A 10-year Metocean dataset for Laguna Madre, Texas, including for the study of extreme cold events
Coastal observations along the Texas coast are valuable for many stakeholders in diverse domains. However, the management of the collected data has been limited, creating gaps in hydrological and atmospheric datasets. Among these, water and air temperature measurements are particularly crucial for water temperature predictions, especially during freeze events. These events can pose a serious threat to endangered sea turtles and economically valuable fish, which can succumb to hypothermic stunning, making them vulnerable to cold-related illness or death. Reliable and complete water and air temperature measurements are needed to provide accurate predictions of when cold-stunning events occur. To address these concerns, the focus of this paper is to describe the method used to create a complete 10-year dataset that is representative of the upper Laguna Madre, TX using multiple stations and various gap-filling methods. The raw datasets consist of a decade’s worth of air and water temperature measurements within the Upper Laguna Madre from 2012 to 2022 extracted from the archives of the Texas Coastal Ocean Observation Network and the National Park Service. Large portions of data from the multiple stations were missing from the raw datasets, therefore a systematic gap-filling approach was designed and applied to create a near-continuous dataset. The proposed imputation method consists of three steps, starting with a short gap interpolation method, followed by a long gap-filling process using nearby stations, and finalized by a second short gap interpolation method. This systematic data imputation approach was evaluated by creating random artificial gaps within the original datasets, filling them using the proposed data imputation method, and assessing the viability of the proposed methods using various performance metrics. The evaluation results help to ensure the reliability of the newly imputed dataset and the effectiveness of the data imputation method. The newly created dataset is a valuable resource that transcends the local cold-stunning issue, offering viable utility for analyzing temporal variability of air and water temperatures, exploring temperature interdependencies, reducing forecasting uncertainties, and refining natural resource and weather advisory decision-making processes. The cleaned dataset with minimal gaps (<2%) is ready and convenient for artificial intelligence and machine learning applications.