Estimation of missing daily Sea Surface Temperature data, IMARPE coastal laboratories
DOI:
https://doi.org/10.53554/boletin.v40i1.431Keywords:
Daily data estimation, Multiple Regression, Segmented Multiple RegressionAbstract
The in situ Sea Surface Temperature (SST) records collected by IMARPE’s coastal laboratories constitute one of the most
extensive daily time series of oceanographic variables available for the Peruvian coast. These datasets are critical for oceanographic research and for monitoring large-scale climate events such as El Niño and La Niña. However, gaps in the daily records persist at several stations, making the development of consistent climatologies and long-term trend analyses difficult. This study aims to estimate missing daily in situ SST values using satellite-derived daily SST as a predictor within a linear statistical framework. Two methods have been applied and evaluated: Multiple Linear Regression (MLR) and Segmented Multiple Linear Regression (SMLR). The analysis shows that satellite SST can effectively serve as a predictor for the reconstruction of in situ SST time series. Among the approaches tested, PMLR provided more robust estimates, particularly for the coastal stations of Chicama, Chimbote, Huacho, Callao and Ilo. The resulting gap-filled time series improve the continuity and reliability of historical SST datasets for climatological assessments and operational monitoring.
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