Surface Temperature Data Research Articles

An automatic eddy detection algorithm based on quadrant angle of velocity vector and its application

ABSTRACT Eddies assume a pivotal role in both the oceanic heat cycle and marine dynamic processes. The development of real-time satellite observations, coupled with advancements in computer intelligence, has propelled automatic eddy detection algorithms to the forefront of ocean remote sensing research. Nevertheless, target omissions and accuracy degradations are inevitable for multiple detection algorithms due to strong morphological variations in ocean eddies. This paper proposes an automatic detection algorithm based on the quadrant angle of the velocity vector in the flow field. Firstly, a rectangular search box is established, and the corresponding quadrant angles at the four vertices are calculated. Secondly, the centres and types of eddies are determined according to the cumulative sum and variety rules of quadrant angles. Then the outermost boundary is identified by regularity of quadrant angles in eight directions expanding outward from the centre of the eddy using the stream function equation. The new algorithm is assessed and verified using geostrophic flow data derived from the CMEMS standard gridded sea level anomaly product. Furthermore, its detection capabilities are demonstrated through a comparative analysis with several other algorithms. All methods exhibit consistent efficacy in detecting the majority of eddies with similar spatial distributions. In cases where the new algorithm identifies a specific eddy not detected by the FF15 and ND10 algorithms, sea surface temperature data is employed for verification. The sea surface temperature distribution map, along with the obtained results, illustrates the superiority of the new algorithm and its adaptability to products with varying resolutions. Additionally, the results undergo verification through a manual detection method, revealing that the new algorithm achieves SDR of 91.73% and an EDR of 3.54%. This percentage significantly surpasses the lower acceptable limit of 80% for the SDR parameter. The new algorithm is applied to study eddies with lifetimes exceeding ten days, spanning from March 2022 to February 2023, within the East China Sea and the South China Sea. The radius of SCS eddies is generally larger than that of ECS eddies, both cyclonic and anticyclonic. This further confirms the significant influence of regional ocean dynamics on the structure of eddies in different regions. Finally, the algorithm is applied to SWOT data to test the adaptability of the algorithm to non-regular grid data. The results show that the algorithm can effectively analyse SWOT data within 60° latitudes.

Constant temperature line identification on prototype gas turbine combustor with multi-colour change coating

Abstract This research focuses on identifying isotherms on the surface of a gas turbine combustion chamber using a colour-changing solvent known as temperature-indicating paint (TIP). The study employs a Multi Colour Change (MCC) 350-8 TIP to detect surface temperature variations. An innovative algorithm is proposed to identify these variations through irreversible colour changes, utilizing a colour-matching technique. The surface temperature data obtained from this method is compared with experimental calibration and computational analysis, showing not just agreement, but a high level of agreement, reinforcing the reliability of the method. This approach aims to enhance the accuracy of temperature detection in combustion chambers, which is crucial for optimizing performance and reducing emissions. The findings contribute to developing more efficient gas turbine systems by providing a reliable method for monitoring and controlling combustion temperatures. This study’s novelty lies in its use of colour-changing technology to achieve precise temperature mapping on complex surfaces.

Seasonal and Temporal Ensemble Models for Accurate Near-Surface Air Temperature Estimation

The near-surface air temperature (NSAT) is crucial for understanding thermal and urban environments. Traditional estimation methods using general remote sensing images often focus on the types of spatial data or machine learning models used, neglecting the importance of seasonal and temporal variations, limiting their accuracy. This study introduces a novel ensemble model that incorporates both seasonal and temporal information integrated with satellite-derived land surface temperature (LST) data to enhance NSAT estimation, along with a rigorous feature importance analysis to identify the most impactful parameters. Data from 2022, collected from 147 South Korean weather stations, were used to develop and evaluate the models. Thirteen initial variables, including the LST and other auxiliary data, were considered. Random forest regression was employed to build separate models for each season. This novel approach of separating data by season allowed optimized feature selection tailored to each season, improving the model efficiency and capturing finer seasonal and daily temperature variations. These seasonal models were then combined to form an ensemble model. The seasonal models demonstrated varying accuracy, with the R2 values indicating a strong correlation between the predicted and actual NSAT, particularly high in spring and fall and lower in summer and winter. The ensemble model showed improved performance, achieving an MAE of 0.534, an RMSE of 0.391, an R2 of 0.996, and a cross-validated R2 of 0.968. These findings highlight the effectiveness of incorporating seasonal and temporal information into NSAT estimation models, offering significant improvements over traditional approaches. The developed models support precise temperature monitoring and forecasting, aiding environmental and urban management.

