Temperature Data Research Articles

Correction to: Dynamic clustering of spatial–temporal rainfall and temperature data over multi‑sites in Yemen using multivariate functional approach

Correction to: Dynamic clustering of spatial–temporal rainfall and temperature data over multi‑sites in Yemen using multivariate functional approach

Machine Learning for Urban Heat Island (UHI) Analysis: Predicting Land Surface Temperature (LST) in Urban Environments

This study investigates how urban configuration influences the distribution of heat, known as the Urban Heat Island (UHI) effect, in Shiraz, Iran. Several Machine Learning algorithms are employed to analyze Land Surface Temperature (LST) data across various land cover types, including built-up, soil, and vegetation. The analysis reveals that Deep Neural Networks (DNNs) and Extreme Gradient Boosting (XGB) models excel at predicting LST, outperforming other methods. These results highlight the significant impact of land use on LST patterns within the metropolitan regions. Furthermore, the study assesses the influence of specific configuration metrics within each land cover category. This allows researchers to pinpoint which urban morphology features most significantly affect LST. These insights can inform targeted interventions and management strategies implemented to mitigate heat and improve thermal comfort in specific areas of Shiraz.

Formation of an extended defect cluster in cuprous oxide

Abstract Intrinsic defects and defect clusters play an important role in the room-temperature transport of cuprous oxide. Neutralization of these defects by doping and/or modifying the synthesis process is essential to improve the room temperature (RT) hole mobility in cuprous oxide. Towards this, we annealed polycrystalline cuprous oxide under Cu-rich conditions leading to neutralization of the intrinsic acceptor defect. The concentration of both the acceptor defects (VCu and Vsplit Cu) that are already present, reduces by four to five orders of magnitude. This is commensurate with the amount of possible Cu incorporation under different annealing conditions, indicating the back-filling of a large fraction of the Cu vacancies. Unforeseeably, the experimental conditions lead to the creation of yet another higher order extended defect (3VCu + 2Cui) with a defect level at ≈0.5 eV above the valence band. The formation of such a defect is also indirectly suggested by the analysis of carrier concentration vs. temperature data and first principle calculations. Such singly ionized higher order defects with a possibly higher capture cross-section act as more effective traps resulting in reduced hole mobility.

Influence of hydrometeorological risk factors on child diarrhea and enteropathogens in rural Bangladesh.

A number of studies have detected relationships between weather and diarrhea. Few have investigated associations with specific enteric pathogens. Understanding pathogen-specific relationships with weather is crucial to inform public health in low-resource settings that are especially vulnerable to climate change. Our objectives were to identify weather and environmental risk factors associated with diarrhea and enteropathogen prevalence in young children in rural Bangladesh, a population with high diarrheal disease burden and vulnerability to weather shifts under climate change. We matched temperature, precipitation, surface water, and humidity data to observational longitudinal data from a cluster-randomized trial that measured diarrhea and enteropathogen prevalence in children 6 months-5.5 years from 2012-2016. We fit generalized additive mixed models with cubic regression splines and restricted maximum likelihood estimation for smoothing parameters. Comparing weeks with 30°C versus 15°C average temperature, prevalence was 3.5% higher for diarrhea, 7.3% higher for Shiga toxin-producing Escherichia coli (STEC), 17.3% higher for enterotoxigenic E. coli (ETEC), and 8.0% higher for Cryptosporidium. Above-median weekly precipitation (median: 13mm; range: 0-396mm) was associated with 29% higher diarrhea (adjusted prevalence ratio 1.29, 95% CI 1.07, 1.55); higher Cryptosporidium, ETEC, STEC, Shigella, Campylobacter, Aeromonas, and adenovirus 40/41; and lower Giardia, sapovirus, and norovirus prevalence. Other associations were weak or null. Higher temperatures and precipitation were associated with higher prevalence of diarrhea and multiple enteropathogens; higher precipitation was associated with lower prevalence of some enteric viruses. Our findings emphasize the heterogeneity of the relationships between hydrometeorological variables and specific enteropathogens, which can be masked when looking at composite measures like all-cause diarrhea. Our results suggest that preventive interventions targeted to reduce enteropathogens just before and during the rainy season may more effectively reduce child diarrhea and enteric pathogen carriage in rural Bangladesh and in settings with similar meteorological characteristics, infrastructure, and enteropathogen transmission.

