High-resolution Temperature Research Articles

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.

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.

Short-term effects of ambient temperature variability on myocardial infarction hospitalizations in Sweden: A nationwide case-crossover study

Abstract Background Climate change threatens human health and general welfare via multiple dimensions. However, the short-term effect of temperature variability, a crucial aspect of climate change, on myocardial infarction (MI) remains largely unexplored. Purpose We aimed to assess the short-term effects of temperature variability on MI hospitalizations within the nationwide SWEDEHEART registry. Methods This population-based nationwide study involved 228,897 MI patients from the SWEDEHEART registry in Sweden from 2005 to 2019. High-resolution (1 km × 1 km) daily mean ambient temperature was estimated using a spatiotemporal machine learning methodology and assigned to patients’ home addresses. Temperature variability was calculated as the difference between the same-day (as the MI event) ambient temperature and the average temperature recorded over the preceding seven days. Specifically, an upward temperature shift represents a rise in the current day’s temperature relative to the seven-day average, while a downward temperature shift indicates a corresponding decrease. A time-stratified case-crossover design incorporating a conditional logistic regression model with distributed lag non-linear model was applied to estimate the association between ambient temperature variability and total MI, ST-segment elevation myocardial infarction (STEMI) and non-ST-segment elevation myocardial infarction (NSTEMI). Moreover, we assessed potential effect modification by sex and medication intake. The results were expressed as odds ratio (OR, with their 95% confidence intervals [CIs]) per 1°C increase in temperature variability. Results Our study found that a 1°C increase in upward temperature shift was significantly associated with increased risks for total MI, STEMI, and NSTEMI (OR [95% CI]: 1.010 [1.006-1.015], 1.014 [1.006-1.022], and 1.009 [1.004-1.014], respectively). Furthermore, a 1°C greater in downward temperature shift was significantly associated with decreased risks for total MI and NSTEMI (OR [95% CI]: 0.995 [0.991-0.999], and 0.993 [0.988-0.998], respectively). Moreover, we found that males and patients who were on any heart medication, particularly digitalis, were at a higher risk of MI associated with exposure to higher upward temperature shifts compared to females and their counterparts not taking these medications. Conclusions This nationwide case-crossover study provides novel evidence that short-term exposure to increasing upward temperature shift is associated with an increased risk of MI hospitalization. Our finding highlights the cardiovascular health threats posed by upward temperature shifts, which are anticipated to increase in frequency and intensity due to climate change.

Sub-∘C-precision temperature imaging using phase-shift luminescence thermometry

Abstract This work explores the potential of luminescence thermometry to provide temperature imaging with high temperature resolution using the phase-shift method. Phosphor thermometry has been widely used for temperature imaging, especially with the decay-time method. However, the phase-shift method was rarely used. In this approach, excitation is modulated in time, and due to the temperature-dependent luminescence dynamics of the painted luminescent probe, the emission waveform is phase-shifted with respect to the excitation waveform. Initially, a parametric study was performed on the effect of excitation waveform frequency and shape, based on a millisecond-decay time phosphor. The study found that the best precision is achieved with a square waveform at a frequency such that the phase shift is π 4 at the target temperature. Then, this method was compared with the well-established decay-time method. Phase-shift method offered a three-fold improvement in temperature precision under identical peak excitation power. Finally, two-dimensional measurements were demonstrated on a temperature-controlled plate using a high speed CMOS camera, and an LED illumination. A temperature precision of 0.13 ∘C was obtained at a temporal resolution of 0.25 s and a spatial resolution of around 1 mm, due to a pre-processing binning of 7 by 7 pixels. This technique can be used as a robust alternative to infrared thermometry to probe the thermal processes with small temperature differences.

