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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.
HPC Performance Profiling Tool
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.
Compressing atmospheric data into its real information
There is a lot of artificial precision in the current CAMS data encoding setup, data takes a long time to archive and download.
We plan to use the CAMS global real-time forecast dataset to test different configurations and estimate data encoding errors.
The work on this project could help us to reduce both volume of data we store in our archive and the amount we disseminate to the users while preserving useful information.
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.
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.
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.
UNSEEN-Open
An open, reproducible and transferable workflow to assess and anticipate climate extremes beyond the observed record.
Bias correction of CAMS model forecasts for air quality variables by using in-situ observations. The bias correction algorithm will mainly be based on machine-learning / deep-learning techniques.
MaLePoM (Machine Learning for Pollution Monitoring)
The project aims to build a Machine Learning model to estimate emissions using suitable proxy data due to anthropogenic activities. Initially, we will model the concentrations of NOx in Europe. Therefore, proxy data should frame these activities exploiting databases such as Land cover maps, Dynamic traffic data, lighthing data and others.
Subsequently, different approach will be tested in order to capture both spatial and temporal variability at high resolution and eventually allow accurate emissions estimates at global scales.
Elefridge.jl: Compressing atmospheric data into its real information content
Weather and climate forecasting centres worldwide produce very large amounts of data that has to be stored and shared with users. Data compression is essential to reduce file sizes sent over the internet and the demand on data archive capacity.
The previously completed challenge within the ESoWC 2020 developed the concept of information-preserving compression by analysing the real information content in data from the Copernicus Atmospheric Monitoring Service (CAMS). Separating the false and hardly compressible information from the real information was shown to allow for high compression factors without significant information loss.
Here, we focus on further details in the implementation of information-preserving compression for CAMS. Readily available in the current GRIB2 compression are different precision and accuracy options that can be translated to preserved information for a given data set. To implement this successfully and in an automated fashion, further improvements are necessary and the best lossless compressor available in GRIB2 that satisfies both speed and size requirements has to be found.
This project aims to successfully implement information-preserving compression for CAMS to put this advanced compression technique into practice.
Project Meeresvogel seeks to make it easier to incorporate weather visualisations into multimedia presentations. We will design and develop a Python module which enables users to create interactive Google Earth presentations which are enhanced with weather data and visualisations from MetView.
Using this module, we aim to create three examples to demonstrate how this could be useful to diverse audiences wanting to explore various aspects of the 2020/21 Vendée Globe Race, a sporting event in which 33 skippers set out to race their 60 foot yachts solo non-stop around the world.
We will explore ways in which weather visualisations can provide insights for the public following the race, the race teams wanting to analyse performance data, and scientists analysing the oceanmet observations which were collected by a number of the boats during the race.
Follow the developments on GitHub
BlenderNC Enhancements
Improving Forecast and Reanalysis Data visualisation support in Blender for ECMWF products.
CliMetLab - Machine Learning on weather and climate data
CliMetLab is a Python package aiming at simplifying access to climate and meteorological datasets, allowing users to focus on science instead of technical issues such as data access and data formats.
This project aims at handling the data loading as well as interpreting the output from the machine learning models with the use of plots, graphs, etc. This will remove the overhead of manual data retrieval, writing specific data loaders per dataset.
The plugin architecture in CliMetLab aims at easy addition of data sources, datasets, plotting styles and data formats.
Specific goals of the project:
1) extend CliMetLab so that it offers the user with high-level Matplotlib-based plotting functions to produce graphs and plot which are relevant to weather and climate applications.
2) Python package Intake is a lightweight set of tools for loading and sharing data in data science projects. Extend CliMetLab so that it seamlessly interfaces with Intake and allows users to access all intake-compatible datasets.
3) Xarray uses the data format Zarr to allow parallel read and parallel write. Convert large already available datasets to xarray-readable zarr format, define appropriate configuration (chunking/compression/other) according to domain use cases, develop tools to benchmark when used on a cloud-platform, compare to other formats (N5, GRIB, netCDF, geoTIFF, etc.).
ML4Land: Using Earth's observation data, Climate reanalysis
& Machine Learning to detect Earth’s heating patterns
Skin temperature has been pivotal in identifying the heating and land-use patterns of Earth. The project aims to learn a mapping from model simulations (using ERA5) to satellite observations of skin temperature. Various works have shown how Machine Learning based models can efficiently recognize and learn useful patterns from complex datasets. We thus aim to use Machine Learning algorithms to learn the mapping between ERA5 variables and satellite observations of maximal skin temperature. These solutions will provide predictions at higher resolutions and offer valuable insights into the relationships between skin temperature and various ERA5 variables.
