High-resolution Air Temperature Research Articles

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.

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.

A new statistical downscaling approach for short‐term forecasting of summer air temperatures through a fusion of deep learning and spatial interpolation

AbstractReliable early forecasting of extreme summer air temperatures is essential for effectively managing and mitigating the socioeconomic damage caused by thermal disasters. Numerical weather prediction models have become valuable tools for forecasting air temperature; however, they incur high computational costs, resulting in coarse spatial resolution and systematic bias owing to imperfect parametrization. To address these problems, we developed a novel statistical downscaling and bias correction method (named DeU‐Net) for the maximum and minimum air temperature (Tmax and Tmin respectively) forecasts obtained from the Global Data Assimilation and Prediction System with a spatial resolution of 10 to 1.5 km over South Korea through the fusion of deep learning (i.e., U‐Net) and spatial interpolation. In this study, we used a methodology to decompose statistically downscaled Tmax and Tmin forecasts into temporal dynamics over South Korea and spatial fluctuations by pixels. When comparing the proposed DeU‐Net with the dynamical downscaling model (i.e., Local Data Assimilation and Prediction System) and support vector regression‐based statistical downscaling model at the seen and unseen stations for forecasting the next‐day Tmax and Tmin, DeU‐Net showed the highest spatial correlation and the lowest root‐mean‐square error in all cases. In a qualitative evaluation, DeU‐Net successfully produced a detailed spatial distribution most similar to the observations. A further comparison extending the forecast lead time to 7 days indicated that the proposed DeU‐Net is a better downscaling approach than support vector regression, regardless of the forecast lead time. These results demonstrate that bias‐corrected high spatial resolution air temperature forecasts with relatively long forecast lead times in summer can be effectively produced using the proposed model for operational forecasting.

Creating a spatially continuous air temperature dataset for Taiwan using thermal remote-sensing data and machine learning algorithms

Weather stations can provide accurate and high temporal resolution air temperature (Ta) measurements, but their limited spatial coverage due to sparse distribution poses an issue and challenge. However, satellite data can offer land surface temperature (LST) observations with high spatial coverage, which have a strong relationship with Ta, making them ideal for enhancing Ta estimation. This study uses satellite-derived and auxiliary data to create a monthly mean Ta dataset with a 1 km resolution over Taiwan from 2003 to 2020. We employed three machine learning (ML) algorithms and seven different datasets comprising 12 explanatory variables with LST obtained from the MODIS to find the optimal combination of algorithm and dataset for Ta estimation in Taiwan. We applied recursive feature elimination (RFE) to reduce the model complexity and overfitting issues. For model assessment, we used five-fold cross-validation to evaluate the ML models, and indicators such as the coefficient of determination (R2), mean absolute error (MAE), and root mean square of error (RMSE) were employed. The results show that the XGB regressor performed the best among the three models with the highest accuracy. The RFE using the XGB model suggested eight selected variables, including nighttime LST, daytime LST, elevation, longitude, latitude, distance to the sea, month, and year. Based on the variance importance analysis, nighttime LST was the most crucial variable, followed by daytime LST and month. We found that the final monthly Ta dataset using the XGB model had an excellent five-fold cross-validated performance (R2 = 0.986, MAE = 0.477 °C, and RMSE = 0.639 °C). Furthermore, the XGB model not only performed well throughout all four seasons but also had high and consistent accuracy across months, years, and subsets, indicating its potential for accurately estimating Ta in Taiwan's complex topographic features with varying climate conditions. The resulting monthly Ta dataset created by our model can be an essential input for environmental studies.

High spatial resolution long-term temperature profiling to inform near-shore atmospheric sound propagation

Atmospheric sound propagation depends on factors such as surface characteristics and meteorological parameters. Effective numerical modeling of atmospheric sound propagation requires reliable and realistic input parameters. One of the most influential factors is the air temperature profile. This work examines the application of experimentally determined air temperature profiles in acoustic modeling of littoral environments. Long term measurements of near-surface air temperature profiles over water and marsh grass are presented. Data have been recorded using temperature loggers affixed to two permanent 7 m masts. The loggers were positioned with a 1 m vertical spacing. A common approach to characterize the near-surface boundary layer uses Monin–Obukhov Similarity Theory (MOST). This work compares high spatial resolution air temperature measurements with measurement-informed profiles based on MOST. The development of a more experimentally grounded set of assumptions will ultimately contribute to the improvement of atmospheric acoustic transmission loss models in near shore environments.

