Infrared Thermography Imaging Research Articles

Novel Diagnostic Approach for Acute Pharyngitis: Combining Machine Learning With Thermal Imaging.

We evaluated the effect of infrared thermography (IRT) on the clinical assessment of bacterial and viral pharyngitis and its impact on the predictive value of the McIsaac score algorithm for streptococcal pharyngitis in children. We also investigated if IRT could distinguish between bacterial and viral pharyngitis. The study included children aged 2-17 years presenting with sore throat and fever over 38°C from November 1, 2021, to April 30, 2022. Of the 76 assessed children, 16 were excluded due to missing data or technical issues, leaving 60 children (32 males, 28 females) divided into three groups: Group A with streptococcal pharyngitis (N = 30), viral pharyngitis (N = 16), and healthy controls (N = 14). McIsaac score and IRT imaging showed a 90% positive predictive value for streptococcal pharyngitis. While IRT alone could not distinguish between bacterial and viral infections, it significantly increased the predictive value when combined with the McIsaac score.

Protocol to evaluate human thermoregulation before and after thermal stress

Problem and motivationThe human body dissipates 60 % of its heat by emitting infrared radiation, it can be studied using Infrared Thermography (IRT). IRT images serve as thermal maps of the body, useful in medical applications to investigate the physiopathological of diseases that present symptoms such as swelling, pain, infection, rash, and increased local skin temperature. AimTo design a protocol to capture IRT images before and after physical activity. The protocol collects skin temperature data of the entire body, in the frontal (anterior and posterior) and sagittal (right and left) planes. MethodologyThe protocol was designed considering clinical, environmental, and technical factors and ensuring its reproducibility in both healthy and pathological populations. Thermographic images were acquired both at rest and after thermal stress (modified Bruce test). In addition, questionnaires were prepared to collect and store information on demographic data, core temperature, environmental conditions, pain perception, and level of physical activity. ResultsThe protocol combines the acquisition of IRT images with the application of the modified Bruce protocol on a treadmill as a thermal stress generator. Further impactThis protocol offers a valuable tool for studying the thermoregulatory capacity of the human body in the presence of different medical conditions.

An improved infrared image post-processing method for metals and composites

Post-processing of the infrared thermography image is crucial for nondestructive testing, which will reduce noise and disturbance considerably. In this work, an improved image post-processing method based on the temperature ratio is proposed, namely the Thermal Image Division (TID) method. To validate this method, we carried out experiments on different materials such as composite CFRP (carbon fiber reinforced polymer) plates, 304 stainless steel plates and aluminum alloy samples. The results show the proposed method is pretty useful, significantly improves the signal-to-noise ratio of the image by comparing with other post-processing methods. It’s not only beneficial for identifying the edge of the defect, but also facilitates the detection of smaller defects with deeper depth. The signal-to-noise ratio is improved by nearly 117.5 % from practice. The TID method was applied to the defect size detection of pulse thermography and ultrasonic thermography. The results indicate TID method has good robustness, significantly reduces the error of defect size quantification.

Electrothermal Modeling of Photovoltaic Modules for the Detection of Hot-Spots Caused by Soiling

Solar energy plays a major role in the transition to renewable energy. To ensure that large-scale photovoltaic (PV) power plants operate at their full potential, their monitoring is essential. It is common practice to utilize drones equipped with infrared thermography (IRT) cameras to detect defects in modules, as the latter can lead to deviating thermal behavior. However, IRT images can also show temperature hot-spots caused by inhomogeneous soiling on the module’s surface. Hence, the method does not differentiate between defective and soiled modules, which may cause false identification and economic and resource loss when replacing soiled but intact modules. To avoid this, we propose to detect spatially inhomogeneous soiling losses and model temperature variations explained by soiling. The spatially resolved soiling information can be obtained, for example, using aerial images captured with ordinary RGB cameras during drone flights. This paper presents an electrothermal model that translates the spatially resolved soiling losses of PV modules into temperature maps. By comparing such temperature maps with IRT images, it can be determined whether the module is soiled or defective. The proposed solution consists of an electrical model and a thermal model which influence each other. The electrical model of Bishop is used which is based on the single-diode model and replicates the power output or consumption of each cell, whereas the thermal model calculates the individual cell temperatures. Both models consider the given soiling and weather conditions. The developed model is capable of calculating the module temperature for a variety of different weather conditions. Furthermore, the model is capable of predicting which soiling pattern can cause critical hot-spots.