At its core, ECMFW is a data organization that produces and distributes essential weather data to its member states and outside businesses. They also provide various other services such as global forecasting, supercomputing facilities, environmental services, meteorological services.
Many users around the world use these services. In this project, I aim to improve the user experience with ECMWF by providing individual users with their own dashboard showcasing valuable data, favourite charts, and a high-level overview of their relationship with ECMWF and its services.
Nowadays it is possible to obtain atmospheric composition datasets for the same locations from different sources. However, most datasets are not easily comparable due to their file formats and structure.
In this regard, Atmospheric Datasets Comparison (ADC) Toolbox is aimed to have a set of tools that allows unit conversion, side-by-side visual comparison, regridding, time and geographic data aggregation and statistics visualization to show how similar the datasets are among them.
The toolbox will consist of different scripts written in Jupyter Notebooks with the tools:
- Transformation: The datasets to be compared will be transformed into a common format.
- Merge: The files will be regridded and, if needed, its units will be converted, to combine them.
- Comparison: Statistics methods will be used to show information about the datasets.
- Visualization: The merge output will be seen side-by-side in tables and maps.
- File format change: The files in a common format will be given in any desired file format.
Users often wrongly convert latitude and longitude coordinates which leads to the selection of a wrong area. This is highly inefficient for ECMWF as a data provider, as the same data request has to be processed multiple times.
The challenge was aimed at developing a widget that selects and displays areas on a map. Such a widget will be useful for many web applications across ECMWF. The widget is based on Leaflet (a javascript library for interactive maps) and provides different tools, e.g. drawing and searching. The widget also offers a grid point system resembling ECMWF’s model grid points.
ECMWF’s visualisations are developed with weather forecasters in mind. This challenge focused on developing new visualisations that help to communicate weather information to non-experts. The goal was to develop an innovative visualisation to present ensemble weather forecasts.
A new design of a meteogram (a graphical presentation of multiple weather variables for a particular location) has been developed with value-suppressing uncertainty (VSU) icons. They allocate a larger range of icons when the uncertainty is low and a smaller range when the uncertainty is high (see figure below). This helps to make ensemble forecasts more accessible and to increase the overall trust in 15-day forecasts.
Migration of calibration software to Python and development of its GUI
ecPoint-Cal is software that compares numerical model outputs against point observations to identify biases/errors at local scale. The software had two different processes and both steps were written in two different programming languages, which cannot be easily integrated. The challenge proposed migrating the existing code into Python and developing a user-friendly GUI for the software.
The new software, ecPoint-PyCal, provides a dynamic environment in Python and could ultimately be used to help steer model developments and to post-process ECMWF model parameters to produce probabilistic products for geographical locations.
Vast quantities of ECMWF data are stored in Network Common Data Format (NetCDF) and often there is a need to quickly create or adapt existing NetCDF datasets, for instance when prototyping a new data processing application. Tasks such as modifying the name or value of a NetCDF attribute or deleting unnecessary variables or attributes, typically require specialised NetCDF tools and libraries.
This challenge aimed to develop a tool to represent the hierarchical structure of a NetCDF dataset as a virtual file system. The new software is written in Python and allows users to easily mount, view, and edit the contents of a NetCDF dataset using file-system operations and general purpose Unix tools. The software is potentially useful for anyone working with weather and climate data in NetCDF format wishing to quickly explore and edit a dataset.
Globally, new sources of raw data are being made available via the web all the time. However, ECMWF often isn’t aware of these. Manually identifying and gathering information on these new sources is both time consuming and error prone.
This challenge was aimed at developing a tool to search the web systematically, identifying data sources for observed environmental data. The software automates the discovery, analysis and assessment of the candidate web pages in order to find new datasets. The resulting data can be used to improve global predictive weather forecasting models.
This project is about using CrowdWater data and to translate this data into something that can be used in flood forecasting models. CrowdWater is an interesting initiative in which people send geo-referenced pictures of streams or rivers, along with the corresponding variations of water level.
The difficulty of this project lies in the variability of the CW data and in the difference of scale between the CW data and the GloFAS (or EFAS) data. We have indeed a very coarse representation of rivers in GloFAS, while in CW data we have more information about the smaller rivers.
The project aims to utilize CW data to improve model forecast.
Estimating and correcting the biases of climate models, as well as assessing associated uncertainties, are crucial for many climate impact-studies and other applications. In our project we aim to develop a software package – building upon the ISIMIP3b-code – that allows users to apply bias correction, using different methods, in a variety of situations.
