Virtual Event: ECMWF-ESA Workshop on Machine Learning for Earth System Observation and Prediction
Session
Conveners
Session 2 (cont.): ML for Earth System Observations
- Peter Dueben (ECMWF)
Session 2 (cont.): ML for Earth System Observations
- Susanne Mecklenburg (ESA)
Neural networks (NNs) have emerged as a promising tool in many meteorological applications. While they perform amazingly well at many complex tasks neural networks are generally treated as a black box, i.e. it is typically considered too difficult a task to understand how they work. However, a better understanding of neural networks would have many advantages. A better understanding could...
With the unprecedented advances in the satellite technology, recent years have witnessed a significant increase in the volume of remote sensing (RS) image archives (Demir and Bruzzone 2016). Thus, the development of efficient and accurate content based image retrieval (CBIR) systems in massive archives of RS images is a growing research interest in RS. CBIR aims to search for RS images of the...
Numerical weather predictions (NWP) and observational systems have improved greatly both on quantity and quality in recent years. Combining expertise knowledge and machine learning (ML) to extract and synthesize valuable information from these "big data" is expected to one of present major challenges and opportunities to improve the severe weather forecast. A ML correction method based on the...
Climate change is arguably the greatest challenge facing humankind in the twenty-first century. The United Nations Framework Convention on Climate Change (UNFCCC) provides the vehicle for multilateral action to combat climate change and its impacts on humanity and ecosystems. In order to make decisions on climate change mitigation and adaptation, the UNFCCC requires a systematic monitoring of...
Since July 2019, a SMOS neural network soil moisture product has been assimilated into the ECMWF operational soil moisture simplified extended Kalman filter (SEKF) as part of the land data assimilation system. There are two versions of SMOS neural network soil moisture products produced routinely at ECMWF. The first has been trained on the SMOS level 2 soil moisture product and is delivered to...
