Equatorial Long Waves in Geostationary Satellite Observations and in a Multichannel Sea Surface Temperature Analysis

Korea’s first geostationary satellite, the “Communication, Ocean, and Meteorological Satellite” (COMS), has been operating since 2010. The Meteorological Imager (MI), an sensor on-board the COMS, has observed sea-surface radiances for the estimation of sea surface temperature (SST) in the western Pacific Ocean and eastern Indian Ocean. To derive the SST coefficients of COMS, quality-controlled surface drifting buoy data were collected for the period of April 2011 to March 2015. A collocation procedure between COMS/MI data and the surface drifter data produced a matchup database for 4 years from 2011 to 2015. The coefficients for the COMS/MI SST were derived by applying appropriate algorithms, i.e., the Multi-channel SST (MCSST) and Non-linear SST (NLSST) algorithms, for daytime and nighttime data using a regression method. Validation results suggest the possibility of the continuous use of the coefficients as representative SST coefficients of COMS. The estimated SSTs near the edge of a full disk with high satellite zenith angles over 60° revealed relatively large errors compared to drifter temperatures. Most of NLSST formulations exhibited overestimation of SSTs at low SSTs (<10 °C). This study suggests an approach by which SST can be measured accurately in order to contribute to tracking climate change.

Download Full-text

DINCAE 1.0: a convolutional neural network with error estimates to reconstruct sea surface temperature satellite observations

Geoscientific Model Development ◽

10.5194/gmd-13-1609-2020 ◽

2020 ◽

Vol 13 (3) ◽

pp. 1609-1622 ◽

Cited By ~ 3

Author(s):

Alexander Barth ◽

Aida Alvera-Azcárate ◽

Matjaz Licer ◽

Jean-Marie Beckers

Keyword(s):

Neural Network ◽

Missing Data ◽

Surface Temperature ◽

Sea Surface Temperature ◽

Error Variance ◽

Temperature Data ◽

Satellite Observations ◽

Sea Surface ◽

Function Decomposition ◽

Surface Temperature Data

Abstract. A method to reconstruct missing data in sea surface temperature data using a neural network is presented. Satellite observations working in the optical and infrared bands are affected by clouds, which obscure part of the ocean underneath. In this paper, a neural network with the structure of a convolutional auto-encoder is developed to reconstruct the missing data based on the available cloud-free pixels in satellite images. Contrary to standard image reconstruction with neural networks, this application requires a method to handle missing data (or data with variable accuracy) in the training phase. The present work shows a consistent approach which uses the satellite data and its expected error variance as input and provides the reconstructed field along with its expected error variance as output. The neural network is trained by maximizing the likelihood of the observed value. The approach, called DINCAE (Data INterpolating Convolutional Auto-Encoder), is applied to a 25-year time series of Advanced Very High Resolution Radiometer (AVHRR) sea surface temperature data and compared to DINEOF (Data INterpolating Empirical Orthogonal Functions), a commonly used method to reconstruct missing data based on an EOF (empirical orthogonal function) decomposition. The reconstruction error of both approaches is computed using cross-validation and in situ observations from the World Ocean Database. DINCAE results have lower error while showing higher variability than the DINEOF reconstruction.

Download Full-text

Sea surface temperature from a geostationary satellite by optimal estimation

Remote Sensing of Environment ◽

10.1016/j.rse.2008.10.012 ◽

2009 ◽

Vol 113 (2) ◽

pp. 445-457 ◽

Cited By ~ 38

Author(s):

C.J. Merchant ◽

P. Le Borgne ◽

H. Roquet ◽

A. Marsouin

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

Optimal Estimation ◽

Geostationary Satellite ◽

Sea Surface

Download Full-text

Impact of atmospheric coastal jet off central Chile on sea surface temperature from satellite observations (2000–2007)

Journal of Geophysical Research Atmospheres ◽

10.1029/2008jc005083 ◽

2009 ◽

Vol 114 (C8) ◽

Cited By ~ 43

Author(s):

Lionel Renault ◽

Boris Dewitte ◽

Mark Falvey ◽

René Garreaud ◽

Vincent Echevin ◽

...

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

Satellite Observations ◽

Sea Surface ◽

Central Chile ◽

Coastal Jet

Download Full-text

Impacts of Sea Surface Temperature Uncertainty on the Western North Pacific Subtropical High (WNPSH) and Rainfall

Weather and Forecasting ◽

10.1175/waf-d-10-05007.1 ◽

2011 ◽

Vol 26 (3) ◽

pp. 371-387 ◽

Cited By ~ 2

Author(s):

Xiaodong Hong ◽

Craig H. Bishop ◽

Teddy Holt ◽

Larry O’Neill

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

North Pacific ◽

Western North Pacific ◽

Forecast Error ◽

Error Variance ◽

Satellite Observations ◽

Sea Surface ◽

Subtropical High ◽

Forecast Error Variance

Abstract This paper examines the sensitivity of short-term forecasts of the western North Pacific subtropical high (WNPSH) and rainfall to sea surface temperature (SST) uncertainty using the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS). A comparison of analyzed SSTs with satellite observations of SST indicates that SST analysis errors are particularly pronounced on horizontal scales from 100 to 200 km, similar to the mesoscale eddy scales in the Kuroshio region. Since significant oceanic variations occur on these scales, it is of interest to examine the effects of representing this small-scale uncertainty with random, scale-dependent perturbations. An SST ensemble perturbation generation technique is used here that enables temporal and spatial correlations to be controlled and produces initial SST fields comparable to satellite observations. The atmospheric model develops large uncertainty in the Korea and Japan area due to the fluctuation in the horizontal pressure gradient caused by the location of the WNPSH. This, in turn, increases the variance of the low-level jet (LLJ) over southeast China, resulting in large differences in the moist transport flux from the tropical ocean and subsequent rainfall. Validation using bin-mean statistics shows that the ensemble forecast with the perturbed SST better distinguishes large forecast error variance from small forecast error variance. The results suggest that using the SST perturbation as a proxy for the ocean ensemble in a coupled atmosphere and ocean ensemble system is feasible and computationally efficient.

Download Full-text