Land surface temperature trends derived from Landsat imagery in the Swiss Alps

Abstract. The warming of high mountain regions caused by climate change is leading to glacier retreat, decreasing snow cover, and thawing permafrost, all of which have far-reaching effects on ecosystems and societies. Landsat Collection 2 provides multi-decadal land surface temperature (LST) data, principally suited for large-scale monitoring at high spatial resolution. In this study, we assess the potential to extract LST trends using Landsat 5, 7, and 8 time series. We conduct a comprehensive comparison of both LST and LST trends with data from 119 ground stations of the Intercantonal Measurement and Information System (IMIS) network, located at high elevations in the Swiss Alps. The direct comparison of Landsat and IMIS LST yields robust satellite data with a mean accuracy and precision of 0.26 and 4.68 K, respectively. For LST trends derived from a 22.6-year record length, as imposed by the IMIS data, we obtain a mean accuracy and precision of −0.02 and 0.13 K yr−1, respectively. However, we find that Landsat LST trends are biased due to unstable diurnal acquisition times, especially for Landsat 5 and 7. Consequently, LST trend maps derived from 38.5-year Landsat data exhibit systematic variations with topographic slope and aspect that we attribute to changes in direct shortwave radiation between different acquisition times. We discuss the origin of the magnitude and spatial variation of the LST trend bias in comparison with modeled changes in direct shortwave radiation and propose a simple approach to estimate the LST trend bias. After correcting for the LST trend bias, the remaining LST trend values average between 0.07 and 0.10 K yr−1. Furthermore, the comparison of Landsat- and IMIS-derived LST trends suggests the existence of a clear-sky bias, with an average value of 0.027 K yr−1. Despite these challenges, we conclude that Landsat LST data offer valuable high-resolution records of spatial and temporal LST variations in mountainous terrain. In particular, changes in the mountain cryosphere, such as glacier retreat, glacier debris cover evolution, and changes in snow cover, are preserved in the LST trends and potentially contribute to improved prediction of permafrost temperatures with large spatial coverage. Our study highlights the significance of understanding and addressing biases in LST trends for reliable monitoring in such challenging terrains.

Machine learning-based models for forecasting radio refractivity over the coastal area of South Africa

Surface refractivity is a crucial parameter that determines the bending of radio signals as they propagate within the troposphere. It is greatly influenced by the atmospheric weather conditions and changes rapidly, especially in the coastal areas. This research utilized 50 years (1974-2023) surface temperature, pressure, and humidity data from six coastal stations in South Africa to forecast radio refractivity in the Mediterranean climate. Five machine learning models: Gated Recurrent Unit (GRU), Light Gradient Boosting Machine (LightGBM), Long-Short Term Memory (LSTM), Prophet, and Random Forest were trained for future prediction of surface refractivity at any coastal area in South Africa. The stations latitude, longitude, altitude, surface refractivity and date were applied as the input parameters to train the models. The models were optimized through the randomized searchCV hyperparameter tuning to improve their efficiency. The LightGBM outperformed other models with RMSE and adjusted determination coefficients of 1.67 and 0.96, respectively. The model is recommended for future prediction of surface refractivity needed for the improvement of point-to-point wireless communication, terrestrial radio and television transmissions, and mobile communication networks in the coastal sub-tropical regions.

Modelling marine heatwaves impact on shallow and upper mesophotic tropical coral reefs

Abstract Coral reefs ecosystems, often compared to rain forests for their high biodiversity, are threatened by ocean warming causing coral bleaching when the symbiotic relationship between dinoflagellates and corals breaks under high ocean temperatures. Thermal stress from marine heatwaves (MHWs) occur both at the surface and subsurface with subsurface MHWs lasting longer with potentially higher cumulative intensities. However, global coral bleaching models generally ignore the differences in thermal stress between surface and sea-bed levels. Here, we define MHWs at sea-bed level to model coral bleaching with daily resolution from 6 May 1993 to 31 October 2023, for 9944 tropical coral reefs between 0 and 60 m depths. We show that deeper reefs experience on average higher thermal stress and bleaching compared to surface reefs. Using surface temperature data to model bleaching for deeper corals underestimates bleaching intensities by an average of 6% ± 9% compared to the subsurface calibrated model. Our study is a starting point for more accurate coral bleaching modelling, providing additional evidence to reshape our perception of deeper coral reefs as potential refugees from climate change.