Measuring the reionization optical depth without large-scale CMB polarization

We study the possibility of measuring the optical depth at reionization τ without relying on large-scale cosmic microwave background (CMB) polarization. Our analysis is driven by the need to obtain competitive measurements that can validate the state-of-the-art constraints on this parameter, widely based on E-mode polarization measurements at ℓ≤30. This need is partially motivated by the typical concerns regarding anomalies observed in the Planck large-scale CMB data as well as by the remarkable fact that, excluding these latter, τ consistently exhibits correlations with anomalous parameters, such as Alens and Ωk, suggesting that slightly higher values of the optical depth at reionization could significantly alleviate or even eliminate anomalies. Within the Λ cold dark matter model, our most constraining result is τ=0.080±0.012, obtained by combining Planck temperature and polarization data at ℓ>30, the Atacama Cosmology Telescope (ACT) and Planck measurements of the lensing potential, baryon acoustic oscillations (BAOs), and type-Ia supernova data from the Pantheon+catalog. Notably, using only ACT temperature, polarization, and lensing data in combination with BAOs and supernovae, we obtain τ=0.076±0.015, which is entirely independent of Planck. The relative precision of these results is approaching the constraints based on large-scale CMB polarization (τ=0.054±0.008). Despite the overall agreement, we report a slight 1.8σ shift toward larger values of τ. We also test how these results change by extending the cosmological model. While in many extensions they remain robust, in general, obtaining precise measurements of τ may become significantly more challenging. Published by the American Physical Society 2024

Leveraging Transfer Learning and U-Nets Method for Improved Gap Filling in Himawari Sea Surface Temperature Data Adjacent to Taiwan

Satellite sea surface temperature (SST) images are valuable for various oceanic applications, including climate monitoring, ocean modeling, and marine ecology. However, cloud cover often obscures SST signals, creating gaps in the data that reduce resolution and hinder spatiotemporal analysis, particularly in the waters near Taiwan. Thus, gap-filling methods are crucial for reconstructing missing SST values to provide continuous and consistent data. This study introduces a gap-filling approach using the Double U-Net, a deep neural network model, pretrained on a diverse dataset of Level-4 SST images. These gap-free products are generated by blending satellite observations with numerical models and in situ measurements. The Double U-Net model excels in capturing SST dynamics and detailed spatial patterns, offering sharper representations of ocean current-induced SST patterns than the interpolated outputs of Data Interpolating Empirical Orthogonal Functions (DINEOFs). Comparative analysis with buoy observations shows the Double U-Net model’s enhanced accuracy, with better correlation results and lower error values across most study areas. By analyzing SST at five key locations near Taiwan, the research highlights the Double U-Net’s potential for high-resolution SST reconstruction, thus enhancing our understanding of ocean temperature dynamics. Based on this method, we can combine more high-resolution satellite data in the future to improve the data-filling model and apply it to marine geographic information science.

Transient state analysis of rehabilitated RC beams using finite element modelling and prediction using an artificial neural network

The present research develops and verifies a simpler numerical approach for analyzing the thermal transient state of rehabilitated concrete beams reinforced with various types of FRP (fiber-reinforced polymer) subjected to high temperatures and specifically built as under-reinforced concrete beams. This approach offers a straightforward, efficient, and exact instrument for numerical analysis. The proposed analytical technique has been validated by load-displacement curves and cross-section temperature data, indicating its dependability and practicality. Subsequently, the validated approach was used to examine the impact of significant variables on the outcome and restoration of FRP-reinforced concrete beams at high temperatures. The methodology gives the Comparing conventional and CFRP, GFRP, AFRP reinforced beams using beam, truss, and shell elements. Thermal and UDL loads were applied, mesh at 25 mm × 25 mm. Transient analysis contrasts performance via displacement and temperature. The temperature versus displacement curve shows the FRP comparisons. Identifying the critical temperature before failure is crucial, emphasizing the curve’s significance in assessing structural performance and potential failure points. Nodal temperatures ranged 939 °C–963 °C (rehabilitated) versus 958 °C (conventional). 200 °C difference affects thermal boundary conditions for structural analysis and Conventional peaks at 320 °C, while AFRP, GFRP, and CFRP reach 358 °C, 385 °C, and 390 °C respectively. CFRP lasts 2400 min. Neural network models demonstrate effective generalizability, enabling satisfactory predictions of RC beam rehabilitation with CFRP laminates within the study’s parameter range.