Towards smart and secure batteries: Linking pressure and temperature profiles with electrochemical behavior through hybrid optical fiber sensors

A hybrid sensing configuration combining fiber Bragg grating (FBG) with a Fabry-Perot interferometer is proposed for highly sensitive detection of pressure and temperature inside a commercial lithium-ion battery (LiB). The battery is also instrumented externally with FBGs to monitor its surface temperature variations. The LiB is operated under several cycling tests at different C-rates and environmental temperatures. High pressure and temperature resolutions of ±0.4 mbar and ±0.5 °C, respectively, are attained for the developed hybrid sensor, along with a minimum response time of 1.7 s. When the LiB is subjected to higher environmental temperatures, reduced thermal variations and higher pressure variations are registered by the optical sensor. Overall, during the cycling tests, the external temperature sensors closely follows the internal sensors behavior. Incremental capacity analysis derivative curves were systematically applied throughout the battery cycling experiments to unveil relevant insights into the interplay between pressure and temperature variations and the LiB electrochemical behavior. It was found that the pressure fluctuations present a “breathing profile” directly linked with the volume electrode lithiation and delithiation processes.

Design of photonic crystals for nanokelvin-resolution thermometry

High-resolution thermometry is key for the development of calorimeters, bolometers, and high-stability light sources, as well as for probing dissipation and transport in microelectronics and quantum devices. Achieving nanokelvin-level temperature resolution at room temperature requires using large optical cavities, which are unsuitable for microscale integration. Here we computationally design a one-dimensional photonic crystal Band Edge Thermometer that achieves significant temperature sensitivity by combining: (i) the abrupt variation in optical properties of a direct bandgap semiconductor at the band edge, and (ii) a large quality factor in a resonant photonic structure. Two devices are designed which are constructed from GaAs/AlAs and GaN/AlN multilayer structures. The optimal sensor design features an extremely large thermoreflectance coefficient of 60.6 K−1 and a thermal time constant of 1.1 µs, with a sensor thickness of only 6.7 µm. The projected thermometry noise floor is 84 nK.Hz-½ for the GaAs/AlAs sensor and 35 nK.Hz-½ for the GaN/AlN sensor. The designed sensor architecture is expected to enable a broad range of applications in microcalorimetry and bolometry where a high temperature resolution combined with microscale sensor footprint is required.

Vulnerability to heat-related mortality and the effect of prevention measures: a time-stratified case-crossover study in Switzerland.

Swiss climate scenarios predict increases in the frequency and intensity of extreme heat episodes in the future. For the effective prevention of heat-related mortality, several aspects of the population's vulnerability to heat must be understood on a local level. A nationwide analysis of individual death records was conducted, enabling a more comprehensive understanding than typical heat studies based on aggregated data. A total of 320,306 individual death records from the Swiss National Cohort with precise address information during the warm season (May to September) from 2003-2016 were linked to indoor and outdoor high-resolution daily temperature estimates. A time-stratified case-crossover study combined with distributed lag non-linear models was then performed to assess the temperature-mortality associations for various causes of death and to estimate the potential effect modification of individual characteristics. Additionally, it was explored whether the effect of extreme heat changed over time in regions with and without cantonal heat-health action plans (HHAPs). Using the temperature with the lowest cause-specific mortality risk (minimum mortality temperature) as the reference temperature, extreme heat (defined as ambient daily maximum temperature reaching 33 °C) was associated with a strong increase in all-cause mortality (odds ratio (OR): 1.21, 95% CI: 1.17-1.25) and disease-specific mortality from Alzheimer's disease and dementia (OR: 1.67, 95% CI: 1.48-1.88), COPD (OR: 1.37, 95% CI: 1.12-1.67), diabetes (OR: 1.34, 95% CI: 1.06-1.70), and myocardial infarction (OR: 1.26, 95% CI: 1.10-1.44). Indoor temperatures above 24 °C were found to be critical for mortality. The population most vulnerable to heat included older adults (≥75 years), unmarried individuals, people with a low education level, older women with low neighbourhood socioeconomic position, and men under 75 years old with low socioeconomic position. Overall, the risk of heat-related all-cause mortality in 2009-2016 was lower than that in 2003-2008. The decrease was significantly stronger in the region where cantonal HHAPs were implemented. This study provides important information for planning targeted and effective measures to reduce heat-related health risks in Switzerland. It demonstrates that HHAPs contribute to reducing heat-related mortality, although they may not reach the high-risk population of individuals with low socioeconomic position. Future prevention efforts should also target the less privileged population, including people younger than 75 years.

