Accurate air pollutant prediction is essential for addressing environmental and public health concerns. Air quality models like WRF-CMAQ provide simulations, but often show significant errors compared to observed concentrations. To identify the sources of these model biases, we applied the XGBoost machine learning algorithm to assess the performance of WRF-CMAQ in predicting air pollutants across two regions in China. XGBoost models trained with observations achieved high accuracy (R > 0.95), indicating that the selected features effectively capture pollutant variations. When trained on WRF-CMAQ inputs, XGBoost still improved performance but revealed biases linked to both model inputs (10–60%) and mechanisms (1–30%). Analysis identified previous-hour pollutant levels as the largest bias contributor, followed by meteorological variables. The study highlights the need for improving both model inputs and mechanisms to enhance future air quality predictions and support pollution control strategies.

Due to limited in-situ ocean observations, reanalysis data are often considered as an important source for studying tropical cyclone (TC)-induced ocean waves. Here, we introduced a method to quantitatively evaluate the errors of TC-induced ocean waves in reanalysis datasets. The TC data are from the IBTrACS dataset. We compared TC-induced ocean waves in two reanalysis datasets (i.e., ERA5 and WAVERYS) with those in buoy data when TCs are near the buoy stations. We showed that the errors of TC-induced ocean waves in WAVERYS and ERA5 are similar, because the surface winds in these two datasets are the same. We noticed that the errors in the North Pacific are remarkably smaller than those in the North Atlantic due to more realistic probability density of TC-induced ocean waves in the North Pacific compared with those in the North Atlantic. Moreover, the errors are strongly related to significant wave height (SWH) and the distance from TC center. The larger the SWH and the shorter the distance, the larger the error. Furthermore, the errors in TC-induced ocean waves decreased significantly over the past decades.

As a semi-enclosed marginal sea, the Japan Sea has long been hindered in tidal studies by sparse tide-gauge observations and limited satellite altimetry coverage. Although Sentinel-3A provides high-quality sea-level observations, its 27-day exact-repeat sun-synchronous orbit introduces severe tidal aliasing into conventional harmonic analysis—most critically, the S₂ constituent degenerates into a constant and cannot be recovered independently. In this study, we apply the Modified Harmonic Analysis model based on the Credo of Smoothness (MHACS) with ridge regression to eight years (2016–2024) of Sentinel-3A altimetry in the Japan Sea for the first time, estimating tidal constants for the eight major constituents. By exploiting the smooth connections among major astronomical constituents, MHACS mitigates the aliasing problem, with severely aliased constituents such as S₂ inferred from the smooth admittance relationship rather than independently resolved. The inverted tidal fields show excellent agreement with the independent FES2014 global tide model, with a spatially averaged vector difference of only 1.39 cm, whereas the traditional tidal response method yields 4.52 cm—3.3 times larger. Independent validation against 22 GESLA-3 tide-gauge records further confirms this ranking (3.012 cm vs. 5.676 cm). As a consistency check, the MHACS-derived P₁/K₁ and K₂/S₂ amplitude ratios (0.316 and 0.278) agree closely with equilibrium tidal theory, although this test is not fully independent. These results demonstrate that MHACS is well suited for tidal inversion from 27-day repeat altimetry.