Data Fusion for Estimating High-Resolution Urban Heatwave Air Temperature

High-resolution air temperature data is indispensable for analysing heatwave-related non-accidental mortality. However, the limited number of weather stations in urban areas makes obtaining such data challenging. Multi-source data fusion has been proposed as a countermeasure to tackle such challenges. Satellite products often offered high spatial resolution but suffered from being temporally discontinuous due to weather conditions. The characteristics of the data from reanalysis models were the opposite. However, few studies have explored the fusion of these datasets. This study is the first attempt to integrate satellite and reanalysis datasets by developing a two-step downscaling model to generate hourly air temperature data during heatwaves in London at 1 km resolution. Specifically, MODIS land surface temperature (LST) and other satellite-based local variables, including normalised difference vegetation index (NDVI), normalized difference water index (NDWI), modified normalised difference water index (MNDWI), elevation, surface emissivity, and ERA5-Land hourly air temperature were used. The model employed genetic programming (GP) algorithm to fuse multi-source data and generate statistical models and evaluated using ground measurements from six weather stations. The results showed that our model achieved promising performance with the RMSE of 0.335 °C, R-squared of 0.949, MAE of 1.115 °C, and NSE of 0.924. Elevation was indicated to be the most effective explanatory variable. The developed model provided continuous, hourly 1 km estimations and accurately described the temporal and spatial patterns of air temperature in London. Furthermore, it effectively captured the temporal variation of air temperature in urban areas during heatwaves, providing valuable insights for assessing the impact on human health.

Deep Learning-Based 500 m Spatio-Temporally Continuous Air Temperature Generation by Fusing Multi-Source Data

The all-weather high-resolution air temperature data is crucial for understanding the urban thermal conditions with their spatio-temporal characteristics, driving factors, socio-economic and environmental consequences. In this study, we developed a novel 5-layer Deep Belief Network (DBN) deep learning model to fuse multi-source data and then generated air temperature data with 3H characteristics: High resolution, High spatio-temporal continuity (spatially seamless and temporally continuous), and High accuracy simultaneously. The DBN model was developed and applied for two different urban regions: Wuhan Metropolitan Area (WMA) in China, and Austin, Texas, USA. The model has a excellent ability to fit the complex nonlinear relationship between temperature and different predictive variables. After various adjustments to the model structure and different combinations of input variables, the daily 500-m air temperature in Wuhan Metropolitan Area (WMA) was initially generated by fusing remote sensing, reanalysis and in situ measurement products. The ten-fold cross-validation results indicated that the DBN model achieved promising results with the RMSE of 1.086 °C, MAE of 0.839 °C, and R2 of 0.986. Compared with conventional data fusion algorithms, the DBN model also exhibited better performance. In addition, the detailed evaluation of the model on spatial and temporal scales proved the advantages of using DBN model to generate 3H temperature data. The spatial transferability of the model was tested by conducting a validation experiment for Austin, USA. In general, the results and fine-scale analyses show that the employed framework is effective to generate 3H temperature, which is valuable for urban climate and urban heat island research.

A cost-effective method for producing spatially continuous high-resolution air temperature information in urban environments

Sustainable city planning requires detailed information on spatial temperature variations. Remotely sensed land surface temperature (LST) is known to differ substantially from air temperature (AT) causing misinterpretations of the ambient conditions. We demonstrate a reliable and cost-efficient method for AT modelling in urban environments using open data and few temperature observations. The study area is the city of Turku SW Finland, where we have a dense in situ AT observation network of 64 Onset Hobo temperature loggers as a reference. Landsat 8 thermal data from different seasons were used to extract pixel-based LST by employing MODIS and ASTER emissivity libraries and CORINE land cover classification. The LSTs were analysed against the in situ AT first with the correlation analysis. Except for December, the Pearson's correlation coefficients were statistically significant (0.449–0.654, p ≤ 0.001). Seasonally adjusted linear regression models were applied to predict spatially continuous air temperatures (ATp) based on the extracted LST. Our results demonstrate that it is possible to predict urban ATs reliably - within ca. half-a-degree accuracy (MAE 0.36–0.62 °C). The prediction works best in spring, summer and autumn. It improves the capacity to produce reliable high spatial resolution AT information even if in situ observations are sparse.

