High-resolution Sea Surface Temperature Research Articles

The Seasonal Correlation Between El Niño and Southern Oscillation Events and Sea Surface Temperature Anomalies in the South China Sea from 1958 to 2024

This study utilizes high-resolution sea surface temperature (SST) reanalysis data (0.25° × 0.25°) to investigate the relationship between SST anomalies in the South China Sea and ENSO events. The main findings are as follows: First, there is a delayed correlation between ENSO and SST anomalies in the South China Sea, with the correlation being stronger during El Niño years than during La Niña years. Second, the correlation with the peak values of the Oceanic Niño Index (ONI) is strongest for El Niño events with a 9-month lead, while for La Niña events, it is strongest with a 2-month lead. Seasonally, during El Niño events, the strongest correlations are observed in summer with a 3-month lead and in winter with a 1-month lag. For La Niña events, the strongest correlations are seen in summer with an 8-month lag and in winter with a 9-month lag. Finally, an analysis of atmospheric anomalies and shear kinetic energy anomalies relative to SST anomalies reveals a significant seasonal SST response, particularly during the summer of El Niño years and the winter of La Niña years. Overall, these results enhance our understanding of ENSO’s influence on the South China Sea and provide valuable insights for climate prediction and ecosystem protection in the region.

Analyzing trend and heatwaves of 15 Years of Sea Surface Temperature Variations along the Italian Adriatic Coast.

Water temperature is a vital parameter impactingthe growth and survival of aquatic life.Using satellite-derived infrared data, this studyanalysed the trend of sea surface temperature (SST)from 2008 to 2022of the Adriatic coastal watersofItalian regions. The "Mediterranean Sea High Resolution and Ultra High Resolution Sea Surface Temperature Analysis" product collected from theCopernicus Marine Serviceof European Copernicus programme was used, as a good compromise among spatial accuracy, temporal frequency and coverage. SST were derived in 176 locations, placed in the Adriatic Sea from the southern limit of the lagoon of Venice (Veneto) to Santa Maria di Leuca (LE), at a distance from the coast between 500 m and 5000 m (0.3 - 2.7 nautical miles). Time series analysis was applied to average value of daily SST calculated from the selected spatial locationsto identify the additive model components: trend, seasonality and random effects. The trend component was isolated and assessed using a linear regression modelto determine its significance and magnitude.A0.010 °C/year increase in SST was observed. Additionally, marine heatwaves and cold spells were consistently registered throughout the entire observation period, with a north-south gradient in intensity.

The Loss of Beneficial Thermal Priming on Global Coral Reefs.

Warm-season marine heatwaves (MHWs) have greatly increased in frequency, severity, and extent over the last few decades, driving more frequent and severe coral bleaching episodes. Given the grave near-term threat to coral reefs imposed by MHWs, it is important to assess the mechanisms by which corals may acquire higher thermal tolerance. Recent field and laboratory studies have demonstrated that exposure to sublethal heat stress, known as "priming," can reduce bleaching susceptibility during a subsequent MHW. Little is known, however, about how often priming conditions occur, and how effective those conditions may be at protecting coral reefs. We employed a global historical coral bleaching database and a high-resolution sea surface temperature dataset to assess the frequency of priming and examine its effect on coral bleaching sensitivity on a global scale. The analysis showed that coral reefs in parts of the western to central tropical Pacific experienced priming on average over twice a decade and had a higher likelihood of priming protection. Mixed-effects regression models indicated that priming conditions could mitigate coral bleaching response by up to 12% in advance of a moderate MHW. However, the protective effect of priming decreased, and even became harmful, with more severe MHWs. We detected spatial variations in priming frequency that could provide insight for conservation planning and explain some variations in bleaching sensitivity to MHWs. Even so, our findings suggest that thermal priming will not be sufficient to protect most coral reefs from MHWs in the future, without substantial efforts to mitigate climate change.

