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The widespread availability of Land Surface Temperature (LST) data from various sources presents a contemporary challenge for urban climate studies: how to efficiently compare these data with the results of traditional methods of temperature monitoring, which typically assume measurements at 2 m under sheltered conditions. In this line, the current study is based primarily on data extracted from a network of 31 points of hourly temperature monitoring at the 2 m level (Tair2m), in use between 2019 and 2021, in the city of Suceava in north-eastern Romania. These data allowed a detailed mapping for each hourly time step through multiple regression, adjusted by IDW, which was identified as the best interpolation method of Tair2m. These data were analyzed in parallel with LST data derived from Landsat imagery available in the analyzed period for 35 summer days with no or low cloud cover. The mapping results of both the Tair2m and LST data describe the main characteristics of the Suceava urban agglomeration (SvMA) heat island, which presents polynuclear features with intensities—as expressed by the temperature difference between the cores of the heat island and the surrounding rural areas—spanning during the summer noontime between 3.0 °C based on Tair2m and 7.1 °C on LST, respectively. The values of the Tair2m–LST differences were 0.68 °C on average, ranging from 5.33 to −19.17 °C, directly proportional to the imperviousness ratio (IMD) values, reaching the highest values in the local climate zones (LCZs) with a high built-up ratio (up to −19.17 °C) and the lowest (0.5 ÷ −0.5 °C) for those with bare soils, with isolated bushes and trees, with few or no buildings. The study results could serve as a tool to downscale the LST data to the level of Tair2m, which is useful for interpretation of the data derived from these commonly used tools in urban climate monitoring. [ABSTRACT FROM AUTHOR]

Urumqi is located in the arid region of northwestern China, known for being one of the most delicate ecological environments and an area susceptible to climate change. The urbanization of Urumqi has progressed rapidly, yet there is a lack of research on the urbanization effect (UE) in Urumqi in terms of the regional climate. This study investigates the UE of Urumqi (urban built-up area) on the regional thermal environment and its mechanisms for the first time, based on the WRF (Weather Research and Forecasting) model (combined with the Urban Canopy Model, UCM) simulation data of 10 consecutive years (2012–2021). The results show that the UE on surface temperature (Ts) and air temperature at 2 m (T2m) is strong (weak) during the night (daytime) in all seasons, and the UE on these is largest (smallest) in spring (winter). In addition, the maximum UE on both Ts and T2m is present over southern Urumqi in winter, whereas the maximum UE is identified over the northern Urumqi in other seasons. The maximum UE on Ts occurred in northwestern Urumqi at 18 LST (Local Standard Time, i.e., UTC+6) in autumn (reaching 5.2 °C), and the maximum UE on T2m occurred in northern Urumqi at 4 LST in summer (reaching 2.6 °C). Urbanization showed a weak cooling effect during daytime in summer and winter, reflecting the unique characteristics of the UE in arid regions, which are different from those in humid regions. The maximum cooling of Ts occurred in northern Urumqi at 11 LST in summer (reaching −0.4 °C), while that of T2m occurred at 10 LST in northern and northwestern Urumqi in winter (reaching −0.25 °C), and the cooling effect lasted for a longer period of time in summer than in winter. The UE of Urumqi causes the increase of Ts mainly through the influence of net short-wave radiation and geothermal flux and causes the increase of T2m through the influence of sensible heat flux and net long-wave radiation. The UE on the land surface energy balance in Urumqi can be used to explain the seasonal variation and spatial differences of the UEs on the regional thermal environment and the underlying mechanism. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau (TP) serves as a crucial ecological barrier in Asia, with vegetation playing a pivotal role in the terrestrial ecosystem by facilitating energy exchange between the land and atmosphere, regulating climate, and participating in the carbon cycle. In this study, we analyze the characteristics of surface vegetation on the TP in the growing season during 1982–2018 using satellite remote sensing data obtained from the National Oceanic and Atmospheric Administration (NOAA) and China Meteorological Forcing Dataset (CMFD). We investigate how these characteristics respond to climate change under different warming and humidification conditions across the TP. The main conclusions are as follows. (1) The normalized difference vegetation index (NDVI) values on the TP exhibit a gradual decrease from southeast to northwest during the growing season. There is a significant overall increasing trend at a climate tendency rate of 0.01·decade−1 (p < 0.01) from 1982 to 2018, characterized by a notable mutation of around 1998. Over the past 37 years, a polarized trend of vegetation was observed on the TP, with notable improvement in its central and eastern regions. However, there has been noticeable degradation in northwestern TP, specifically within the Kunlun Mountains and Qaidam Basin. (2) The climate of the TP demonstrates distinct regional disparities in terms of warming and humidification characteristics before and after 1998. During the period of 1982–1998 (1998–2018), the temperature increase is primarily concentrated in the northern (southern) TP, while precipitation increase is mainly observed in the southern and northwestern (northeastern and western) regions of the TP. (3) The responses of surface vegetation to climate factors exhibit significant variations across diverse climatic backgrounds. It is noteworthy that moisture conditions have a substantial impact on the response of vegetation to air temperature on the TP. During the period of 1982–1998, under relatively insufficient moisture conditions, a positive correlation was observed between air temperature and surface vegetation in the humid and semi-humid regions of the southeastern TP, while a negative correlation was found in the semi-arid regions of northeastern TP. During 1998–2018, as moisture conditions became relatively sufficient, surface vegetation in the semi-arid regions showed positive correlations with both temperature and precipitation. However, surface vegetation in the humid and semi-humid regions exhibited a significant negative correlation with precipitation. During this period, the synergistic effects between warm and humid climates in the semi-arid regions of northeastern TP and warm and dry climates in humid and semi-humid regions of southeastern TP substantially enhanced surface vegetation on the TP. Furthermore, our results indicate that thermal factors (air temperature) primarily influence variations in surface vegetation within the high-altitude arid region of the TP. During 1998–2018, a significant cooling trend was observed in the northwestern TP, which could potentially account for the degradation of surface vegetation in the Kunlun Mountains. The findings of this study establish a scientific basis for the sustainable development of grassland ecosystems on the TP. [ABSTRACT FROM AUTHOR]

The presence of melt ponds on the surface of Arctic Sea ice affects its albedo, thermal properties, and overall melting rate; thus, the detection of melt pond onset is of significant importance for understanding the Arctic's changing climate. This study investigates the utility of a novel method for detecting the onset of melt ponds on sea ice using a satellite-based, dual-sensor C-band approach, whereby Sentinel-1 provides horizontally polarized (HH) data and Advanced SCATterometer (ASCAT) provides vertically polarized (VV) data. The co-polarized ratio (VV/HH) is used to detect the presence of melt ponds on landfast sea ice in the Canadian Arctic Archipelago in 2017 and 2018. ERA-5 air temperature and wind speed re-analysis datasets are used to establish the VV/HH threshold for pond onset detection, which have been further validated by Landsat-8 reflectance. The co-polarized ratio threshold of three standard deviations from the late winter season (April) mean co-pol ratio values are used for assessing pond onset detection associated with the air temperature and wind speed data, along with visual observations from Sentinel-1 and cloud-free Sentinel-2 imagery. In 2017, the pond onset detection rates were 70.59% for FYI and 92.3% for MYI. Results suggest that this method, because of its dual-platform application, has potential for providing large-area coverage estimation of the timing of sea ice melt pond onset using different earth observation satellites. [ABSTRACT FROM AUTHOR]

Due to the difference in the quality of the global assimilation data and the ability to reproduce the real conditions of the atmosphere, the hourly atmospheric temperature at 2 m above the land surface from ERA5 cannot be used with complete confidence for the atmospheric weighted mean temperature ( T m ) calculations and global navigation satellite system (GNSS) precipitable water vapor (PWV) inversion. A systematic and complete refinement method is proposed, including the compensation of elevation matching bias of gridded temperature, correction of fixed-time cusp data fitting and refinement based on the remove-and-restore model. The usability and accuracy improvement of the refined ERA5 2 m atmospheric temperature in the T m and PWV calculation were validated based on three GNSS stations. The result shows that the average accuracy of the T m and PWV for the entire region could be increased by 74.4% and 75.1%, respectively. The RMS of the highest station was reduced from 4.28 K to 0.62 K for the T m and 0.662 mm to 0.203 mm for the PWV, and the RMS of other stations was reduced from 1.25 to 0.44 K for the T m and 0.211 mm to 0.101 mm for the PWV. This overall refinement method has important implications for atmospheric remote sensing. [ABSTRACT FROM AUTHOR]