Evidence of elevation-dependent warming from the Chinese Tian Shan

Abstract. The phenomenon in which the warming rate of air temperature is amplified with elevation is termed elevation-dependent warming (EDW). It has been clarified that EDW can accelerate the retreat of glaciers and melting of snow, which can have significant impacts on the regional ecological environment. Owing to the lack of high-density ground observations in high mountains, there is widespread controversy regarding the existence of EDW. Current evidence is mainly derived from typical high-mountain regions such as the Swiss Alps, the Colorado Rocky Mountains, the tropical Andes and the Tibetan Plateau–Himalayas. Rare evidence in other mountain ranges has been reported, especially in arid regions. In this study, EDW features (regional warming amplification and altitude warming amplification) in the Chinese Tian Shan (CTM) were detected using a unique high-resolution (1 km, 6-hourly) air temperature dataset (CTMD) from 1979 to 2016. The results showed that there were significant EDW signals at different altitudes on different timescales. The CTM showed significant regional warming amplification in spring, especially in March, and the warming trends were greater than those of continental China with respect to three temperatures (minimum temperature, mean temperature and maximum temperature). The significance values of EDW above different altitude thresholds are distinct for three temperatures in 12 months. The warming rate of the minimum temperature in winter showed a significant elevation dependence (p<0.01), especially above 3000 m. The greatest altitudinal gradient in the warming rate of the maximum temperature was found above 4000 m in April. For the mean temperature, the warming rates in June and August showed prominent altitude warming amplification but with different significance above 4500 m. Within the CTM, the Tolm Mountains, the eastern part of the Borokoonu Mountains, the Bogda Mountains and the Balikun Mountains are representative regions that showed significant altitude warming amplification on different timescales. This new evidence could partly explain the accelerated melting of snow in the CTM, although the mechanisms remain to be explored.

High-resolution air temperature mapping in a data-scarce, arid area by means of low-cost mobile measurements and machine learning

The availability of gridded, screen-level air temperature data at an effective spatial and temporal resolution is important for many fields such as climatology, ecology, urban planning and design. This study aims at providing such data in a data-scarce, arid city within the greater Cairo region (Egypt), namely the Sixth of October, where, to our knowledge, no such data are available. By using (i) air temperature data, collected from mobile measurements, (ii) multiple spectral indices, (iii) spatial analysis techniques and (iv) random forest regression modelling, we produced air temperature maps (for both daytime and nighttime) at 30-m spatial resolution for the entire city. The proposed method is systematic and relies on low-cost instrumentation and freely-available satellite data and hence it can be replicated in similar data-scarce, arid areas to allow for better spatial and temporal monitoring of air temperature.

Spatial downscaling method for air temperature through the correlation between land use/land cover and microclimate: A case study of the Greater Tokyo Area, Japan

Since the development of remote sensing, studies on urban heat islands (UHIs) have mainly focused on surface UHI (SUHI), which is more than the atmospheric UHI (AUHI). Remote sensing advancements provide strong technical support for SUHI studies. However, the acquisition of air temperature (AT), especially high-resolution large-scale AT data, remains inconvenient. In this study, we selected the Greater Tokyo Area as a case study. We used the extra-trees (ET) model to downscale the AT from 1 km to 250 m based on the regression relationship among AT, digital elevation model (DEM), and land use/land cover (LULC). The downscaled AT results were obtained after residual fitting. Finally, we compared the downscaled AT with meteorological station data to verify accuracy. The results indicated the following: (1) the ET model and the combination of independent variables, including DEM and LULC, could be applied to AT downscaling research; (2) the downscaling accuracy of highly heterogeneous regions was significantly higher than that of highly homogeneous regions; and (3) high-resolution AT data could accurately describe the AUHI of the study area. Finally, an accurate, efficient AT downscaling method based on high-resolution LULC was proposed.

