Abstract
Sea surface temperature (SST) is an important factor in the global ocean–atmosphere system, being vital in a variety of climate analyses and air–sea interaction research studies. However, estimating daily SST with both high precision and high spatial completeness remains a challenge. This article attempts to solve this problem by merging two complementary daily SST products, that is, the 25 km-resolution Advanced Microwave Scanning Radiometer for EOS (AMSR-E) SST and 4 km-resolution Moderate Resolution Imaging Spectroradiometer (MODIS) SST, using a genetic algorithm–assisted deep neural network model (GA-DNNM). The merged SST with a spatial resolution of 4 km and a temporal resolution of 1 day is achieved. Experiments in the Asia and Indo-Pacific Ocean (AIPO) region in 2005 were conducted to demonstrate the feasibility and advantages of the proposed method. Results showed that the spatial coverages of the original MODIS SST and AMSR-E SST are ranging from 25.0 to 48.1%, and 31.5 to 47.6%, respectively, while the merged SST achieves a spatial coverage ranging from 56.1 to 73.1%, with improvements ranging from 50.2 to 131.7% relative to the original MODIS SST. Comparisons with drifting buoy observations indicate that the merged SST is accurate, with an average bias of 0.006°C and an average RMSE of 0.502°C, in places where the MODIS SST data are missing before being merged in the AIPO area, and with an average bias of −0.082 °C, and an average RMSE of 0.603°C for the merged SST in the whole study area.
Highlights
Sea surface temperature (SST) is an important physical parameter of the oceans, playing a fundamentally important role in the exchange of energy, momentum, and moisture between the oceans and atmosphere (Wentz et al, 2000)
For evaluating the proposed method, experiments are conducted on each day of 2005, expect for November 17, 2005 when the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) SST’s spatial coverage is 0.0% in the study area, and November 20, 2005 when the AMSR-E SST’s spatial coverage is 0.0524% in the study area and has no match with the drifting buoy observations. 4 km daily merged SST products with improved quality are generated in the AIPO area
The spatial coverage is a critical index for measuring the quality of SST
Summary
Sea surface temperature (SST) is an important physical parameter of the oceans, playing a fundamentally important role in the exchange of energy, momentum, and moisture between the oceans and atmosphere (Wentz et al, 2000). SST with high spatiotemporal resolution, spatial coverage, and accuracy is of vital importance to forecasting weather and monitoring climate change (Reynolds & Smith, 1995; Reynolds et al, 2002; Guan & Kawamura, 2004; Guo, 2010; Li et al, 2013; Tang et al, 2015; Zhu et al, 2018; Xiao et al, 2019). Microwaves can penetrate clouds with little attenuation, and MW SST can provide a fairly high spatial coverage of the sea under all weather conditions, except for rain (Wentz et al, 2000). We can utilize these two types of SST complementarily to obtain SST with desirable qualities based on the idea of synergy (Zhang and Chen, 2016)
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