A novel infrared thermography image analysis for transformer condition monitoring

Electrical systems are deeply ingrained in most industrial facilities, their maintenance is increasingly becoming a critical and significant component of economic policy. Condition monitoring of electrical transformers is essential for improving their dependability and availability, averting costly maintenance and additional significant breakdowns. This research adopts the approach based on infrared thermography techniques (IRT) to keep eye on electrical transformers and detect their defects. Thermal images of the transformer were captured at two distinct operating states with an infrared camera. These images were then compiled into a dataset for further analysis. This method uses infrared imaging (IRT), along with feature analysis and machine learning, to identify issues in electrical transformers in a new way. To find the best performing machine learning model, different techniques are compared here in terms of their accuracy and stability. Two approaches are investigated for identifying features in thermography images. Approach-1 employed five common machine learning algorithms, such as Support Vector Machine (SVM), K-Nearest Neighbours (KNN), Decision Tree (DT), Logistic Regression (LR), and Least Squares Support Vector Machine (LS-SVM). Approach-2 utilized four deep learning techniques, such as MobileNetV2 (MNV2), InceptionV3 (InV3), DenseNet121(DN121), and our proposed modified VGG-16. Among all evaluated methods, the modified VGG-16 architecture achieved the highest level of dependability, demonstrating exceptional efficiency and accuracy in transformer condition monitoring and fault diagnosis.

Infrared thermography and computed tomography imaging for hind limb study after immobilization-induced disuse atrophy

Immobilization for treatment after an injury can lead to disuse atrophy, resulting in reduced functionality and strength of the immobilized limb. In our study, we utilized infrared thermography (IR) and computed tomography (CT) ex vivo to assess both physiological and structural changes following hind limb immobilization in a young Wistar rat model. Twelve rats weighing 275 ± 30 g had their right hind limbs immobilized with a modified Thomas-splint for varying durations (3, 7, or 14 days). IR imaging using an infrared camera provided insight into limb temperature changes. For micro-CT, we implemented a stain-ethanol fixation method and a gray score which enabled us to visualize and quantify muscle alterations. Thermographic images showed an increase in temperature of up to 8% in the hind limb at supine position at 14 days due to the inflammatory process while micro-CT exhibited muscle shrinkage of 10 and 18% at 7 and 14 days, respectively. Our findings underscore the efficacy of IR and micro-CT as rapid and precise imaging modalities for detecting morphological shifts in muscle tissue, particularly in pathological conditions like atrophy.

FAULT DETECTION AND CLASSIFICATION USING DENSENET, SURF-BASED ANN, AND INFRARED THERMOGRAPHY

The reliability and efficiency of electric motors are critical in industrial applications, where unexpected faults can lead to costly downtime and safety hazards. Traditional fault detection methods often require extensive manual inspection and may not capture subtle anomalies in motor behavior. Infrared thermography has emerged as a non-invasive technique to detect temperature variations in motor components, which can indicate potential faults. However, the challenge lies in accurately classifying these faults to prevent failures. Current methods lack the precision needed to classify various motor faults accurately and quickly, especially when dealing with complex thermal patterns. The integration of advanced deep learning architectures with feature extraction techniques presents an opportunity to enhance the detection and classification of motor faults. This study proposes a hybrid model combining DenseNet, a deep learning architecture known for its high performance in image analysis, with a Speeded-Up Robust Features (SURF)-based Artificial Neural Network (ANN) for feature extraction and classification. Infrared thermography images of motors were first processed through DenseNet for initial feature extraction. The SURF algorithm further refined these features, which were then classified using ANN. The model was trained and validated on a dataset of infrared thermography images, representing various motor fault conditions, including bearing wear, misalignment, and insulation failure. The proposed model achieved an overall accuracy of 98.7% in detecting and classifying motor faults, outperforming traditional methods by 5.2%. The model also demonstrated high sensitivity (97.8%) and specificity (99.1%) in identifying subtle temperature variations indicative of early-stage faults. These results highlight the effectiveness of the DenseNet and SURF-based ANN approach in enhancing the reliability of motor fault detection using infrared thermography.