Multiple timescale variations in fronts in the Seto Inland Sea, Japan

Abstract. The Seto Inland Sea (SIS) is a critical semi-enclosed coastal sea in Japan, characterized by intricate coastlines and narrow straits that give rise to various fronts. Despite extensive research on tidal fronts, knowledge gaps persist regarding their spatiotemporal dynamics, particularly in certain poorly documented regions. Additionally, the understanding of thermohaline fronts, which emerge during winter, requires further investigation. We aimed to enhance our understanding of tidal and thermohaline fronts in the SIS by analyzing their dynamic processes, including intra-tidal and spring–neap tidal cycles, seasonal variations, and anomalous frontal variability. Using a gradient-based algorithm with an advanced contextual feature-preserving median filter, we processed the high-resolution sea surface temperature dataset to detect and quantify tidal and thermohaline fronts. Our analysis revealed the presence of numerous tidal fronts, predominantly influenced by the M2 tide, across the SIS, with substantial spatial variations in amplitude due to complex coastlines and narrow straits. Intra-tidal movements of tidal fronts corresponded to ebb and flood currents, while spring–neap tidal cycles and seasonal shifts influenced frontal positions and intensities. Additionally, thermohaline fronts were identified in certain regions during winter, characterized by large horizontal temperature and salinity gradients. This study enhances the understanding of tidal and thermohaline fronts in the SIS, emphasizing the importance of intra-tidal and wind-driven influences on frontal dynamics. However, limited observational coverage and resolution emphasize the need for further research to explore long-term temporal changes and better grasp the influences of ambient currents and wind patterns. Such insights are vital for effective coastal management and environmental monitoring in the SIS region.

A coordination attention residual U-Net model for enhanced short and mid-term sea surface temperature prediction

Sea surface temperature (SST) is crucial for studying global oceans and evaluating ecosystems. Accurately predicting short and mid-term daily SST has been a significant challenge in oceanography. Traditional deep learning methods can handle temporal data and spatial features but often struggle with long-range spatiotemporal dependencies. To address this, we propose a coordination attention residual U-Net(CResU-Net) model designed to better capture the dynamic spatiotemporal correlations of high-resolution SST. The model integrates coordinate attention mechanisms, multiple residual modules, and depthwise separable convolutions to enhance prediction capabilities. The spatiotemporal variations of SST across different areas of the South China Sea are complex, making accurate predictions challenging. Experiments across various regions of the South China Sea show the model’s effectiveness and robust generalization in predicting high-resolution daily SST. For a 10-day forecast period, the model achieves approximately 0.3 °C in RMSE, outperforming several advanced models.

Reconstructing tropical monthly sea surface temperature variability by assimilating coral proxy datasets

Coral reconstruction often serves as a major proxy of high-resolution sea surface temperature (SST) variability beyond the instrumental era. However, coral reconstructions are sparse and are usually studied for interannual variability, with few studies on the monthly features. In this study, we reconstruct the monthly SST spatial field by applying the paleoclimate data assimilation method to the coral records of the latest CoralHydro2k data set for the instrument period of 1880–2000. A comparison with observed SST variability shows that our assimilated tropical SST variability performs reasonably well for the seasonal cycle and monthly ENSO characteristics, notably the phase-locking and onset timing, and more realistic spatial fields relative to the model simulations. This study suggests the feasibility of applying paleoclimate data assimilation to reconstruct the monthly SST in the historical period.

Spatial Downscaling of Sea Surface Temperature Using Diffusion Model

In recent years, advancements in high-resolution digital twin platforms or artificial intelligence marine forecasting have led to the increased requirements of high-resolution oceanic data. However, existing sea surface temperature (SST) products from observations often fail to meet researchers’ resolution requirements. Deep learning models serve as practical techniques for improving the spatial resolution of SST data. In particular, diffusion models (DMs) have attracted widespread attention due to their ability to generate more vivid and realistic results than other neural networks. Despite DMs’ potential, their application in SST spatial downscaling remains largely unexplored. Hence we propose a novel DM-based spatial downscaling model, called DIFFDS, designed to obtain a high-resolution version of the input SST and to restore most of the meso scale processes. Experimental results indicate that DIFFDS is more effective and accurate than baseline neural networks, its downscaled high-resolution SST data are also visually comparable to the ground truth. The DIFFDS achieves an average root-mean-square error of 0.1074 °C and a peak signal-to-noise ratio of 50.48 dB in the 4× scale downscaling task, which shows its accuracy.

