Exploring CYGNSS mission for surface heat flux estimates and analysis over tropical oceans - Li, Xiaohui; Yang, Jingsong; Yan, Yunwei; Li, Weiqiang (Frontiers in Marine Science, 2022)
The remotely sensed wind field plays a very important role in global heat flux estimation. In this paper, heat flux estimates from the Global Tropical Moored Buoy Array are used for the quantitative verification and evaluation of satellite-derived surface latent and sensible heat fluxes (LHF and SHF) data obtained from the Cyclone Global navigation satellite system reflectometry (CYGNSS) mission, which demonstrates the performance and reliability of CYGNSS heat flux products during the period of 2017–2022, including CYGNSS Level 2 Ocean Surface Heat Flux Climate Data Record (CDR) Version 1.0 and Version 1.1. Comparisons show that the LHF and SHF estimates from the CYGNSS mission are in good agreement with those from in situ measurements. The accuracy of each tropical ocean basin is investigated using the Tropical Atmosphere Ocean/Triangle Trans-Ocean Buoy Network array in the Pacific Ocean, the Prediction and Research Moored Array in the Tropical Atlantic in the Atlantic Ocean, and the Research Moored Array for African-Asian-Australian Monsoon Analysis and Prediction in the Indian Ocean. Different latent heat characteristics in the tropical oceans are seen when the comparisons are evaluated separately based on each buoy, suggesting better agreement in the Atlantic for LHF/SHF products. Based on the Coupled Ocean-Atmosphere Response Experiment 3.5 algorithm, the impact of wind speed on the LHF/SHF products is analyzed by using Science Data Record V3.1 and NOAA V1.2 science wind products. The results show that the performance of the remote sensing wind speed directly affect the accuracy of LHF products, with an improvement of 17% with respect to that of LHF CDR V1.0. Especially, in the Indian Ocean, accuracy can be improved by 26.8%. The heat flux estimates along the orbit of the CYGNSS are an important supplement to the in situ observation data and will benefit the study of global climate change.