Saffir‐Simpson Category‐4 Typhoon Malakas crossed the Kuroshio, the Western Boundary Current of the North Pacific Ocean, and moved along the path of the Kuroshio for eight days between September 12 and 20, 2016. Malakas induced a 3–4°C sea surface cooling and an increase in sea surface Chlorophyll‐a concentration to 0.8 mg/m³. The total cooling area was approximately 18,000 km². Satellite observations show that Malakas triggered two upwelling regions associated with two mesoscale eddies, both to the northeast of Taiwan. The Regional Oceanic Modeling System (ROMS) was employed to offer insights into the dynamical mechanisms on the ocean response to a typhoon. The numerical model results reveal that a coastal upwelling event was induced by southeasterly winds blowing prior to the arrival of Malakas' outer band, and caused a surface cooling near Taiwan's northeast coast. A branch of the Taiwan Warm Current interacted with the offshore Kuroshio current to form an eddy prior to the typhoon's arrival. Malakas then facilitated the development of the near‐coastal eddy. An open ocean eddy was triggered by inertial‐Ekman pumping under the forcing effect of Malakas. The interaction between the southward current caused by Malakas and the northeastward flowing Kuroshio maintained these two induced mesoscale eddies. Numerical results show a near‐inertial oscillation (IO) of about a 26‐h period with an e‐folding time scale of ∼5 days was magnified and modulated by Malakas. The typhoon‐induced oceanic phenomena were very well revealed in this combined observational and numerical modeling study.

The Lagrangian tracking approach is often used in numerical modeling (NM) to simulate and predict the movement of marine particles such as plastic, oil spills, and floating wreckage. The uncertainties in NM reduce prediction accuracy as a result of the coarse temporal and spatial resolution, along with waves, winds, and currents. From 29 August to 22 December 2020, the United States Defense Advanced Research Projects Agency's Ocean of Things program deployed 422 floating drifters in the Gulf of Mexico, providing an opportunity of using the observed trajectories as ground truth to train Artificial Intelligence (AI) models to correct NM float trajectory predictions. A Regional Ocean Model System (ROMS) and AI Hybrid model was developed to implement AI to correct the ROMS‐predicted 1‐day float trajectories. The AI model is built on a convolutional neural network and Gated Recurrent Unit. The results of the ROMS‐AI Hybrid model show that 82.0% of the trajectory predictions were improved at the 24 hr, with the corresponding overall mean separation error decreasing by 11.82 km, from 20.56 to 8.74 km, which is a 57% improvement, and the error growth rate decreasing from 5.06 km per 6 hr to 1.95 km per 6 hr. The evident improvement indicates that the ROMS‐AI Hybrid model can correct the ROMS simulation to improve the 1‐day prediction of the float trajectories and shows great potential to predict the cluster of the floats.

The Category-4 typhoon Malakas moved along the continental shelf margin of the East China Sea from September 16 to 18, 2016. The typhoon-induced 2°C-3°C sea surface cooling and 0.8 mg/m ³ Chlorophyll-a concentration change were observed by the geostationary satellite sensors carried by the Japanese Himawari 8 Advanced Himawari Imager (AHI) and the South Korean Geostationary Ocean Color Imager (GOCI). Numerical simulation was employed to analyze this typhoon's impact on the ocean environment to the northeast of Taiwan. The model result suggests that Typhoon Malakas generated an upper layer cyclonic eddy in the study area. This surface eddy was connected to a 100-m deep pre-existing eddy to form a stronger one that triggered the upwelling observed by satellites.

