Rapid estimation of global sea surface temperatures from sparse streaming in situ observations

TL;DR

This paper introduces S-DEIM, a novel method combining empirical interpolation and neural networks to accurately and efficiently reconstruct high-resolution sea surface temperatures from sparse in situ data, suitable for real-time applications.

Contribution

The paper presents S-DEIM, a new approach that improves SST reconstruction accuracy from sparse data using a hybrid empirical and neural network method, without relying on models.

Findings

S-DEIM achieves about 40% higher accuracy than previous empirical methods.

91% of estimates are within ±1°C of true SST.

S-DEIM is robust to sensor placement and computationally efficient.

Abstract

Reconstructing high-resolution sea surface temperatures (SST) from staggered SST measurements is essential for weather forecasting and climate projections. However, when SST measurements are sparse, the resulting inferred SST fields are rather inaccurate. Here, we demonstrate the ability of Sparse Discrete Empirical Interpolation Method (S-DEIM) to reconstruct the high-resolution SST field from sparse in situ observations, without using a model. The S-DEIM estimate consists of two terms, one computed from instantaneous in situ observations using empirical interpolation, and the other learned from the historical time series of observations using recurrent neural networks (RNNs). We train the RNNs using the National Oceanic and Atmospheric Administration's weekly high-resolution SST dataset spanning the years 1989-2021 which constitutes the training data. Subsequently, we examine the performance of S-DEIM on the test data, comprising January 2022 to January 2023. For this test data, S-DEIM infers the high-resolution SST from 100 in situ observations, constituting only 0.2% of the high-resolution spatial grid. We show that the resulting S-DEIM reconstructions are about 40% more accurate than earlier empirical interpolation methods, such as DEIM and Q-DEIM. Furthermore, 91% of S-DEIM estimates fall within $\pm 1^\circ$C of the true SST. We also demonstrate that S-DEIM is robust with respect to sensor placement: even when the sensors are distributed randomly, S-DEIM reconstruction error deteriorates only by 1-2%. S-DEIM is also computationally efficient. Training the RNN, which is performed only once offline, takes approximately one minute. Once trained, the S-DEIM reconstructions are computed in less than a second. As such, S-DEIM can be used for rapid SST reconstruction from sparse streaming observational data in real time.