The Dynamics of Air Pollution in the Southwestern Part of the Caspian Sea Basin (Based on the Analysis of Sentinel-5 Satellite Data Utilizing the Google Earth Engine Cloud-Computing Platform)

The Caspian region represents a complex and unique system of terrestrial, coastal, and aquatic environments, marked by an exceptional landscape and biological diversity. This diversity, however, is increasingly threatened by substantial anthropogenic pressures. One notable impact of this human influence is the rising concentration of pollutants atypical for the atmosphere. Advances in science and technology now make it possible to detect certain atmospheric pollutants using remote Earth observation techniques, specifically through data from the Sentinel-5 satellite, which provides continuous insights into atmospheric contamination. This article investigates the dynamics of atmospheric pollution in the southwestern part of the Caspian Sea basin using Sentinel-5P satellite data and the cloud-computing capabilities of the Google Earth Engine (GEE) platform. The study encompasses an analysis of concentrations of seven key pollutants: nitrogen dioxide (NO2), formaldehyde (HCHO), carbon monoxide (CO), ozone (O3), sulfur dioxide (SO2), methane (CH4), and the Aerosol Index (AI). Spatial and temporal variations in pollution fields were examined for the Caspian region and the basins of the seven rivers (key areas) flowing into the Caspian Sea: Sunzha, Sulak, Ulluchay, Karachay, Atachay, Haraz, and Gorgan. The research methodology is based on the use of data from the Sentinel-5 satellite, SRTM DEM data on absolute elevations, surface temperature data, and population density data. Data processing is performed using the Google Earth Engine cloud-computing platform and the ArcGIS software suite. The main aim of this study is to evaluate the spatiotemporal variability of pollutant concentration fields in these regions from 2018 to 2023 and to identify the primary factors influencing pollution distribution. The study’s findings reveal that the Heraz and Gorgan River basins have the highest concentrations of nitrogen dioxide and Aerosol Index levels, marking these basins as the most vulnerable to atmospheric pollution among those assessed. Additionally, the Gorgan basin exhibited elevated carbon monoxide levels, while the highest ozone concentrations were detected in the Sunzha basin. Our temporal analysis demonstrated a substantial influence of the COVID-19 pandemic on pollutant dispersion patterns. Our correlation analysis identified absolute elevation as a key factor affecting pollutant distribution, particularly for carbon monoxide, ozone, and aerosol indices. Population density showed the strongest correlation with nitrogen dioxide distribution. Other pollutants exhibited more complex distribution patterns, influenced by diverse mechanisms associated with local emission sources and atmospheric dynamics.

Development and Evaluation of Machine Learning Models for Air-to-Land Temperature Conversion Using the Newly Established Kunlun Mountain Gradient Observation System

Mountainous land types are characterized by a scarcity of observational data, particularly in remote areas such as the Kunlun Mountains, where conventional Automatic Weather Stations (AWSs) typically do not record land surface temperature (LST) data. This study aims to develop and evaluate models for converting air temperature (TA) to LST using newly established meteorological station data from the Kunlun Mountain Gradient Observation System, thereby providing time-continuous LST data for AWSs. We constructed a conceptual model to explore the relationship between 1.5 m TA and LST and instantiated it using three machine learning algorithms: Support Vector Machine (SVR), Convolutional Neural Network (CNN), and CatBoost. The results demonstrated that the CatBoost algorithm outperformed the others under complex terrain and climatic conditions, achieving a coefficient of determination (R2) of 0.997 and the lowest root mean square error (RMSE) of 0.627 °C, indicating superior robustness and accuracy. Consequently, CatBoost was selected as the optimal model. Additionally, this study analyzed the spatiotemporal distribution characteristics of cloud cover in the Kunlun Mountain region using the MOD11A1 product and assessed the uncertainties introduced by the 8-day average compositing method of the MOD11A2 product. The results revealed significant discrepancies between the monthly average LST derived from polar-orbiting satellites and the hourly composite monthly LST measured on-site or under ideal cloud-free conditions. These differences were particularly pronounced in high-altitude regions (4000 m and above), with the greatest differences occurring in winter, reaching up to 10.2 °C. These findings emphasize the importance of hourly LST calculations based on AWSs for accurately assessing the spatiotemporal characteristics of LST in the Kunlun Mountains, thus providing more precise spatiotemporal support for remote sensing applications in high-altitude regions.

