A Downscaling Method Based on MODIS Product for Hourly ERA5 Reanalysis Land Surface Temperature

Abstract

Land surface temperature (LST) is a critical parameter for the dynamic simulation of land surface processes and for analyzing variations on regional or global scales. Obtaining LST with high spatiotemporal resolution is a subject of intensive and ongoing research. This study proposes a pixel-wise temporal alignment iterative linear regression model for downscaling based on MODIS LST products. This approach allows us to address the problem of high temporal resolution but low spatial resolution of the ERA5 reanalysis LST product, while remaining immune to pixel loss caused by clouds. The hourly ERA5 LST of the study area for 2012–2021 was downscaled to 1000 m resolution, and its accuracy was verified by comparison with measured data from meteorological stations. The downscaled LST offers intricate details and is faithful to the LST characteristics of distinct land-cover categories. In comparison with other downscaling techniques, the proposed technique is more stable and preserves the spatial distribution of ERA5 LST with minimal missing pixels. The pixel-wise average R-squared and mean absolute error for MODIS view times are 0.87 and 2.7 K, respectively, for cloud-free conditions at a 1000 m scale. Accuracy verification using data from meteorological stations indicates that the overall error is lower during cloudless periods rather than during overcast periods, during the night rather than during the day, and at MODIS view times rather than at non-view times. The maximum and minimum mean errors are 0.13 K for cloud-free periods and −0.98 K for cloudy periods, indicating a slight underestimation and overestimation, respectively. Conversely, the maximum and minimum mean absolute errors are 2.01 K for the daytime and 0.85 K for the nighttime. Therefore, the model ensures higher accuracy during cloudy periods with only clear sky LST as input data, making it suitable for long-term, all-weather ERA5 LST downscaling.

Harnessing the Power of Graph Representation in Climate Forecasting: Predicting Global Monthly Mean Sea Surface Temperatures and Anomalies

“…The generated SST and SSTA graphs in NumPy arrays, including the node feature tensors (containing both features and labels), the adjacency matrices, and the node coordinates, are accessible at https://doi.org/10.5281/zenodo. 10702373 (Ning, 2024d).…”

“…The checkpoints of the GraphSAGE models and the forecasted SST and SSTA matrices in NumPy arrays are accessible at https://doi.org/10.5281/zenodo.10702459 (Ning, 2024b). The package we developed to generate the graphs is available at https://doi.org/10.5281/zenodo.10702598 (Ning, 2024a). The scripts for the GraphSAGE model and other purposes, including computing SSTAs, the persistence model, and the recursive model, are accessible at https://doi.org/10.5281/zenodo.10702592 (Ning, 2024c).…”