Intrinsic error analysis of sea surface significant wave height retrieval from FY-3E GNSS-R based on neural network inversion

doi:10.13474/j.cnki.11-2246.2025.1017

Abstract

Abstract: The global navigation satellite occultation sounder Ⅱ (GNOS Ⅱ), carried by the Fengyun-3 satellite, has achieved operational products such as sea surface wind speed, soil moisture, sea ice coverage and sea ice thickness based on the global navigation satellite system reflectometry (GNSS-R)data acquired in orbit.This study employs neural network (NN)technology to develop an SWH inversion model using Beidou Navigation Satellite System reflectometry (BDS-R)and Global Positioning System reflectometry (GPS-R)data provided by GNOS Ⅱ on the FY-3E satellite.A triplet comparison analysis method is adopted to compare and analyze the inherent errors in SWH inversion based on BDS-R and GPS-R data.The research results indicate that the inversion accuracy of BDS-R for significant wave height, assessed solely with data from the European Centre for Medium-Range Weather Forecasts (ECMWF)is 0.43 meters, compared to 0.46 meters for GPS-R.When validated independently using buoy data from the National Data Buoy Center (NDBC), the inversion accuracies for BDS-R and GPS-R are 0.45 and 0.50 meters, respectively.Using the triplet comparison analysis method, the inherent errors in SWH inversion for BDS-R and GPS-R are estimated to be 0.40 and 0.43 meters.This method effectively reduces the impact of inherent errors in the comparison data on the assessment results.Overall, the inversion accuracy of BDS-R for SWH is approximately 7%better than that of GPS-R.The findings of this study provide a reference for the operational application of SWH retrievals from FY-3 GNOS Ⅱ.

Key words: FY-3E satellite, GNOS Ⅱ, GNSS-R, signific wave height, neutral network, triple collocation analysis

CLC Number:

P229

YU Hui, DU Qifei, XIA Junming, HUANG Feixiong, YIN Cong, BAI Weihua. Intrinsic error analysis of sea surface significant wave height retrieval from FY-3E GNSS-R based on neural network inversion[J]. Bulletin of Surveying and Mapping, 2025, 0(10): 100-105,132.

References

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