstract. The Seto Inland Sea (SIS) is a critical semi-enclosed coastal sea in Japan, characterized by intricate coastlines and narrow straits that give rise to various fronts. Despite extensive research on tidal fronts, knowledge gaps persist regarding their spatiotemporal dynamics, particularly in certain poorly documented regions. Additionally, the understanding of thermohaline fronts, which emerge during winter, requires further investigation. We aimed to enhance our understanding of tidal and thermohaline fronts in the SIS by analyzing their dynamic processes, including intra-tidal and spring–neap tidal cycles, seasonal variations, and anomalous frontal variability. Using a gradient-based algorithm with an advanced contextual feature-preserving median filter, we processed the high-resolution sea surface temperature dataset to detect and quantify tidal and thermohaline fronts. Our analysis revealed the presence of numerous tidal fronts, predominantly influenced by the M2 tide, across the SIS, with substantial spatial variations in amplitude due to complex coastlines and narrow straits. Intra-tidal movements of tidal fronts corresponded to ebb and flood currents, while spring–neap tidal cycles and seasonal shifts influenced frontal positions and intensities. Additionally, thermohaline fronts were identified in certain regions during winter, characterized by large horizontal temperature and salinity gradients. This study enhances the understanding of tidal and thermohaline fronts in the SIS, emphasizing the importance of intra-tidal and wind-driven influences on frontal dynamics. However, limited observational coverage and resolution emphasize the need for further research to explore long-term temporal changes and better grasp the influences of ambient currents and wind patterns. Such insights are vital for effective coastal management and environmental monitoring in the SIS region.

Abstract 4118766: Lower Winter and Higher Summer Temperatures Increase the Risk of Hospitalization With Aortic Aneurysm or Aortic Dissection

Background: Seasonal variation in aortic dissection incidence are well-documented. This study utilized a high-resolution surface temperature model and state-specific administrative medical data to investigate the relationship between long-term exposure to winter and summer temperature averages and hospitalization rates for aortic aneurysm or dissection. Methods: Using data from the 2000-2016 State Inpatient Databases (SID), 265,795 hospitalizations with a principal diagnosis of aortic aneurysm or dissection for residents of 19 U.S. states were identified via ICD-9 and 10 codes. Daily surface temperature data, extracted from Oak Ridge National Laboratory’s Daymet weather model on a 1-km grid, were aggregated to zip codes to match SID's spatial resolution. Linear regressions, adjusted for demographics, comorbidities (including hypertension, hyperlipidemia, atherosclerosis, smoking, and genetic disorders), air pollution, and neighborhood socioeconomic status, estimated absolute changes in annual hospitalization rates associated with variations in winter and summer temperature averages. Nonlinear exposure-effect relationships were assessed using penalized cubic splines with at most nine degrees of freedom. Results: Each Celsius degree decrease in winter and increase in summer was associated with 19.0 (95% CI: 17.5, 20.8, p<0.001) and 10.4 (95% CI: 8.0, 12.7, p<0.001) additional hospitalizations per one million person-years, respectively. These statistically significant associations were consistent across subgroups, including thoracic vs. abdominal and aneurysm vs. dissection. Further analysis revealed that cooler-than-average winter temperatures consistently correlated with increased hospitalization rates for overall aortic aneurysms or dissections and their subtypes. Conclusion: Colder winter and hotter summer temperatures significantly increase the risk of hospitalization for aortic aneurysm or dissection. This emphasizes the importance of understanding how environmental factors contribute to aortic disease.

