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Soil moisture is a critical parameter that significantly impacts the global energy balance, including the hydrologic cycle, land–atmosphere interactions, soil evaporation, and plant growth. Currently, soil moisture is typically measured by installing sensors in the ground or through satellite remote sensing, with data retrieval facilitated by reanalysis models such as the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5 (ERA5) and the Global Land Data Assimilation System (GLDAS). However, the suitability of these methods for capturing local-scale variabilities is insufficiently validated, particularly in regions like South Korea, where land surfaces are highly complex and heterogeneous. In contrast, artificial intelligence (AI) approaches have shown promising potential for soil moisture retrieval at the local scale but have rarely demonstrated substantial products for spatially continuous grids. This paper presents the retrieval of daily soil moisture (SM) over a 500 m grid for croplands in South Korea using random forest (RF) and automated machine learning (AutoML) models, leveraging satellite images and meteorological data. In a blind test conducted for the years 2013–2019, the AutoML-based SM model demonstrated optimal performance, achieving a root mean square error of 2.713% and a correlation coefficient of 0.940. Furthermore, the performance of the AutoML model remained consistent across all the years and months, as well as under extreme weather conditions, indicating its reliability and stability. Comparing the soil moisture data derived from our AutoML model with the reanalysis data from sources such as the European Space Agency Climate Change Initiative (ESA CCI), GLDAS, the Local Data Assimilation and Prediction System (LDAPS), and ERA5 for the South Korea region reveals that our AutoML model provides a much better representation. These experiments confirm the feasibility of AutoML-based SM retrieval, particularly for local agrometeorological applications in regions with heterogeneous land surfaces like South Korea. [ABSTRACT FROM AUTHOR]

Since its inception in 2011, Museum Teen Summit (MTS), a youth-led collective dedicated to better understanding and transforming the role of young people within museums, sought to act as a bridge between teens and cultural organizations. Over eight years, MTS built an impressive network of youth and adult professionals interested in specialized programming for teens in museums, conducted research on teen agency in museums, organized events for teens within museums, and presented at professional conferences across the country. The work of the over ninety young people who cycled through the collective was a resounding success, but with notable flaws and space for improvement. A closer analysis of the work of MTS also reveals less conventionally recognized successes. This collectively written article, authored by youth alumni and an adult collaborator, analyzes definitions of success and failure in youth-led organizing and research and how they shaped our work and our experiences.

The diagonal squall line that passed through the Korean Peninsula on the 18 May 2020 was examined using wind data retrieved from multiple Doppler radar synthesis focusing on its kinematic and dynamic aspects. The low-level jet, along with warm and moist air in the lower level, served as the primary source of moisture supply during the initiation and formation process. The presence of a cold pool accompanying the squall line played a role in retaining moisture at the surface. As the squall line approached the Korean Peninsula, the convective bands in the northern segment (NS) and southern segment (SS) of the squall line exhibited distinct evolutionary patterns. The vertical wind shear in the NS area was more pronounced compared to that in the SS. The ascending inflow associated with the tilted updraft in the NS reached an altitude of 7 km, whereas it was only up to 4 km in the SS. The difference was caused by the strong descending rear flow, which obstructed the ascending inflow and let to significant updraft in the SS. [ABSTRACT FROM AUTHOR]