We aim to:
1) develop an easy to use, flexible software package that users can employ in different computing environments,
2) extend the ISIMIP approach with several small improvements,
3) implement a systematic evaluation framework for bias correction to support the estimation of uncertainty.
The project's goal is to provide a web-based graphical user interface (GUI) to make the cache content and configuration settings of the CliMetLab Python package easier.
Currently, CliMetLab’s settings and cache are configured via the terminal, which is cumbersome to use and requires experience with shell commands.
This project will enable a wide audience to fully utilise CliMetLab's features by providing a GUI built using modern web frameworks such as ReactJS and Flask.
The Wildfire Explorer will allow users to create plots of wildfire emission and activity data on-demand.
This application will consist of a GUI where the user can select the geographical domain of interest, the date period of a specific event, a reference period for comparison (optional), the variable considered and the plot type.
The processing of the data will be automated and optimised using a PostGIS database. The GUI will be built from a Jupyter notebook with interactive widgets and the data will be processed from the CAMS Global Fire Assimilation System (GFAS).
ECMWF as an organization provides a variety of applications but there is no central dashboard to get a global overview of the information from the user’s favorite app.
The aim of this project is to move forward the existing user dashboard prototype closer to operations by building on the already existing functionalities.
The current state of the project offers a central dashboard to add different widgets to it.
The plan is to move forward with integration from the individual apps using a simple discoverable widget-api comparable to GetCapabilities for OGC Web Services, to add the widgets from the individual applications as well.
Magics is ECMWF's meteorological plotting software that supports plotting contours, wind fields, observations, satellite images, symbols, text, axis and graphs.
The project aims to utilize the power, flexibility and extensibility of the python library matplotlib (https://github.com/matplotlib/matplotlib) to improve the drivers for ECMWF's magics-python library (https://github.com/ecmwf/magics-python).
These improvements would allow the users to create more customizable and interactive plots. This project also aims to continue the development of ECMWF's magpye (https://github.com/ecmwf/magpye), which provides a more pythonic and user-friendly API to magics.
The final aim is to create resources such as tutorials and documentation for magics and magpye.
WeatherBench is a benchmark dataset that explores the potential of Machine Learning methods for weather forecasting. WeatherBench is comprised of ERA5 reanalysis data and covers the entire globe. Various spatial resolutions are available, the time step is 1 hour. Authors compete to predict meteorological variables as well as possible 3 and 5 days into the future.
Diffusion Models are a recently popularised class of Machine Learning models and have proven especially effective at generating images. Particularly successful examples include Stable Diffusion and DALL-E 2. Diffusion Models can also be trained to generate output conditioned on input data such as text or other images.
We will employ Diffusion Models for weather forecasting: we plan to give the model the current state of atmospheric variables as conditioning information and train it to predict realistic future states.
Specifically we plan to:
- Explore the potential of Diffusion Models on the WeatherBench challenge - which has never been done before.
- Publish code and trained models to make it easy to replicate and build on our results.
Atmospheric Composition Dataset Explorer
The goal of this project is to create an API and an interactive application that generate atmospheric composition diagnostics plots according to user specifications. The data source is the CAMS Atmosphere Data Store, specifically the GUI application shall deal with CAMS Greenhouse Gas Fluxes and CAMS Global Reanalysis EAC4 datasets.
The application shall automatize the process of creating frequently used time series, hovmoeller and geospatial plots for parameters such as spatial and temporal domains, time resolution and atmospheric variables.
The ideal outcome would be to provide generic enough APIs which can be used for data retrieval, data homogenization, data slicing and sub-setting, aggregation and visualization of different CAMS datasets; also, it shall be generic enough that adding new plot types won't be too difficult. We also plan to provide a GUI for easy generation of a report based on parameters selected by the user.
The aim of the project is to create a Machine learning (ML) model that can generate high-resolution regional reanalysis data (similar to the one produced by CERRA) by downscaling global reanalysis data from ERA5.
This will be accomplished by using state-of-the-art Deep Learning (DL) techniques like U-Net, conditional GAN, and diffusion models (among others). Additionally, an ingestion module will be implemented to assess the possible benefit of using CERRA pseudo-observations as extra predictors. Once the model is designed and trained, a detailed validation framework takes the place.
It combines classical deterministic error metrics with in-depth validations, including time series, maps, spatio-temporal correlations, and computer vision metrics, disaggregated by months, seasons, and geographical regions, to evaluate the effectiveness of the model in reducing errors and representing physical processes. This level of granularity allows for a more comprehensive and accurate assessment, which is critical for ensuring that the model is effective in practice.
Moreover, tools for interpretability of DL models can be used to understand the inner workings and decision-making processes of these complex structures by analyzing the activations of different neurons and the importance of different features in the input data.
