Abstract

Within the Copernicus Marine Environment Monitoring Service (CMEMS), a new operational MEDiterranean Diurnal Optimally Interpolated SST (MED DOISST) product has been developed. This product provides hourly mean maps (Level-4) of sub-skin SST at 1/16° horizontal resolution over the Mediterranean Sea from January 2019 to present. The product is built by combining hourly SST data from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) on board Meteosat Second Generation and model analyses through optimal interpolation. SEVIRI and model data are respectively used as the observation source and first-guess. The differences between satellite and model SST are free, or nearly free, of any diurnal cycle, thus allowing them to be interpolated in space and time using satellite data acquired at different times of the day. The accuracy of the MED DOISST product is assessed here by comparison against surface drifting buoy measurements, covering the years 2019 and 2020. The diurnal cycle reconstructed from DOISST is in good agreement with the one observed by independent drifter data, with a mean bias of 0.041 ± 0.001 K and root-mean-square difference (RMSD) of 0.412 ± 0.001 K. The new SST product is more accurate than the input model during the central warming hours, when the model, on average, underestimates drifter SST by one tenth of degree. The MED DOISST product is also able to reproduce accurately the extreme diurnal warming events frequently observed in the Mediterranean Sea, which may reach amplitudes larger than 5 K during the warm season. This product can contribute to improve the prediction capability of numerical weather forecast systems (e.g., through improved forcings/assimilation), as well as the monitoring of surface heat budget estimates and temperature extremes which can have significant impacts on the marine ecosystem. The full MED DOISST product (released on 04 May 2021) is available upon free registration at https://doi.org/10.25423/CMCC/SST_MED_PHY_SUBSKIN_L4_NRT_010_036 (Pisano et al., 2021). The reduced subset used here for validation and review purposes is openly available at https://doi.org/10.5281/zenodo.5807729 (Pisano, 2021).

Highlights

  • In the last decades, the development of accurate satellite-based Sea Surface Temperature (SST) products required an increasing effort to meet an ever-growing request from scientific, operational and emerging policy needs

  • The aim of this paper is to describe the operational implementation of a diurnal optimally interpolated SST (DOISST) product for the Mediterranean Sea (MED) at 1/16° horizontal resolution, building on the algorithm by Marullo et al (2014, 2016)

  • The Mediterranean diurnal optimally interpolated SST operational product consists of hourly mean gap-free (L4) satellite-based estimates of the sub-skin SST over the Mediterranean Sea

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Summary

Introduction

The development of accurate satellite-based Sea Surface Temperature (SST) products required an increasing effort to meet an ever-growing request from scientific, operational and emerging policy needs. Operational datasets are typically distributed in near real time (NRT), delayed-mode or as reprocessed datasets, and may include different processing levels, from single satellite passes processed to provide valid SST values in the original observation geometry, the so-called Level-2 (L2), to images remapped onto a regular grid, known as Level-3 (L3), up to the spatially complete Level-4 (L4), interpolated over fixed regular grids These latter are required by several applications since the lower levels are typically affected by several data voids (due to clouds, rain, land, sea-ice, or other environmental factors depending on the type of sensors). The Operational Sea surface Temperature and sea Ice Analysis (OSTIA) diurnal product (While et al., 2017) provides daily gap-free maps of hourly mean skin SST at 0.25° x 0.25° horizontal nominal resolution, using in situ and satellite data from infrared radiometers. The assessment of the MED DOISST product covers two complete years (2019-2020), extending previous similar validations (Marullo et al, 2016)

Satellite data
Model data
In situ data
Product overview
Processing chain
Validation framework
The mean diurnal cycle
Diurnal warming events
Findings
2021, Summary and conclusions
Full Text
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