In this study, a novel method for retrieving atmospheric temperature profiles with tree-structured Parzen estimator (TPE) and multilayer perceptron (MLP) algorithms was proposed, using FY-4A/GIIRS (Geosynchronous Interferometric Infrared Sounder) and ERA5 data. Firstly, by adding solar altitude angle, satellite zenith angle, 2m temperature, and surface temperature to the input layer of MLP, there is an improvement in retrieval accuracy. Secondly, TPE is effective in optimizing the hyper-parameters of MLP, and a set of optimized hyper-parameters is obtained through iterative optimization. Thirdly, comparing the retrieved temperature profiles with ERA5 data, we found that retrieval accuracy is influenced by detector, signal-to-noise ratio, terrain, solar altitude angle, satellite zenith angle, and the horizontal temperature gradient. The mean biases of the two adjacent detectors show significant differences, and the retrieval accuracy of the center detectors is greater than that of the north and south sides. The retrieval accuracy is relatively poor in areas with high terrain and large satellite zenith angle. There is a monthly variation in the retrieval accuracy due to the horizontal temperature gradient and signal-to-noise ratio and a significant diurnal variation due to solar altitude angle and signal-to-noise ratio. Compared to in situ sounding data, the mean biases vary from −0.56 K to 0.60 K, and the standard deviations vary from 1.26 K to 2.17 K. The analysis of factors influencing retrieval accuracy provides important insights into improving the ability to retrieve atmospheric temperatures from geostationary hyperspectral IR sounder observations for near real-time (NRT) applications. [ABSTRACT FROM AUTHOR]

Mars atmospheric dynamics are crucial for understanding its climate and weather patterns, especially over plateaus. Previous studies have explored localized atmospheric heating mechanisms over Mars plateaus only to a little extent. The local atmospheric heating dynamics over the Tharsis plateau, especially during global dust storms (GDSs), have not been quantitatively analyzed before. Based on reanalysis datasets, our analysis reveals that the central highlands of Tharsis experience ~130 K diurnal temperature fluctuations, driven by intense daytime convective activity. Surface temperature and near-surface air temperatures show fluctuations approximately 25 K and 20 K higher than those at similar latitudes, respectively. We quantify a super-adiabatic lapse rate around noon that suggests strong atmospheric instability, previously unquantified in this region. By dusk, the atmosphere stabilizes, presenting a homogenized condition. At aphelion, sensible heating and adiabatic terms control the atmospheric heating, while, at perihelion, radiative and sensible heating predominate. Notably, the onset of GDS significantly alters this dynamic, reducing the ground–air temperature gap from 17 K to 5 K and enhancing diabatic heating (adiabatic cooling) in the mid-to-lower (mid-to-upper) troposphere, with increases in radiative components up to 60 W/m2. [ABSTRACT FROM AUTHOR]

In this study, we develop an Atmospheric Motion Vector (AMV)-based method for retrieving wind vectors using 183.31 GHz water-vapor absorption channels. The method involves tracking water-vapor features from image triplets and subsequently deriving wind fields from motion vectors. The height of the derived wind for each channel is determined by calculating the weighing function peak using monthly averaged ERA5 reanalysis data. By utilizing Microwave Humidity Sounder-II (MWHS-II) brightness temperatures from the five channels centered around 183.31 GHz, wind vectors are retrieved within the Arctic region for the entire year of 2022. The retrieval quality is evaluated through comparative analysis with ERA5 reanalysis data and the Visible Infrared Imaging Radiometer Suite (VIIRS) wind product. The resultant vector root mean square errors (RMSEs) are approximately 4.5 m/s for the three lower-height channels and 5.5 m/s for the two upper-height channels. These findings demonstrate a wind retrieval performance comparable to the existing methods, highlighting its potential for augmenting wind availability at lower height levels. [ABSTRACT FROM AUTHOR]

Lakes on the Qinghai Tibet Plateau (QTP) are widely distributed spatially, and they are mostly seasonally frozen. Due to global warming, the thickness and phenology of the lake ice has been changing, which profoundly affects the regional climate evolution. There are a few studies about lake ice in alpine regions, but the understanding of climatological characteristics of lake ice on the QTP is still limited. Based on a field experiment in the winter of 2022, the thermal conductivity of Qinghai Lake ice was determined as 1.64 W·m−1·°C−1. Airborne radar ice thickness data, meteorological observations, and remote sensing images were used to evaluate a quasi-steady ice model (Leppäranta model) performance of the lake. This is an analytic model of lake ice thickness and phenology. The long-term (1979–2017) ice history of the lake was simulated. The results showed that the modeled mean ice thickness was 0.35 m with a trend of −0.002 m·a−1, and the average freeze-up start (FUS) and break-up end (BUE) were 30 December and 5 April, respectively, which are close to the field and satellite observations. The simulated trend of the maximum ice thickness from 1979 to 2017 (0.004 m·a−1) was slightly higher than the observed result (0.003 m·a−1). The simulated trend was 0.20 d·a−1 for the FUS, −0.34 d·a−1 for the BUE, and −0.54 d·a−1 for the ice duration (ID). Correlation and detrending analysis were adopted for the contribution of meteorological factors. In the winters of 1979–2017, downward longwave radiation and air temperature were the two main factors that had the best correlation with lake ice thickness. In a detrending analysis, air temperature, downward longwave radiation, and solar radiation contributed the most to the average thickness variability, with contributions of 42%, 49%, and −48%, respectively, and to the maximum thickness variability, with contributions of 41%, 45%, and −48%, respectively. If the six meteorological factors (air temperature, downward longwave radiation, solar radiation, wind speed, pressure, and specific humidity) are detrending, ice thickness variability will increase 83% on average and 87% at maximum. Specific humidity, wind, and air pressure had a poor correlation with ice thickness. The findings in this study give insights into the long-term evolutionary trajectory of Qinghai Lake ice cover and serve as a point of reference for investigating other lakes in the QTP during cold seasons. [ABSTRACT FROM AUTHOR]

The intermittency of gravity wave potential energy (GWPE) in the upper troposphere and stratosphere was investigated using the Constellation Observing System for Meteorology, Ionosphere, and Climate-2 (COSMIC-2) temperature data over three typical mountains (Tibetan Plateau, Rocky Mountains, and Andes). These typical mountains have high sea level elevations but different land–sea contrast. The probability density function (PDF) of GWPE has the independent variable of GWPE and dependent variable of occurrence probability of GWPE over a region. Our analysis showed that the PDFs of GWPE over these three mountains roughly followed lognormal distributions in all heights and months. But, the key parameters (mean value and standard deviation) of lognormal distribution varied with heights and months. Above each mountain, the two key parameters exhibited similar temporal and spatial distributions. They had the largest values around the tropopause region, smaller values in the lower stratosphere (~20–30 km), and larger values in the upper stratosphere (~35–45 km). The intermittency of GWs is represented as the ratio of the GWPE at 50th percentile to the GWPE at 90th percentile. The weakest intermittency was at ~20–30 km (above the zonal mean winds of zero) over the Tibetan Plateau and Rocky Mountains in all months and over the Andes from November to March, respectively. Generally, the weakest intermittency (~0.4) occurred in the region where the key parameters were the smallest around summer. The key parameters of lognormal distribution were dominated by annual variation over the Andes throughout the height range, 8–50 km. However, the semiannual variations are also significant in the lower stratosphere over the Tibetan Plateau and Rocky Mountains. The seasonal variations in the intermittency were not as obvious as those of the key parameters. The lognormal distributions and the intermittencies derived here provide an observational constraint on the tunable parameters in GW parameterization schemes. [ABSTRACT FROM AUTHOR]

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-surface air temperature (Ta) is a key variable in global climate studies. Global climate models such as ERA5 and CMIP6 predict various parameters at coarse spatial resolution (>9 km). As a result, local phenomena such as the urban heat islands are not reflected in the model's outputs. In this study, we address this limitation by downscaling the resolution of ERA5 (9 km) and CMIP6 (27 km) Ta to 1 km, employing two different machine learning algorithms (XGBoost and Deep Learning). Our models leverage a diverse set of features, including data from satellites (land surface temperature and normalized difference vegetation index), from ERA5 and CMIP6 climate models (e.g., solar and thermal radiation, wind), and from digital elevation models to develop accurate machine learning prediction models. These models were rigorously validated against observations from 98 meteorological stations in the East Mediterranean (Israel) using a standard cross-validation technique as well as a leave-one-group-out on the station ID evaluation methodology to avoid overfitting and dependence on geographic location. We demonstrate the sensitivity of the downscaled Ta to local land cover and topography, which is missing in the climate models. Our results demonstrate impressive accuracy with the Deep Learning-based models, obtaining Root Mean Squared Error (RMSE) values of 0.98 °C (ERA5) and 1.86 °C (CMIP6) for daily Ta and 2.20 °C (ERA5) for hourly Ta. Additionally, we explore the impact of the various input features and offer an extended application for future climate predictions. Finally, we propose an enhanced evaluation framework, which addresses the problem of model overfitting. This work provides practical tools and insights for building and evaluating Ta downscaling models. The code and data are publicly shared online. [ABSTRACT FROM AUTHOR]