Marine heatwaves and commercial fishing in New Zealand

A marine heatwave (MHW) is an atypical and relatively short period of warmer Sea Surface Temperature (SST) which may be disruptive to marine life. Changes brought about by MHWs can reshape marine ecosystems in ways that have an economic impact on their users. MHWs are expected to become more frequent, longer, and more intense due to anthropogenic climate change. We leverage high-resolution SST data, and spatially and temporally detailed fish catch data, for an analysis of the impact of MHW events on fish catch in Aotearoa New Zealand's large marine Exclusive Economic Zone (EEZ). We use Geographically and Temporally Weighted Regression to assess the spatially heterogenous impact of MHWs on fish catch across different areas within the EEZ. We find that moderate MHWs correlate with increased fish catches; but, as the intensity of MHWs increase, their impact becomes predominantly negative. Intense MHWs are associated with substantial decreases in fish catch, suggesting significant disruptions to fish populations and their habitats. This pattern underscores the vulnerability of marine ecosystems and consequently commercial fisheries to the increasing frequency and severity of MHWs caused by climate change. As MHWs become more frequent and intense, better informed management approaches will be required to ensure the sustainability and viability of fisheries.

A New Insight on the Upwelling along the Atlantic Iberian Coasts and Warm Water Outflow in the Gulf of Cadiz from Multiscale Ultrahigh Resolution Sea Surface Temperature Imagery

The ATLAZUL project is an Interreg effort among 18 partners from Spain and Portugal along the Atlantic Iberian coasts. One of its objectives is the development of new methods and data processing for oceanic information to produce useful products for private and public stakeholders. This study proposes a new insight on the sea surface dynamic of the ATLAZUL area based on almost two years of multiscale high resolution sea surface temperature imagery. The use of techniques such as the Karhunen–Loève transform (Empirical Orthogonal Function) and the Maximum Entropy Spectral Analysis were applied to study long- and short-term features in the sea surface temperature imagery. Mathematical Morphology and the Geometrical Theory of Measure are utilized to compute the Medial Axis Transform and the Hausdorff dimension. The results can be summarized as follows: (i) the tow upwelling areas are identified along the Galician–Portugal coast as indicated in the second and third modes of KLT/EOF analysis, and they are partially affected by wind; (ii) the tow warm water outflows from the Bay of Cádiz to the Gulf of Cádiz are identified as the second and third modes of KLT/EOF analysis, which are also influenced by wind; (iii) the skeletons of the surface signature of the upwelling and of the warmer water outflow, along with their fractal dimensions, indicate a chaotic pattern of spatial distribution and (iv) the harmonic prediction model should be combined with the wind prediction.

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

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

Enhanced Atlantic Meridional Mode predictability in a high-resolution prediction system.

Accurate prediction of sea surface temperatures (SSTs) in the tropical North Atlantic on multiyear timescales is of paramount importance due to its notable impact on tropical cyclone activity. Recent advances in high-resolution climate predictions have demonstrated substantial improvements in the skill of multiyear SST prediction. This study reveals a notable enhancement in high-resolution tropical North Atlantic SST prediction that stems from a more realistic representation of the Atlantic Meridional Mode and the associated wind-evaporation-SST feedback. The key to this improvement lies in the enhanced surface wind response to changes in cross-equatorial SST gradients, resulting from Intertropical Convergence Zone bias reduction when atmospheric model resolution is increased, which, in turn, amplifies the positive feedback between latent and sensible surface heat fluxes and SST anomalies. These advances in high-resolution climate prediction hold promise for extending tropical cyclone forecasts at multiyear timescales.

