May Rainfall
Updated 2h ago
Track Atlantic inflow anomalies with a 12-day ensemble blend highlighting above-normal moisture signal.
Live Kalshi climate signal portfolio
Climate signal analysis built for prediction market conviction.
We surface forecast probabilities, market spreads, and evidence trails to help traders validate Kalshi climate positions with transparent reasoning and measurable signal strength.
Signal quality score updated daily with model uncertainty bands.
Market reasoning linked to primary datasets, runs, and forecasts.
Portfolio signal pulse
Last 24hConviction
74%
Spread
+11.8¢
Pattern reliability
0.82
12-model ensemble consensus
Next catalyst
ENSO readout
Forecast window: 14 days
Confidence intervals reflect current Kalshi liquidity and volatility.
Dashboards
Dashboards
An explorer for all dashboards
Region
Signal type
Timeframe
Model confidence
May Outcome
Updated 5h ago
Outcome probability curve tightening around median forecast with coastal heat intensity leading.
May-June Transition
Updated 9h ago
Shift analysis highlights a 2.3σ swing in jet-stream alignment driving early summer volatility.
Recent activity
Signal monitoring feed
Rainfall ensemble widened for Northeast corridor.
3:18 UTC · Model variance +0.7σ
Miami humidity signal revised upward after ocean heat update.
2:41 UTC · Confidence 68%
West coast volatility alert: June transition skew tightening.
1:12 UTC · Spread -4 bps
Signal overview
KPI summary for climate forecast ops
Compact indicators that communicate the scope of dashboards tracked, signal breadth, evidence depth, and how confidence is presented across models.
Portfolio-ready metrics
Evidence-first
12
Dashboards
Forecast dashboards tracked
Portfolio-ready apps spanning temperature, hurricane, and ENSO-linked markets.
7
Signal sets
Signal categories mapped
Synoptic patterns, seasonal drivers, market microstructure, and model bias scans.
38
Notes
Reasoning snapshots archived
Concise decision logs per market to quantify narrative strength and drift.
94%
Confidence
Model agreement examples
Representative confidence ranges illustrate consensus across stacked models.
Climate market signals
Analytical edge for Kalshi climate forecasting
A premium signal layer that inspects market patterns, quantifies anomalies, and connects thesis quality to real trading outcomes.
Reliable signal review
Data-first methodology
PR
Signal 01
Pattern reliability review
Score recurring climate setups, flag regime shifts, and surface only the patterns that keep their edge through stress tests.
AT
Signal 02
Anomaly tracking
Detect volatility bursts and outlier temperature moves before they expand spreads, with alerts calibrated to market depth.
HC
Signal 03
Historical comparison
Align current forecasts with past analogs, highlighting percentile rankings and scenario-adjusted outcomes.
TB
Signal 04
Thesis breakdowns
Transparent reasoning trees map inputs to predicted contracts, showing confidence bands and failure points.
DD
Signal 05
Dashboard directory
Navigate the portfolio of single-page apps with a quick index of coverage, markets, and signal freshness.
MI
Signal 06
Market interpretation
Translate climate mechanics into trade-ready narratives, highlighting when pricing diverges from modeled reality.
Editorial forecast brief
Forecast theses built from climate signals, not vibes.
Kalshi Climate Signals merges anomaly detection, baseline calibration, and market microstructure to keep each forecast readable and testable. We treat every contract as a measurable hypothesis with a documented chain of evidence.
01
Signal triage
We score anomaly strength, persistence, and geographic spread before touching pricing.
02
Baseline integrity
Historical regimes anchor probability bands so the thesis survives recent noise.
03
Market alignment
We compare contract pricing to modeled odds and flag spreads that warrant action.
Thesis narrative
From anomaly to tradeable narrative
Weather anomaly strength
Historical distribution fit
Market pricing variance
Each thesis connects observed weather anomalies with their historical baselines, then translates the resulting probability band into how Kalshi contracts are currently priced. When pricing drifts from climate-backed odds, the dashboard surfaces a concise narrative: what shifted, where the baseline sits, and how the market is (or isn’t) accounting for it. That narrative is what we publish, so every forecast reads like an evidence brief—not a headline bet.
Inputs
Anomalies + Baselines
Model
Probability bands
Output
Market misprice call
Methodology FAQ
How we structure climate signals and forecast reasoning
Concise answers for traders and analysts evaluating the signal quality, update cadence, and confidence framing behind our Kalshi climate dashboards.