Temperature analysis is of special interest in polar areas because temperature is an essential variable in the energy exchange between the Earth's surface and atmosphere. Although land surface temperature (LST) obtained using satellites and air temperature (Ta) have different physical meanings and are measured with different techniques, LST has often been successfully employed to estimate Ta. For this reason, in this work, we estimated Ta from LST MODIS collection 6 (C6) and used other predictor variables. Daily mean Ta was calculated from Spanish State Meteorological Agency (AEMET) stations data on the Livingston and Deception Islands, and from the PERMASNOW project stations on Livingston Island; both islands being part of the South Shetland Islands (SSI) archipelago. In relation to our previous work carried out in the study area with collection 5 (C5) data, we obtained higher R2 values (R2CV = 0.8, in the unique model with Terra daytime data) and lower errors (RMSECV = 2.2 °C, MAECV = 1.6 °C). We corroborated significant improvements in MODIS C6 LST data. We analyzed emissivity as a possible factor of discrepancies between C5 and C6, but we did not find conclusive results, therefore we could not affirm that emissivity is the factor that causes differences between one collection and another. The results obtained with the applied filters indicated that MODIS data can be used to study Ta in the area, as these filters contribute to the reduction of uncertainties in the modeling of Ta from satellites. [ABSTRACT FROM AUTHOR]

The higher the atmosphere is, the larger the deviations in atmospheric temperature and humidity are between the vertical column atmosphere above the cross-section of a satellite instrument and a ray's trajectory from the cross-section to the satellite. In general, satellite instruments that observe using cross-orbit scanning result in the difference between the observed radiance and the simulations using this method becoming incrementally larger and larger as the cross-section moves to the edge of the satellite's orbit. The deviations depend on the distance from the column to the ray trajectory and on the horizontal gradient of variables in the distance. In fact, the horizontal gradient of water vapour is larger than the gradient of temperature in clear scenarios, which could introduce an impact of temperature and water vapour on the simulated radiance of a satellite. In this study, a new method to simulate upgoing and downgoing radiation synchronously was developed, using the observing path tracking method. The conventional vertical initial atmospheric profile (Exp.1) and the profiles along the upgoing and downgoing rays of the satellite's observation (Exp.2) were established, in order to simulate the observed radiance of MWHS-II of FY-3D using global numerical forecasts with resolutions of 15 km and 25 km. The results showed that, for channels in the oxygen and water vapour absorption line on the microwave spectrum, deviations of the two atmospheric profiles were larger at the scan edge (0.01 K) than those at the nadir (0.001 K), and were larger in the upper atmosphere than in the lower atmosphere. The deviation was usually negative in low-latitude regions and was positive in southern high-latitude regions. Such results were obtained in experiments using both the numerical forecast method with 15 km grids and the forecast method with 25 km grids. Deviations were analysed for representative channels at 118 GHz and 183 GHz. Then, the results indicated that bigger deviations between the two experiments were observed in the water vapour absorption line than in the oxygen absorption line in the microwave spectrum. In conclusion, this indicates that, because of the greater horizontal gradient of water vapour, the stronger localisation of water vapour makes the atmospheric profile along the satellite's observing ray have more increments in the simulated radiance at the scan edge, compared to the atmospheric column profile. [ABSTRACT FROM AUTHOR]

The paper evaluates the usability of remote satellite-based and proximal ground-based agrometeorological data sources for precision agriculture and crop production in Croatia. The compared agrometeorological datasets stem from the open-access data sources Copernicus CDS and the Agri4Cast portal, and commercial in situ agrometeorological stations (PinovaMeteo) which monitor environmental parameters relevant to the physiological state of crops. The study compares relevant parameters for 10 different locations in Croatia for three consecutive years (2019, 2020, and 2021) to investigate whether model-based data from ERA5-Land and Agri4Cast are well-correlated with ground measurements from independent in situ stations (PinovaMeteo) for specific agrometeorological parameters (air and soil temperature, and precipitation). Our results indicate the following: both the ERA5-Land and Agri4Cast datasets show mostly strong positive correlations with ground observations for air temperature, modest correlations for soil temperature, but modest or even low correlations for precipitation. Analysis of the residuals indicates higher overall residual values, especially in areas with complex topography and near large bodies of water or the sea, and deviations of residuals that may limit the usability of satellite- and model-based data for decision-making in agriculture. [ABSTRACT FROM AUTHOR]

A passive microwave instrument will be carried by China's geostationary microwave satellite. A microwave hyper-spectral band included by the instrument ranges from 52.6 to 57.3 GHz, and totally has 89 channels in this spectral domain. The design of the hyper-spectral band is described from the aspects of scientific objectives and specifications. The weighting functions for each channel are calculated utilizing radiative transfer simulations under clear sky conditions. Then, the information content as well as the degree of freedom for signal are computed and analyzed to characterize this hyper-spectral sounding for atmospheric temperature profiling. Both the vertical distribution of the weighting functions and the width of retrieval averaging kernels indicate that the hyper-spectral band can provide more denser sampling for atmospheric temperature. The information content for the hyper-spectral band is approximately 46% higher than that of the ATMS-type channel 3 to 15, indicating that hyper-spectral measurement can improve the accuracy of retrieval. The most informative channels mainly locate near 57 GHz, having good consistency with the existing channels. The height range where the retrieval using the hyper-spectral observations is sensitive to the true profile, begins from about 800 to 1 hPa. Some channels can be considered as alternatives to each other since they have very similar information content and weighting functions. These results are expected to provide a valuable reference for future applications of the microwave hyper-spectral measurements. [ABSTRACT FROM AUTHOR]

The interrelation between grassland vegetation greenness and hydrothermal conditions on the Tibetan Plateau demonstrates a significant correlation. However, understanding the spatial patterns and the degree of this correlation, especially in relation to minimum and maximum air temperatures across various vertical gradient zones of the Plateau, necessitates further examination. Utilizing the normalized difference phenology index (NDPI) and considering four distinct hydrothermal conditions (minimum, maximum, mean temperature, and precipitation) during the growing season, an analysis was conducted on the correlation of NDPI with hydrothermal conditions across plateau elevations from 2000 to 2021. Results indicate that the correlation between vegetation greenness and hydrothermal conditions on the Tibetan Plateau grasslands is spatially varied. There is a pronounced negative correlation of greenness to maximum temperature and precipitation in the northeastern plateau, while areas exhibit stronger positive correlations to mean temperature. Additionally, as elevation increases, the positive correlation and sensitivity of alpine grassland vegetation greenness to minimum temperature significantly intensify, contrary to the effects observed with maximum temperature. The correlations between greenness and mean temperature in relation to elevational changes primarily exhibit a unimodal pattern across the Tibetan Plateau. These findings emphasize that the correlation and sensitivity of grassland vegetation greenness to hydrothermal conditions are both elevation-dependent and spatially distinct. [ABSTRACT FROM AUTHOR]

This study aims to explore the roles of multiple gust fronts (i.e., outflow boundaries) during a short-lived extreme rainfall that occurred in the Greater Bay Area of South China in the afternoon of 1 August 2021. Through the use of microbarographs and Doppler weather radars, the research highlights how the interactions of five gust fronts, approaching the region from different directions, have contributed to the high precipitation efficiency and damaging surface winds during the event. The close convergence of these gust fronts funneled unstable air masses into the region of interest, priming the mesoscale convective environment. Some isolated convection initiated before the gust fronts' arrival. Preceding the arrival of these gust fronts, subtle wave-like pressure jumps were identified from the high-frequency (1 Hz) microbarograph observations. The amplitude of the pressure jump is approximately 40 Pa with minimal changes in air temperature. During the early stage of the gust front passages, very high-frequency oscillations in surface pressure are recognized, indicating interaction between the density currents and the low-level troposphere. As suggested through numerical simulations, the subtle pressure jumps are associated with upward displacements of isentropic surfaces aloft, deepening the moist layer and enhancing the lapse rate that are conducive to convective development. The simulated vertical profiles show no evident capping inversion above the dry neutral boundary layer, suggesting that the pressure jumps are likely to be dynamically induced through the collision of the outflows and environmental air masses. The findings of this study suggest the potential application of microbarographs in the nowcasting of the convective development associated with gust fronts. [ABSTRACT FROM AUTHOR]

The study of the response of vegetation phenology in the Qinghai Tibet Plateau to various climatic variables is paramount to unveiling the reaction of alpine ecosystems to worldwide climate alterations. Nonetheless, the lagged and cumulative effects of various climatic variables on vegetation phenology in the Qinghai Tibet Plateau remain unclear. Therefore, based on MODIS NDVI data, we extracted vegetation phenological parameters from 2001 to 2020, including the start of the vegetation growing season (SOS) and the end of the vegetation growing season (EOS), and then analyzed the response mechanisms of vegetation phenology to pre-seasonal air temperature (T), precipitation (P), and daytime and nighttime land surface temperatures (DLST, NLST) in the Qinghai Tibet Plateau on the basis of an investigation of the lag and cumulative effects. The results showed that: (1) the multiyear mean values of the SOS mainly occurred from 120 to 160 days, accounting for 86.17% of the study area, while the multiyear mean values of the EOS were mainly concentrated between 260 and 280 days, accounting for 77.05% of the study area; (2) air temperature (T), precipitation (P), and daytime and nighttime land surface temperatures (DLST, NLST) had different degrees of lagging effects on the SOS and the EOS. Among them, the time lag effect of precipitation on vegetation phenology was more pronounced; (3) different climatic variables had distinct cumulative effects on vegetation phenology. In contrast to the insignificant cumulative effects of temperature and nighttime surface temperature on the SOS and the EOS, the cumulative effects of precipitation and daytime land surface temperature on the SOS were more pronounced than those on the EOS; (4) the SOS and air temperature, precipitation, and NLST were mainly negatively correlated, in which the proportion of the negative correlation between SOS and NLST was up to 68.80%, and SOS and DLST were mainly positively correlated with a positive correlation proportion of 73.27%, EOS and air temperature, precipitation, and NLST were positively correlated with a positive correlation proportion of EOS and precipitation of up to 71.52%, and EOS and DLST were mainly negatively correlated with a negative correlation ratio of 55.87%. [ABSTRACT FROM AUTHOR]