Plateau-linear reaction norm model analysis of number born alive in purebred Landrace pigs using meteorological data in Japan.

We performed a plateau-linear reaction norm model (RNM) analysis of number born alive (NBA) in purebred Landrace pigs, where breeding value changes according to maximum temperature at mating day, using public meteorological observation data in Japan. We analysed 52,668 NBA records obtained from 10,320 Landrace sows. Pedigree data contained 99,201 animals. Off-farm daily temperature data at the nearest weather station from each of the farms were downloaded from the Japan Meteorological Agency website. A plateau-linear RNM analysis based on daily maximum temperature on mating day (threshold temperature of 16.6°C) was performed. The percentage of the records with daily maximum temperatures at mating days of ≤16.6, ≥25.0, ≥30.0 and ≥35.0°C were 34.3%, 33.6%, 14.0% and 0.8%, respectively. The value of Akaike's information criterion for the plateau-linear RNM was lower than that for a simple repeatability model (RM). With the plateau-linear RNM, estimated value of heritability ranged from 0.14 to 0.15, while that from the RM analysis was 0.15. Additive genetic correlation between intercept and slope terms was estimated to be -0.52 from the plateau-linear RNM analysis. Estimated additive genetic correlations were >0.9 between NBA at different temperatures ranging from 16.6 to 37.6°C. For the 10,320 sows, average values of prediction reliability of the intercept and slope terms for breeding values in the plateau-linear RNM were 0.47 and 0.16, respectively. Increasing weight for slope term in linear selection index could bring positive genetic gain in the slope part, but prediction accuracy would decrease. Our results imply that genetically improving heat tolerance in sows reared in Japan focusing on NBA using RNM is possible, while RNM is more complex to implement and interpret. Therefore, further study should be encouraged to make genetic improvement for heat tolerance in sows more efficient.

Machine Learning-Based Anomaly Detection on Seawater Temperature Data with Oversampling

Machine Learning-Based Anomaly Detection on Seawater Temperature Data with Oversampling

Full-Scale Modeling and FBGs Experimental Measurements for Thermal Analysis of Converter Transformer

As the imbalance between power demand and load capacity in electrical systems becomes increasingly severe, investigating the temperature variations in transformers under different load stresses is crucial for ensuring their safe operation. The thermal analysis of converter transformers poses challenges due to the complexity of model construction. This paper develops a full-scale model of a converter transformer using a multi-core high-performance computer and explores its thermal state at 80%, 100%, and 120% loading ratios using the COUPLED iteration method. Additionally, to validate the simulation model, 24 FBGs are installed in the experimental transformer to record the temperature data. The results indicate a general upward trend in winding the temperature from bottom to top. However, an internal temperature rise followed by a decrease is observed within certain sections. Moreover, as the loading ratio increases, both the peak temperature and temperature differential of the transformer windings rise, reaching a peak temperature of 107.9 °C at a 120% loading ratio. The maximum discrepancy between the simulation and experimental results does not exceed 3.5%, providing effective guidance for the transformer design and operational maintenance.

Machine learning-enabled weather forecasting for real-time radioactive transport and contamination prediction

Effective responses to nuclear accidents require accurate and fast predictions of radionuclide transport in the atmosphere. However, real-time forecasting has been challenging, since predicting the transport requires meteorological forecasting, which relies on computationally intensive numerical weather simulations. To address this challenge, we have developed statistical emulators for the Weather Research and Forecasting model (WRF) to forecast wind and temperature fields around the source location. The emulator is constructed based on pre-computed WRF simulation results and their boundary conditions from global circulation models (GCM), enabling the rapid prediction of local-scale wind/temperature fields by ingesting the GCM-predicted boundary conditions available online for the next several days. We have explored two methods: (1) deep neural network and (2) a vector autoregressive model with exogenous variables (VARX). A Gaussian plume model is then used to take these field as input and to predict the distribution of radioactive material/dose in the environment. The results show that both emulators are capable of forecasting the wind field for several days in the future in real-time, while not compromising accuracy. The autoregressive model has shown a strong performance of predicting the temperature field, owing to the strong temporal autocorrelations in the temperature data. This framework offers a powerful tool for informed decision-making during emergencies such as enabling meteorology-informed timing of containment venting, and real-time guidance of evacuation strategies.

