Eastern China (27–41° N, 110–123° E) is heavily polluted by nitrogen dioxide (NO₂), particulate matter with aerodynamic diameter below 2.5 µm (PM_2.5), and other air pollutants. These pollutants vary on a variety of temporal and spatial scales, with many temporal scales that are nonperiodic and nonstationary, challenging proper quantitative characterization and visualization. This study uses a newly compiled EOF–EEMD analysis visualization package to evaluate the spatiotemporal variability of ground-level NO₂, PM_2.5, and their associations with meteorological processes over Eastern China in fall–winter 2013. Applying the package to observed hourly pollutant data reveals a primary spatial pattern representing Eastern China synchronous variation in time, which is dominated by diurnal variability with a much weaker day-to-day signal. A secondary spatial mode, representing north–south opposing changes in time with no constant period, is characterized by wind-related dilution or a buildup of pollutants from one day to another. We further evaluate simulations of nested GEOS-Chem v9-02 and WRF/CMAQ v5.0.1 in capturing the spatiotemporal variability of pollutants. GEOS-Chem underestimates NO₂ by about 17 µg m⁻³ and PM_2.5 by 35 µg m⁻³ on average over fall–winter 2013. It reproduces the diurnal variability for both pollutants. For the day-to-day variation, GEOS-Chem reproduces the observed north–south contrasting mode for both pollutants but not the Eastern China synchronous mode (especially for NO₂). The model errors are due to a first model layer too thick (about 130 m) to capture the near-surface vertical gradient, deficiencies in the nighttime nitrogen chemistry in the first layer, and missing secondary organic aerosols and anthropogenic dust. CMAQ overestimates the diurnal cycle of pollutants due to too-weak boundary layer mixing, especially in the nighttime, and overestimates NO₂ by about 30 µg m⁻³ and PM_2.5 by 60 µg m⁻³. For the day-to-day variability, CMAQ reproduces the observed Eastern China synchronous mode but not the north–south opposing mode of NO₂. Both models capture the day-to-day variability of PM_2.5 better than that of NO₂. These results shed light on model improvement. The EOF–EEMD package is freely available for noncommercial uses.

Motivated by the unwarned tsunami disaster caused by the flank collapse of the Anak Krakatau volcano on 22 December 2018, we used a landslide tsunami model to explore potential tsunami observing and warning systems for the region. With the estimated volume of 0.24 km³ and the relatively short duration (~3 to 5 min), the landslide of the volcanic edifice in the southwest sector triggered a tsunami of higher than 40 m in the vicinity. The tsunami, however, attenuated rapidly as it propagated away from the generation area, resulting in lower than 2 m wave heights at tide gauges around the Sunda Strait. Using the tsunami model, we demonstrated the capability of a ship height positioning method to detect the tsunami of amplitude ~20 cm associated with the event. Furthermore, assimilating the tsunami current velocity observed by high‐frequency oceanographic radars can produce accurate forecasts of coastal tsunami heights.

The Eastern Mediterranean Basin (EMB) is under the threat of tsunami events triggered by various causes including earthquakes and landslides. We propose a deployment of Offshore Bottom Pressure Gauges (OBPGs) around Crete Island, which would enable tsunami early warning by data assimilation for disaster mitigation. Our OBPG network consists of 12 gauges distributed around Crete Island, with a 100‐km interval, based on three criteria to select the locations. The station network must have a good azimuthal coverage and have enough (>50 km) distance from the coast, and the OBPGs are placed at the locations where the most energetic wave dynamics occur, which is confirmed by Empirical Orthogonal Function (EOF) analysis of pre‐calculated tsunami scenarios. We demonstrate three test cases comprising a hypothetical seismogenic tsunami in east Sicily, a hypothetical landslide tsunami in the Aegean Sea, and the real tsunami event of the May 2020 off the Crete earthquake. Our designed OBPG network achieves an accuracy of 88.5% for the hypothetical seismogenic tsunami and 87.3% for the hypothetical landslide tsunami with regard to the forecasting of first tsunami peak. For the real event of May 2020, it predicts the tsunami arrival at tide gauge NOA‐04 accurately; the observed and forecasted amplitudes of the first wave are 5.0 cm and 4.5 cm, respectively. The warning lead time for the May 2020 event was ~10 min. Therefore, our results reveal that the assimilation of OBPG data can satisfactorily forecast the amplitudes and arrival times for tsunamis in the EMB.