A winter storm triggered a significant sea smoke with the northwesterly wind to the Yellow Sea, China, on 7 January 2021. The ocean responses to this event lasted about 3 days. Satellite observations show that the sea surface temperature dropped from 5.7° to 4.7°C on the following day and then recovered to the previous level; the chlorophyll-a, a bio-growth indicator, increased from 3.6 to 3.9 mg m⁻³ due to cooling-induced coastal upwelling between 7 and 9 January. Two buoys measurements showed that the air temperature dropped to −13.3°C and high relative humidity with a maximum value of 89.0% above the sea surface, creating favorable conditions for sea smoke generation. A Regional Ocean Modeling System (ROMS) and Weather Research and Forecasting (WRF) Model coupled model with the Goddard Chemistry Aerosol Radiation and Transport (GOCART) chemical module was implemented to reproduce this sea smoke phenomenon and analyze the air–sea interaction. The 20°C temperature difference between extreme cold air (−13.3°C) and the relatively warm stable sea surface (4.7°–5.7°C) enhanced the seawater evaporation. In addition, we suppose the concentration of sea salt, a kind of condensation nucleus, with a particle diameter of 0.5–1.5 μm above the sea surface increased quickly on 7 January. The boiling-water-like sea surface was imaged on a synthetic aperture image. We developed an image analysis method to describe the cell-shaped texture characteristics imaged by Synthetic Aperture Radar (SAR). We also found that the sea surface imprints of sea smoke are governed by the thermal, not the dynamical instability. Significance Statement: On 7 January 2021, a significant sea smoke event happened in the Yellow Sea. The ocean response to the event lasted 3 days. First, on a synoptic scale, this study presents the comprehensive satellite observations of the sea surface temperature drop and chlorophyll-a increase associated with the sea smoke. Second, a coupled air–sea interaction model with the Goddard Chemistry Aerosol Radiation and Transport (GOCART) chemical module was implemented to reproduce this sea smoke phenomenon and identify which condensation nucleus induced such heavy sea smoke. Third, we developed an image analysis method to analyze high-resolution synthetic aperture radar images and found that the sea surface imprints of sea smoke are governed by the thermal, not the dynamical instability.

Several groups of diurnal recurring atmospheric gravity waves (AGWs) were observed on an Environmental Satellite advanced synthetic aperture radar image and six Moderate Resolution Imaging Spectroradiometer (MODIS) images acquired along the South China Sea coast for 4 days from 3 to 6 March 2010. The unique observations are that the previously observed AGWs seldom recurred in a region for multiple days. Each cycle of these observed AGWs had undergone three stages: generation, propagation, and dissipation daily. Both SAR‐imaged sea surface roughness patterns and MODIS‐observed cloud imprints show that the AGWs were a group of transverse waves along the coast. We implemented the Weather Research and Forecasting (WRF) model to understand the physical characteristics and dynamical mechanisms of these AGWs. The WRF simulated results indicated that the AGWs were upstream waves triggered by the interaction of the southerly wind and topography and dissipated by the diurnal warming. We applied the spectral analysis to the model‐simulated vertical wind field and explored that the leading wave crest length and crest‐to‐crest wavelength are 156.25 and 7.14 km, respectively. The wave period and phase speed of the upstream AGWs within the vital observation of SAR are 1.81 hr and 1.26 m/s, respectively. The fifth‐generation European Centre for Medium‐Range Weather Forecasts reanalysis hourly data was used to explore the rarity of the recurring AGWs and proved that the stable air‐sea temperature difference was the main reason for the rarity of this event. And sufficient cloud content (>50%) was necessary for the MODIS to capture the AGWs' imprints.

Stormwater management ponds (SMPs) protect coastal communities from flooding caused by heavy rainfall and runoff. If the SMPs are submerged under seawater during a tropical cyclone (TC) and its storm surge, their function will be compromised. Under climate change scenarios, this threat is exacerbated by sea level rise (SLR) and more extreme tropical cyclones. This study quantifies the impact of tropical cyclones and their storm surge and inundation on South Carolina SMPs under various SLR scenarios. A coupled hydrodynamic model calculates storm surge heights and their return periods using historical tropical cyclones. The surge decay coefficient method is used to calculate inundation areas caused by different return period storm surges under various SLR scenarios. According to the findings, stormwater management ponds will be aggravated by sea level rise and extreme storm surge. In South Carolina, the number of SMPs at risk of being inundated by tides and storm surges increases almost linearly with SLR, by 10 SMPs for every inch of SLR for TC storm surges with all return periods. Long Bay, Charleston, and Beaufort were identified as high-risk coastal areas. The findings of this study indicate where current SMPs need to be redesigned and where more SMPs are required. The modeling and analysis system used in this study can be employed to evaluate the effects of SLR and other types of climate change on SMP facilities in other regions.