With the increasing frequency of global climate change and natural disasters, real-time monitoring and prediction of the Earth system have become critical. Near-real time modeling provides a more rapid and accurate analytical approach to Earth system science, particularly in areas such as climate change, environmental protection, disaster forecasting, and resource management. This study explores near-real time Earth system modeling methods based on high-performance computing and data integration. By utilizing multi-source data inputs, including satellite remote sensing, meteorological observations, and geological data, and combining advanced numerical models with machine learning algorithms, real-time simulations and predictions of dynamic Earth system changes are made. The paper presents modeling techniques for various Earth subsystems (such as atmosphere, oceans, land, and ice sheets) and discusses the challenges and opportunities of near-real time modeling. Additionally, the impact of real-time modeling on policy decisions and disaster response is analyzed, highlighting the potential of future technological advancements for achieving more accurate predictions and addressing global changes. This research offers new insights for Earth system scientists and provides valuable support for public management and environmental protection agencies in tackling complex Earth system dynamics.

Near-real time modeling