Farmstead-Specific Weather Risk Prediction Technique Based on High-Resolution Weather Grid Distribution

Autor: Dae-Jun Kim, Jin-Hee Kim, Eun-Jeong Yun, Dae Gyoon Kang, Eunhye Ban
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Atmosphere, Vol 15, Iss 1, p 116 (2024)
Druh dokumentu: article
ISSN: 2073-4433
DOI: 10.3390/atmos15010116
Popis: In recent years, the importance and severity of weather-related disasters have escalated, attributed to rising temperatures and the occurrence of extreme weather events due to global warming. The focus of disaster management has shifted from crisis management (e.g., repairing and recovering from damage caused by natural disasters) to risk management (e.g., prediction and preparation) while concentrating on early warning, thanks to the development of media and communication conditions. The Rural Development Administration (Korea) has developed the “early warning service for weather risk management in the agricultural sector” that detects weather risks for crops from high-resolution weather information in advance and provides customized information to respond to possible disaster risks in advance in response to the increasing number of extreme weather events. The core technology of this service is damage prediction technology that determines the overall agricultural weather risk level by quantifying the current growth stage of cultivated crops and the probability of possible weather disasters according to the weather conditions of the farm. Agrometeorological disasters are damages caused by weather conditions that can affect crops and can be predicted by estimating the probability of damage that may occur from the interaction between hazardous weather and crop characteristics. This review introduces the classification of possible weather risks by their occurrence mechanisms, based on the developmental stage of crops and prediction techniques that have been developed or applied to date. The accumulated crop growth and weather risk information is expected to be utilized as support material for farming decision-making, which helps farmers proactively respond to crop damage due to extreme weather events by providing highly reliable disaster forecasts through the advancement of prediction technology.
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