Sea SurfaceTemperature (SST) is a critical parameter for monitoring the marine environment and understanding various ocean phenomena. While SST can be regularly retrieved from satellite data, it often suffers from missing data due to various reasons including cloud contamination. In this study, we proposed a novel two-step data fusion framework for generating high-resolution seamless daily SST from multi-satellite data sources. The proposed approach consists of (1) SST reconstruction based on Data Interpolate Convolutional AutoEncoder (DINCAE) using the SSTs derived from two satellite sensors (i.e., Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Microwave Scanning Radiometer 2(AMSR2)), and (2) SST improvement through data fusion using random forest for consistency with in situ measurements with two schemes (i.e., scheme 1 using the reconstructed MODIS SST variables and scheme 2 using both MODIS and AMSR2 SST variables). The proposed approach was evaluated over the Kuroshio Extension in the Northwest Pacific, where a highly dynamic SST pattern can be found, from 2015 to 2019. The results showed that the reconstructed MODIS and AMSR2 SSTs through DINCAE yielded very good performance with Root Mean Square Errors (RMSEs) of 0.85 and 0.60 °C and Mean Absolute Errors (MAEs) of 0.59 and 0.45 °C, respectively. The results from the second step showed that scheme 2 and scheme 1 produced RMSEs of 0.75 and 0.98 °C and MAEs of 0.53 and 0.68 °C, respectively, compared to the in situ measurements, which proved the superiority of scheme 2 using multi-satellite data sources. Scheme 2 also showed comparable or even better performance than two operational SST products with similar spatial resolution. In particular, scheme 2 was good at simulating features with fine resolution (~50 km). The proposed approach yielded promising results over the study area, producing seamless daily SST products with high quality and high feature resolution.
<p>Estimating diurnal variations of Sea Surface Temperature (SST) is important for studying air-sea heat exchange. Existing operational diurnal SSTs are derived from numerical models incorporating satellite data, and assimilated with in-situ measurements, which are very accurate. However, numerical model-based methods incur significant computational costs for identifying the diurnal cycle of SST from various heat flux sources (i.e., sensible, latent heat). In this study, we first proposed a Generative Adversarial Network (GAN) method to reconstruct high-resolution diurnal SST using satellite observations as an actual diurnal signal from the ocean surface layer. A generator in the GAN model was trained using the diurnal variability-related variables, including the hourly SSTs and shortwave radiation measurements from Himawari-8 geostationary satellite observations, to estimate diurnal SSTs. The discriminator in the GAN model was learned to reduce the difference in spatiotemporal variability of diurnal SSTs between a satellite data-assimilated numerical model product (Global Ocean OSTIA Diurnal Skin Sea Surface Temperature; Copernicus marine service) and estimated SST from the generator. The results showed that the reconstructed SST had a better spatial distribution of ocean phenomena such as front and eddy than compared with the numerical model-derived SST. It implied that the GAN model could simulate a high spatial variability of SSTs using satellite-based data with a spatial resolution of 2km. The proposed GAN model produced high validation accuracy, resulting in the coefficient of determination of 0.99, bias of -0.2&#8451;, and root mean square errors of 0.58&#8451; when compared with in situ SST Quality Monitor drifting buoy data. Since we use geostationary satellite data, the proposed model can capture real diurnal variability of SST more frequently than existing numerical model data using analysis data. In addition, the proposed deep learning model is much more computationally efficient than the numerical models.</p>
<p>Reliable early forecasting of summer air temperature is important to effectively prepare and mitigate damage such as heat-related mortality and excessive electricity demand caused by heat waves and tropical nights. Numerical weather prediction (NWP) models have been used for operational forecasting of air temperature. However, NWP models have coarse spatial resolution due to massive computational resources arising from complex forecasting systems and unstable parameterization of NWP models, which make the uncertainty of prediction, consisting of systematic and random biases. Therefore, the objective of this study is to develop a novel deep learning-based statistical downscaling approach for the Global Data Assimilation and Prediction System (GDAPS) model&#8217;s summer air temperature forecasts over South Korea. This study developed the proposed statistical downscaling model through the decomposition into the temporal dynamics of daily air temperature forecast and spatial fluctuation by pixels. The daily temperature dynamic was estimated using a daily mean GDAPS temperature forecast with simple mean bias correction. The spatial fluctuation by pixels was obtained using the spatial anomaly of downscaled air temperature forecast by the U-Net model. The GDAPS model&#8217;s forecast data, present-day high spatial resolution satellite observations, and topography variables were used as input variables for training the U-Net model. The observations at weather stations were spatially interpolated using the regression-kriging, and then we used it as a target image for the U-Net model. The proposed U-net model was compared with the Local Data Assimilation and Prediction System (LDAPS), the dynamically downscaled model of the GDAPS, and the support vector regression (SVR)-based statistical downscaling model. For next-day Tmax and Tmin forecasts, the suggested U-net model showed better performance, having high coefficient of determination (R<sup>2</sup>) of 0.76 and 0.74 and root mean square error (RMSE) of 2.5 &#176;C and 1.5 &#176;C for next-day Tmax and Tmin forecasts, respectively. When analyzing the skill score (SS) values by stations of the U-Net model, it had remarkably high SS values at stations where the GDAPS had a high absolute value. For Tmax and Tmin forecasts with 1-7 days forecast lead time, the suggested model consistently provided better performance (higher spatial correlation and lower RMSE) than GDAPS and SVR. In addition, the U-net model showed a detailed spatial distribution most similar to that of the observations. These results demonstrated that the suggested model successfully corrected the bias of the GDAPS, improving not only the forecast accuracy but also the ability to capture the spatial distribution of Tmax and Tmin forecasts. Using the deep learning-based suggested model in this study, bias-corrected high spatial resolution air temperature forecasts with a relatively long forecast lead time in summer seasons can be successfully produced.</p>
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