Compression of Geospatial Data with Varying Information Density
Geospatial data can vary in its information density from one part of the world to another. A dataset containing streets will be very dense in cities but contains little information in remote places like the Alps or even the ocean. The same is also true for datasets about the ocean or the atmosphere. The variability of sea surface temperatures and currents is much larger in the vicinity of the golf stream than in the middle of the Atlantic basin. This variability might also change in time. A hurricane, for example, has a lot of variability in winds, temperature and rain rates, and travels in addition across entire ocean basins.
The challenge of this project is to improve `xbitinfo` to preserve the natural variability of these features but not to save random noise where the real information density is rather low. This means in particular that the number of bits needed to preserve in compression changes with location. A hurricane has a different information density than a same-sized area in the steadily blowing trade-wind regimes. Compressibility of climate data therefore can change drastically in time and space, which we want to exploit.
Currently in the bitinformation framework, to preserve all real information, the maximum information content calculated by `xbitinfo` needs to be used for the entire dataset. However, bitinformation can also be calculated on subsets, such that the ‘boring’ parts can therefore be more efficiently compressed.
Sketchbook Earth is a project aiming to democratize the production of climate intelligence reports, traditionally restricted due to the reliance on internal ECMWF tools. We propose developing a series of Jupyter notebooks that will illustrate our planet's climate stories in an accessible and engaging manner.
Leveraging the new cads-toolbox Python package, these notebooks will retrieve and process data from the Copernicus climate data store (CDS), transforming raw information into expressive visual narratives. We will focus on downloading and preprocessing Essential Climate Variables (ECVs), calculating climate anomalies, and generating visualizations that echo the vibrant storytelling found in a sketchbook.
The resulting Jupyter notebooks will not only provide meaningful climate insights but also serve as a comprehensive training resource. Through Sketchbook Earth, we aim to offer a more visual, comprehensible, and reproducible approach to climate intelligence.
Global reanalysis data sets such as ERA5 constitute an important backbone for a wide range of topics, most notably including applications related to renewable energy and agriculture, as well as driving fields for climate control simulations. However, due to its resolution of 31 km and even lower resolved uncertainty information, ERA5 lacks details and applicability for regions with heterogeneous terrain or renewable energy applications.
To increase the ERA5 spatial resolution in a step-wise manner for the whole globe without the need for large computational resources, parameter-wise downscaling with statistics and/or machine learning using a higher resolved reanalysis data set as target/proxy is a promising approach. For such a purpose, the CERRA data covering Europe with a spatial resolution of 5.5 km (and 11 km for the ensemble) is ideal.
Here, we aim at implementing a model output (baseline) approach, and two deep learning approaches for post-processing and downscaling using residuals.
Validation of soil moisture and soil temperature is crucial for Numerical Weather Prediction (NWP), as they control surface heat fluxes that directly affect near-surface weather. This can be done with LANDVER, which is a validation package for land surface variables, currently consisting of soil moisture and soil temperature.
The tool provides an independent validation of soil moisture and soil temperature data using in situ observations from the international soil moisture network. What is currently missing in this software package is the capability to validate latent and sensible surface heat fluxes against Eddy-Covariance measurements, which can provide useful information about how well ECMWF’s Land-Surface Modelling Component ECLand is able to translate soil moisture stress into surface heat fluxes.
Implementing an additional routine into the already existing software package paves the way for a standardized land-surface benchmarking tool for the ECMWF.
TropiDash: towards a comprehensive tropical cyclone hazard dashboard
This project aims to contribute in tropical cyclone data dissemination to help in population preparedness and resilience against extreme meteorological events.
TropiDash will be a platform on Jupyter notebook able to visualize key meteorological parameters in plots and maps to better understand tropical cyclone hazards evolution.
It will gather the currently used and most effective visualizations and reproduce them in a dashboard applying interactive elements.
The end user will be provided with sound documentation which will enable the platform usage and enhancement after the end of the project.
ECMWF has an extensive amount of real-time and historical weather data, as well as an comprehensive documentation knowledge base. Currently, the data can be accessed via three different API — the chart discovery API, the dataset API, and the dataset DOIs — all of which require some level of coding, as well very precise queries. This constitutes a high barrier to entry to third parties who want to make use of ECMWF's very large amount of information.
Recent developments in the field of natural language processing, such as the Transformers technologies and large language models fine-tuned to interact conversationally — such as ChatGPT — allow for a search engine to reply to queries formulated in natural language. The large language model maps the request to an API query, and provide seamlessly the required information.