Comparing firn temperature profile retrieval based on the firn densification model and microwave data over the Antarctica

Abstract. The firn temperature is a crucial parameter for understanding firn densification processes of the Antarctic Ice Sheet (AIS). Simulations with firn densification models (FDM) can be conceptualized as a function that relies on forcing data, comprising temperature and surface mass balance, together with tuning parameters determined based on measured depth-density profiles from different locations. The simulated firn temperature is obtained in the firn densification models by solving the one-dimensional heat conduction equation. Microwave satellite data on brightness temperature at different frequencies can also provide remote sensing monitoring of firn temperature variations across the AIS (i.e., the L-band up to 1500 meters). The firn temperature can be estimated by the microwave emission model and the regression method, but these two methods need more observations of temperature profiles for correction and validation. Therefore, we compiled a dataset with temperature profiles and temperature observations with depth around 10 meters. In this work, two methods were used to simulate/retrieve firn temperature across the Antarctic ice sheet. One method estimated the temperature profiles by solving the one-dimensional heat conduction equation driven by reanalyses and regional climate models, which are used in the simulation of FDMs. The other one established a relationship between the multi-frequency brightness temperature data from microwave remote sensing satellites and the firn temperature.

Climatology and variability of sea surface temperature in the Azores region and its relation to climate indices

Daily sea surface temperature (SST) data from the Advanced Very High Resolution Radiometer from 1982 to 2020 were used to study the spatiotemporal variability and climatology of SST in the Azores. In addition, the relationships between the SST anomaly (SSTA) and three climate index time series for the period 1982–2020 were investigated: the Oceanic Niño Index (ONI), the Southern Oscillation Index (SOI) and the North Atlantic Oscillation (NAO) index. The spatiotemporal variability and climatology of the SST were studied by analyzing annual, seasonal, and monthly mean SST time series and climate fields. Relationships between the SSTA and climate index time series were investigated using Pearson's correlation analysis and wavelet coherence analysis. The annual mean SST time series shows a positive linear trend of about 0.027°C per year over the period 1982–2020. The spatial variability of the linear trend shows that the positive trend is consistent throughout the study area, ranging from 0.024 to 0.034°C per year. The spatial variability is not uniform, and high values of the trend are observed in the upper northwestern part and southwestern half of the basin. Seasonal mean SST fields show a positive North-South latitudinal gradient, with higher temperatures in the south, especially in the southwestern basin. In addition, the seasonal mean SST fields show possible coastal upwelling signatures along the coast of the Azores islands in different seasons. The results of Pearson's correlation analysis between basin-averaged, de-trended monthly mean SSTA and climate indices show that SSTA shows a significant positive correlation with SOI and a negative correlation with ONI from 1982 to 2020. NAO shows positive correlation with SSTA for the December-January-February season. Wavelet coherence analysis confirms significant coherences between the Azores SSTA and the climate-ocean indices at certain time scales and periods.

Inter‐comparison and validation of high‐resolution surface air temperature reanalysis fields over Italy