Validation and Application of Satellite-Derived Sea Surface Temperature Gradients in the Bering Strait and Bering Sea

The Arctic is one of the most important regions in the world’s oceans for understanding the impacts of a changing climate. Yet, it is also difficult to measure because of extreme weather and ice conditions. In this work, we directly compare four datasets from the Group for High-Resolution Sea Surface Temperature (GHRSST) with a NASA Saildrone deployment along the Alaskan Coast and the Bering Sea and Bering Strait. The four datasets used are the Remote Sensing Systems Microwave Infrared Optimally Interpolated (MWIR) product, the Canadian Meteorological Center (CMC) product, the Daily Optimally Interpolated Product (DOISST), and the Operational Sea Surface Temperature and Ice Analysis (OSTIA) product. Spatial sea surface temperature (SST) gradients were derived for both the Saildrone deployment and GHRSST products, with the GHRSST products collocated with the Saildrone deployment. Overall, statistics indicate that the OSTIA product had a correlation of 0.79 and a root mean square difference of 0.11 °C/km when compared with Saildrone. CMC had the highest correlation of 0.81. Scatter plots indicate that OSTIA had the slope closest to one, thus best reproducing the magnitudes of the Saildrone gradients. Differences increased at latitudes > 65°N where sea ice would have a greater impact. A trend analysis was then performed on the gradient fields. Overall, positive trends in gradients occurred in areas along the coastal regions. A negative trend occurred at approximately 60°N. A major finding of this study is that future work needs to revolve around the impact of changing ice conditions on SST gradients. Another major finding is that a northward shift in the southern ice edge occurred after 2010 with a maxima at approximately 2019. This indicates that the shift of the southern ice edge is not gradual but has dramatically increased over the last decade. Future work needs to revolve around examining the possible causes for this northward shift.

Sea surface temperature prediction by stacked generalization ensemble of deep learning

Accurate sea surface temperature (SST) prediction is of great significance for fishery farming, marine ecological protection, and planning of maritime activities. In this paper, the stacked generalization ensemble is demonstrated for SST prediction to improve every single deep-learning model. Long-term high-resolution satellite-derived SST is used with the sub-regions of the Taiwan Strait and East China Sea taken as the study area. We select the Multilayer Perceptron, Long short-term memory (LSTM), Convolutional Neural Networks (CNN), CNN-LSTM as individual learners, and Convolutional LSTM as the meta-learner. The individual learners are trained and validated on the retained data subset I, while the meta-learner is trained and validated by constructing the samples with the predictions of validated individual learners on the retained data subset II. The two types of models are evaluated on the same test dataset with root-mean-square error and coefficient of efficiency as the scoring criteria. We find that the meta-model outperforms any individual model and other baselines for the one-day-/three-day-ahead forecasts in the Taiwan Strait and one-day-/three-day-/five-day-ahead predictions in the East China Sea. Furthermore, when the lead time is 1 day and 3 days, the meta-model has a better spatial distribution of prediction metrics across all grid points in the Taiwan Strait sub-area. For the East China Sea sub-region, the meta-model advantage is extended to the lead time of 5 days. Probably due to the higher quality of offshore satellite data, the prediction ability enhancement of the stacked ensemble applied in the East China Sea is better than that in the Taiwan Strait. The better-performing meta-model prediction suggests that the stacked generalization ensemble is encouraging and promising for improving the short-term prediction of the daily SST field.

Assessing the accuracy of MUR high resolution satellite sea surface temperature data

Marine bivalve mollusks are valuable proxies for El Niño-Southern Oscillation (ENSO) events in paleoclimate research, reflecting changes in sea surface temperature (SST) and other ambient marine conditions in their shell chemistry. However, the use of several Peruvian species of bivalves as paleoENSO proxies needs to be established by comparing their shells’ δ18O-derived SST records with instrumental SST measurements. Along the Peruvian coast, the limited spatial coverage of in-situ coastal monitoring stations hinders such comparisons. This study aims to assess the accuracy of the Multi-scale Ultra-high Resolution (MUR) satellite-derived SST dataset as an alternative source of SST information for paleoENSO studies in Peru, which could advance research on Peruvian bivalve species. This study compares MUR satellite data with in-situ measured SST from ten coastal monitoring stations operated by the Instituto del Mar del Perú (IMARPE). The correlation between the datasets is evaluated, and systematic and non-systematic variations are examined. Environmental factors that may impact the accuracy of satellite SST measurements, such as clouds and aerosols, are also investigated. Results show that MUR data generally correlate with overall trends in SST variability captured by in-situ measurements. However, there are regional variations in the accuracy of MUR data, with some areas showing consistent temperature offsets. Differences in measurement methodologies and environmental variables are explored as potential causes of variations between the datasets. Overall, the study demonstrates the potential of using high-resolution satellite SST data for assessing δ18O SST records from bivalves in locations without local SST records. It provides insights into the accuracy and limitations of using MUR data as an alternative source of SST information for paleoENSO studies in coastal Peru.