The aim of this challange is to develop a search engine - the chatECMWF - that can reply to a number of queries related to ECMWF datasets, charts and documentation, from general enquiries — such as "What is the license of ECMWF open data" or "Where can I find ozone data?" — to very specific requests — think of "What air quality data is available in CAMS for Europe for the period from September to October 2014?".
We will develop a framework to forecast wildfires in Europe with machine learning from GFAS fire data and meteorological forecasts. Our team will evaluate different machine learning tools for forecasting and aim to integrate the tools into the operational pipeline of the ECMWF.
The project explores and analyzes weather data (distribution of features target) used in the training phase of AI weather forecasting model PanguWeather/ AIFS, targeting a process-based analysis of the model input-output.
This project aims to build a knowledge graph from Scientific documents and enhance LLMs chatbot. By embedding gathered information, the project aims to enhance the existing ECMWF-assistant chatbot by providing more interactive and engaging (and explainable) answers.
The solution aims to enhance user capabilities in handling data constraints with the Climate Data Store API (CDSAPI) by introducing a robust mechanism for request optimization. A key feature is breaking down user requests into multiple valid sub-requests that adhere to the available data constraints. This approach will offload processing from the server side to the client side, reducing the burden on the CDS and ADS infrastructure. Additionally, it will improve user satisfaction by minimizing failed or partially covered requests and ensuring efficient data retrieval.
By offering CDS API users a Python library that can validate their requests and the availability of new data in advance of submission, we will increase chances of successful data requests, reduce data retrieval time, and minimize server costs.
The solar energy sector is emerging as a frontrunner among renewable energy sources, with a surge of solar projects anticipated across Europe in the coming years. However, accessing pertinent information for informed decision-making regarding solar investments remains a challenge for both companies and end-users.
To address this need, there is a growing demand for a user centric web application that facilitates visualization, comparison, and data downloading related to solar energy. Embracing this challenge, we aim to provide the solar community with a tool able to compare and visualize data hosted in CAMS, C3S, ADS and CDS before downloading them.
The vAirify project is working on a dashboard for exploring differences between CAMS air pollution forecasts and in-situ measurements. An intuitive user interface will allow forecasters to explore the variations, at a glance they will be able to see the biggest differences, and will be able to drill down to the local area using a map view. This will help forecasters improve the forecasting model and be more responsive to large variations when they occur.
The primary objective of the CAMS-nb-Charts project is to create a series of Jupyter notebooks that can reproduce CAMS forecast charts programmatically. These notebooks will be generated based on a flexible template and a configuration file, allowing for easy customization and reusability. The configuration file will contain crucial information such as the mapping between web product names and dataset/variables hosted in ADS, unit conversion formulas, and graphical attributes for chart reproduction
In a world increasingly challenged by erratic weather conditions, the 'Tales of Dry Lands' project, crafted from Python notebooks, offers a way to understand climate data concerning droughts. Whether you're a citizen, a student, or a passionate environmentalist, this guide enables you to learn how to visualize trends and explore complex climatic patterns of droughts.
Our app is a web app consisting of a Python based backend and a JavaScript based interactive frontend. The data is indexed by the backend and the frontend fetches the data from the backend using the index. The UI of the frontend is interactively built at the loading of the page and the user selects the parameters they are interested in to get the needed visualisation for them.
We aim to make the experience of getting verification data easier with more options as well as solve the storage constraints of the current system by moving the plotting to runtime. Our app should be flexible enough to accommodate verification data of other variables as well and we aim to modernise the visualisation process of verification data primarily of the CAMS project hence the name “CAMS Verisualiser”.
The HydroGap-AI project aims to bridge gaps in streamflow observations using advanced machine learning (ML) techniques. This project involves the development of a Python-based open-source software package designed to fill gaps in daily streamflow time series, ensuring more accurate and continuous data for hydrological analysis and decision-making.
The project was to create an NLM (Natural Language Model), that is capable of extracting information using the polytope system to extract Specific weather information from large datacubes using features. The Polytope system allows for the data to be extracted and this therefore allows for the integration of a chatbot to assist in the extraction of this data. This is particularly useful for non-technical users. Therefore, an NLM was created to assist in the extraction of complex weather information. The NLM produced was robust and can understand a variety of user inputs which makes it ideal for use by both technical users and non-technical users.
AirQuality Urban View addresses the challenge of downscaling regional pollutant data to urban levels, visualizing accurate and precise air quality insights for European cities through intuitive maps and analysis.
This project evaluates and improves the performance of ECMWF’s current land surface Machine Learning model prototype. It seeks to validate the prototype emulator thoroughly to understand the model's capabilities in approximating the ECLand model output, and also compare its results to in-situ observations by developing an evaluation framework for LSM emulators.
