Abstract

Polar sea ice affects atmospheric and ocean circulation and plays an important role in global climate change. Long time series sea ice concentrations (SIC) are an important parameter for climate research. This study presents an SIC retrieval algorithm based on brightness temperature (Tb) data from the FY3C Microwave Radiation Imager (MWRI) over the polar region. With the Tb data of Special Sensor Microwave Imager/Sounder (SSMIS) as a reference, monthly calibration models were established based on time–space matching and linear regression. After calibration, the correlation between the Tb of F17/SSMIS and FY3C/MWRI at different channels was improved. Then, SIC products over the Arctic and Antarctic in 2016–2019 were retrieved with the NASA team (NT) method. Atmospheric effects were reduced using two weather filters and a sea ice mask. A minimum ice concentration array used in the procedure reduced the land-to-ocean spillover effect. Compared with the SIC product of National Snow and Ice Data Center (NSIDC), the average relative difference of sea ice extent of the Arctic and Antarctic was found to be acceptable, with values of −0.27 ± 1.85 and 0.53 ± 1.50, respectively. To decrease the SIC error with fixed tie points (FTPs), the SIC was retrieved by the NT method with dynamic tie points (DTPs) based on the original Tb of FY3C/MWRI. The different SIC products were evaluated with ship observation data, synthetic aperture radar (SAR) sea ice cover products, and the Round Robin Data Package (RRDP). In comparison with the ship observation data, the SIC bias of FY3C with DTP is 4% and is much better than that of FY3C with FTP (9%). Evaluation results with SAR SIC data and closed ice data from RRDP show a similar trend between FY3C SIC with FTPs and FY3C SIC with DTPs. Using DTPs to present the Tb seasonal change of different types of sea ice improved the SIC accuracy, especially for the sea ice melting season. This study lays a foundation for the release of long time series operational SIC products with Chinese FY3 series satellites.

Highlights

  • Polar sea ice affects the atmosphere and ocean circulation and plays an important role in global climate change [1]

  • The data used in this study were generated with Sensor Microwave Imager/Sounder (SSMIS) data from Defense Meteorological Satellite Program (DMSP) F17 through the NASA team algorithm developed by the NASA Goddard

  • Most OBS sea ice concentrations (SIC) data are from the sea ice melting season, i.e., from May to October for the Arctic and October to December for the Antarctic

Read more

Summary

Introduction

Polar sea ice affects the atmosphere and ocean circulation and plays an important role in global climate change [1]. Passive microwave (PM) radiometers on satellites are effective tools to obtain all-weather, near-real-time, and long-term continuous SIC data over polar regions. NSIDC has become one of the main data sources for global climate change research and polar prediction models. (FY-3) series satellite PM radiometers are similar to that of the SSMIS on the DMSP satellites; these instruments have the potential to become the main data source of polar sea ice observations. Radiation Imager (MWRI) on FY3C and the SSMIS on F17, and we systematically evaluated the temporal continuity of the daily SIC products over the polar region from FY3C/MWIR data.

Brightness Temperature Data
SIC Product of NSIDC
ASPeCt Ship Observation Data
Spatial
RRDP Data
Intersensor Calibrations between SSMIS and MWRI
Scatter diagrams fromFY3C
SIC Retrieval
NASA Team Method with Fixed Tie Points Based on the Intersensor Calibrated Tb
NASA Team Method with Dynamic Tie Points Based on the Original Tb
Land Contamination Effect Remove
Intersensor Comparison in the SIC
Mean value and standard relative inand
Evaluation with OBS SIC Data
Evaluation with SAR Data
Evaluation with RRDP Data
Conclusions
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call