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Challenge 13 - PyroScope #6

@DiegoDomenici

Description

@DiegoDomenici

Challenge 13 - PyroScope

Stream 1 - Data Visualization for Earth Sciences Applications

Goal

PyroScope aims at developing and deploying an interactive web application as a visualisation tool for ECMWF fire products from both CEMS and CAMS.

Mentors

Joe McNorton, Edward Comyn-Platt, Chris Barnard, Mark Parrington (ECMWF)
Douglas Kelley (CEH)

Skills Required

  • Experience with python
  • Experience with web frontend languages, e.g. JSreact, TypeScript
  • Some experience working on novel data visualisations
  • Experience working with big data

Description

What is the current problem / limitation?

The increasing frequency and intensity of wildfires globally pose a significant threat to lives, property, and the environment. Climate change has exacerbated this issue by creating conditions that allow fires to spread rapidly, leading to air pollution, biodiversity loss, and long-term ecological damage. Each year, media, policymakers, and fire managers rightly ask questions about their causes and impacts—questions that the fire science community struggles to answer with the required speed.

ECMWF produces multiple fire-related forecasting products that could provide essential and rapid information during or soon after a fire event. These products include the Fire Weather Index (FWI), Fire Occurrence Probability Index (FOPI), and Probability of Fire (PoF), alongside datasets on active fires, burned areas, and fire emissions. However, these products are not easily accessible or user-friendly for end users. Currently, these data are mainly available as static maps via the ecCharts web portal, or manually converted into news articles for the CAMS weather room and the ECMWF blog. This limits users' ability to analyse trends, compare forecasts with historical data, or extract region-specific insights efficiently.

Making these tools rapidly available will help provide the critical insights that agencies and media outlets desperately need during fire events. Through our collaborations with partner organisations successful applicants will have the opportunity to extend their impact beyond ECMWF and immediate European partners. They will contribute to making a real difference to countries and communities across the world that need to respond and adapt to the emerging wildfire crisis.

What data / system do you plan to use?

This project will use ECMWF’s existing fire forecasting and observational datasets, which include:

  • Forecasting Products: FWI, FOPI, and PoF with a 10-day lead time and resolutions of 9 km and 1 km globally, available in NetCDF format. The CAMS emissions and atmospheric forecast for trace gases related to fires, available in grib or NetCDF format.
  • Observation-Based Data: Daily updates on active fires from multiple satellite sensors, as well as burned area and fire emissions data from the Global Fire Assimilation System (GFAS).
  • Visualization Platform: The ecCharts system currently provides static maps, but the underlying data could be integrated into a more interactive platform, in for example, the CAMS weather room.

What could be the solution?

Interactive data visualisation that allows users to dynamically explore ECMWF’s fire-related products. Instead of relying on static maps, users could:

  • Select a specific location or region and generate time-series comparisons between forecasts and historical data.
  • Visualize fire risk and activity trends over different timescales, helping both researchers and decision-makers interpret patterns more effectively.
  • Enhance accessibility and usability by integrating the tool into a widely accessible platform, such as the Climate Data Store, CAMS weather room or the State of Wildfire website.

Ideas for Implementation

  • Web-based interactive visualization: Develop a tool inspired by platforms like Climate Explorer, featuring time-series analysis, spatial overlays, and real-time updates.
  • Integration with ECMWF systems: The tool could be embedded within ecCharts or linked to other ECMWF-hosted platforms.
  • User engagement features: Options to download data, overlay multiple datasets, and generate reports for research or media use.
  • Scalability: The system should be designed to accommodate potential future expansions, such as incorporating machine learning models to enhance forecast accuracy.

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Evaluation Criteria

  • Feasibility
  • Transferability
  • Easy to maintain / Future-proof approach

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