# An Interpretable Deep Learning Approach for Detecting Marine Heatwaves Patterns
## 2 Methods
### Prediction Using the LSTM Network  
#### 2.1.1 Data  
Table 1 summarizes the observation and reanalysis datasets used in this study. The OISST V2 dataset provided by the National Oceanic and Atmospheric Administration  
Figure 1: The framework of using interpretive deep learning for marine heatwaves. (NOAA) of the United States is derived from remotely sensed SST by the Advanced Very High-Resolution Radiometer (AVHRR), and estimates global high-resolution daily sea surface temperature using an optimal interpolation (OI) method. The dataset resolution is 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\), spanning from 1982 to 2020. ERA5 is the fifth generation of atmospheric reanalysis conducted by the European Centre for Medium-Range Weather Forecasts (ECMWF). ERA5 provides hourly estimates of numerous atmospheric, land, and oceanic variables from 1979 to the present. The variables used in this paper include sea surface pressure (\(P\)), 10m zonal wind (\(U\)), and 10 m meridional wind (\(V\)), with the dataset resolution being 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\). The U and V can describe the wind motion state. Based on this, the wind speed and direction can be calculated using vector direction and strength concepts. The wind speed is calculated using the formula \(W=\sqrt{U^{2}+V^{2}}\), and the wind direction by \(\theta=\text{atan2}(U,V)\). The \(atan2\) function is a four-quadrant inverse tangent operation, which yields the azimuthal angle from the origin to a point in space. By inputting the components \(U\) and \(V\), the wind direction angle can be determined. The output is given in radians with a range of \((-\pi,\pi]\). The obtained angle is measured counterclockwise from true north. If measurement clockwise from true north is desired, it should be converted to \(\theta=(450-\theta)\) mod 360. In this study, we chose datasets for different stations along the Chinese coast as our research subjects. The Chinese coastal areas include the East China Sea, South China Sea, and Yellow Sea, among others, with these areas having plenty of marine resources and crucial ecosystems. Our dataset selection is based on the following considerations. 1. The geographical significance of the coastal sites to China's marine biodiversity hotspots and fishing grounds, which are vital for preserving species diversity and ensuring food security. 2. The sensitivity of these regions to climatic anomalies and anthropogenic impacts, which make them sentinel areas for detecting the early signs of climate change-driven phenomena like marine heatwaves. 3. The potential socio-economic repercussions resulting from changes in the marine ecosystem that could affect coastal communities, including alterations in fisheries, tourism, and local livelihoods. We chose thirteen stations, and Table 2 shows the latitudes and longitudes of these stations, with their positions on the map depicted in Figure 2. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **Data Set** & **Institute** & **Spatial Resolution** & **Period** & **Reference** & **Webstation** \\ \hline SST & OISST & NOAA ESRL & 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) & 1982–2020 & [PERSON] et al. (2018) & [[https://www.esrl.noaa.gov/psd](https://www.esrl.noaa.gov/psd)]([https://www.esrl.noaa.gov/psd](https://www.esrl.noaa.gov/psd)), accessed on 20 July 2023 \\ MSLP, 10 m wind speed & ERA5 & ECMWF & 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) & 1982–2020 & [PERSON] et al. (2019) & [[https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5](https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5)]([https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5](https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5)), accessed on 20 July 2023 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The Observational and Reanalysis Data Sets Used in This Study.