A Dataset of Hourly Sea Surface Temperature From Drifting Buoys

TL;DR

This paper introduces a new hourly sea surface temperature dataset derived from NOAA drifter observations, modeling diurnal and low-frequency variability to provide detailed SST estimates with uncertainty measures.

Contribution

It presents a novel method for estimating hourly SST from drifting buoy data, incorporating diurnal harmonics and polynomial trends, and provides a comprehensive dataset with uncertainty quantification.

Findings

The dataset includes hourly SST estimates with uncertainties.

The method models diurnal and low-frequency SST variability.

The dataset aligns with existing drifter position and velocity data.

Abstract

A dataset of sea surface temperature (SST) estimates is generated from the temperature observations of surface drifting buoys of NOAA's Global Drifter Program. Estimates of SST at regular hourly time steps along drifter trajectories are obtained by fitting to observations a mathematical model representing simultaneously SST diurnal variability with three harmonics of the daily frequency, and SST low-frequency variability with a first degree polynomial. Subsequent estimates of non-diurnal SST, diurnal SST anomalies, and total SST as their sum, are provided with their respective standard uncertainties. This Lagrangian SST dataset has been developed to match the existing hourly dataset of position and velocity from the Global Drifter Program.