It is essential to investigate the influences of environmental elements on snow cover to understand the mechanism of the snowmelt process. These elements, as influencing factors, have spatial heterogeneity, which results in significant differences and uncertainties in the extent and range of their effects at different scales. However, little research has been conducted on the spatial interaction and mechanisms of these factors at multiple scales. This study selected the Manas River basin in the Tianshan Mountains as the study area. The study period is 2015–2020. The snow cover status index is calculated based on available Landsat8-OLS/TIRS data; influencing factors are collected from multiple datasets. Their relationships are explored using a novel zonal spatial panel model, fully considering the spatial, proximity, and scale effects. The findings are as follows: (1) There is a robust spatial interaction and proximity effect between snowmelt and various factors, and such effects display distinct spatial heterogeneity. The elevation (ELE), slope (SLP), land surface temperature (LST), and normalized digital vegetation index (NDVI) showed significant overall dominant effects on the snow melting process. The influencing factors with apparent proximity effects are LST, ELE, SLP, NDVI, and air temperature (TEMP), and their influence ranges are different. (2) The relative importance and significance rank of dominant influencing factors vary under different partition schemes and scales. As the scale decreases, the significance of terrain- and vegetation-related factors increases, whereas the significance of temperature- and elevation-related factors decreases, and the number of dominant factors also decreases. (3) The influencing factors represent distinct characteristics among each zone at the optimally partitioned scale we defined. The overall influencing pattern demonstrates a characteristic of being globally dictated by elevation and temperature, with local terrain factors, vegetation, and wind speed modifying this pattern. Our study provides practical data support and a theoretical basis for deepening our understanding of the influence mechanism of the snow melting process. [ABSTRACT FROM AUTHOR]

Based on Microwave Radiometer (MWR) observations in Wuhan over the course of 21 months, we compared the temperature and water vapor levels with those from radiosonde (RS) sounding data at 00:00 and 12:00 UTC, and then analyzed the seasonal and diurnal changes of temperature and water vapor levels from the MWR data. The MWR and RS mean temperatures and dew points are roughly consistent with each other below 2 km, whereas above 2 km, the MWR temperature is slightly lower than the RS temperature. The difference in their water vapor densities decreases quickly with height, and the bias of their relative humidities is generally in the range of −15% to 20%. The MWR observations show that in autumn, the surface temperature is 6.8 K lower during precipitation events than during non-precipitation events, indicating that precipitation in autumn is mainly caused by cold air from the north. The relative humidity during precipitation events exceeds 90% from the ground to 5 km, which is obviously larger than during non-precipitation events. During non-precipitation events, the seasonal mean water vapor density at 0–1.0 km shows an approximately linear increase with the mean temperature; however, their diurnal changes are opposite due to the effect of the boundary layer. At 4.5–5.5 km and 8.5–9.5 km, the mean temperature shows a synchronized diurnal evolution, with the maximum value prior to that at 0–1.0 km, indicating the strong influence of the air–land interaction on the temperature near the ground. Hence, this study is helpful for deepening our understanding of temperature and humidity variabilities over Wuhan. [ABSTRACT FROM AUTHOR]

Climatic studies of agricultural regions normally use gauge-based air temperature datasets, which are produced with interpolation methods. The informative quality of these datasets varies depending on the density of the weather stations in a particular region. A way to overcome this limitation is to use the land surface temperature calculated from satellite imagery. To show this, the MODIS land surface temperature was compared with the PTHRES gridded dataset for air temperature in the Douro Demarcated Region (Portugal) between the years 2002 and 2020. The MODIS land surface temperature was able to detect a more pronounced maritime–continental gradient, a higher lapse rate, and thermal inversions in valley areas in winter. This information could prove to be crucial for farmers looking to adapt their practices and crops to extreme events, such as heat waves or heavy frost. However, the use of land surface temperature in climate studies should consider the differences in air temperature, which, on some occasions and locations, can be up to ten degrees in the summer. [ABSTRACT FROM AUTHOR]

Fengyun-4B (FY-4B) is the first operational satellite from China's latest generation of geostationary meteorological satellites. It is equipped with the Geostationary Interferometric Infrared Sounder (GIIRS), which is able to obtain highly accurate atmospheric temperature and humidity profiles through hyperspectral detection in long- and mid-wave infrared spectral bands. In this study, the accuracy of the FY-4B/GIIRS temperature and humidity profile retrievals over two months is evaluated using radiosonde observations and ERA5 reanalysis data. We go a step further to investigate the impact of the satellite retrievals on assimilation and forecasts for Typhoons Chaba and Ma-on in 2022. Results reveal that the root-mean-square difference (RMSD) for the FY-4B/GIIRS temperature and humidity profile retrievals were within 1 K and 1.5 g/kg, respectively, demonstrating high overall accuracy. Moreover, assimilating temperature and humidity profiles from FY-4B/GIIRS positively impacts model analysis and prediction, improving typhoon track and intensity forecasts. Additionally, improvements have been discovered in predicting precipitation, particularly with high-magnitude rainfall events. [ABSTRACT FROM AUTHOR]

Spring snow cover variability over Northeast China (NEC) has a profound influence on the local grain yield and even the food security of the country, but its drivers remain unclear. In the present study, we investigated the spatiotemporal features and the underlying mechanisms of spring snow cover variability over NEC during 1983–2018 based on the satellite-derived snow cover data and atmospheric reanalysis products. The empirical orthogonal function (EOF) analysis showed that the first EOF mode (EOF1) explains about 50% of the total variances and characterizes a coherent snow cover variability pattern over NEC. Further analyses suggested that the formation of the EOF1 mode is jointly affected by the atmospheric internal variability and the sea surface temperature (SST) anomaly at the interannual timescale. Specifically, following a negative phase of the atmospheric teleconnection of the Polar–Eurasian pattern, a prominent cyclonic circulation appears over NEC, which increases the snowfall over the east of NEC by enhancing the water vapor transport and decreases the air temperature through reducing the solar radiation and intensifying the cold advection. As a result, the snow cover has increased over NEC. Additionally, the tripole structure of the North Atlantic spring SST anomaly could excite a wave-train-type anomalous circulation propagating to NEC that further regulates the snow cover variability by altering the atmospheric dynamic and thermodynamic conditions and the resultant air temperature and snowfall. Our results have important implications on the understanding of the spring snow cover anomaly over NEC and the formulation of the local agricultural production plan. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) is a fundamental variable of environmental monitoring and surface equilibrium. Although the HJ-1B infrared scanner (IRS) has accumulated many observations, further application of HJ-1B/IRS is limited by the lack of LST products. This study refined the ERA5 atmospheric profile database, instead of the widely used traditional TIGR atmospheric profile database, and simulated the coefficients of the generalized single-channel (GSCs) algorithms to improve LST retrieval. GSCs can be divided into the GSCw and GSCwT algorithms, depending on whether the input is atmospheric water vapor content (WVC) or in situ near-surface air temperature and WVC. Land surface emissivity (LSE) was obtained from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Dataset (GED) and vegetation/snow cover products. Then, the retrieved LSTs were evaluated using the LSTs from the RTE algorithm, TIGRw/TIGRwT profiles, and in situ near-surface air temperature from the HiWATER experiment in China from 2012 to 2014. The bias (root mean square error (RMSE)) values are displayed as ERA5wT < RTE < ERA5w < TIGRwT < TIGRw. The accuracy of ERA5wT, with a bias (RMSE) of 0.02 K (2.30 K), is higher than that of RTE, with a bias (RMSE) of 0.74 K (2.47 K). The accuracy of RTE is preferable to that of ERA5w, with a bias (RMSE) of 0.89 K (2.48 K), followed by TIGRwT, with a bias (RMSE) of −1.18 K (2.50 K), and then, TIGRw, with a bias (RMSE) of 1.60 K (2.77 K). In summary, the accuracy of LST obtained by GSC from the refined ERA5 atmospheric profiles is higher than that obtained from the TIGR profiles. The accuracy of LST obtained by GSCwT is greater than that obtained by GSCw. The accuracy of LST obtained using in situ near-surface air temperature is higher than that obtained using ERA5 air temperature. The accuracy of LSEASTER is slightly better than that of LSEMOD21. The aforementioned conclusions can provide scientific support to generate HJ-1B/IRS LST products. [ABSTRACT FROM AUTHOR]

