Weather Signals for Systematic Strategies
A Practical Map of the Signal Space, Research Pitfalls, and Implementation Considerations
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Executive Summary (read this first)
Weather can act as a meaningful exogenous driver in certain systematic strategies — but only when treated with discipline.
Most attempts fail due to shallow variable selection, incorrect aggregation, poor handling of nonstationarity, and underestimation of operational complexity.
This document is not a guide to generating alpha.
It is a map of the space: what exists, where signal can arise, where it usually breaks, and how serious research teams structure their thinking.
Use this document to:
• orient research efforts
• identify viable signal families
• avoid common failure modes
• understand implementation complexity before committing resources
You can skim section headers and jump directly to what you need.
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How to use this document
• 10-minute skim
Read Sections 1, 2, and 8 to understand what exists and what’s crowded.
• Research orientation
Read Sections 1–5 to frame a weather signal hypothesis correctly.
• Implementation reality check
Read Sections 3, 6, and 7 before committing engineering or research time.
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1. What counts as a weather signal (scope & definitions)
Weather signal (in this document):
A time-indexed variable derived from meteorological data that plausibly transmits into economic or market outcomes through a defined mechanism.
Included:
• temperature, precipitation, wind, humidity, radiation, snow, soil moisture
• derived or synthetic variables built from these
• features aggregated across locations or time
Excluded:
• discretionary forecasts
• narrative climate commentary
• pure climate trend attribution
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2. The three-layer structure of weather signals
Most viable weather signals follow a layered structure:
Layer 1 — Meteorological primitives
Raw or modeled environmental variables (e.g. temperature, wind speed).
Layer 2 — Impact transforms
Transformations that convert weather into operational stress or constraint:
• thresholds
• persistence
• cumulative effects
• anomaly measures
Layer 3 — Economic transmission
Mechanisms by which those constraints affect:
• supply
• demand
• logistics
• production
• volatility
Signal strength almost never exists at Layer 1 alone.
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3. Major families of weather-driven signals
This section maps where researchers typically look, not where alpha is guaranteed.
3.1 Energy demand & load
• Degree-day variants
• Peak demand stress indicators
• Heat/cold persistence features
Typical horizon: days to weeks
3.2 Energy supply & generation
• Wind generation variability
• Solar irradiance constraints
• Hydro inflow and evaporation proxies
Typical horizon: days to months
3.3 Agriculture & soft commodities
• Growing degree accumulation
• Soil moisture stress
• Frost / heat window timing
Typical horizon: weeks to seasons
3.4 Industrial productivity & labour
• Heat stress / cold exposure proxies
• Weather-driven work stoppage risk
Typical horizon: days to quarters
3.5 Logistics & physical trade flows
• Port, river, rail, airport constraint indicators
• Weather-induced transport friction
Typical horizon: days to weeks
3.6 Extreme hazard event surfaces
• Flood, cyclone, wildfire probability fields
• Exposure-weighted event indicators
Typical horizon: event-driven
3.7 Macro & inflation transmission
• Food price pressure pathways
• Energy cost pass-through channels
Typical horizon: months
3.8 Volatility & risk premia
• Weather uncertainty as a volatility input
• Event risk repricing
Typical horizon: short-term, options-relevant
3.9 Cross-sectional & dispersion signals
• Geographic dispersion of weather stress
• Relative regional exposure differences
Typical horizon: varies
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4. Signal maturity: where crowding usually occurs
Weather signals tend to cluster by maturity:
• Foundational: widely known, low edge
• Implementation-driven: edge from better transforms or aggregation
• Constraint-based: edge from operational bottlenecks
• Second-order: interactions, dispersion, persistence
• Regime-aware: conditional on climate or circulation regimes
Most naive research stops at the first layer.
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5. Why most weather research fails (common failure modes)
Common reasons weather signals appear promising but fail in production:
• seasonality masquerading as signal
• nonstationarity from climate drift or infrastructure change
• incorrect lag assumptions
• over-aggregation smoothing away extremes
• silent data revisions
• multiple hypothesis testing
• confounding by policy, outages, or capacity expansion
• treating forecasts as point values rather than distributions
Any serious research effort must explicitly guard against these.
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6. Data considerations that matter more than expected
6.1 Data types
• reanalysis vs observations
• forecasts vs hindcasts
• ensemble vs deterministic outputs
6.2 Resolution tradeoffs
• spatial coverage vs relevance
• temporal granularity vs noise
• strategic locations vs full coverage
6.3 Nonstationarity handling
• rolling baselines
• anomaly construction
• regime conditioning (with caution)
Data quality and continuity often dominate model choice.
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7. From raw weather to research-ready features
Weather signals rarely work as raw inputs.
Common feature archetypes include:
• anomalies relative to historical context
• threshold exceedance counts
• persistence metrics
• cumulative stress measures
• dispersion across locations
• exposure-weighted aggregation
Feature construction typically matters more than model choice.
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8. Validation & research discipline
Robust research requires:
• time-respecting validation
• stability checks across regimes
• explicit lag discovery
• uncertainty awareness
• clear economic mechanism alignment
Signals that only work in hindsight should be treated with skepticism.
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9. Implementation realities (often underestimated)
Teams exploring this space typically encounter:
• large numbers of location-specific series
• frequent recomputation as forecasts update
• signal monitoring and decay tracking
• significant data QA overhead
• non-trivial compute and storage requirements
Weather research scales poorly without dedicated infrastructure.
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10. Crowded vs underexplored areas (high-level)
Often crowded:
• simple degree-day features
• single-location event studies
Less explored:
• constraint-driven operational signals
• geographic dispersion features
• persistence + threshold interactions
• forecast uncertainty as a feature
Exploration does not imply guaranteed edge.
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11. Signal index (conceptual, non-exhaustive)
Examples of commonly researched signal types:
• Port disruption risk indicators
• River draft constraint proxies
• Wind ramp stress metrics
• Heat stress labour indices
• Hydro evaporation stress signals
• Transport delay probability surfaces
Each requires careful definition, validation, and monitoring.
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12. A realistic starting point
Teams new to this space typically benefit from:
• selecting one clear economic mechanism
• limiting scope to a small asset universe
• testing multiple feature constructions
• validating across time and geography
• planning for long-term maintenance
Weather signals reward patience and rigor.
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Closing note
Weather is not an alternative data shortcut.
It is a structural driver that demands careful treatment.
This document exists to help researchers decide where to look, where not to, and what it would take to do this properly.
For a shortcut? Visit weatherwise.studio