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.

A century-long China homogenized daily surface air temperature dataset (CUG-CMA CHDT)

Daily meteorological observation data of the early period (pre-1950) were critically important for investigating the long-term trends and multi-decadal scale variability of extreme climate events. The high-resolution surface air temperature (SAT) data for time period before 1950 are lacking in China. We extended the SAT observations of China back to 1840 through developing a pre-1950 daily SAT dataset. The early-period daily SAT were manually corrected for the input and clerical errors, and then according to the length or coverage of time, the main series for each of the cities was determined. The observation time system of unknown sites was determined by the minimum difference method. After these operations, the data of all sites were unified into the same format. By using the ridge regressions established based on data from modern reference stations, the missing maximum temperature (Tmax) and minimum temperature (Tmin) were interpolated. The early-period data were combined with modern data to form the long-term daily SAT dataset of 1840–2020 in China. RHtest software was used to detect and adjust the inhomogeneities in the station data series. Finally, the century-long homogenized daily SAT dataset including 45 key city stations in China was obtained. Among the stations, there are 20 stations with observation record more than one hundred years. The length of temperature observation series of 17 stations is between 80 and 100 years. The series length of the remaining 7 stations is between 68 and 80 years. Finally, the angular distance weighting (ADW) method was used to interpolate the data into grid products, and the grid size is 2.5 ° × 2.5 °. The dataset was named CUG-CMA CHDT, which is applicable in monitoring, studies and assessments of regional extreme temperature change and variability in China.

Sub-millikelvin-resolved superconducting nanowire single-photon detector operates with sub-pW infrared radiation power

Abstract The noise equivalent temperature difference (NETD) indicates the minimum temperature difference resolvable by an infrared detector. The lower the NETD is, the better the sensor can register small temperature differences. In this work, we proposed a strategy to achieve a high temperature resolution using a superconducting nanowire single-photon detector (SNSPD) with ultrahigh sensitivity. We deduced the model for calculating the NETD of a photon-counting type detector and applied it to our SNSPD-based setup. Experimentally, we obtained a NETD as low as 0.65 mK, which is limited by the background radiation of the environment, and the required infrared radiation power is calculated to be less than 1 pW. Furthermore, the intrinsic NETD of this SNSPD is estimated to be less than 0.1 mK. This work demonstrated a sub-mK temperature resolution with the SNSPD, paving the way for future remote infrared thermal imaging with high temperature resolution.

High-resolution temperature, salinity and depth data from southeastern Australian estuaries, 2018–2021

Estuaries are the important interface between the land and sea, providing significant environmental, economic, cultural and social values. However, they face unprecedented pressures including eutrophication, harmful algal blooms, habitat loss, and extreme weather due to climate change. Here we present an open access, quality-controlled water quality dataset collected from twelve diverse estuaries spanning 1000 km along the southeastern Australian coastline. Water depth, temperature and salinity data were collected across two years (2018–2021) capturing drought, wildfire and flood periods, using high accuracy Seabird MicroCAT field sensors located within oyster leases. These fully autonomous instruments collected and transmitted data every 10 minutes before downstream quality checking and uploading onto a public website. Simultaneous, high-resolution, longitudinal environmental data collected across multiple estuaries throughout a range of extreme weather events are exceptionally rare in the Southern Hemisphere, yet provide an invaluable resource for the aquaculture industry, researchers and environmental regulators alike.