Unique High-Resolution Temperature Mapping of Stage 1 Turbine Vane in a Long-Term Engine Test

Abstract In this study, surface temperature maps resulting from a long-term engine test were evaluated on a stage 1 turbine vane using thermal history coatings (THCs). THCs are ceramic-type sensor coatings doped with a lanthanide ion, giving the coating photoluminescent properties. The THC's structure permanently changes when exposed to high temperatures, which in turn alters its photoluminescent properties. Therefore, historic maximum temperature maps are evaluated by optically probing the THC point-by-point across the vane's surface. The vane was exposed to a non-dedicated (multi-cycle, multi temperature-level) engine test lasting over 8 months. Two passes of THC measurements were taken, termed low resolution (LR) and high resolution (HR), each, respectively, with point pitches of 5 mm and 1 mm. The latter provided a unique insight into the historic maximum temperature profile of the vane, particularly around high-temperature gradient regions, such as those close to effusion cooling holes. The THC temperatures were compared to the engine manufacturer's (Doosan Enerbility) heat transfer code (Doosan Integrated Thermal Analysis for Cooling System (DiTACS)) for validation. The THC temperature mapping was successful with good coverage across the vane. The leading edge and pressure side trailing edge regions were typically the hottest. The HR measurements clearly show sharp thermal gradients around the effusion cooling holes on the vane's airfoil and platforms. Critically, the THC measurements were compared very well to the DiTACS measurements, validating THCs as a credible temperature measurement technique for long-term, non-dedicated engine tests for components operating in some of the most extreme environments of an engine.

Beyond threats: Extreme heatwaves and economic resilience in China

This study demonstrates extreme heatwaves have a positive impact on economic resilience in China at the provincial level, utilizing high spatial resolution temperature data. This effect may be attributed to heightened climate policy uncertainty and shifts in public climate perception. Furthermore, our findings reveal that these effects are particularly pronounced in the eastern regions of China and in provinces with large economic scales but relatively small agriculture output values.

High Spatial Resolution and Accurate Temperature Profile Measurements in a Nuclear Reactor Core Enabled by Machine Learning

High Spatial Resolution and Accurate Temperature Profile Measurements in a Nuclear Reactor Core Enabled by Machine Learning

Study of the Future Evolution of the Urban Climate of Paris by Statistical–Dynamical Downscaling of the EURO-CORDEX Ensemble

Abstract High-resolution urban climate projections are needed for local decision-making on climate change adaptation. Regional climate models have resolutions that are too coarse to simulate the urban climate at such resolutions. A novel statistical–dynamical downscaling (SDD) approach is used here to downscale the EURO-CORDEX ensemble to a resolution of 1 km while adding the effect of the city of Paris (France) on air temperature. The downscaled atmospheric fields are then used to drive the Town Energy Balance urban canopy model to produce high-resolution temperature maps over the period 1970–2099, while maintaining the city’s land cover in its present state. The different steps of the SDD are evaluated for the summer season. The regional climate models simulate minimum (maximum) temperatures (TN/TX) that are too high (low). After correction and downscaling, the urban simulations inherit some of these biases but give satisfactory results for summer urban heat islands (UHIs), with average biases of −0.6 K at night and +0.3 K during the day. Changes in future summer temperatures are then studied for two greenhouse gas emission scenarios, RCP4.5 and RCP8.5. Outside the city, the simulations project average increases of 4.1 and 4.8 K for TN and TX for RCP8.5, respectively. In the city, warming is lower, resulting in a decrease in UHIs of −0.19 K at night (from 2.1 to 1.9 K) and −0.16 K during the day. The changes in UHIs are explained by higher rates of warming in rural temperatures due to lower summer precipitation and soil water content and are partially offset by increased ground heat storage in the city.

Reconstructing tropical monthly sea surface temperature variability by assimilating coral proxy datasets

Coral reconstruction often serves as a major proxy of high-resolution sea surface temperature (SST) variability beyond the instrumental era. However, coral reconstructions are sparse and are usually studied for interannual variability, with few studies on the monthly features. In this study, we reconstruct the monthly SST spatial field by applying the paleoclimate data assimilation method to the coral records of the latest CoralHydro2k data set for the instrument period of 1880–2000. A comparison with observed SST variability shows that our assimilated tropical SST variability performs reasonably well for the seasonal cycle and monthly ENSO characteristics, notably the phase-locking and onset timing, and more realistic spatial fields relative to the model simulations. This study suggests the feasibility of applying paleoclimate data assimilation to reconstruct the monthly SST in the historical period.