Delta Tool Online
Delta Tool Online provides a graphical user interface to the Python libraries fmm_assess: FAIRMODE Modelling-Assessment Evaluation Tool and fmf_eval: FAIRMODE Forecast-Evaluation Tool, released as open-source on the European Commission platform code.europa.eu JRC Air Quality Modelling project.
Full description of the two underlying libraries is available at Assessment computation details and Forecasting computation details. In order to understand the indicators calculated by the two library, please refer to these sections: Assessment indicators reference and Forecasting indicators reference. To fully understand the output plots, see the two detailed guides: Plots reading guide for assessment and Plots reading guide for forecasting.
Delta Tool Online is available at this URL, which can be accessed by providing EU-login account credentials.
The tool enables to upload users own datasets following the legacy Delta Tool format (startup.ini plus folder of observation and model CSV files) or the MQOR format (attributes and short term CSV, as documented here). Once loaded, stations data can be used for running experiments and calculate indicators and charts according to the Air Quality Directive 2881/2024.
Note
While a user can run many experiments, only one dataset is allowed for each user. Please keep in mind that your uploaded data will be automatically deleted after 10 days of inactivity.
Contents:
- Delta Tool Online
- Main page
- Assessment
- Forecasting
- Dataset summary, stations filtering and selection
- Assessment computation details
fmm_assess: FAIRMODE Modelling-Assessment Evaluation Tool (Python)- 1. Purpose and scope
- 2. Package layout
- 3. Dependencies
- 4. Inputs
- 5. Measurement uncertainty
U(O) - 6. Aggregations (
aggregations.py) - 7. Indicators (
indicators.py) - 8. Pipeline (
pipeline.py) - 9. Diagrams (
plots/) - 10. CLI / Spyder workflow
- 11. Outputs
- 12. Tests (
tests/) - 13. Open issues / deferred work
- 14. References
- Conventions cheat-sheet
- Indicators reference
- Plots reading guide
- Contents
- Triage workflow
- target.png — short-term Target plot
- scatter.png — long-term Scatter plot
- radar.png — Radar plot
- ts_report.png — Temporal-Spatial Report
- taylor.png — Assessment Taylor plot
- scatter_dyneval.png — Dynamic-Evaluation Scatter
- bars.png — Bar plot
- timeseries_⟨station⟩.png — Time series
- Mixed-network rendering
- Sample data — demo dataset walkthrough
- Forecasting computation details
fmf_eval: FAIRMODE Forecast-Evaluation Tool (Python)- 1. Purpose and scope
- 2. Package layout
- 3. Dependencies
- 4. Inputs
- 5. Measurement uncertainty
U(O) - 6. Aggregations (
aggregations.py) - 7. Indicators (
indicators.py,categorical.py,aqi.py) - 8. Pipeline (
pipeline.py) - 9. Diagrams (
plots/) - 10. CLI / Spyder workflow
- 11. Outputs
- 12. Tests (
tests/) - 13. Open issues / deferred work
- 14. References
- Conventions cheat-sheet
- Indicators reference
- Plots reading guide
- Contents
- Triage workflow
- forecast_target_FH⟨n⟩.png — Forecast Target plot
- forecast_mpi_FH⟨n⟩.png — Forecast MPI plot
- forecast_threshold_perf_FH⟨n⟩.png — Forecast Threshold Performance plot
- forecast_threshold_perf_norm_FH⟨n⟩.png — Forecast Threshold Normalised Performance plot
- forecast_pod_sr_sensitivity_FH⟨n⟩.png — POD / SR sensitivity bar plot
- forecast_summary_FH⟨n⟩.png — Forecast Summary Report
- forecast_summary_pnorm_FH⟨n⟩.png — Forecast Summary P-Normalised Report
- forecast_aqi_FH⟨n⟩.png — Forecast AQI plot
- Backend differences (matplotlib vs plotly)
- Sample data — demo dataset walkthrough