A Dataset of Hourly Sea Surface Temperature From Drifting Buoys

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.

Dynamic Data Assimilation of MPAS-O and the Global Drifter Dataset

“…Specifically, we will be using the V1 historical run of MPAS-O [3]. The GDP is a large array of more than 1000 satellite-tracked ocean buoys that measure ocean variables such as drift and sea surface temperature, and are commonly used in weather prediction [5,4]. These two datasets have different temporal resolutions, so some time averaging within the GDP is required to match the MPAS-O.…”

“…These observations are more accurate at specific locations, but are limited in coverage. An example of a highly resolved ocean buoy dataset is the Global Drifter dataset program (GDP) overseen by NOAA [5,4]. In many cases, scientists use interpolation techniques to estimate state variables, such as temperature or salinity, between observations.…”