Up-conversion optical temperature sensing characteristics of new KBaGd(MoO4)3:Yb3+,Ho3+ phosphors

To develop new up-conversion luminescent materials for non-contact optical thermometer with high sensitivity and temperature resolution, a battery of KBaGd(MoO4)3:Yb3+,Ho3+ phosphors were fabricated through solid reaction process. The crystal structure, up-conversion luminescence, energy transfer, thermal stability and optical temperature sensing performances were studied in detail. Under 980 nm laser excitation, the KBaGd(MoO4)3:Yb3+,Ho3+ phosphor exhibits distinctive emission bands of Ho3+ at 545, 660, and 755 nm, and excellent illuminant performance. Based on the thermally coupled levels (TCLs) of Ho3+, both the relative sensitivity (Sr) and absolute sensitivity (Sa) display similar change trends, with the highest values of 6.73%/K (@298 K) and 5.69%/K (@298 K), respectively. Furthermore, the highest Sa of 13.90%/K (@623 K) and the ultimate Sr of 0.62%/K (@ 298 K) are achieved based on non-TCLs of Ho3+. Therefore, KBaGd(MoO4)3:Yb3+,Ho3+ phosphor is a promising candidate for self-referenced optical thermometry.

Spatiotemporal Facility-Level Patterns of Summer Heat Exposure, Vulnerability, and Risk in United States Prison Landscapes.

Heat is associated with increased risk of morbidity and mortality. People who are incarcerated are especially vulnerable to heat exposure due to demographic characteristics and their conditions of confinement. Evaluating heat exposure in prisons, and the characteristics of exposed populations and prisons, can elucidate prison-level risk to heat exposure. We leveraged a high-resolution air temperature data set to evaluate short and long-term patterns of heat metrics for 1,614 prisons in the United States from 1990 to 2023. We found that the most heat-exposed facilities and states were mostly in the Southwestern United States, while the prisons with the highest temperature anomalies from the historical record were in the Pacific Northwest, the Northeast, Texas, and parts of the Midwest. Prisons in the Pacific Northwest, the Northeast, and upper Midwest had the highest occurrences of days associated with an increased risk of heat-related mortality. We also estimated differences in heat exposure at prisons by facility and individual-level characteristics. We found higher proportions of non-white and Hispanic populations in the prisons with higher heat exposure. Lastly, we found that heat exposure was higher in prisons with any of nine facility-level characteristics that may modify risk to heat. This study brings together distinct measures of exposure, vulnerability, and risk, which would each inform unique strategies for heat-interventions. Community leaders and policymakers should carefully consider which measures they want to apply, and include the voices of directly impacted people, as the differing metrics and perspectives will have implications for who is included in fights for environmental justice.

Sea surface temperature differences between in situ and GHRSST (L4) records in a socio-ecological critical area of the northeastern Pacific Ocean May 2015 to July 2016

Using satellite sensor data for studying sea surface temperature (SST) provides advantages over in situ information, such as obtaining data from large areas and remote access regions in short periods. However, differences between the SST values recorded in situ and those by satellite sensors are due to the intrinsic nature of both methods and meteorological factors. The present study aims to search the difference between SST values from in situ and satellite sensor data of 1×1 km resolution (type L4) from the Group of High-Resolution Sea Surface Temperature (GHRSST) for an annual cycle in a socio-ecological critical area known as the Gulf of Ulloa (GU). The linear regression, linear spline regression, and logic models showed an overestimated SST by satellites compared to in situ data, particularly at temperatures below 20°C. The overestimation can be attributed to the oceanic-atmospheric variations, which are consequences of upwelling and the time lag between the data record morning by in situ and night by satellite sensors. This finding may be relevant in decision-making for marine resource management and conservation in the GU. The information may help to seek the sustainability of this social-environmental system.

A Highly Sensitive NiO Flexible Temperature Sensor Prepared by Low-Temperature Sintering Electrohydrodynamic Direct Writing.

Flexible temperature sensors have diverse applications and a great potential in the field of temperature monitoring, including healthcare, smart homes and the automotive industry. However, the current flexible temperature sensor preparation generally suffers from process complexity, which limits its development and application. In this paper, a nickel oxide (NiO) flexible temperature sensor based on a low-temperature sintering technology is introduced. The prepared NiO flexible temperature sensor has a high-resolution temperature measurement performance and good stability, including temperature detection over a wide temperature range of (25 to 70 °C) and a high sensitivity performance (of a maximum TCR of -5.194%°C-1 and a thermal constant of 3938 K). The rapid response time of this temperature sensor was measured to be 2 s at 27-50 °C, which ensures the accuracy and reliability of the measurement. The NiO flexible temperature sensor prepared by electrohydrodynamic direct writing has a stable performance and good flexibility in complex environments. The temperature sensor can be used to monitor the temperature status of the equipment and prevent failure or damage caused by overheating.