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.

Spatiotemporal Evolution of Marine Heatwaves Globally

Abstract The spatiotemporal evolution of marine heatwaves (MHWs) is explored using a tracking algorithm called Ocetrac that provides objective characterization of MHW spatiotemporal evolution. Candidate MHW grid points are defined in detrended gridded sea temperature data using a seasonally varying temperature threshold. Identified MHW points are collected into spatially distinct objects using edge detection with weak sensitivity to edge detection and size percentile threshold criteria at each timestep. Ocetrac then uses 3D connectivity to determine if these objects are part of the same event, but Ocetrac only defines the full MHW event after all timesteps have been processed, limiting its use in predictability studies. Here Ocetrac is applied to monthly satellite sea surface temperature data from September 1981 through January 2021. The resulting MHWs are characterized by their intensity, duration, and total area covered. The global analysis shows that MHWs in the Gulf of Maine and Mediterranean Sea are spatially isolated, while major MHWs in the Pacific and Indian Oceans are connected in space and time. The largest and most long lasting MHW using this method lasts for 60 months from November 2013 to October 2018, encompassing previously identified MHW events including those in the Northeast Pacific (2014-2015), the Tasman Sea (2015-2016, 2017-2018), and the Great Barrier Reef (2016).

A two-stage deep learning architecture for detection global coastal and offshore submesoscale ocean eddy using SDGSAT-1 multispectral imagery

Submesoscale ocean eddies are essential oceanic phenomenon that control and influence the ocean energy cascade. Most existing eddy detection methods rely on low-resolution satellite altimeter data, which fail to capture submesoscale ocean features and oceanographic phenomena in shallow water. Introducing high-resolution multispectral data can alleviate these problems, yet it has been largely overlooked. A generalized and efficient deep learning architecture that combines developments in deep learning with Sustainable Development Goals Science Satellite 1 (SDGSAT-1) multispectral data from earth observations offers a potential pathway for more fine detection of ocean eddies. Considering that oceanic eddy exhibits spatially sparse characteristics on high-resolution remote sensing scenes, the oceanic eddy detection (OED) model suitable for global coastal and offshore regions is divided into two stages: eddy information judgment and eddy position determination. Correspondingly, SDGSAT-1 multispectral data from November 2021 to December 2022 were carried out to construct two submesoscale eddy datasets for training and testing each stage model. The union validation of multiple metrics demonstrates that the proposed OED model and its stage models achieve state-of-the-art (SOTA) performance, especially in optically complex coastal and offshore waters. We applied the model to real-world scenes captured by SDGSAT-1 in 2023, and found that the detected results were mainly located at the water depth below 200 m. The authenticity of the recognition results is validated using sea surface chlorophyll concentration, temperature, and topography data, indicating that the OED model has achieved remarkable effectiveness under various sea conditions. In addition, the temporal distributions and statistical characteristics of detected submesoscale eddies are analyzed over an extended period (November 2021 to November 2023). Finally, HISEA-2, Landsat-9, and Sentinel-2 served as testing grounds to validate the generalization of the proposed methodology, with experimental results demonstrating that the OED model possesses significant developmental potential for multi-source remote sensing data. This paper presents a comprehensive deep learning framework for the global-scale detection of submesoscale eddies and underscores the pivotal role of high-resolution multispectral imagery as an innovative data source for global coastal and offshore eddy identification.