A 1-km hourly air-temperature model for 13 northeastern U.S. states using remotely sensed and ground-based measurements

BackgroundAccurate and precise estimates of ambient air temperatures that can capture fine-scale within-day variability are necessary for studies of air temperature and health. MethodWe developed statistical models to predict temperature at each hour in each cell of a 927-m square grid across the Northeast and Mid-Atlantic United States from 2003 to 2019, across ~4000 meteorological stations from the Integrated Mesonet, using inputs such as elevation, an inverse-distance-weighted interpolation of temperature, and satellite-based vegetation and land surface temperature. We used a rigorous spatial cross-validation scheme and spatially weighted the errors to estimate how well model predictions would generalize to new cell-days. We assess the within-county association of temperature and social vulnerability in a heat wave as an example application. ResultsWe found that a model based on the XGBoost machine-learning algorithm was fast and accurate, obtaining weighted root mean square errors (RMSEs) around 1.6 K, compared to standard deviations around 11.0 K. We found similar accuracy when validating our model on an external dataset from Weather Underground. Assessing predictions from the North American Land Data Assimilation System-2 (NLDAS-2), another hourly model, in the same way, we found it was much less accurate, with RMSEs around 2.5 K. This is likely due to the NLDAS-2 model's coarser spatial resolution, and the dynamic variability of temperature within its grid cells. Finally, we demonstrated the health relevance of our model by showing that our temperature estimates were associated with social vulnerability across the region during a heat wave, whereas the NLDAS-2 showed a much weaker association. ConclusionOur high spatiotemporal resolution air temperature model provides a strong contribution for future health studies in this region.

Evaluation and application of a low-cost measurement network to study intra-urban temperature differences during summer 2018 in Bern, Switzerland

The understanding of intra-urban air temperature variations is crucial to assess strategies for cities' adaptation to impacts of present and future anthropogenic climate change. Depending on extensive measurement networks, high-resolution air temperature measurements in urban environments are challenging due to high instrumentation and maintenance costs. Here, we present a low-cost measurement device (LCD) consisting of a temperature logger and a custom-made, naturally ventilated radiation shield. Besides intercomparisons with automated weather stations (AWS) at three reference sites during record-dry summer 2018, we tested the potential of the devices using a network of 79 LCDs to assess the intra-urban variability of urban heat island (UHI) patterns in the city of Bern, Switzerland. We found positive mean measurement biases between LCDs and AWS of 0.61 to 0.93 K (RMSE: 0.78 to 1.17 K) during daytime, of which up to 82.8% of the variance could be explained statistically by solar irradiance (radiative heating) and wind speed (insufficient ventilation). During night, average measurement biases were markedly lower and eventually negative with −0.12 to 0.23 K (RMSE: 0.19 to 0.34 K). Our results highlight the importance of sensor intercomparisons being conducted at multiple locations with differing urban land-cover, structure, and metabolism given that biases varied considerably between the reference sites. Data retrieved by the city-wide measurement network showed that the LCD approach is well suited for the analysis of spatiotemporal UHI patterns during night and adds considerable value compared to the few existing AWS in detecting fine-scale air temperature variability. In conclusion, the current LCD measurement approach represents a valuable option for cost-effective analyses of urban air temperature variability across multiple scales, which may be of particular value for the development, appliance, and monitoring of adaptation strategies to climate change in cities with restricted financial resources.

Assessment of High Resolution Air Temperature Fields at Rocky Mountain National Park by Combining Scarce Point Measurements with Elevation and Remote Sensing Data

There is necessity of considering air temperature to simulate the hydrology and management within water resources systems. In many cases, a big issue is considering the scarcity of data due to poor accessibility and limited funds. This paper proposes a methodology to obtain high resolution air temperature fields by combining scarce point measurements with elevation data and land surface temperature (LST) data from remote sensing. The available station data (SNOTEL stations) are sparse at Rocky Mountain National Park, necessitating the inclusion of correlated and well-sampled variables to assess the spatial variability of air temperature. Different geostatistical approaches and weighted solutions thereof were employed to obtain air temperature fields. These estimates were compared with two relatively direct solutions, the LST (MODIS) and a lapse rate-based interpolation technique. The methodology was evaluated using data from different seasons. The performance of the techniques was assessed through a cross validation experiment. In both cases, the weighted kriging with external drift solution (considering LST and elevation) showed the best results, with a mean squared error of 3.7 and 3.6 °C2 for the application and validation, respectively.

MODIS and Landsat satellite images comparison for modeling near surface air temperature

Near surface air temperature (T air) is a descriptor of terrestrial environment conditions and one of climatic variables that most widely used in climate change studies. In this search, the worker suggested a high-resolution air temperature estimation from integrating Iraqi Agrometeorological network daily (T air) and land surface temperature (LST) that derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) or from Landsat8. The objective of this search is to find model to estimate high spatial resolution air temperature for any region based on satellite images and climate datasets. The data of air temperature used for modelling is collected by four weather stations located in Babylon Governorate from December 2016 to March 2017. The standard error for MODIS winter model is 1.55 and RMSE for landsat8 winter model is 1.3.