Deep learning methods can achieve a finer refinement required for downscaling meteorological elements, but their performance in terms of bias still lags behind physical methods. This paper proposes a statistical downscaling network based on Light-CLDASSD that utilizes a Shuffle–nonlinear-activation-free block (SNBlock) and Swin cross-attention mechanism (SCAM), and is named SNCA-CLDASSD, for the China Meteorological Administration Land Data Assimilation System (CLDAS). This method aims to achieve a more accurate spatial downscaling of a temperature product from 0.05° to 0.01° for the CLDAS. To better utilize the digital elevation model (DEM) for reconstructing the spatial texture of the temperature field, a module named SCAM is introduced, which can activate more input pixels and enable the network to correct and merge the extracted feature maps with DEM information. We chose 90% of the CLDAS temperature data with DEM and station observation data from 2016 to 2020 (excluding 2018) as the training set, 10% as the verification set, and chose the data in 2018 as the test set. We validated the effectiveness of each module through comparative experiments and obtained the best-performing model. Then, we compared it with traditional interpolation methods and state-of-the-art deep learning super-resolution algorithms. We evaluated the experimental results with HRCLDAS, national stations, and regional stations, and the results show that our improved model performs optimally compared to other methods (RMSE of 0.71 °C/0.12 °C/0.72 °C, BIAS of −0.02 °C/0.02 °C/0.002 °C), with the most noticeable improvement in mountainous regions, followed by plains. SNCA-CLDASSDexhibits the most stable performance in intraday hourly bias at temperature under the conditions of improved feature extraction capability in the SNBlock and a better utilization of the DEM by the SCAM. Due to the replacement of the upsampling method from sub pixels to CARAFE, it effectively suppresses the checkerboard effect and shows better robustness than other models. Our approach extends the downscaling model for CLDAS data products and significantly improves performance in this task by enhancing the model's feature extraction and fusion capabilities and improving upsampling methods. It offers a more profound exploration of historical high-resolution temperature estimation and can be migrated to the downscaling of other meteorological elements. [ABSTRACT FROM AUTHOR]

Deepening the understanding of diurnal characteristics and driving mechanisms of surface urban heat islands (SUHIs) across different local climate zones (LCZs) and time scales is of great significance for guiding urban surface heat mitigation. However, a comprehensive investigation of SUHIs from the diurnal, local, multi-seasonal, and interactive perspectives remains a large gap. Here, we generalized major diurnal patterns of LCZ-based SUHI intensities (SUHIIs) throughout 2020 over the urban area of Beijing, China, based on diurnal temperature cycle modeling, block-level LCZ mapping, and hierarchical clustering. A geographical detector was then employed to explore the individual and interactive impacts of 10 morphological, socioeconomic, and meteorological factors on the multi-temporal spatial differentiations of SUHIIs. Results indicate six prevalent diurnal SUHII patterns with distinct features among built LCZ types. LCZs 4 and 5 (open high- and mid-rise buildings) predominantly display patterns one, two, and five, characterized by an afternoon increase and persistently higher values during the night. Conversely, LCZs 6, 8, and 9 (open, large, and sparsely built low-rise buildings) mainly exhibit patterns three, four, and six, with a decrease in SUHII during the afternoon and lower intensities at night. The maximum/minimum SUHIIs occur in the afternoon–evening/morning for patterns 1–3 but in the morning/afternoon for patterns 5–6. In all four seasons, the enhanced vegetation index (EVI) and gross domestic product (GDP) have the top two individual effects for daytime spatial differentiations of SUHIIs, while the air temperature (TEM) has the largest explanatory power for nighttime differentiations of SUHIIs. All factor interactions are categorized as two-factor or nonlinear enhancements, where nighttime interactions exhibit notably greater explanatory powers than daytime ones. The strongest interactions are EVI ∩ GDP (q = 0.80) during the day and TEM ∩ EVI (q = 0.86) at night. The findings of this study contribute to an improved interpretation of the diurnal continuous dynamics of local SUHIIs in response to various environmental conditions. [ABSTRACT FROM AUTHOR]

Accurate, seamless, and long-term land surface temperature (LST) data sets are crucial for investigating climate change and agriculture production. However, factors like cloud contamination have led to invalid values in the LST product, which has restricted the application of the LST dataset. Therefore, the reconstruction of LST products is challenging, and it is attracting widespread attention. This study compared the performance of different algorithms (XGBoost, GBDT, RF, POLY, MLR) and different training sets (using only good-quality pixels or using both good-quality and other-quality pixels) in the estimation of missing pixels in the LST data, obtaining a seamless daily 1 km LST dataset of MODIS Terra-day, Aqua-day, Terra-night, and Aqua-night data for Zhejiang Province and its surrounding areas from 2000 to 2022. The results demonstrated that the performance of machine-learning models is significantly better than that of linear models, and among the five models, XGBoost performed the best, with an RMSE of less than 1 °C. The Wilcoxon test between the reconstructed LST and the true LST values revealed that including both good-quality and other-quality pixels for reconstruction resulted in a 33% increase in the number of days with non-significant differences compared with using only good-quality pixels. Moreover, the reconstructed nighttime LST has a lower RMSE compared with the reconstructed daytime LST, and the RMSE of the reconstructed LST on the Terra satellite is lower than the RMSE of the reconstructed LST on the Aqua satellite. The RMSEs for the reconstructed LSTs are 0.50 °C, 0.61 °C, 0.36 °C, and 0.39 °C, corresponding to Terra-day, Aqua-day, Terra-night, and Aqua-night for images with coverage reaching 70%, 0.65 °C, 0.83 °C, 0.49 °C, respectively, and 0.52 °C for images with coverage less than 70%. The accuracy of the reconstructed LSTs using our proposed framework outperforms the existing reconstruction methods. The 1 km daily seamless LST products can be applied in various fields, such as air temperature estimation, climate change, urban heat island, and crop temperature stress monitoring. [ABSTRACT FROM AUTHOR]

In urban thermal environment research (UTE), urban shadows formed by buildings and trees contribute to significant variations in thermal conditions, particularly during the mid-day period. This study investigated the multi-scale effects of indicators, including urban shadows, on UTE, focusing specifically on the mid-day hours. It integrated field temperature measurements and drone aerial data from multiple city blocks. Considering both urban shadows and direct solar radiation, a linear mixed-effects model was employed to study the multi-scale effects of urban morphological indicators. Results showed that: (1) UTE is a multi-scale, multi-factor phenomenon, with thermal effects manifesting at specific scales. Under shadow conditions, smaller scales (10–20 m) of landscape heterogeneity and larger scales (300–400 m) of landscape consistency better explained temperature variations mid-day. Conversely, under direct sunlight, temperature was primarily influenced by larger scales (150–300 m). (2) Trees significantly reduced temperature; 100% tree canopy cover within a 10-m radius reduced air temperatures by approximately 2 °C mid-day. However, there is no significant correlation between temperature and green spaces. (3) Building area and height were significantly correlated with temperature. Specifically, an increase in building area beyond 150 m, especially within a 300-m radius, leads to higher temperatures. Conversely, building height within a 10–20 m range exhibits significant cooling effects. These findings provide crucial reference data for micro-scale UTE investigations during mid-day hours and offer new strategies for urban planning and design. [ABSTRACT FROM AUTHOR]

The air temperature changes in the Palu MW7.5 earthquake in Indonesia on 28 September 2018 were analyzed, based on the additive tectonic stress caused by celestial tidal-generating forces (ATSCTF) and air temperature data from the National Center for Environmental Prediction (NCEP). This paper explored the variation characteristics of three-dimensional stratified of air temperature caused by seismic activity and the coupling relationship between air temperature changes and the tidal force. The background information for air temperature calculation was obtained from the tidal force changes, and the air temperature increment method was used to study the temperature evolution process of different periods in the study area. The results found that the tidal force acting on the critical state earthquake faults may be an important external factor inducing earthquakes, and there was indeed a significant air temperature increase anomaly during the Palu MW7.5 earthquake. The paper also summarized the abnormal characteristics of air temperature caused by seismic activity: the air temperature closer to the land's surface has greater anomaly amplitude and a wider anomaly area than that of the upper air. [ABSTRACT FROM AUTHOR]

Climate and topography are pivotal factors influencing snow cover variation, highlighting the significance of investigating the altitudinal response of snow cover to climate change. This study adopted a new MODIS snow cover extent product over China, reanalysis climate data, and digital elevation model (DEM) data to analyze the variation characteristics of snow cover frequency (SCF) and climatic factors with elevation in the Keriya River Basin (KRB) during the hydrological years from 2000 to 2020. The Partial Least Squares Regression (PLSR) method was utilized to explore the elevation-based relationships between SCF and climatic factors. Our findings can be summarized as follows: (1) The SCF exhibited an "increasing–decreasing–increasing–decreasing" pattern intra-annually, with insignificant monthly inter-annual variations. Only November, January, April, and May demonstrated upward trends, whereas October and December remained relatively stable, and other months exhibited declines. (2) Vertical variations in SCF and climatic factors revealed fluctuating upward trends in SCF and wind speed. On the other hand, the air temperature consistently decreased at a lapse rate ranging from 0.60 to 0.85 °C/100 m. Precipitation demonstrated "rising–falling" or "rapidly rising–slowly rising" patterns, bounded by 3821 m (range 3474–4576 m). (3) A new decision scheme, which took into account the alteration of the primary SCF controlling factors and shifts between positive and negative impacts caused by these factors, was used to determine five threshold elevation zones: 2585 m (range 2426–2723 m), 3447 m (range 3125–3774 m), 4251 m (range 4126–4375 m), 5256 m (range 4975–5524 m), and 5992 m (range 5874–6425 m). These threshold elevation zones were evident in spring, with four of these appearing in autumn (excluding 4251 m) and summer (excluding 2585 m). Only two threshold elevation zones were observed in winter with elevation values of 3447 m and 5992 m, respectively. Our findings are crucial for a deeper understanding of snow cover variation patterns at different elevations and offer essential insights for the responsible management of regional water resources. [ABSTRACT FROM AUTHOR]