Identifying groups at-risk to extreme heat: Intersections of age, race/ethnicity, and socioeconomic status

Anthropogenic climate change has resulted in a significant rise in extreme heat events, exerting considerable but unequal impacts on morbidity and mortality. Numerous studies have identified inequities in heat exposure across different groups, but social identities have often been viewed in isolation from each other. Children (5 and under) and older adults (65 and older) also face elevated risks of heat-related health impacts. We employ an intersectional cross-classificatory approach to analyze the distribution of heat exposure between sociodemographic categories split into age groups in the contiguous US. We utilize high-resolution daily air temperature data to establish three census tract-level heat metrics (i.e., average summer temperature, heat waves, and heat island days). We pair those metrics with American Community Survey estimates on racial/ethnic, socioeconomic, and disability status by age to calculate population weighted mean exposures and absolute disparity metrics. Our findings indicate few substantive differences between age groups overall, but more substantial differences between sociodemographic categories within age groups, with children and older adults from socially marginalized backgrounds facing greater exposure than adults from similar backgrounds. When looking at sociodemographic differences by age, people of color of any age and older adults without health insurance emerge as the most exposed groups. This study identifies groups who are most exposed to extreme heat. Policy and program interventions aimed at reducing the impacts of heat should take these disparities in exposure into account to achieve health equity objectives.

Background radiation compensation calibration method for film cooling infrared temperature measurement based on BP neural network

The precise non-contact temperature measurement method is essential for studying gas turbine component heat transfer. While infrared thermal imaging provides high-resolution two-dimensional temperature fields, errors arise from the measurement environment's complexity, emissivity variations, and reflection radiation effects. This study utilizes BP neural networks to correct infrared radiation on gas film cooled plates. By modeling background radiation and analyzing infrared emission processes, factors influencing temperature distribution are identified. Experimental data from uncooled plate are used for network training. Experimental measurements were conducted on uncooled plates to obtain training data. A BP neural network was established based on Planck's law and the radiation transmission process to relate the mainstream temperature to the background radiation. Calibration analysis was performed based on experimental data from the cooled plate, achieving the restoration of the two-dimensional temperature field under multiple operating conditions and significantly reducing measurement errors. This method comprehensively considers the entire process of radiation transmission, thereby eliminating background radiation embedded in the infrared images. The obtained measurement results vividly depict the real temperature distribution, offered significant support for gas turbine engineering applications.

Machine learning framework for high-resolution air temperature downscaling using LiDAR-derived urban morphological features

Climate models lack the necessary resolution for urban climate studies, requiring computationally intensive processes to estimate high resolution air temperatures. In contrast, Data-driven approaches offer faster and more accurate air temperature downscaling. This study presents a data-driven framework for downscaling air temperature using publicly available outputs from urban climate models, specifically datasets generated by UrbClim. The proposed framework utilized morphological features extracted from LiDAR data. To extract urban morphological features, first a three-dimensional building model was created using LiDAR data and deep learning models. Then, these features were integrated with meteorological parameters such as wind, humidity, etc., to downscale air temperature using machine learning algorithms. The results demonstrated that the developed framework effectively extracted urban morphological features from LiDAR data. Deep learning algorithms played a crucial role in generating three-dimensional models for extracting the aforementioned features. Also, the evaluation of air temperature downscaling results using various machine learning models indicated that the LightGBM model had the best performance with an RMSE of 0.352 °K and MAE of 0.215 °K. Furthermore, the examination of final air temperature maps derived from downscaling showed that the developed framework successfully estimated air temperatures at higher resolutions, enabling the identification of local air temperature patterns at street level. The corresponding source codes are available on GitHub: https://github.com/FatemehCh97/Air-Temperature-Downscaling.