Investigating the influence of land cover on land surface temperature

The increasingly serious urban heat island (UHI) effect is unfavorable to urban development. This study utilized land cover data and land surface temperature (LST) data of China in 2020 by using correlation analysis and spatial regression models to analyze the relationships between LST and two influencing factors (land cover and digital elevation model (DEM)). The results showed the following: (1) The correlation between LST and forest was highest in the Northeast China Plain (NCP), Huang-Huai-Hai Plain (HHP), Qinghai Tibet Plateau (QTP), and Loess Plateau (LP). DEM mean displayed its highest correlation in the Northern arid and semiarid region (NAR), Sichuan Basin and surrounding regions (SCR), Yunnan-Guizhou Plateau (YGP), and Middle-lower Yangtze Plain (MYP). Southern China (SC) had the highest correlation between LST and construction land. (2) There was spatial heterogeneity between land cover and LST. Unused land on LP had larger impact on LST. For every 1% increase in the proportion of unused land area, the LST increased by 0.250 °C. LST in some central and western regions of China (the NAR, the LP, the SCR, and the YGP) was mainly affected by local land cover; LST in eastern coastal regions (the HHP, MYP, NCP, SC) and QTP was not only affected by local land cover, but also by LST or land cover of neighboring regions. The warming effect of construction land on LST was more significant, with LST increasing by 0.079 °C to 0.338 °C for every 1% increase in the proportion of construction land area. Coordination of land use planning and synergistic remediation in different regions and rational planning of construction land are essential to mitigate the UHI effect. (3) Water bodies in the NCP, NAR, and MYP had the greatest cooling impact on LST, with LST decreasing by 0.277 °C, 0.246 °C, and 0.079 °C, respectively, for every 1% increase in the proportion of water bodies area. Forest on the QTP, LP, SC, and YGP had the greatest cooling impact on LST, and for every 1% increase in the proportion of forest area, LST decreased by 0.144 °C, 0.089 °C, 0.086 °C, and 0.038 °C, respectively. Actively planting trees and increasing the area of forests and water bodies are of positive significance in alleviating the UHI effect and improving the ecological environment.

Concrete refuges and the influence of temperature on artificial refuge occupation by terrestrial lizards

ABSTRACT Artificial refuges are commonly used to sample small reptiles and to provide supplementary shelter. For refuges to be effective, they should be safe and acceptable to reptiles. Current designs used for New Zealand lizards are vulnerable to trampling and degradation, restricting where they can be used without risk to lizards. We tested the usage of a new trampling-resistant concrete refuge, containing an internal crevice and basking ledge, by terrestrial lizards on the Port Hills in Canterbury, South Island. Twice a month we inspected 40 refuges on 20 occasions over one year which resulted in 420 gecko and 39 skink encounters. Geckos consistently used refuges throughout the year while skinks were infrequently found. Internal refuge surface and ambient air temperature data suggests that both overnight and day-time temperatures in the refuges were favourable for year-round occupation by geckos at the study site. Due to the low number of skinks, their occupancy was not analysed. Our refuge design appears to be a robust and effective design, particularly for crevice-dwelling geckos in trampling-prone and rocky habitats. Additional research is needed to investigate gecko movements in refuges at more extreme temperatures, test our design on other reptile species and assess potential restoration benefits. Zoobank LSID urn:lsid:zoobank.org:pub:71482E37-D73D-4362-AAE8-4AA15BE33A7F.

Monthly and Interannual Variations in Upwelling and Chlorophyll off the Vietnam Coast in Summer Based on Deep Learning

AbstractThe deep learning technique is used to automatically identify upwelling regions off the Vietnam coast using Sea Surface Temperature (SST) data from satellite imagery. After delineating these upwelling areas, this study is pioneering in deriving the upwelling probability off the Vietnam coast, pinpointing two significant upwelling hotspots. To deepen the understanding of the individual impacts of wind and sea surface height anomalies on monthly and spatial variations in upwelling probability and chlorophyll distribution, the Empirical Orthogonal Function method was employed to analyze this data set. The upwelling probability demonstrates a close relationship with wind and sea surface height anomalies, while chlorophyll concentration correlates with the strength of the southwesterly wind. The southwesterly wind, Ekman pumping, eddy dipole, and northeastward jet have been identified as key drivers of the formation and spatial variability of upwelling in summer, albeit with varying contributions to different parts of the coastal region. The distribution of upwelling probability correlates with chlorophyll variation off the northern Vietnam coast. High chlorophyll concentration off the southern Vietnam coast is primarily influenced by the southwesterly wind that carries the Mekong River plume eastward. Furthermore, two abnormal scenarios in upwelling and chlorophyll concentration during 2010 and 2018 were analyzed, attributed to the abnormal southwesterly monsoon and coastal circulation influenced by an El Niño event and a weak La Niña event with a positive Indian Ocean Dipole, respectively. This study elucidates the dynamic processes underlying the intricate chlorophyll distribution over the continental shelf, which is influenced by both the river plume and upwelling.