An Experimental Investigation Of Local Thermo-Fluidic Heat Transfer In A U-Shaped Mini-Channel Sink

This study investigates experimentally a heated U-shaped mini-channel heat sink using Infrared Thermography and Particle Image Velocimetry for a water coolant flow of Reynolds numbers of 280, 650, and 1350 (based on the hydraulic channel diameter). The choice of this cell geometry is based on its role as a simplified unit of a serpentine heat exchanger, which is proved to be one of the most promising for cooling processes. The use of the infrared camera allows the detection of temperature fields on top of the external surface of the cell. Therefore, aiming to derive the temperature distribution on the channel roof, a dedicated transfer function is implemented. Moreover, we employed the lumped capacitance model for thermal analysis on both infrared measurements and thermocouple data. The latter are recorded to capture the cooling process of the aluminium base and water temperature at the end of the outlet tube. As a result, thermal transient rate, cooling magnitude and equilibrium temperature are obtained. These parameters indicate that higher Reynolds numbers correspond to increased thermal transient rates, enhanced cooling effects, and lower equilibrium temperatures. A non-uniform distribution of heat transfer along the channel is reported, with the most efficient cooling area localized close to the first 90-degree corner. These findings are consistent with numerical simulations and previous experimental observations. PIV results reveal the presence of two fluid acceleration zones following both 90-degree corners, which contribute to improve the water cooling ability in their respective regions. Additionally, the formation of two recirculating bubbles is reported at the inner wall from corners vertices, whose intensity is dependent on Reynolds number, pushing the main flow towards the outer channel wall and reducing the local heat transfer. Turbulent kinetic energy distributions are also investigated, pointing out the presence of intense areas that better match with regions of minimum equilibrium temperature than maximum velocity zones. This suggests the presence of local turbulent unsteadiness and three-dimensional phenomena, which contributes significantly to cooling enhancement.

Functional infrared thermography imaging can be used to assess the effectiveness of Maxicam Gel® in pre-emptively treating transient synovitis and lameness in horses.

Diagnosing and treating lameness in horses is essential to improving their welfare. In equine orthopedic practice, infrared thermography (IRT) can indirectly detect soreness. Non-steroidal anti-inflammatory drugs can treat painful and inflammatory processes in horses. Using IRT, the efficacy of meloxicam (Maxicam Gel®) was evaluated in pre-treating transient synovitis in horses induced by a middle carpal joint injection of lipopolysaccharides (LPS) from E. coli 055:B5 at a dose of 10 endotoxin units. In a cross-over design, six healthy horses were randomly assigned to receive either 0.6 mg/kg of oral Maxicam Gel® (MAXVO) or a mock administration (control group, C) following a two-week washout period. IRT of the middle carpal joint, visual lameness assessment and joint circumference were recorded over time. Clinical and hematological evaluations were performed. Synovial fluid aspirates were analyzed for total nucleated cell count, total protein, and prostaglandin E2. A mixed effects analysis of variance was performed for repeated measures over time, followed by Tukey's test. A multinomial logistic regression was conducted to determine whether there is a relationship between a thermography temperature change and the lameness score. There were no changes in joint circumference. The MAXVO group showed a lower rectal temperature 4 h after synovitis induction. The C group presented an increase in neutrophils and a decrease in total hemoglobin and hematocrit 8 h after induction. No changes were observed in the synovial fluid between groups. The horses that received meloxicam did not show clinically significant lameness at any time, while the C group showed an increase in lameness 2, 4, and 8 h after synovitis induction. IRT indicated that the skin surface temperature of the middle carpal joint was lower in horses who received meloxicam, suggesting a reduction in the inflammatory process induced by LPS. It was observed that the maximum temperature peaks in the dorsopalmar and lateropalmar positions can be utilized to predict the severity of lameness, particularly when the temperature rises above 34°C. Horses pre-treated with meloxicam showed either reduced or no indication of mild to moderate pain and presented a lowehr thermographic temperature, which indicates the effectiveness of Maxicam Gel® as an anti-inflammatory.

