# PROVISIONAL PATENT APPLICATION

## United States Patent and Trademark Office

---

**Title of Invention:**
METHOD AND SYSTEM FOR MULTI-SENSOR COUPLING-WEIGHTED COHERENCE ANALYSIS FOR WEATHER EVENT PREDICTION

**Inventor:**
Rhet Dillard Wike
Council Hill, Oklahoma 74427

**Date:**
April 7, 2026

**Applicant:**
AIIT — Artificial Intelligence Innovation Technologies

---

## 1. FIELD OF THE INVENTION

This invention relates to methods and systems for predicting severe weather events, particularly tropical cyclone formation, using coupling-weighted multi-sensor coherence analysis. More specifically, the invention uses a physics-based coherence decay equation applied to real-time and historical sensor data from hydrological, geomagnetic, seismic, tidal, and atmospheric sources to compute a planetary coherence state that predicts whether environmental conditions will produce a weather event or remain quiescent.

## 2. BACKGROUND OF THE INVENTION

Current weather prediction systems rely primarily on numerical weather prediction (NWP) models that simulate atmospheric dynamics forward in time. These models require massive computational resources and are fundamentally limited by initial condition sensitivity (Lorenz, 1963). