Enhancing tropical cyclone intensity forecasting with explainable deep learning integrating satellite observations and numerical model outputs.

Tropical cyclone (TC) intensity change forecasting remains challenging due to the lack of understanding of the interactions between TC changes and environmental parameters, and the high uncertainties resulting from climate change. This study proposed hybrid convolutional neural networks (hybrid-CNN), which effectively combined satellite-based spatial characteristics and numerical prediction model outputs, to forecast TC intensity with lead times of 24, 48, and 72 h. The models were validated against best track data by TC category and phase and compared with the Korea Meteorological Administrator (KMA)-based TC forecasts.

Diurnal urban heat risk assessment using extreme air temperatures and real-time population data in Seoul.

Previous heat risk assessments have limitations in obtaining accurate heat hazard sources and capturing population distributions, which change over time. This study proposes a diurnal heat risk assessment framework incorporating spatiotemporal air temperature and real-time population data. Daytime and nighttime heat risk maps were generated using hazard, exposure, and vulnerability components in Seoul during the summer of 2018.

Retrieval of hourly PM using top-of-atmosphere reflectance from geostationary ocean color imagers I and II.

To produce real-time ground-level information on particulate matter with a diameter equal to or less than 2.5 μm (PM), many studies have explored the applicability of satellite data, particularly aerosol optical depth (AOD). However, many of the techniques used are computationally demanding; to overcome these challenges, machine learning(ML)-based research has been on the rise.

Geostationary satellite-derived ground-level particulate matter concentrations using real-time machine learning in Northeast Asia.

Rapid economic growth, industrialization, and urbanization have caused frequent air pollution events in East Asia over the last few decades. Recently, aerosol data from geostationary satellite sensors have been used to monitor ground-level particulate matter (PM) concentrations hourly. However, many studies have focused on using historical datasets to develop PM estimation models, often decreasing their predictability for unseen data in new days.

Estimation of surface-level NO and O concentrations using TROPOMI data and machine learning over East Asia.

In East Asia, air quality has been recognized as an important public health problem. In particular, the surface concentrations of air pollutants are closely related to human life. This study aims to develop models for estimating high spatial resolution surface concentrations of NO and O from TROPOspheric Monitoring Instrument (TROPOMI) data in East Asia.

Estimation of spatially continuous daytime particulate matter concentrations under all sky conditions through the synergistic use of satellite-based AOD and numerical models.

Satellite-derived aerosol optical depth (AOD) products are one of main predictors to estimate ground-level particulate matter (PM and PM) concentrations. Since AOD products, however, are only provided under high-quality conditions, missing values usually exist in areas such as clouds, cloud shadows, and bright surfaces. In this study, spatially continuous AOD and subsequent PM and PM concentrations were estimated over East Asia using satellite- and model-based data and auxiliary data in a Random Forest (RF) approach.

Delineation of high resolution climate regions over the Korean Peninsula using machine learning approaches.

In this research, climate classification maps over the Korean Peninsula at 1 km resolution were generated using the satellite-based climatic variables of monthly temperature and precipitation based on machine learning approaches. Random forest (RF), artificial neural networks (ANN), k-nearest neighbor (KNN), logistic regression (LR), and support vector machines (SVM) were used to develop models. Training and validation of these models were conducted using in-situ observations from the Korea Meteorological Administration (KMA) from 2001 to 2016.

Zooplankton and micronekton respond to climate fluctuations in the Amundsen Sea polynya, Antarctica.

The vertical migration of zooplankton and micronekton (hereafter 'zooplankton') has ramifications throughout the food web. Here, we present the first evidence that climate fluctuations affect the vertical migration of zooplankton in the Southern Ocean, based on multi-year acoustic backscatter data from one of the deep troughs in the Amundsen Sea, Antarctica. High net primary productivity (NPP) and the annual variation in seasonal ice cover make the Amundsen Sea coastal polynya an ideal site in which to examine how zooplankton behavior responds to climate fluctuations.

Effects of seasonal variations on sediment-plume streaks from dredging operations.

When mixtures of aggregates and water dredged from the seabed are discharged at the surface into the adjacent water from a barge, coarse sediments sink immediately and fine sediments are suspended forming a plume. Recently, elongated plumes of fine sediment were observed by satellites near a dredging location on the continental shelf. Such plume streaks were longer in certain conditions with seasonality than expected or reported previously.