Performance improvement of a Vestas V52 850kW wind turbine by retrofitting passive flow control devices

This study presents the results of a collaborative effort between academia and industry aimed at further enhancing the benefits provided by Vortex Generators and Gurney Flaps. To achieve this objective, an integrated approach was employed, involving wind tunnel experiments, on-site measurements, and computational simulations to design devices tailored for an onshore (Vestas V52, 850 kW) turbine and assess their influence on turbine performance. Device selection was based on wind tunnel measurements, while their positioning on the blade was based on infrared thermography images from the field. A Reynolds Averaged Navier Stokes solver was used to predict the performance of the devices on both airfoil and blade level. The final assessment of the upgrade pack was based on SCADA data and Lidar measurements. The results show that an Annual Energy Production uplift of 5.77% is measured for this turbine.

Prediction of coronary artery disease based on facial temperature information captured by non-contact infrared thermography

BackgroundCurrent approaches for initial coronary artery disease (CAD) assessment rely on pretest probability (PTP) based on risk factors and presentations, with limited performance. Infrared thermography (IRT), a non-contact technology that...

Condition Assessment of Electrical Power Devices using Infrared Thermography: A Review

Departures from the stable operation of electrical equipment usually appear as changes in parameters such as vibrations, pressure, temperature, power, and flow. Excessive changes in these parameters will indicate significant damage, leading to total or partial breakdown of equipment. This paper reviews the use of infrared thermography (IRT) for the operating condition temperature monitoring of electrical equipment. Infrared thermography is a contactless method that is based on thermal radiation principles and used for determining the temperature distribution on the surface of objects by measuring the intensity of radiation emitted by the object. The basic principles underlying IRT are presented in this review, showing its pros and cons, the various IRT methods, processes involved in IRT image acquisition, intelligent techniques for IR image analysis, and factors that affect the accuracy of results, including technical, environmental, and procedural factors. Also presented is an overview of common applications of IRT condition monitoring to solar energy, medicine, civil infrastructure, and aerospace equipment. Common faults in electrical equipment, including those in transformers, circuit breakers, feeder pillars and arrestors, are discussed with illustrations from recent research studies. These studies have demonstrated the effectiveness of IRT in the predictive maintenance of electrical installations. Hence, the replication of the techniques reviewed in this work is recommended so as to reduce current costs incurred in maintaining and replacing damaged equipment, and the associated time losses.

A novel application of deep learning approach over IRT images for the automated detection of rising damp on historical masonries

Nowadays, the fusion of Artificial Intelligence (AI) comprises a widespread approach for resolving various types of problems in many scientific domains including Protection of Monuments. Non-Destructive Testing (NDT) approaches and Infra-Red Thermography (IRT) specifically, plays a key role for the diagnosis and the assessment of the monuments’ preservation state. Additionally, IRT comprises a powerful tool for continuous monitoring especially when it concerns the physical and/or chemical processes that take place within or on the material and affect the irradiation of the historical surfaces. This study explores the application of Deep Learning (DL) to IRT images of passive approach, focusing on the automated detection of rising damp in historical masonries. The IRT data were acquired from two monuments, the Holy Aedicule of the Holy Sepulchre and the Historical Building "Msma’a". Exploiting the capabilities of AI for enhancing the non-intrusive nature of passive IRT, this research seeks to provide a cost-effective and non-destructive approach for the early identification of rising damp, contributing significantly to the long-term preservation, conservation, and protection of the cultural heritage. To achieve this, the study takes advantage of a combination of the PSPNet image segmentation model with the ResNet-50 backbone, the PSP_R50 model. The mmsegment framework, renowned for its versatility and effectiveness, serves as the ideal platform for training, evaluating, and fine-tuning the proposed segmentation model. Despite having a relatively small dataset, a highly effective segmentation model (0.93 accuracy, 0.89 IoU), has been successfully developed.

INFRARED THERMOGRAPHY IMAGE BASED CLASSIFICATION OF SOIL DIRT AND FABRIC

Soil is the substance most likely to meet nature and dirt people, vehicles, and clothing, especially in outdoor. Both source material and soil samples can be damaged during industrial and criminal investigations. Therefore, there is a need for detection, examination, and identification systems that can minimize contact with forensic evidence and provide accurate results with fewer samples. The study aims to determine the type of soil using a low-cost, easily accessible, and highly sensitive system that can be used easily without interference from the surface properties of the textile or destruction of the structure of the dirt. The working sites and areas of samples to be collected were determined according to the purpose of the study. In this context, samples of the most common soil types were taken from the lands in the Aegean Region of Turkey. Different types of substances were applied and dirtying on the collected samples. The newly formed samples were heated with a heating surface and allowed to cool. During this process, a thermal video was recorded, and feature extraction was performed. 165 samples were obtained from 55 tests. As a result, it is seen that the proposed method can detect samples with 97% accuracy.