Influence of mesoscale eddies on wind retrieval from C-band synthetic aperture radar images

ABSTRACT Synthetic Aperture Radar (SAR) is an innovative technique for monitoring sea surface dynamics and has a large swath coverage (>200 km). In this paper, we investigate the characteristics of anticyclonic and cyclonic eddies on SAR and the influence of mesoscale eddies on SAR wind retrieval. Mesoscale eddies in the global oceans are identified from daily sea level anomaly (SLA) data in 2022–2023, which are integrated by the Haiyang-2B/2C/2D (HY-2B/2C/2D) altimeters. A total of 1000 Sentinel-1 (S-1) images acquired in interferometric-wide (IW) and extra-wide (EW) modes are available, and they cover the mesoscale eddies. In addition, swath wind products from HY-2 scatterometers and sea surface temperature (SST) data from Haiyang-1C (HY-1C) are collected. Co-polarized (vertical–vertical (VV) and horizontal–horizontal (HH)) Geophysical Model Function (GMF) C-SARMOD2 was applied to invert the wind speed with reference to the scatterometer-measured wind direction. The variation in the difference (SAR retrievals minus scatterometer measurements) indicates that the SST of below 11°C has a certain impact on the SAR wind retrieval. The influence is weak at higher SSTs, and the difference increases with increasing eddy kinetic energy (EKE). It is concluded that the shear current associated with mesoscale eddies is a significant factor that affects the accuracy of SAR-derived winds.

Intensified upwelling: normalized sea surface temperature trends expose climate change in coastal areas

Abstract. Eastern boundary upwelling systems (EBUSs) provide valuable natural resources due to their high primary production. However, there is significant uncertainty in how climate change may affect the mechanisms that sustain these ecosystems in the future. Therefore, assessing the effects of climate change on EBUSs under the current global warming scenario is crucial for efficient ecosystem management. In 1990, Andrew Bakun suggested an increase in the upwelling intensity due to the rise of the ocean–land pressure gradient. Since there is a significant link between thermal gradients and offshore Ekman transport, we use sea level pressure (SLP) and deseasonalized sea surface temperature (SST) data from remote sensing to elucidate this hypothesis and validate it using in situ observations. SST is an indicator of coastal upwelling, and our long-term analysis of monthly and deseasonalized SST records shows that the seasonal and synoptic processes have minimal influence on the SST–upwelling intensity relationship. Upwelling within the same EBUS is not usually evenly distributed along coastlines, leading to upwelling in specific areas, referred to as upwelling centers. We compare the SST trends in the main upwelling centers of the four EBUSs with those in open ocean waters through a new index, αUI, designed to characterize upwelling changes in the EBUS. An a-dimensional number allows us to normalize the trends independently of the upwelling system and compare all of them. Furthermore, we have complemented the SST index with sea level pressure gradient data. This new index (supported by SLP gradient trends) indicates intensification in all the EBUSs, revealing a coherent pattern within EBUSs in the same ocean (i.e., Canarian and Benguela or Californian and Humboldt upwelling systems).

Identification of position, size and dynamic heat power of chips embedded in a real electronic board

Temperature is a critical factor in the lifespan of electronic systems. Therefore, it is essential to control all thermal parameters. In certain applications, such as commercial off-the-shelf (COTS) components, manufacturers may not have precise knowledge of the composition of electronic boards. As a result, it is important for them to define the positions of heat-dissipating elements, and the power dissipated to better predict the final thermal behavior of the system. Some electronic boards today even have components integrated into their volume. Since components can now be located not only on the surface but also within the volume of electronic boards, manufacturers need to define volume-based methods for detecting these heat sources. In this article, we address the problem of detecting sources within a volume using surface temperature data. To achieve this goal, we develop a representative numerical model of the system that can be used for an inverse source identification procedure. In a first time, we present a numerical approach that demonstrates the feasibility of the identification procedure on a board containing several electronic chips. Secondly, the volume source identification method is applied to a real case, using surfaces temperatures measured by infrared cameras. The experimental results obtained are consistent and show that this technique is suitable for detecting volumetric sources within an electronic board.