On Predicting Offshore Hub Height Wind Speed and Wind Power Density in the Northeast US Coast Using High-Resolution WRF Model Configurations during Anticyclones Coinciding with Wind Drought

We investigated the predictive capability of various configurations of the Weather Research and Forecasting (WRF) model version 4.4, to predict hub height offshore wind speed and wind power density in the Northeast US wind farm lease areas. The selected atmospheric conditions were high-pressure systems (anticyclones) coinciding with wind speed below the cut-in wind turbine threshold. There are many factors affecting the potential of offshore wind power generation, one of them being low winds, namely wind droughts, that have been present in future climate change scenarios. The efficiency of high-resolution hub height wind prediction for such events has not been extensively investigated, even though the anticipation of such events will be important in our increased reliance on wind and solar power resources in the near future. We used offshore wind observations from the Woods Hole Oceanographic Institution’s (WHOI) Air–Sea Interaction Tower (ASIT) located south of Martha’s Vineyard to assess the impact of the initial and boundary conditions, number of model vertical levels, and inclusion of high-resolution sea surface temperature (SST) fields. Our focus has been on the influence of the initial and boundary conditions (ICBCs), SST, and model vertical layers. Our findings showed that the ICBCs exhibited the strongest influence on hub height wind predictions above all other factors. The NAM/WRF and HRRR/WRF were able to capture the decreased wind speed, and there was no single configuration that systematically produced better results. However, when using the predicted wind speed to estimate the wind power density, the HRRR/WRF had statistically improved results, with lower errors than the NAM/WRF. Our work underscored that for predicting offshore wind resources, it is important to evaluate not only the WRF predictive wind speed, but also the connection of wind speed to wind power.

A Daily High-Resolution Sea Surface Temperature Reconstruction Using an I-DINCAE and DNN Model Based on FY-3C Thermal Infrared Data

The sea surface temperature (SST) is one of the most important parameters that characterize the thermal state of the ocean surface, directly affecting the heat exchange between the ocean and the atmosphere, climate change, and weather generation. Generally, due to factors such as the weather, satellite scanning orbit range, and satellite sensor malfunction, there are large areas of missing satellite remote sensing SST data, greatly reducing data utilization. In this situation, how to use effective data or avenues to rebuild missing SST data has become a research hotspot in the field of ocean remote sensing. Based on the SST data from an FY-3C visible and infrared radiometer with a spatial resolution of 5 km (FY-3C VIRR), an improved data interpolation convolutional autoencoder (I-DINCAE) was used to reconstruct the missing SST data. Through cross-validation, the accuracy of the reconstruction results was quantitatively evaluated with an RMSE of 0.36 °C and an MAE of 0.24 °C. The results showed that the I-DINCAE algorithm outperformed the original DINCAE algorithm greatly. For further optimization, a deep neural network (DNN) was chosen to adjust the error between the reconstructed SST and the in situ data. The RMSE of the final adjusted SST and in situ data is 0.466 °C, and the MAE is 0.296 °C. Compared to the in situ data, the accuracy of the adjusted data has shown a significant improvement over the reconstructed data. This method successfully applies deep-learning technology to the reconstruction of SST data, achieving the full coverage and high accuracy of SST products, which can provide more reliable and complete SST data for marine scientific research.

