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This study addresses a crucial gap in understanding the impact of urban morphologies on the canopy urban heat islands (CUHI) effect. The selection of reference stations lacks a unified standard, and their surface air temperature (SAT) sequences are also inevitably influenced by urbanization. However, synchronous observational data from relocated meteorological stations could provide high-quality sample data for studying CUHI. Utilizing remote sensing techniques, the findings of this paper revealed that the observation environment of stations after relocation exhibited remarkable representativeness, with their observation sequences accurately reflecting the local climatic background. The differences in synchronized observation sequences could characterize the CUHI intensity (CUHII). Among the various factors, land use parameters and landscape parameters played particularly significant roles. Furthermore, the fitting performance of the random forest (RF) model for both training and testing data was significantly superior to that of the linear model and support vector regression (SVR) model. Additionally, the influence of local circulation on CUHI could not be overlooked. The mechanisms by which urban morphologies affect CUHII under different circulation backgrounds deserve further investigation. [ABSTRACT FROM AUTHOR]

Near-land surface air temperature (NLSAT) is an important meteorological and climatic parameter widely used in climate change, urban heat island and environmental science, in addition to being an important input parameter for various earth system simulation models. However, the spatial distribution and the limited number of ground-based meteorological stations make it difficult to obtain a large range of high-precision NLSAT values. This paper constructs neural network, long short-term memory, bi-directional long short-term memory, support vector machine, random forest, and Gaussian process regression models by combining MODIS data, DEM data, and meteorological station data to estimate the NLSAT in China's mainland and compare them with actual NLSAT observations. The results show that there is a significant correlation between the model estimates and the actual temperature observations. Among the tested models, the random forest performed the best, followed by the support vector machine and the Gaussian process regression, then the neural network, the long short-term memory, and the bi-directional long short-term memory models. Overall, for estimates in different seasons, the best results were obtained in winter, followed by spring, autumn, and summer successively. According to different geographic areas, random forest was the best model for Northeast, Northwest, North, Southwest, and Central China, and the support vector machine was the best model for South and East China. [ABSTRACT FROM AUTHOR]

Surface air temperature is a critical element in the surface–atmosphere interaction, energy exchange, and water cycle. Multi-source fusion reanalysis products (hereafter referred to as reanalysis) have spatiotemporal continuity and broad applicability that can provide key data support for various studies such as glacier melting, soil freeze-thaw and desertification, ecosystem, and climate change in the alpine region of the Qinghai–Tibet Plateau (QTP). Surface air temperature observations collected at 17 weather stations in the High-cold region Observation and Research Network for Land Surface Process and Environment of China (HORN) over the period of 2017–2018 are implemented to evaluate the advanced and widely used surface air temperature reanalysis datasets, which include the European Centre for Medium-Range Weather Forecasts (ECMWF) Fifth Generation Land Surface Reanalysis (ERA5L), the U.S. Global Land Data Assimilation System (GLDAS), and China Meteorological Administration Land Data Assimilation System (CLDAS). Results are as follows: (1) Evaluation results of temporal changes and spatial distribution characteristics indicate that the three reanalysis datasets are consistent with in-situ observations in the alpine region of the QTP. CLDAS is more consistent with observations and can better describe details of temperature distribution and variation than ERA5L and GLDAS. (2) For the evaluation period, CLDAS is 0.53 °C higher than the in-situ observation, while ERA5L and GLDAS are lower than the in-situ observation by −3.45 °C and −1.40 °C, respectively. (3) The accuracy of CLDAS is better than ERA5L and GLDAS under different elevations and land covers. We resampled three reanalysis datasets with a spatial resolution of 0.25° and used the two most common interpolation methods to analyze the impact of spatial resolution and different interpolation methods on the evaluation results. We found that the impact is small. In summary, the three reanalysis datasets all have certain applicability in the alpine region of the QTP, and the accuracy of CLDAS is significantly higher than ERA5L and GLDAS. The results of the present paper have important implications for the selection of reanalysis data in the studies of climate, ecosystem, and sustainable development in the QTP. [ABSTRACT FROM AUTHOR]

Surface air temperature (Ta) is an important physical quantity, usually measured at ground weather station networks. Measured Ta data is inadequate to characterize the complex spatial patterns of Ta field due to low density and unevenness of the networks. Remote sensing can provide satellite imagery with large scale spatial coverage and fine resolution. Estimating spatially continuous Ta by integrating ground measurements and satellite data is an active research area. A variety of methods have been proposed and applied in this area. However, the existing studies primarily focused on daily Ta and failed to quantify uncertainties in model parameter and estimated results. In this paper, a Bayesian Kriging regression (BKR) method is proposed to model and estimate monthly Ta using satellite-derived land surface temperature (LST) as the only input. The BKR is a spatial statistical model with the capacity to quantify uncertainties via Bayesian inference. The BKR method was applied to estimate monthly maximum air temperature (Tmax) and minimum air temperature (Tmin) over the conterminous United States in 2015. An exploratory analysis shows a strong relationship between LST and Ta at the monthly scale, indicating LST has the great potential to estimate monthly Ta. 10-fold cross-validation approach was adopted to compare the predictive performance of the BKR method with the linear regression method over the whole region and the urban areas of the contiguous United States. For the whole region, the results show that the BKR method achieves a competitively better performance with averaged RMSE values 1.23 K for Tmax and 1.20 K for Tmin, which are also lower than previous studies on estimation of monthly Ta. In the urban areas, the cross-validation demonstrates similar results with averaged RMSE values 1.21 K for Tmax and 1.27 K for Tmin. Posterior samples for model parameters and estimated Ta were obtained and used to analyze uncertainties in the model parameters and estimated Ta. The BKR method provides a promising way to estimate Ta with competitively predictive performance and to quantify model uncertainties at the same time. [ABSTRACT FROM AUTHOR]