Thermal imaging based pre-diagnostics tool for Graves’ disease

Infrared thermography Images of the facial region are taken from sixty persons. Scintigraphy and standard thyroid blood test are used to categorize these persons into thirty-three females and thirteen males suffering from Graves’ disease. This study is approved by the All-India Institute of Medical Science Rishikesh Ethics Committee with reference number AIIMS/IEC/19/997. Eleven Females and three males are found to be in healthy conditions and used as control. A convolutional neural networks (CNN) model is developed to automatically segment and extract the histogram-associated information within the thyroid and cheek region from the collected images. The sub-surface temperature of the thyroid gland and control is extracted using these set of images. We have acquired moderately correlated imaging biomarker with respect to age and gender from this sparse data. An Artificial Intelligence-based app is developed and deployed in a clinical environment to enrich the prognosis model in real time. An affordable Thermal plug-and-play addon device is developed to connect with any smartphone for faster diagnosis at the patient end to carry out this test now. This smartphone and AI-based app combination is successfully deployed as a point-of-care device. It is expected that this IR based preliminary test will automatically categorize healthy cases from patients. This step may save the clinicians to unnecessarily recommending the radioactive contamination-prone Scintigraphy and/or expensive and relatively slower thyroid blood tests. Such preliminary tests may (a) save costs to the patients and (b) relieve the burden on pathology labs. These two points are impactful for the healthcare industry, particularly in densely populated countries having low per capita income.

Material-enabled damage inspection of multifunctional shape memory alloy tufted composite T-joints

The through-the-thickness reinforcement of carbon-epoxy composite joints with shape memory alloy (SMA) tufts has shown significant improvement of the mechanical strength, fracture toughness, and delamination resistance. This study explores the thermal-electric properties of SMA filaments tufted in composite T-joints to exhibit multiple functionalities including material-enabled thermographic inspection and structural health monitoring via in-situ strain sensing. Infrared thermography image analysis was performed on both pristine and damaged T-joint specimens subject to pull-off testing. Experimental results showed that the heat generated by SMA tufts measured by an infrared camera provided accurate indication of delamination perpendicular to the tuft direction. SMA tufts were also used as strain sensors embedded within the T-joint. Local changes of the electrical resistance in SMA filaments, both separately and within the joint, were observed during pulling loads. Digital Image Correlation measurements exhibited good correlation between electrical resistance variations and the opening of delamination. These results pave the way for the development of multifunctional composite joining systems combining enhanced through-the-thickness damage tolerance and self-sensing capabilities.

Progress in Active Infrared Imaging for Defect Detection in the Renewable and Electronic Industries.

In recent years, infrared thermographic (IRT) technology has experienced notable advancements and found widespread applications in various fields, such as renewable industry, electronic industry, construction, aviation, and healthcare. IRT technology is used for defect detection due to its non-contact, efficient, and high-resolution methods, which enhance product quality and reliability. This review offers an overview of active IRT principles. It comprehensively examines four categories based on the type of heat sources employed: pulsed thermography (PT), lock-in thermography (LT), ultrasonically stimulated vibration thermography (UVT), and eddy current thermography (ECT). Furthermore, the review explores the application of IRT imaging in the renewable energy sector, with a specific focus on the photovoltaic (PV) industry. The integration of IRT imaging and deep learning techniques presents an efficient and highly accurate solution for detecting defects in PV panels, playing a critical role in monitoring and maintaining PV energy systems. In addition, the application of infrared thermal imaging technology in electronic industry is reviewed. In the development and manufacturing of electronic products, IRT imaging is used to assess the performance and thermal characteristics of circuit boards. It aids in detecting potential material and manufacturing defects, ensuring product quality. Furthermore, the research discusses algorithmic detection for PV panels, the excitation sources used in electronic industry inspections, and infrared wavelengths. Finally, the review analyzes the advantages and challenges of IRT imaging concerning excitation sources, the PV industry, the electronics industry, and artificial intelligence (AI). It provides insights into critical issues requiring attention in future research endeavors.