Yao and Guan [[26]] derive atmospheric temperature and humidity profiles under all sky conditions from GIIRS-observed brightness temperatures using three deep machine learning algorithms. This Topic covers a wide range of topics, including atmospheric dynamics and physics, synoptic weather, climate variability, climate change, and remote sensing observations for weather and climate studies. Due to noise interference, TB observations reflecting rain, clouds, tropical cyclone warm core, temperature and water vapor distributions are not readily distinguishable, especial in channels above the middle troposphere (channels 4-7 and 24), whose dynamic ranges of TB are smaller than low tropospheric channels 1-3. [[9]] is an observational study that quantifies the change in amplitude of synoptic-scale surface temperature variability across the U.S., finding a surge in the surface temperature variability in the Rockies and surrounding regions but a reduction over low land regions. [Extracted from the article]

Irrigation has excellent potential for altering surface characteristics and the local climate. Although studies using site observations or remote sensing data have demonstrated an irrigation cooling effect (ICE) on the air temperature (Tem) and land surface temperature (LST), it is difficult to eliminate other stress factors due to different backgrounds. We characterized the irrigation effect as the differences (Δ) of LST and DCT (DCT = LST − Tem) between irrigated and adjacent non-irrigated areas. An improved method was proposed to detect it over the North China Plain (NCP) based on satellite observations. We also investigated the effects of irrigation on Tem, precipitation, NDVI, and ET, and explored the relationships between them. The results show that irrigation induced a decrease in the daytime/nighttime LST and DCT (−0.13/−0.09 and −0.14/−0.07 °C yr−1), Tem (−0.023 °C in spring), and precipitation (−1.461 mm yr−1), and an increase in NDVI (0.03 in spring) and ET (0.289 mm yr−1) across the NCP. The effect on nighttime LST and NDVI increased by 0.04 °C 10 yr−1 and 0.003 10 yr−1, and that on ET weakened by 0.23 mm 10 yr−1 during 2000–2015. The ICE on the LST had evident spatiotemporal heterogeneity, which was greater in the daytime, in the spring, and in the northern area of the NCP (dry–hot conditions). The daytime ICE in the NCP and northern NCP was 0.37 and 0.50 °C during spring, respectively, with the strongest ICE of 0.60 °C in Henan; however, the ICE was less evident (<0.1 °C) in the southern NCP throughout the year. The ΔNDVI, ΔET, and ΔTem were the main factors driving ICE, explaining approximatively 22%, 45%, and 25% of the daytime ICE, respectively. For every unit of these measures that was increased, the daytime ICE increased by about 7.3, 4.6, and 1.5 °C, respectively. This study highlights the broad irrigation effect on LST, ET, NDVI, and the climate, and provides important information for predicting climate change in the future. The improved method is more suitable for regions with uneven terrain and a varying climate. [ABSTRACT FROM AUTHOR]

The analysis of the near-surface air temperature is vital for many applications such as urban heat islands and climate change studies. In particular, extreme weather events are typically localized and so should the corresponding adaptation measures. However, climate scientists are often confronted with the difficulty of providing reliable predictions at high spatial resolutions in the order of 1 km. We propose to train a convolutional neural network model to emulate the hourly high-resolution near-surface air temperature field simulated by the Weather Research and Forecasting (WRF) software over a period of 18 months. The model is driven by current and past lags of coarse SEVIRI land surface temperature fields. This nowcasting application implements a downscaling of the spatial resolution of the input by about a factor of four, while establishing a correlation between current and past land surface temperature maps and the current near-surface air temperature field. The U-Net variant that is proposed in this study uses regularization to prevent over-fitting and implements a novel space-time approach, where multiple time steps are fed into the model through 3D convolution layers. Besides LST, the model also uses urban density as additional static input to be able to predict the temperature more accurately in urban areas and to improve the generalizability of the trained model. The performance of our U-Net model is assessed via comparison to an MLR benchmark (ridge regularization). The model is shown to be superior on all performance metrics. It achieves a mean absolute error of 1.36 ° C versus 1.49 ° C for benchmark (a 9% relative improvement) and a root mean square error of 1.77 ° C versus 1.91 ° C for benchmark (a 7% relative improvement). The absolute error of the model is less than 2 ° C for 77% of the prediction points versus 72% for the benchmark (a 7% relative improvement). The improvement over the benchmark is even more significant during extreme hot periods. We demonstrate the generalizability of the approach by testing the trained model on unseen spatial domains. [ABSTRACT FROM AUTHOR]

Soil moisture over the Tibetan Plateau (TP) can affect hydrological cycles on local and remote scales through land–atmosphere interactions. However, TP long-term surface soil moisture characteristics and their response to climate change are still unclear. In this study, we firstly evaluate two satellite-based products—SSM/I (the Special Sensor Microwave Imagers) and ECV COMBINED (the Essential Climate Variable combined)—and three reanalysis products—ERA5-Land (the fifth generation of the land component of the European Centre for Medium-Range Weather Forecasts atmospheric reanalysis), MERRA2 (the second version of Modern-Era Retrospective Analysis for Research and Applications), and GLDAS Noah (the Noah land surface model driven by Global Land Data Assimilation System)—against two in situ observation networks. SSM/I and GLDAS Noah outperform the other soil moisture products, followed by MERRA2 and ECV COMBINED, and ERA5-Land has a certain degree of uncertainty in evaluating TP surface soil moisture. Analysis of long-term soil moisture characteristics during 1988–2008 shows that annual and seasonal mean soil moisture have similar spatial distributions of soil moisture decreasing from southeast to northwest. Additionally, a significant increasing trend of soil moisture is found in most of the TP region. With a non-linear machine learning method, we quantify the contribution of each climatic variable to warm-season soil moisture. It indicates that precipitation dominates soil moisture changes rather than air temperature. Pixel-wise partial correlation coefficients further show that there are significant positive correlations between precipitation and soil moisture over most of the TP region. The results of this study will help to understand the role of TP soil moisture in land–atmosphere coupling and hydrological cycles under climate change. [ABSTRACT FROM AUTHOR]

A number of previous studies have contributed to a better understanding of the thermal impacts of dam-related reservoirs on stream temperature, but very few studies have focused on air temperature, especially at the catchment scale. In addition, due to the lack of quantitative analysis, the identification of the effects of water impoundment on regional air temperature is still lacking. We investigated the impacts of reservoirs on the regional air temperature changes before and after two large dam constructions in the lower Jinsha River located in southwest China, by using a 40 year record of reanalysis data at 90 m resolutions. Furthermore, the long short-term memory (LSTM) model was also employed to construct an impoundment effect on the temperature (IET) index. Research results indicate that compared to the pre-impoundment period (1980–2012), the variations in the air temperature at the catchment scale were reduced during the post-impoundment period (2013–2019). The annual maximum air temperature decreased by 0.4 °C relative to the natural regimes. In contrast, the cumulative effects of dam-related reservoirs increased the annual mean and minimum air temperature by 0.1 °C and 1.0 °C, respectively. Warming effects prevailed during the dry season and in the regions with high elevations, while cooling effects dominated within a 4 km buffer of the reservoirs. Therefore, this study offers important insights about the impacts of anthropogenic impoundments on air temperature changes, which could be useful for policymakers to have a more informed and profound understanding of local climate changes in dammed areas. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau (TP) plays a crucial role in the climate change of China as well as global climate change. It is therefore of great practical significance to study vegetation and its dynamic changes for regional ecological protection. The combination of a dry climate and notable temperature disparities can lead to intricate effects on the region's vegetation. However, there are few studies exploring the complex effects of diurnal temperature variations on vegetation growth that differ from the effects of mean temperature on the TP, especially under different frozen ground types. Based on the long-time series maximum temperature (Tmax), minimum temperature (Tmin), and Normalized Difference Vegetation Index (NDVI) of the TP, we conducted a comparative study of the warming effects on plant growth under different frozen types. The results exhibit that it warms up faster at night (0.223 °C de−1; p < 0.01) than during the day (0.06 °C de−1; p < 0.01), resulting in a significant decrease in the temperature difference between day and night (−0.078 °C de−1; p < 0.01) in the past few decades. The principal finding of this paper is that Tmin is the dominant temperature indicator for vegetation growth on the TP, which dominates 63.3% of the area for NDVI and 61.4% of the area for GPP, respectively. The results further identify a stronger correlation between air temperature and vegetation growth in seasonal frozen grounds (R = 0.68, p < 0.01) and permafrost regions (R = 0.7, p < 0.01) compared to unfrozen grounds (R = 0.58, p < 0.01). Moreover, the physiological mechanism underlying the asymmetric influence of Tmin and Tmax on vegetation growth is further elucidated in this study. Given that future climate changes are expected to exacerbate these changes, it is imperative to explore additional avenues in pursuit of potential mechanisms that can offer adaptive strategies for safeguarding the ecology of the TP. [ABSTRACT FROM AUTHOR]