AbstractSurface air temperature (t2m) data are essential for understanding climate dynamics and assessing the impacts of climate change. Reanalysis products, which combine observations with retrospective short‐range weather forecasts, can provide consistent and comprehensive datasets. ERA5 represents the state‐of‐the‐art in global reanalyses and supplies initial and boundary conditions for higher‐resolution regional reanalyses designed to capture finer‐scale atmospheric processes. However, these products require validation, especially in complex terrains like Italy. This study analyses the capability of different reanalysis products to reproduce t2m fields over Italy during the 1991–2020 period. The analyses encompass ERA5, ERA5‐Land, the MEteorological Reanalysis Italian DAtaset (MERIDA), the Copernicus European Regional ReAnalysis (CERRA), and the Very High‐Resolution dynamical downscaling of ERA5 REAnalysis over ITaly (VHR‐REA_IT). The validation we conduct pertains to both the spatial distribution of 30‐year seasonal and annual normal values and the daily anomaly records. Each reanalysis is compared with observations projected onto its respective grid positions and elevations, overcoming any model bias resulting from an inaccurate representation of the real topography. Key findings reveal that normal values in reanalyses closely match observational values, with deviations typically below 1°C. However, in the Alps, winter cold biases sometimes exceed 3°C and show a relation with the elevation. Similar deviations occur in the Apennines, Sicily, and Sardinia. Conversely, VHR‐REA_IT shows a warm bias in the Po Valley up to 3°C in summer. Daily anomalies generally exhibit lower errors, with MERIDA showing the highest accuracy and correlation with observational fields. Moreover, when aggregating daily anomalies to annual time scales, the errors in the anomaly records rapidly decrease to <0.5°C. The results of this study empower reanalysis users across multiple sectors to gain a more profound insight into the capabilities and constraints of different reanalysis products. The knowledge and the characterization of the reanalyses t2m bias against observations can indeed be crucial when incorporating these products into their research and practical applications.

Research on power generation prediction of hydropower in river basin based on multi-head attention graph convolutional neural network

For the power generation prediction of traditional hydropower stations, most of them only use time series prediction and neglect to study the spatial topological relationship of hydropower stations in the river basin, so that it is difficult to fully explore the characteristic relationship of space power stations. In this paper, a research method for power generation prediction of hydropower in river basin hydropower stations based on multi-head attention map convolutional neural network is proposed. This method establishes a first-level node neighborhood feature map based on the spatial geographic distribution information of hydropower stations in the basin, and uses the method of graph convolution to carry out node feature mining and feature learning, so as to transform the power generation capacity evaluation problem of the hydropower station in the basin into the node prediction problem in the graph, which is different from the global normalization rule. the multi-head attention mechanism introduced further improves the information aggregation quality of the graph node, and uses the historical temperature, power generation, electricity price, unit status and other data of each hydropower station in the basin for training. The reasoning results show that the proposed method achieves higher accuracy than other schemes, and the power prediction method is conducive to the formulation of power plans of hydropower stations in the basin, and can also play a positive role in guiding the site selection of hydropower plants.

Ecogeographic rules in a common Neotropical treefrog: No clinal variation along a mountain range

AbstractBody and limb size are associated with many aspects of the biology of animals. Geographic variation in morphological traits has often been investigated through ecogeographical rules. Bergmann's rule posits an inverse relationship between body size and temperature in homeotherms, while Allen's rule posits that endothermic animals inhabiting colder climates have relatively shorter protruding parts (e.g., limbs) in comparison to populations from warmer climates. However, the applicability and validity of these rules in poikilotherms are currently contentious. In this study, we aimed to test predictions of Bergmann's and Allen's rules in populations of the Neotropical frog Boana platanera. We analysed 142 adult male museum specimens from 17 localities along a mountain range in western Venezuela. We obtained six external morphological measurements and assessed the correlation of body size and limb size with temperature data for each locality. Our results were contrary to the expectations but consistent with previous studies. All this together indicates that exceptions to the Bergmann's and Allen's rules are frequent and idiosyncratic in anurans. We discuss alternative hypotheses that could explain geographic variation of body and limb size in future studies.