Ocean Satellite Data Fusion for High-Resolution Surface Current Maps

Real-time reconstruction of ocean surface currents is a challenge due to the complex, non-linear dynamics of the ocean, the small number of in situ measurements, and the spatio-temporal heterogeneity of satellite altimetry observations. To address this challenge, we introduce HIRES-CURRENTS-Net, an operational real-time convolutional neural network (CNN) model for daily ocean current reconstruction. This study focuses on the Mediterranean Sea, a region where operational models have great difficulty predicting surface currents. Notably, our model showcases higher accuracy compared to commonly used alternative methods. HIRES-CURRENTS-Net integrates high-resolution measurements from the infrared or visible spectrum—high resolution Sea Surface Temperature (SST) or chlorophyll (CHL) images—in addition to the low-resolution Sea Surface Height (SSH) maps derived from satellite altimeters. In the first stage, we apply a transfer learning method which uses a high-resolution numerical model to pre-train our CNN model on simulated SSH and SST data with synthetic clouds. The observation of System Simulation Experiments (OSSEs) offers us a sufficient training dataset with reference surface currents at very high resolution, and a model trained on this data can then be applied to real data. In the second stage, to enhance the real-time operational performance of our model over previous methods, we fine-tune the CNN model on real satellite data using a novel pseudo-labeling strategy. We validate HIRES-CURRENTS-Net on real data from drifters and demonstrate that our data-driven approach proves effective for real-time sea surface current reconstruction with potential operational applications such as ship routing.

Intra-tidal upwelling variability off Zhoushan Islands, East China Sea

The coastal upwelling that takes place off the Zhoushan Islands is of great importance in the transport of nutrients, nourishing local fisheries and exerting a substantial environmental influence. While it is known that tides play a central role in triggering and controlling the Zhoushan upwelling, its short-term variability within the tidal cycle remains uncertain, thereby constituting the primary focus of this study, which relies on the analysis of high-resolution Sea Surface Temperature (SST) satellite images and in-situ data. In a tidal cycle, cold upwelling patches exhibit a westward shift during the flood tide and an eastward shift during the ebb tide. The extent of cold upwelling patches is greatest during maximum flood and ebb periods. At maximum ebb, the core temperature within the cold patches notably decreases, which indicates intensified upwelling. At the same time, a similar pattern of sudden increases is also observed throughout the entire water column in the upwelling region. The strong ebb current swiftly transports the upper water mass during maximum ebb, facilitating the compensation of lower water and augmenting the intensity of the upwelling. The calculated vertical velocity within the study area ranged from −9.04 × 10−4 m/s to 12.66 × 10−4 m/s during the observational period, when upwelling showed a significant congruence with low-temperature phenomena. Alternating upwelling and downwelling phases were discernible within a tidal cycle, with upwelling being prevalent. The influence of surface heat flux on seawater below the thermocline and night-time SST was negligible. Furthermore, compared to the vertical velocity, the calculated wind-induced upwelling velocity was much lower, reaching just 3.38 × 10−6 m/s during the observed tidal cycle, which suggests that wind does not exert a primary influence on upwelling within the tidal cycle. This study contributes to an enhanced understanding of upwelling variability under the influence of tides, a topic of paramount importance in the marine environment.

High Spatiotemporal Resolution Sea Surface Temperature From MERSI and AGRI Sensors Based on Spatial and Temporal Adaptive Sea Surface Temperature Fusion Model

High Spatiotemporal Resolution Sea Surface Temperature From MERSI and AGRI Sensors Based on Spatial and Temporal Adaptive Sea Surface Temperature Fusion Model

Reconstruction of Continuous High-Resolution Sea Surface Temperature Data Using Time-Aware Implicit Neural Representation

Accurate climate data at fine spatial resolution are essential for scientific research and the development and planning of crucial social systems, such as energy and agriculture. Among them, sea surface temperature plays a critical role as the associated El Niño–Southern Oscillation (ENSO) is considered a significant signal of the global interannual climate system. In this paper, we propose an implicit neural representation-based interpolation method with temporal information (T_INRI) to reconstruct climate data of high spatial resolution, with sea surface temperature as the research object. Traditional deep learning models for generating high-resolution climate data are only applicable to fixed-resolution enhancement scales. In contrast, the proposed T_INRI method is not limited to the enhancement scale provided during the training process and its results indicate that it can enhance low-resolution input by arbitrary scale. Additionally, we discuss the impact of temporal information on the generation of high-resolution climate data, specifically, the influence of the month from which the low-resolution sea surface temperature data are obtained. Our experimental results indicate that T_INRI is advantageous over traditional interpolation methods under different enhancement scales, and the temporal information can improve T_INRI performance for a different calendar month. We also examined the potential capability of T_INRI in recovering missing grid value. These results demonstrate that the proposed T_INRI is a promising method for generating high-resolution climate data and has significant implications for climate research and related applications.