DataSheet_1_A transformer-based method for correcting significant wave height numerical forecasting errors.docx

Accurate significant wave height (SWH) forecasting is essential for various marine activities. While traditional numerical and mathematical-statistical methods have made progress, there is still room for improvement. This study introduces a novel transformer-based approach called the 2D-Geoformer to enhance SWH forecasting accuracy. The 2D-Geoformer combines the spatial distribution capturing capabilities of SWH numerical models with the ability of mathematical-statistical methods to identify intrinsic relationships among datasets. Using a comprehensive long time series of SWH numerical hindcast datasets as the numerical forecasting database and ERA5 reanalysis SWH datasets as the observational proxies database, with a focus on a 72-hour forecasting window, the 2D-Geoformer is designed. By training the potential connections between SWH numerical forecasting fields and forecasting errors, we can retrieve SWH forecasting errors for each numerical forecasting case. The corrected forecasting results can be obtained by subtracting the retrieved SWH forecasting errors from the original numerical forecasting fields. During long-term validation periods, this method consistently and effectively corrects numerical forecasting errors for almost every case, resulting in a significant reduction in root mean square error compared to the original numerical forecasting fields. Further analysis reveals that this method is particularly effective for numerical forecasting fields with higher errors compared to those with relatively smaller errors. This integrated approach represents a substantial advancement in SWH forecasting, with the potential to improve the accuracy of operational SWH forecasts. The 2D-Geoformer combines the strengths of numerical models and mathematical-statistical methods, enabling better capture of spatial distributions and intrinsic relationships in the data. The method's effectiveness in correcting numerical forecasting errors, particularly for cases with higher errors, highlights its potential for enhancing SWH forecasting accuracy in operational settings.</p

Insights into Shape Selectivity and Acidity Control in NiO-Loaded Mesoporous SBA-15 Nanoreactors for Catalytic Conversion of Cellulose to 5‑Hydroxymethylfurfural

Facilitated isomerization of cellulose hydrolysis intermediate glucose without unexpected byproducts, which is the rate-determining step in the production of high-value-added biofuels, enables the efficient production of 5-hydroxymethylfurfural (5-HMF) from cellulose. In this work, considering the essential role of the acidity control and shape selectivity of a zeolite catalyst, a NiO-loaded mesoporous NiO/poly(vinyl pyrrolidone) (PVP)-phosphotungstic acid (HPA)@SBA-15 nanoreactor was prepared. This SBA-15 nanoreactor with a pore size of 5.47 nm reduced the concentration of byproducts formic acid (FA) and levulinic acid (LA) through shape selection for intermediates. Well-defined NiO nanoparticles (Ni-to-carrier mass ratio was 1:1) provided the NiO/PVP-HPA@SBA-15 nanoreactor a high Lewis acidity of 99.29 μmol g–1 for glucose catalytic isomerization, resulting in an increase in total reducing sugar (TRS) yield by 5 times. Such a nanoreactor remarkably improved the reaction efficiency of 5-HMF production from cellulose (a 5-HMF selectivity of 95.81%) in the 1-butyl-3-methylimidazolium chloride ([BMIM]Cl)/valerolactone (GVL) biphasic system