Earth, its weather and climate constitute a complex system whose monitoring and modelling has undergone remarkable progress in recent years. In particular, enhanced spaceborne observations and the integration Machine/Deep Learning (ML/DL) techniques are key drivers of innovation in Earth System Observation and Prediction (ESOP) for Weather and Climate.
ML/DL techniques revolutionized numerous fields and have proven advantageous in various applications. These techniques garnered significant attention and adoption within the ESOP community due to their ability to enhance our understanding and prediction capabilities of the Earth's complex dynamics. One prominent area where ML/DL techniques have proven invaluable is in the development of high fidelity digital models of the Earth on a global scale. These models serve as comprehensive monitoring, simulation, and prediction systems that enable us to analyse and forecast the intricate interactions between natural phenomena and human activities.
ML/DL solutions also showcased promising advancements in weather forecasting and climate prediction. Algorithms can be trained to identify instances where physical models may exhibit inaccuracies and subsequently learn to correct their predictions accordingly. Moreover, AI-based models have the potential to create hybrid forecast models that combine the strengths of traditional, physics-based NWP/Climate prediction methodologies with the capabilities of ML/DL, ultimately enhancing the accuracy and reliability of predictions.As such, the application of ML/DL for ESOP and the hybrid usage in combination with established numerical prediction and assimilation methods are thematic areas of particular interest in this session.
Inspired by the successful 4-days long ESA-ECMWF ML4ESOP workshop, this sprint session invites new ML4ESOP explorers to present their latest innovation in ESOP. Focus is on the exploration of new data sources and benchmarks for weather and climate modelling, the adaptation of large-scale data-driven Earth system models, as well as novel demonstrations of their applicability to weather and climate observation and prediction.
This session invites all experts from diverse fields to discuss how recent advances innovate on established ESOP approaches, to address current challenges, and to identify opportunities for future work.
ML/DL techniques revolutionized numerous fields and have proven advantageous in various applications. These techniques garnered significant attention and adoption within the ESOP community due to their ability to enhance our understanding and prediction capabilities of the Earth's complex dynamics. One prominent area where ML/DL techniques have proven invaluable is in the development of high fidelity digital models of the Earth on a global scale. These models serve as comprehensive monitoring, simulation, and prediction systems that enable us to analyse and forecast the intricate interactions between natural phenomena and human activities.
ML/DL solutions also showcased promising advancements in weather forecasting and climate prediction. Algorithms can be trained to identify instances where physical models may exhibit inaccuracies and subsequently learn to correct their predictions accordingly. Moreover, AI-based models have the potential to create hybrid forecast models that combine the strengths of traditional, physics-based NWP/Climate prediction methodologies with the capabilities of ML/DL, ultimately enhancing the accuracy and reliability of predictions.As such, the application of ML/DL for ESOP and the hybrid usage in combination with established numerical prediction and assimilation methods are thematic areas of particular interest in this session.
Inspired by the successful 4-days long ESA-ECMWF ML4ESOP workshop, this sprint session invites new ML4ESOP explorers to present their latest innovation in ESOP. Focus is on the exploration of new data sources and benchmarks for weather and climate modelling, the adaptation of large-scale data-driven Earth system models, as well as novel demonstrations of their applicability to weather and climate observation and prediction.
This session invites all experts from diverse fields to discuss how recent advances innovate on established ESOP approaches, to address current challenges, and to identify opportunities for future work.