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Machine Learning Applications on Earth Sciences Dept.
These activities benefit from the disciplinary structure of the Earth Sciences department as well as the collaboration with other BSC departments like the Computer Sciences and external institutions.
Some of the potential topics of interest for the department are listed below.
- Clustering(Unsupervised Learning) and Classification(supervised Learning) Methods
- Multivariate Methods
- Deep Learning(SP) : Convolutional Neuronal Networks
- Causal Effect Networks for Time-series Analysis
Atmospheric Composition
Inference of surface pollutant concentrations from satellite and global scale meteorological and chemical simulations (AQ-WATCH project). Application to NO2 (TROPOMI sensor) and aerosols.
Earth System Services
Statistical correction of air quality forecasts with machine learning algorithms (ML4AQ grant). Application to Mexico City.
Climate and Weather Prediction
Hurricanes Prediction
The use of Deep Learning methods, in particular convolutional neuronal networks, can be applied to Hurricanes and cyclones observational databases to predict the number of such extreme events for the subsequent years.
Bias Correction
The use of the Analogues technique utilized to improve the CALIOPE forecast might also be implemented within the MED-GOLD project to improve the Bias correction and forecast calibration. Mutivariate-Analysis will also play an important role in the development of multivariate scores using EOF approach.
Teleconnections and Multivariate analysis
Causal interships between different predictants like geopotential, sea level pressure, seat surface temperature, precipitation level, ozone concentration, etc .. might be characterized and identified using causal effect networks. These studies help scientist to evaluate to what extent variability modes occur and the impact of their influence into different components of the earth dynamics system(Atmosphere, Land, Ocean, Sea Ice).
Earth Sciences Services
This particular area benefits from the application of machine learning techniques like multivariate analysis among others, applied to the different research fields of the department to extract valuable information for decision making in a wide spectrum of applications such as society, agriculture, energy, health etc.
Computational Earth Sciences
Current Developments
- Improvement in the characterization of uncertainty in multi-ensembles models like EC-Earth by using an adaptive methodology. adaptive
- Extending and adapting existing Classification /Clustering algorithms to the department tools.
- Deep Learning and GPUs applications to Online Diagnostics and Hurricanes forecast.