An adaptive multi-modal hybrid model for classifying thyroid nodules by combining ultrasound and infrared thermal images

BackgroundTwo types of non-invasive, radiation-free, and inexpensive imaging technologies that are widely employed in medical applications are ultrasound (US) and infrared thermography (IRT). The ultrasound image obtained by ultrasound imaging primarily expresses the size, shape, contour boundary, echo, and other morphological information of the lesion, while the infrared thermal image obtained by infrared thermography imaging primarily describes its thermodynamic function information. Although distinguishing between benign and malignant thyroid nodules requires both morphological and functional information, present deep learning models are only based on US images, making it possible that some malignant nodules with insignificant morphological changes but significant functional changes will go undetected.ResultsGiven the US and IRT images present thyroid nodules through distinct modalities, we proposed an Adaptive multi-modal Hybrid (AmmH) classification model that can leverage the amalgamation of these two image types to achieve superior classification performance. The AmmH approach involves the construction of a hybrid single-modal encoder module for each modal data, which facilitates the extraction of both local and global features by integrating a CNN module and a Transformer module. The extracted features from the two modalities are then weighted adaptively using an adaptive modality-weight generation network and fused using an adaptive cross-modal encoder module. The fused features are subsequently utilized for the classification of thyroid nodules through the use of MLP. On the collected dataset, our AmmH model respectively achieved 97.17% and 97.38% of F1 and F2 scores, which significantly outperformed the single-modal models. The results of four ablation experiments further show the superiority of our proposed method.ConclusionsThe proposed multi-modal model extracts features from various modal images, thereby enhancing the comprehensiveness of thyroid nodules descriptions. The adaptive modality-weight generation network enables adaptive attention to different modalities, facilitating the fusion of features using adaptive weights through the adaptive cross-modal encoder. Consequently, the model has demonstrated promising classification performance, indicating its potential as a non-invasive, radiation-free, and cost-effective screening tool for distinguishing between benign and malignant thyroid nodules. The source code is available at https://github.com/wuliZN2020/AmmH.

A flexible deep learning framework for thermographic inspection of composites

Infrared thermography (IRT) is a promising inspection technique, showing good defect detectability in a range of materials. To advance the IRT inspection technique, automated defect detection and analysis are of high interest. This study proposes an object detection algorithm based on Faster R–CNN with attention-based feature fusion network and flexible late fusion strategy for efficient extraction of defect features from an IRT image sequence. In order to train the deep learning-based algorithm, a large, diverse and representative virtual thermographic dataset for composite materials was constructed by an in-house developed parameterized 3D finite element simulator. The virtually trained deep learning framework is tested on experimental IRT datasets which were obtained on composite coupons having a range of artificial defect types as well as on stiffened aerospace composite panels with real (production) defects. The obtained test results indicate the high performance, generalization and reliability of the proposed deep learning framework for automated thermographic inspection of composite parts.

Infrared thermography monitoring of rock faces – Potential and pitfalls

Rock surface temperatures are of fundamental importance for studies on weathering, rockfall and permafrost. Point temperature measurements may not reflect the small-scale variability of temperature as a function of micro-topography. To close this gap, infrared thermography (IRT) seems to be a simple and promising approach. However, there are several pitfalls in terms of interpretation, as radiation temperatures depend on emissivity and reflectivity of the rock, which in turn are influenced by rock type, surface roughness, wetness, surrounding weather conditions, and angle to the camera axis.We performed laboratory and exemplary field experiments in order to estimate the magnitude of possible errors. We used rough and smooth (sawn) specimen of six different stone types in wet and dry condition and took IRT images at different tilt angles between camera axis and rock surface. Furthermore, we applied the approach to a small rock outcrop (approx. 3 × 3 m) and to a rockwall (approx. 100 × 100 m).The results of the laboratory measurements show that the temperature error increases with increasing tilt angle of the rock surface. Depending on the nature of the reflected surroundings, radiation temperatures can be warmer or cooler than sensor temperatures. In typical settings, the error is low (<0.5 K) up to a tilt of 40° but it may increase to >1 K at tilt angles of 50° and more. Smooth and wet surfaces tend to be more prone to deviations.The field examples confirm the results of the laboratory tests. But they show that spatial differences in temperature can still be detected as the “true” differences are usually larger than the magnitude of error. We suggest to reduce the error of the IRT image by correcting temperatures using a high-resolution surface model.