The Tibet Plateau (TP) and the Arctic are typically cold regions with abundant snow cover, which plays a key role in land surface processes. Knowledge of variations in snow density is essential for understanding hydrology, ecology, and snow cover feedback. Here, we utilized extensive measurements recorded by 697 ground-based snow sites during 1950–2019 to identify the spatio-temporal characteristics of snow density in these two regions. We examined the spatial heterogeneity of snow density for different snow classes, which are from a global seasonal snow cover classification system, with each class determined from air temperature, precipitation, and wind speed climatologies. We also investigated possible mechanisms driving observed snow density differences. The long-term mean snow density in the Arctic was 1.6 times that of the TP. Slight differences were noted in the monthly TP snow densities, with values ranging from 122 ± 29 to 158 ± 52 kg/m3. In the Arctic, however, a clear increasing trend was shown from October to June, particularly with a rate of 30.3 kg/m3 per month from March to June. For the same snow class, the average snow density in the Arctic was higher than that in the TP. The Arctic was characterized mainly by a longer snowfall duration and deeper snow cover, with some areas showing perennial snow cover. In contrast, the TP was dominated by seasonal snow cover that was shallower and warmer, with less (more) snowfall in winter (spring). The results will be helpful for future simulations of snow cover changes and land interactions at high latitudes and altitudes. [ABSTRACT FROM AUTHOR]

Forecasts on transportation meteorology, such as pavement temperature, are becoming increasingly important in the face of global warming and frequent disruptions from extreme weather and climate events. In this study, we propose a pavement temperature forecast model based on stepwise regression—model output statistics (SRMOS) at the short-term timescale, using highways in Jiangsu, China, as examples. Experiments demonstrate that the SRMOS model effectively calibrates against the benchmark of the linear regression model based on surface air temperature (LRT). The SRMOS model shows a reduction in mean absolute errors by 0.7–1.6 °C, with larger magnitudes observed for larger biases in the LRT forecasts. Both forecasts exhibit higher accuracy in predicting minimum nighttime temperatures compared to maximum daytime temperatures. Additionally, it overall shows increasing biases from the north to the south, and the SRMOS superiority is greater over the south with larger initial LRT biases. Predictor importance analysis indicates that temperature, moisture, and larger-scale background are basically the key predictors in the SRMOS model for pavement temperature forecasts, of which the air temperature is the most crucial factor in the model's construction. Although larger-scale circulation backgrounds are generally characterized by relatively low importance, their significance increases with longer lead times. The presented results demonstrate the considerable skill of the SRMOS model in predicting pavement temperatures, highlighting its potential in disaster prevention for extreme transportation meteorology events. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Air temperature (Ta) is essential for studying surface processes and human activities, particularly agricultural cultivation, which is strongly influenced by temperature. Remote sensing techniques that integrate multi-source data can estimate Ta with a high degree of accuracy, overcoming the shortcomings of traditional measurements due to spatial heterogeneity. Based on in situ measurements in Guangdong Province from 2012 to 2018, this study applied three machine learning (ML) models and fused multi-source datasets to evaluate the performance of four data combinations in Ta estimation. Correlations of covariates were compared, focusing on rice planting areas (RA). The results showed that (1) The fusion of multi-source data improved the accuracy of model estimations, where the best performance was achieved by the random forest (RF) model combined with the ERA5 combination, with the highest R2 reaching 0.956, the MAE value of 0.996 °C, and the RMSE of 1.365 °C; (2) total precipitation (TP), wind speed (WD), normalized difference vegetation index (NDVI), and land surface temperature (LST) were significant covariates for long-term Ta estimations; (3) Rice planting improved the model performance in estimating Ta, and model accuracy decreased during the crop rotation in summer. This study provides a reference for the selection of temperature estimation models and covariate datasets. It offers a case for subsequent ML studies on remote sensing of temperatures over agricultural areas and the impact of agricultural cultivation on global warming. [ABSTRACT FROM AUTHOR]

Measurement uncertainty is an extremely important parameter for characterizing the quality of measurement results. In order to measure the reliability of atmospheric temperature detection, the uncertainty needs to be evaluated. In this paper, based on the measurement models originating from the Chanin-Hauchecorne (CH) method, the atmospheric temperature uncertainty was evaluated using the Guide to the Expression of Uncertainty in Measurement (GUM) and the Monte Carlo Method (MCM) by considering the ancillary temperature uncertainty and the detection noise as the major uncertainty sources. For the first time, the GUM atmospheric temperature uncertainty framework was comprehensively and quantitatively validated by MCM following the instructions of JCGM 101: 2008 GUM Supplement 1. The results show that the GUM method is reliable when discarding the data in the range of 10–15 km below the reference altitude. Compared with MCM, the GUM method is recommended to evaluate the atmospheric temperature uncertainty of Rayleigh lidar detection in terms of operability, reliability, and calculation efficiency. [ABSTRACT FROM AUTHOR]

Accurate high-resolution atmospheric temperature profiles are essential for precisely characterizing the evolution of the atmosphere and developing numerical forecasts. Atmospheric datasets, such as ERA5 (the fifth-generation ECMWF Reanalysis) and MERRA-2 (the Modern-Era Retrospective Analysis for Research and Applications, Version 2), provide global and continuous temperature profiles, with fine vertical distribution and horizontal resolution. RAOB (Radiosonde Observation) sounding data have high confidence and representativeness and are usually used for data accuracy verification. Due to the difficulty of updating existing products, and the scarcity of research on mesospheric temperature profiles, this work maximizes the high observation accuracy of RAOB data, combines the benefits of ERA5's horizontal resolution and MERRA-2's vertical distribution, and employs the optimal interpolation method to combine the data, in order to produce a fused result with high spatial resolution. After converting all of the data to the same spatial distribution, the optimal interpolation method was used to combine the two datasets from separate places and different pressure layers in order to produce the fused results, which had a vertical distribution of 45 layers and a spatial resolution of 0.25°. The fused data's RMSE and MAE were 6.0 K and 5.0 K lower than those of the MERRA-2 temperature profile data, respectively, and 0.3 K and 0.4 K lower than those of the ERA5 temperature profile data, respectively. The validation, using data from 2019, showed that the fused data exhibits better correlation and data accuracy than the other two datasets, which demonstrated that the fused algorithm can potentially be used to generate reliable datasets for future meteorological research. [ABSTRACT FROM AUTHOR]

With the development of spectrum observation technology, solar-induced chlorophyll fluorescence (SIF)—an effective substitute for photosynthesis—has been widely used to monitor crop stress, vegetation phenology and ecosystem productivity. The relationship between fluorescence and photosynthesis is complicated because they are sensitive to environmental changes. Understanding the response of SIF to environmental factors is of great significance for clarifying the variation dynamic and relationship between SIF and photosynthesis under different conditions. In this study, the canopy SIF and the environmental factors of a Quercus variabilis BI. plantation were observed simultaneously, and the response of SIF to environmental factors at a daily scale and at a half-hour scale was analyzed. The results showed that SIF had obvious seasonal and diurnal dynamics and was mainly driven by photosynthetically active radiation (PAR). The influence of PAR, air temperature (Ta), vapor pressure deficit (VPD), soil moisture (SM) and wind speed (Ws) on SIF varied with the lapse of the growing season. After eliminating the covariant effect of PAR on the Ta and VPD during the whole growing season, the relationship between VPD and SIF was found to be negative, and the effect of Ta on SIF disappeared. This study enriched the ground observation dataset and provided support for understanding the variations in the relationship between SIF and photosynthesis under different conditions. [ABSTRACT FROM AUTHOR]

This study investigated the actual evapotranspiration (AET) and potential evapotranspiration (PET) seasonality, trends, abrupt changes, and driving mechanisms with global sea surface temperature (SST) and atmospheric circulation patterns over Equatorial Africa (EQA) during 1980–2020. The spatiotemporal characteristics of mean ET were computed based on a 40-year average at annual and seasonal scales. The Mann-Kendall statistical test, the Sen slope test, and the Bayesian test were used to analyze trends and detect abrupt changes. The results showed that the mean annual PET (AET) for 1980–2020 was 110 (70) mm. Seasonal mean PET (AET) values were 112 (72) in summer, 110 (85) in autumn, 109 (84) in winter, and 110 (58) in spring. The MK test showed an increasing (decreasing) rate, and the Sen slope identified upward (downward) at a rate of 0.35 (0.05) mm yr−10. The PET and AET abrupt change points were observed to happen in 1995 and 2000. Both dry and wet regions showed observed weak (strong) correlation coefficient values of 0.3 (0.8) between PET/AET and climate factors, but significant spatiotemporal differences existed. Generally, air temperature, soil moisture, and relative humidity best explain ET dynamics rather than precipitation and wind speed. The regional atmospheric circulation patterns are directly linked to ET but vary significantly in space and time. From a policy perspective, these findings may have implications for future water resource management. [ABSTRACT FROM AUTHOR]

