Harnessing deep learning to forecast local microclimate using global climate data.

ABSTRACT: Microclimate is a complex non-linear phenomenon influenced by both global and local processes. Its understanding holds a pivotal role in the management of natural resources and the optimization of agricultural procedures. This phenomenon can be effectively monitored in local areas by employing models that integrate physical laws and data-driven algorithms relying on climate data and terrain conformation. Climate data can be acquired from nearby meteorological stations when available, but in their absence, global climate datasets describing 10 km-scale areas are often utilized. The present research introduces an innovative microclimate model that combines physical laws and deep learning to reproduce temperature and relative humidity variations at the meter-scale within a study area located

SUBMITTER: Zanchi M 

PROVIDER: S-EPMC10687000 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature