Data Visualization Showcase: Communicating Ensemble Forecasts

Visualization goals

Published
February 25, 2024
Author
Reading time
1 min read
Difficulty
Intermediate
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Online at https://diogoribeiro7.github.io/analytics-blog-jekyll/visualization/2024/02/25/data-visualization-showcase-ensemble/

Visualization goals

  • Convey ensemble spread without overwhelming the viewer.
  • Provide tactile controls for filtering, animation, and comparison.
  • Maintain WCAG-compliant contrast and descriptive alt text for every figure.

Plotly fan chart

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import plotly.graph_objects as go

fig = go.Figure()
for member in ensemble_members:
    fig.add_trace(go.Scatter(
        x=member["date"],
        y=member["temperature"],
        mode="lines",
        line=dict(color="rgba(33, 150, 243, 0.15)")
    ))
fig.add_trace(go.Scatter(
    x=ensemble_mean["date"],
    y=ensemble_mean["temperature"],
    mode="lines",
    line=dict(color="#ff9800", width=3),
    name="Ensemble mean"
))
fig.update_layout(
    template="plotly_white",
    hovermode="x unified",
    title="Ensemble temperature forecast",
    xaxis_title="Date",
    yaxis_title="Temperature (°C)"
)
fig.show()

Observable linked highlighting

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viewof focus = Inputs.select(models, {label: "Forecast model"})

display(horizonChart(data, {
  color: model => model === focus ? "#d81b60" : "#90caf9",
  description: model => `${model.name} temperature anomalies`
}))

Accessibility checklist

  1. Provide textual summaries below each chart describing trends.
  2. Enable keyboard navigation for filters and toggles.
  3. Export static PNG/SVG snapshots for reports and offline review.

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    © 2024 Diogo Ribeiro. Text and figures under CC BY 4.0.

    How to cite

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    Diogo Ribeiro (2024). Data Visualization Showcase: Communicating Ensemble Forecasts. DataLog | Data Science & Research Theme. https://diogoribeiro7.github.io/analytics-blog-jekyll/visualization/2024/02/25/data-visualization-showcase-ensemble/.

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    Open science & reproducibility badges

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    • Open Data Dataset and code repository published with permissive license. Public repository, DOI issued, README with reproduction steps.
    • Reproducible Workflow Containerized environment and automated tests provided. Continuous integration pipeline with reproducibility checks.
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