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Recent state explains much more than weather alone

Finding 03 · Held-out subsequent-growth models

Weather matters, but measured weather explains relatively little additional variation in the next observed growth increment compared with recent fire state in these analyses.

Question

For exact next-calendar-day transitions, how much does day-specific weather add after the fire’s recent developmental state is known?

Answer

The region-held-out autoregressive state model explains 44.8% of variation. Adding weather raises R² by 0.5 percentage points; allowing the full set of weather × state interactions raises it by 1.8 percentage points above the same baseline.

First ask what weather predicts by itself

Mean vapor pressure deficit projected onto an already fitted developmental morphospace, beside held-out weather prediction scores for several developmental responses.

What to notice. Weather is projected after the developmental axes exist. The color field is not a clean shape gradient, and held-out skill varies by response and blocking scheme. Technical caption

How to read Figure 3
- **Panels:** A colors the existing morphospace by median event-mean VPD; B compares held-out prediction for several developmental responses. - **Look here:** Peak timing and fold-fitted shape PC1 are barely recovered in regional holdout, while other outcomes show somewhat more skill. - **Supported conclusion:** Weather–morphology associations are weak and response-dependent in this cohort. This does not show weather is unimportant.
Recent fire stateR² 0.448current and prior growth, cumulative area, elapsed time
+ weatherΔR² 0.005increment above the same state baseline
+ weather × stateΔR² 0.018full interaction increment above baseline

Held-out prediction of subsequent calendar-day growth from recent state, weather, and their interactions.

What to notice. The autoregressive state points sit near R² 0.45; weather-only prediction is much lower. The right panels show the smaller increment that weather contributes above state, including sensitivity to spatial exposure and season. The common cohort contains 87,944 transitions from 31,700 fires. Technical caption

How to read Figure 4
- **Panels:** A compares total held-out R²; B isolates increments above the same state baseline; C changes exposure geometry; D checks seasons. - **Look here:** State is near R² 0.45, while weather-only prediction is much lower. Weather moves the state baseline only slightly. - **Comparison:** The defensible weather comparison is ΔR² above state—not the total R² of the interaction model. - **Supported conclusion:** Recent observed state carries much more next-day predictive information than these weather variables add. This is retrospective association, not causal or operational prediction.

Challenge this result. Incorrect dates, static centroids, or region leakage could create artificial weather skill. See the date, geometry, external-source, and blocked-fold tests →

Evidence trail

ClaimState R² 0.448; weather ΔR² 0.005; interactions ΔR² 0.018
FiguresFigure 3 · N = 9,212; Figure 4 · 87,944 transitions from 31,700 fires
AnalysisCommon cohorts, exact next-day transitions, blocked folds, paired comparisons

What we measured

Every comparator uses exact one-day transitions and the same cohort. The baseline contains current growth, previous-calendar-day growth, cumulative observed area, and elapsed time. Day-t weather is sampled at that day’s newly burned-area centroid. Models are evaluated by held-out region, year block, and their stricter intersection.

How well does it hold up?

Holdout State R² + weather ΔR² + interactions ΔR²
Region 0.448 0.005 0.018
Year block 0.458 0.005 0.015
Space + time 0.448 0.005 0.018

VPD-specific products add 0.006–0.012 R² above the other interactions across four blocking schemes; the regional increment is 0.0118 with a conditional fire-bootstrap interval of 0.0097–0.0138. Coefficients vary by region, size, and partial edge years, and small-fire subsets can have poor absolute prediction despite a positive increment.

What this does not show

This is not evidence that weather is unimportant. It is evidence that the measured, roughly 4-km daily weather variables add little predictive skill after recent reconstructed state is known in this cohort and model. The result is retrospective, non-causal, and not a live forecast. Satellite latency, event delineation, time-zone alignment, local heterogeneity, directional wind, active-edge exposure, fuels, terrain, and suppression are not resolved.

Technical details

See weather, prediction, and uncertainty and the state-analysis correction record.