Comparison of 2-m surface temperature data between reanalysis and observations over the Arabian Peninsula

The 2-m temperature data is a significant indicator for studying the weather extremes and the exchange of water and energy fluxes between the surface and atmosphere. This study compared three reanalysis datasets, i.e., ERA5, ERA5-Land, and MERRA-2, with observations from in-situ sources from 1990 to 2022 using various statistical error metrics and extreme temperature indices over the Arabian Peninsula (AP) region. We selected these reanalysis datasets due to the continuous improvements and higher spatiotemporal output better capturing the temperature variability. The spatiotemporal climatology shows lower temperatures in winter (<15 °C) and maximum in summer (>35 °C); however, the reanalysis data show more deviations in temperature during the cold season than in the warm season. The reanalysis data underestimated the frequency of the cold (<10 °C) and hot (>30 °C) days across the four regions, except ERA5-Land, which closely followed observed data. The strength of the correlation shows better performance (>0.90) in the cold extremes (cold nights and cold days) frequency than the hot extremes. On an interannual scale, reanalysis products exhibit strong correlations (>0.90) with in-situ data across most regions, particularly in winter and autumn, moderate in spring, and weaker in summer. The reanalysis data shows negative biases in the inland regions and positive biases in the coastal areas with consistent root mean square differences (RMSD) spatiotemporally. The differences in performance are due to the topography and poor representation of the energy fluxes, especially in MERRA-2 as well as missing data in observations. This study recommends ERA5-Land as the first choice for extreme weather simulations in the region, followed by MERRA-2 and ERA5 on the same scale, but proper attention is needed when using reanalysis data for cold and hot extremes.

Analysis of radiative heat flux using ASTER thermal images: Climatological and volcanological factors on Java Island, Indonesia

This study focuses on analysing natural Radiative Heat Flux (RHF) anomalies to map out the heat distribution across the Java Island. Leveraging remote sensing techniques, we calculated natural RHF anomalies using Land Surface Temperature (LST) and Land Surface Emissivity (LSE) data obtained from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery. A key aspect of our approach was distinguishing between natural and anthropogenic heat sources by cross-referencing the LST Map with the Land Use Land Cover (LULC) map of Java Island. The study interprets natural RHF anomalies by examining regional trends in non-volcanic areas and local trends within volcanic regions, considering climatological and volcanological factors. Relation with climatological factors involves assessing soil moisture parameters from Soil Moisture Active Passive (SMAP) data, precipitation from monthly Global Precipitation Measurement (GPM) data, and classifications according to the Köppen-Geiger climate schema. Our regional analysis reveals high natural RHF anomalies in the northern regions of West Java, parts of Central Java, and most of East Java, attributed to low soil moisture and low precipitation in savanna and monsoon climates. On a more localised scale, RHF values are significantly high in volcanic areas, particularly around Central and East Java's volcanoes, such as Mt. Merapi, Mt. Slamet, Mt. Semeru, the Sidoarjo Mud Volcano, and Mt. Ijen. The Natural RHF anomalies at volcanoes in West Java were identified as not being high except at Mt Patuha. These areas exhibit average natural RHF anomalies ranging between 32.22 W/m2 and 115.13 W/m2, indicating strong and intense volcanic activity. The insights obtained from these findings explain the overall thermal characteristics of Java Island and highlight the presence of subsurface thermal zones associated with volcanic activity and geothermal potential.

Long-term recurrent exposure to excessive heat in the east Mediterranean and climate resilience development – a case study in Israel

This work studies long-term trends of observed meteorological parameters and of exposure to excessive heat over 74 years in Israel (1950–2023). We report an increasing trend of recurring exposure of the Israeli population to excessive heat during most of the summer noon hours, with the heat index often above the physiologically no-risk threshold. Specifically, since the beginning of the millennium, a significant increase in summertime decadal means of ambient noontime temperature (Ta), absolute humidity (AH), and heat index (HI) is evident relative to the 1950’s (Ta: 0.06 °C/year, AH: 0.06 g/m3year, HI: 0.09 °C/year). The experienced increase summertime thermal discomfort by the Israeli population results from the significant and synergistic increase in co-exposure to ambient temperature and humidity. Indeed, long-term satellite data (Landsat 1984–2021) of the east Mediterranean Sea Surface Temperature (SST) reveal a significant change (SST: 0.05 °C/year), which elucidates the corresponding increase in the absolute humidity. Leishmaniasis is a climate-related vector-borne infectious disease. However, the 1956–2017 leishmaniasis incidence rates in Israel do not correlate with the significant increase in the ambient temperature and heat index, representing development of climate resilience in terms of administrated prevention measures (namely, systematic adaptation) to this climate-related disease.