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Applying AI capabilities to address Operations challenges in ECMWF Products Team
The ECMWF Product’s Team acts as a data vendor to several clients, providing massive amounts of meteorological data to them. These services produce log files that contain useful patterns and information that can help improve the reliability of ECMWF’s services.
We aim to produce Machine Learning and Deep Learning based systems capable of monitoring these services for sudden disruptions or failures. We also propose methods to forecast these variables, so that we can predict future spikes and surges.
Through this, we hope to provide valuable insights into how ECMWF can improve it’s data services in the near future.
The current Global ECMWF Fire Forecasting (GEFF) system is based on empirical models implemented in FORTRAN.
The project intends to explore whether fire danger forecasting using Deep Learning can achieve skills comparable to the operational GEFF system and whether artificial intelligence can reveal important relationships between fire danger and event occurrence through the inclusion of additional variables, optimisation of model architecture & hyperparameters.
Finally, conditional to the suitability of the available data, a preliminary fire spread prediction tool will be developed to support first responders and monitoring activities.
The continuous integration cycle of the IFS model is able to provide a regular stream of performance data, such as component runtimes, I/O and parallelization overheads.
In this project, we are aiming to develop a tool for interactive visualization of HPC performance data to better track and analyze IFS performance based on performance monitoring metrics built into the IFS.
Conversational Virtual Assistant for users of ECMWF online products and services
The goal of our challenge is to create a chatbot with which external users can have conversions to get their questions answered without the need to make use of other, existing support channels.
To achieve this, we will build up a modern processing pipeline which retrieves content from ECMWF's helpdesk and support-related pages, apply natural language understanding algorithms to build up a semantic knowledge graph and use this knowledge graph to train the Dialogflow-based chatbot.
Users who will make use of our chatbot will hopefully find answers faster than before, and ECMWF's support team gets more time to focus on critical support cases.
Exploring or machine/deep learning techniques to detect and track tropical cyclones
Cyclones are the complex events characterized by strong winds surrounding a low-pressure area.
Intensity classification of cyclones is traditionally performed using Dvorak technique focusing on statistical relationships between different environmental parameters and the intensity.
This project aims to create an algorithm based on deep learning to recognize and classify tropical cyclones based on their intensities. We'll utilize - a) Satellite imaging data b) BestTrack database information of tropical cyclones for the task.
The model will be developed for static (per satellite image) detection and classification and later extended to perform dynamic (continuous real-time) detection and classification while maintaining robustness.
Detect Anomaly in Air Quality Station(DAAQS)
Using Machine learning clustering algorithms to provide Reliability and Representativeness
Validating and removing errors outliers from surface air quality observations from individual sensors so that these observation can be compared to ECMWF's CAMS air quality forecasts.
By clustering analysis on these observations more reliable observations can be identified. Enhancing these observations by attaching data about factors that affect air quality these observations can have more credibility about their accuracy.
CAMS lacks credible surface air quality observations in many parts of the world, often in the most polluted area such as in India or Africa. Some observations are available for these areas from data harvesting efforts such as openAQ but there is no quality control applied to the data, and it is often not well known if the observations are made in a rural, urban or heavily polluted local environment.
This information on the environment is important because the very locally influenced measurements are mostly not representative for the horizontal scale (40 km) of the CAMS forecasts and should therefore not be used for the evaluation of the CAMS model.