The Essential Climate Variable (ECV) Permafrost is currently undergoing strong changes due to rising ground and air temperatures. Surface movement, forming characteristic landforms such as rock glaciers, is one key indicator for mountain permafrost. Monitoring this movement can indicate ongoing changes in permafrost; therefore, rock glacier velocity (RGV) has recently been added as an ECV product. Despite the increased understanding of rock glacier dynamics in recent years, most observations are either limited in terms of the spatial coverage or temporal resolution. According to recent studies, Sentinel-1 (C-band) Differential SAR Interferometry (DInSAR) has potential for monitoring RGVs at high spatial and temporal resolutions. However, the suitability of DInSAR for the detection of heterogeneous small-scale spatial patterns of rock glacier velocities was never at the center of these studies. We address this shortcoming by generating and analyzing Sentinel-1 DInSAR time series over five years to detect small-scale displacement patterns of five high alpine permafrost environments located in the Central European Alps on a weekly basis at a range of a few millimeters. Our approach is based on a semi-automated procedure using open-source programs (SNAP, pyrate) and provides East-West displacement and elevation change with a ground sampling distance of 5 m. Comparison with annual movement derived from orthophotos and unpiloted aerial vehicle (UAV) data shows that DInSAR covers about one third of the total movement, which represents the proportion of the year suited for DInSAR, and shows good spatial agreement (Pearson R: 0.42–0.74, RMSE: 4.7–11.6 cm/a) except for areas with phase unwrapping errors. Moreover, the DInSAR time series unveils spatio-temporal variations and distinct seasonal movement dynamics related to different drivers and processes as well as internal structures. Combining our approach with in situ observations could help to achieve a more holistic understanding of rock glacier dynamics and to assess the future evolution of permafrost under changing climatic conditions. [ABSTRACT FROM AUTHOR]

Desertification is a global eco-environmental hazard exacerbated by environmental and anthropogenic factors. However, comprehensive quantification of each driving factor's relative impact poses significant challenges and remains poorly understood. The present research applied a GIS-based and geographic detector model to quantitatively analyze interactive effects between environmental and anthropogenic factors on desertification in the Shiyang River Basin. A MODIS-based aridity index was used as a dependent variable, while elevation, near-surface air temperature, precipitation, wind velocity, land cover change, soil salinity, road buffers, waterway buffers, and soil types were independent variables for the GeoDetector model. A trend analysis revealed increased aridity in the central parts of the middle reach and most parts of the Minqin oasis and a significant decrease in some regions where ecological rehabilitation projects are underway. The GeoDetector model yielded a power determinant (q) ranging from 0.004 to 0.270, revealing elevation and soil types as the region's highest contributing factors to desertification. Precipitation, soil salinity, waterway buffer, and wind velocity contributed moderately, while near-surface air temperature, road buffer, and land cover dynamics exhibited a lower impact. In addition, the interaction between driving factors often resulted in mutual or non-linear enhancements, thus aggravating desertification impacts. The prominent linear and mutual enhancement occurred between elevation and soil salinity and between elevation and precipitation. On the other hand, the results exhibited a non-linear enhancement among diverse variables, namely, near-surface air temperature and elevation, soil types and precipitation, and land cover dynamics and soil types, as well as between wind velocity and land cover dynamics. These findings suggest that environmental factors are the primary drivers of desertification and highlight the region's need for sustainable policy interventions. [ABSTRACT FROM AUTHOR]

The atmospheric temperature profiles (ATPs) retrieved through the geostationary Interferometric Infrared Sounder (GIIRS) onboard the FY-4A satellite (GIIRS/FY-4A) can effectively fill the gap of the scarce conventional sounding data in the Taklimakan Desert (TD), the second largest desert in the world, with an area of 330,000 square kilometers. In this study, we take the experimental radiosonde observations (RAOB) from one RAOB station in the hinterland of TD and seven conventional radiosondes in the oasis region around the desert as the true values and analyze the bias distribution characteristics of GIIRS/FY-4A ATPs with quality control (QC) flags 0 or 1 for this region. In addition, a bias comparison is made with GIIRS/FY-4A ATPs, and the fifth generation ECMWF atmospheric reanalysis of the global climate (ERA5) ATPs. The results show that (1) Missing measurements in GIIRS/FY-4A ATPs are the most frequent in the near-surface layer, accounting for more than 80% of all the retrieved grid points. The averaged total proportion of GIIRS/FY-4A ATPs with QC marks 0 or 1 is about 33.06%. (2) The root mean square error (RMSE) of GIIRS/FY-4A ATPs is less than 3 K, smaller than that of ERA5 ATPs. The RMSE of ERA5 ATPs can exceed 10 K in the desert hinterland. The absolute mean biases of GIIRS/FY-4A ATPs and ERA5 ATPs are, respectively, smaller than 3 K and 2 K, the former being slightly larger. The correlation coefficients of GIIRS/FY-4A ATPs with ERA5 ATPs and RAOB ATPs are higher than 0.98 and 0.99, respectively, and the correlation between GIIRS/FY-4A ATPs and RAOB ATPs is inferior to the latter. (3) The overall atmospheric temperature retrieved by GIIRS/FY-4A is 0.08 K higher than the temperature of RAOB, on average, while the overall temperature from ERA5 is 0.13 K lower than that of RAOB, indicating that the temperature profile obtained by integrating GIIRS/FY-4A ATPs and ERA5 ATPs may be much closer to RAOB ATPs. (4) The probability density of the GIIRS/FY-4A ATP biases in the TD region generally follows the Gaussian distribution so that it can be effectively assimilated in the 3-D variational assimilation modules. The probability density distribution characteristics of the GIIRS/FY-4A ATP biases in the desert hinterland and oasis are not much different. However, due to the fusion analysis of the relatively rich multi-source conventional observation data from the oasis stations, the probability density of ERA5 ATPs biases at the oasis stations is nearer to Gaussian distribution than that of the GIIRS/FY-4A ATPs. In the desert hinterland, where conventional observation is not enough, the probability density distributions of the ATPs biases from ERA5 and GIIRS/FY-4A are alike. Therefore, the GIIRS FY4A can contribute to a more accurate estimation of ERA5 ATPs in the TD region. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) has a critical impact on the energy balance of land surface processes and ecosystem stability. Meanwhile, LST is controlled by multiple factors at the surface, resulting in heterogeneity of its spatial distribution. To understand the drivers of LST spatial heterogeneity and their contributions, the effects of air temperature, normalized difference vegetation index (NDVI), soil moisture, net surface radiation, precipitation, aerosol optical depth (AOD), evapotranspiration, water vapor, digital elevation model (DEM), climate type, and land cover type on LST spatial heterogeneity was analyzed in this study with GeoDetector. The results showed that the explanatory ability of air temperature to impact the spatial heterogeneity of LST was the largest in each year with a mean value of 0.74, followed by water vapor with a mean value of 0.7, and the driving effect of the factors on LST showed an increasing trend year by year. However, the land cover type did not have an effect on the spatial heterogeneity of LST for the univariate analysis in this study. In addition, the interaction analysis indicated that the spatial distribution of LST was jointly driven by all the driving factors. Among them, air temperature had the strongest interaction with other factors, with the strength of the effect in the range of 0.73–0.8. In terms of the highly sensitive area of LST for each driver, AOD has the largest driving area, accounting for 15.8% of the total area, followed by WV, TA, and ET at about 11%, and the remaining variables are less than 10%. During the study period, the area of the highly sensitive region of LST for each factor showed an overall decreasing trend, indicating that the influence of the driving factors on LST will be stronger and more concentrated. Generally, this study provides meaningful understanding of the spatial heterogeneity of LST since 2003 and provides a scientific reference for coping with climate change, analyzing surface environmental patterns, and protecting ecological environment. [ABSTRACT FROM AUTHOR]

Machine learning (ML) was used to assess and predict urban air temperature (Tair) considering the complexity of the terrain features in Yerevan (Armenia). The estimation was performed based on the Partial Least-Squares Regression (PLSR) model with a high number (30) of input variables. The relevant parameters include a newly purposed modification of spectral index IBI-SAVI, which turned out to strongly impact Tair prediction together with land surface temperature (LST). Cross-validation analysis on temperature predictions across a station-centered 1000 m circular area revealed quite a high correlation (R2Val = 0.77, RMSEVal = 1.58) between the predicted and measured Tair from the test set. It was concluded the remote sensing is an effective tool to estimate Tair distribution where a dense network of weather stations is not available. However, further developments will include incorporation of additional weather parameters from the weather stations, such as precipitation and wind speed, as well as the use of non-parametric ML techniques. [ABSTRACT FROM AUTHOR]