Land Surface Temperature Retrieval From GF5-01A Based on Split- Window Algorithm

Abstract. Land Surface temperature (LST) is a core parameter in the energy exchange between the surface and the atmosphere, and the use of thermal infrared remote sensing can realize the wide-range, fast, and accurate acquisition of surface temperature. GF5-01A is an important part and the final satellite of the major special project on high-resolution Earth observation system which is equipped with a wide-area thermal infrared imager with a resolution of 100 meters and a width of 1,500 kilometers. In this paper, based on the GF5-01A WTI spectral response function, combined with the TIGR2000 atmospheric profiles data and the ASTER spectral library, the data simulation was carried out by using the atmospheric radiative transfer model MODTRAN 5.2, and then constructed the split-window algorithm. Then, the method proposed in this paper was validated and evaluated using Landsat 8/9 temperature products and measured surface temperature data from SURFRAD sites acquired on the same day. The results show that the RMSE between the GF5-01A retrieved LST and the Landsat8/9 retrieved LST is between 1.27–2.24K, and the Bias is between −2.08–1.12K. The RMSE is between 0.68–2.64K and the bias is between −0.68–1.49K compared to the measured surface temperature. The split-window algorithm of GF5-01A proposed in this paper can meet the requirements of thermal infrared remote sensing monitoring and has enormous potential and value.

Investigation on temperature effect of local structural stress during non-uniform cross-section steel bridge deck pavement paving

During the process of gussasphalt concrete paving on the orthotropic steel bridge deck of a non-uniform cross-section box girder bridge, the high-temperature asphalt mixture often produces temperature stresses that can affect bearings safety. In this study, a refined model of a steel box girder bridge with a 660 m scale was developed and numerical simulations were conducted using the finite element software. The temperature field was applied to the orthotropic steel bridge deck in a quasi-dynamic fashion, leading to the determination of a safe width for concrete paving. To assess the accuracy of the computational predictions, field paving was performed under the safe width condition determined from the calculations, and the temperature and local stress data were measured in the field. The results demonstrate that the finite element predictions are in good agreement with the yield strength of the bearing’s local structure, and the measurements also show a close correspondence with the structural yield strength, validating the reliability of the quasi-static simulation approach. The proposed method offers a computational tool and a reliability criterion for future finite element analyses of large-span non-uniform cross-section steel box girder bridges, as well as a safe width example for subsequent gussasphalt mixture paving on orthotropic steel bridge decks.

La Nia and climate degree of contact

Studies have shown that the 2022 anomalous climate change is different from previous La Nia phenomena. Using the data of the maximum temperature, minimum temperature and temperature difference from 2020 to March 2021 in Hong Kong, the grey model was used to compare the maximum and minimum temperature values in 2022 with the actual values and found that the values were close. The La Nia phenomenon occurred in March 2022, concluding that La Nia is not a major contributor to climate change in Hong Kong. Climate change in Hong Kong is also linked to typhoons, increased carbon dioxide levels in the air, ocean temperatures and other factors. The study concluded that the main cause of the extreme weather in Hong Kong in 2022 was not the occurrence of La Nia, but a combination of factors such as cumulative carbon dioxide emissions, human activities, the altitude of the Tibetan Plateau, and typhoons.

An Assessment of Convergence of Climate Reanalyses from Two Coupled Data Assimilation Systems with Identical High-Efficiently Filtering

Abstract Coupled data assimilation (CDA) uses coupled model dynamics and physics to extract observational information from measured data in multiple Earth system domains to reconstruct historical states of the Earth system, forming a reanalysis of climate variability. Due to imperfect numerical schemes in modeling dynamics and physics, models are usually biased from the real world. Such model bias is a critical obstacle in the reconstruction of historical variability by combining model and observations, and, to some degree, causes divergence of CDA results because of individual model behavior in each system. Here, based on a multitimescale high-efficiency filtering algorithm which includes a deep ocean bias relaxing scheme, we first develop a high-efficiency online CDA system with the Community Earth System Model (CESM-MSHea-CDA). Then, together with the other previously-established CDA system (CM2-MSHea-CDA) within the Coupled Model version 2.1 model that is developed by Geophysical Fluid Dynamics Laboratory, we conduct climate reanalysis for the past four decades (1978–2018). Evaluations show that due to improved representation for multiscale background statistics and effective deep ocean model bias relaxing, both CDA systems produce convergent estimation of variability for major climate signals such as variability of basin-scale ocean heat content, ENSO, PDO, etc. Particularly, both CDA systems generate similar time-mean of global and Atlantic meridional overturning circulations that converge to the geostrophic velocity estimate from climatological temperature and salinity data. The CDA-estimated mass transport at typical measurement sections is mostly consistent with the observations.