The NOAA/NESDIS/NCEI Daily Optimum Interpolation Sea Surface Temperature (SST), version 2.0, dataset (DOISST v2.0) is a blend of in situ ship and buoy SSTs with satellite SSTs derived from the Advanced Very High Resolution Radiometer (AVHRR). DOISST v2.0 exhibited a cold bias in the Indian, South Pacific, and South Atlantic Oceans that is due to a lack of ingested drifting-buoy SSTs in the system, which resulted from a gradual data format change from the traditional alphanumeric codes (TAC) to the binary universal form for the representation of meteorological data (BUFR). The cold bias against Argo was about −0.14°C on global average and −0.28°C in the Indian Ocean from January 2016 to August 2019. We explored the reasons for these cold biases through six progressive experiments. These experiments showed that the cold biases can be effectively reduced by adjusting ship SSTs with available buoy SSTs, using the latest available ICOADS R3.0.2 derived from merging BUFR and TAC, as well as by including Argo observations above 5-m depth. The impact of using the satellite MetOp-B instead of NOAA-19 was notable for high-latitude oceans but small on global average, since their biases are adjusted using in situ SSTs. In addition, the warm SSTs in the Arctic were improved by applying a freezing point instead of regressed ice-SST proxy. This paper describes an upgraded version, DOISST v2.1, which addresses biases in v2.0. Overall, by updating v2.0 to v2.1, the biases are reduced to −0.07° and −0.14°C in the global ocean and Indian Ocean, respectively, when compared with independent Argo observations and are reduced to −0.04° and −0.08°C in the global ocean and Indian Ocean, respectively, when compared with dependent Argo observations. The difference against the Group for High Resolution SST (GHRSST) Multiproduct Ensemble (GMPE) product is reduced from −0.09° to −0.01°C in the global oceans and from −0.20° to −0.04°C in the Indian Ocean.

/pdf/improvements-of-the-daily-optimum-interpolation-sea-surface-bqxd72tmw1.pdf

Improvements of the Daily Optimum Interpolation Sea Surface Temperature (DOISST) Version 2.1

Digitalization to achieve sustainable development goals: Steps towards a Smart Green Planet.

https://backend.orbit.dtu.dk/ws/files/191605483/1_s2.0_S0034425719303852_main.pdf

Half a century of satellite remote sensing of sea-surface temperature

The Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) system generates global, daily, gap-filled foundation sea surface temperature (SST) fields from satellite data and in situ observations. The SSTs have uncertainty information provided with them and an ice concentration (IC) analysis is also produced. Additionally, a global, hourly diurnal skin SST product is output each day. The system is run in near real time to produce data for use in applications such as numerical weather prediction. Data production is monitored routinely and outputs are available from the Copernicus Marine Environment Monitoring Service (CMEMS; marine.copernicus.eu). As an operational product, the OSTIA system is continuously under development. For example, since the original descriptor paper was published, the underlying data assimilation scheme that is used to generate the foundation SST analyses has been updated. Various publications have described these changes but a full description is not available in a single place. This technical note focuses on the production of the foundation SST and IC analyses by OSTIA and aims to provide a comprehensive description of the current system configuration.

https://www.mdpi.com/2072-4292/12/4/720/pdf

The Current Configuration of the OSTIA System for Operational Production of Foundation Sea Surface Temperature and Ice Concentration Analyses

Sea surface salinity estimates from spaceborne L-band radiometers: An overview of the first decade of observation (2010–2019)

A climate data record of global sea surface temperature (SST) spanning 1981-2016 has been developed from 4 × 1012 satellite measurements of thermal infra-red radiance. The spatial area represented by pixel SST estimates is between 1 km2 and 45 km2. The mean density of good-quality observations is 13 km-2 yr-1. SST uncertainty is evaluated per datum, the median uncertainty for pixel SSTs being 0.18 K. Multi-annual observational stability relative to drifting buoy measurements is within 0.003 K yr-1 of zero with high confidence, despite maximal independence from in situ SSTs over the latter two decades of the record. Data are provided at native resolution, gridded at 0.05° latitude-longitude resolution (individual sensors), and aggregated and gap-filled on a daily 0.05° grid. Skin SSTs, depth-adjusted SSTs de-aliased with respect to the diurnal cycle, and SST anomalies are provided. Target applications of the dataset include: climate and ocean model evaluation; quantification of marine change and variability (including marine heatwaves); climate and ocean-atmosphere processes; and specific applications in ocean ecology, oceanography and geophysics.

/pdf/satellite-based-time-series-of-sea-surface-temperature-since-1ue12uo5yv.pdf

Satellite-based time-series of sea-surface temperature since 1981 for climate applications.

ESA is building on almost 15 years of continuous Fiducial Reference Measurements (FRM) from UK-funded shipborne radiometers by establishing a service to provide historic and ongoing FRM measurement...

Ruth Wilson

Papers

Satellite-based time-series of sea-surface temperature since 1981 for climate applications.

Sentinel-3 SLSTR SST Validation using a Fiducial Reference Measurements (FRM) Service