A high-resolution air temperature model for epidemiological studies in France

A high-resolution air temperature model for epidemiological studies in France

Estimation High Resolution Air Temperature Based on landsat8 images and Climate Datasets

Near surface air temperature (Ta) is one of the critical variables in environmental health, hydrology, climatology and epidemiology. In this study, we suggested a model to estimate high resolution air temperature for different region based on climate datasets estimation by Iraqi Agrometeorological network daily air temperature and the land surface temperature derived from thermal bands of landsat8 images. The new air temperature estimation was tested over part of Babylon governorate as a case study. Observations from four weather stations at Babylon governorate for four months from June to September for the year 2017 were used for modelling and accuracy assessment air temperature estimation.

A high-resolution air temperature data set for the Chinese Tian Shan in 1979–2016

Abstract. The Chinese Tian Shan (also known as the Chinese Tianshan Mountains, CTM) have a complex ecological environmental system. They not only have a large number of desert oases but also support many glaciers. The arid climate and the shortage of water resources are the important factors restricting the area's socioeconomic development. This study presents a unique high-resolution (1 km, 6-hourly) air temperature data set for the Chinese Tian Shan (41.1814–45.9945∘ N, 77.3484–96.9989∘ E) from 1979 to 2016 based on a robust elevation correction framework. The data set was validated by 24 meteorological stations at a daily scale. Compared to original ERA-Interim temperature, the Nash–Sutcliffe efficiency coefficient increased from 0.90 to 0.94 for all test sites. Approximately 24 % of the root-mean-square error was reduced from 3.75 to 2.85 ∘C. A skill score based on the probability density function, which was used to validate the reliability of the new data set for capturing the distributions, improved from 0.86 to 0.91 for all test sites. The data set was able to capture the warming trends compared to observations at annual and seasonal scales, except for winter. We concluded that the new high-resolution data set is generally reliable for climate change investigation over the Chinese Tian Shan. However, the new data set is expected to be further validated based on more observations. This data set will be helpful for potential users to improve local climate monitoring, modeling, and environmental studies in the Chinese Tian Shan. The data set presented in this article is published in the Network Common Data Form (NetCDF) at https://doi.org/10.1594/PANGAEA.887700. The data set includes 288 nc files and one user guidance txt file.

AN ASSESSMENT OF SPATIAL VARIATION OF LAND SURFACE CHARACTERISTICS OF MINNA, NIGER STATE NIGERIA FOR SUSTAINABLE URBANIZATION USING GEOSPATIAL TECHNIQUES

AN ASSESSMENT OF SPATIAL VARIATION OF LAND SURFACE CHARACTERISTICS OF MINNA, NIGER STATE NIGERIA FOR SUSTAINABLE URBANIZATION USING GEOSPATIAL TECHNIQUES

Developing a 1 km resolution daily air temperature dataset for urban and surrounding areas in the conterminous United States

High spatiotemporal resolution air temperature (Ta) datasets are increasingly needed for assessing the impact of temperature change on people, ecosystems, and energy system, especially in the urban domains. However, such datasets are not widely available because of the large spatiotemporal heterogeneity of Ta caused by complex biophysical and socioeconomic factors such as built infrastructure and human activities. In this study, we developed a 1 km gridded dataset of daily minimum Ta (Tmin) and maximum Ta (Tmax), and the associated uncertainties, in urban and surrounding areas in the conterminous U.S. for the 2003–2016 period. Daily geographically weighted regression (GWR) models were developed and used to interpolate Ta using 1 km daily land surface temperature and elevation as explanatory variables. The leave-one-out cross-validation approach indicates that our method performs reasonably well, with root mean square errors of 2.1 °C and 1.9 °C, mean absolute errors of 1.5 °C and 1.3 °C, and R2 of 0.95 and 0.97, for Tmin and Tmax, respectively. The resulting dataset captures reasonably the spatial heterogeneity of Ta in the urban areas, and also captures effectively the urban heat island (UHI) phenomenon that Ta rises with the increase of urban development (i.e., impervious surface area). The new dataset is valuable for studying environmental impacts of urbanization such as UHI and other related effects (e.g., on building energy consumption and human health). The proposed methodology also shows a potential to build a long-term record of Ta worldwide, to fill the data gap that currently exists for studies of urban systems.