Generating 1 km Spatially Seamless and Temporally Continuous Air Temperature Based on Deep Learning over Yangtze River Basin, China

Rui Li,Xiang Zhang,Tailai Huang,Shuzhe Huang,Yu Song

doi:10.3390/rs13193904

Abstract

Air temperature is one of the most essential variables in understanding global warming as well as variations of climate, hydrology, and eco-systems. However, current products and assimilation approaches alone can provide temperature data with high resolution, high spatio-temporal continuity, and high accuracy simultaneously (refer to 3H data). To explore this kind of potential, we proposed an integrated temperature downscaling framework by fusing multiple remotely sent, model-based, and in-situ datasets, which was inspired by point-surface data fusion and deep learning. First, all of the predictor variables were processed to maintain spatial seamlessness and temporal continuity. Then, a deep belief neural network was applied to downscale temperature with a spatial resolution of 1 km. To further enhance the model performance, calibration techniques were adopted by integrating station-based data. The results of the validation over the Yangtze River Basin indicated that the average Pearson correlation coefficient, RMSE, and MAE of downscaled temperature achieved 0.983, 1.96 °C, and 1.57 °C, respectively. After calibration, the RMSE and MAE were further decreased by ~20%. In general, the results and comparative analysis confirmed the effectiveness of the framework for generating 3H temperature datasets, which would be valuable for earth science studies.

Highlights

Surface air temperature is one of the most critical land surface variables in the fields of hydrology, meteorology, earth system sciences, and plays a vital role in climate change, natural disasters, and human health [1,2]
It can be seen that in-situ temperature indicated strong positive relationship with land surface temperature and ERA-5 temperature with Pearson correlation coefficient (PCC) of 0.91 and 0.95, respectively
The results indicated that deep belief network (DBN) exhibited most powerful downscaling performance for obtaining the lowest errors (i.e., PCC, root mean squared error (RMSE), mean absolute error (MAE) of 0.983, 1.96 ◦ C, and 1.57 ◦ C) against in-situ observations

Summary

Introduction

Surface air temperature is one of the most critical land surface variables in the fields of hydrology, meteorology, earth system sciences, and plays a vital role in climate change, natural disasters, and human health [1,2]. In-situ weather stations are superior in providing long-term, stable, and accurate temperature recordings Their costly maintenance and sparse distribution are the major limitations for their application in regional or global earth science research [8,9]. A variety of data assimilation and reanalysis approaches have been developed to produce large scale assimilated products such as the Japanese 55-year Reanalysis Project (JRA-55), the Global Land Data Assimilation System (GLDAS), the NCEP/DOE Reanalysis 2 Project (NNRP-2), and the ERA-5 reanalysis product [10,11,12,13] These assimilated products are capable of providing both regional and global data, the coarse resolution and relatively low accuracy usually limits their use for fine impact assessment and decision making [14,15]

Objectives

Methods

Results

Discussion

Conclusion