Construction of a climate data record of sea surface temperature from passive microwave measurements

REMOTE SENSING OF ENVIRONMENT

卷:236

Article

Alerskans, Emy¹  Hoyer, Jacob L.¹  Gentemann, Chelle L.²  Pedersen, Leif Toudal³  Nielsen-Englyst, Pia^1,3  Donlon, Craig⁴ 

[1] Danish Meteorol Inst, Lyngbyvej 100, DK-2100 Copenhagen O, Denmark

[2] Earth & Space Res, Seattle, WA 98121 USA

[3] Tech Univ Denmark, DTU Space, DK-2800 Lyngby, Denmark

[4] ESA, Estec, European Space Res & Technol Ctr, NL-2201 AZ Noordwijk, Netherlands

关键词: Remote sensing;    Microwave;    AMSR-E;    AMSR2;    Sea surface temperature;    Climate data record;   

DOI  :  10.1016/j.rse.2019.111485

来源: Elsevier

【 摘 要 】

A statistical regression-based microwave sea surface temperature (SST) retrieval algorithm has been developed within the European Space Agency Climate Change Initiative (ESA-CCI) SST project. The retrieval algorithm was used to generate a climate data record (CDR) of passive microwave (PMW) SST from the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) and its follow-on instrument AMSR2 for the period June 2002-October 2017. Multisensor Matchup Datasets (MMDs), which includes satellite orbital data collocated with in situ and auxiliary data, was used to derive consistent algorithms for AMSR-E and AMSR2. The retrieval algorithms consist of wind speed (WS) retrievals and SST retrievals, with corresponding uncertainty retrievals. The WS retrieval consists of a two-step regression model, where the second step is a localized algorithm, trained to perform well over restricted WS intervals. A two-step multiple linear regression retrieval with localized algorithms is used to retrieve SST. The first-stage algorithm is trained to perform well over restricted latitude intervals for ascending and descending orbit, respectively, whereas the second-stage retrieval uses localized SST and WS algorithms. Furthermore, a new and effective method for detecting and screening for Radio Frequency Interference (RFI) was developed. Validation of the PMW SSTs against drifter in situ SSTs shows an overall bias of - 0.02 K for quality level (QL) 4 and 5 AMSR-E retrievals with a standard deviation of 0.46 K. Validation results for QL 4 and 5 AMSR2 retrievals against drifter in situ SSTs give a bias of 0.002 K and a standard deviation of 0.45 K. The corresponding mean modelled SST uncertainties, including in situ and sampling uncertainties, are estimated to 0.45 K for QL 4 and 5 AMSR-E retrievals and 0.44 K for QL 4 and 5 AMSR2 retrievals. Validation against near-surface temperature measurements from Argo floats yielded comparable results, confirming the drifting-buoy validation. The validation results demonstrate a consistent PMW SST CDR with accurate SST observations and reliable uncertainty estimates.

【 授权许可】

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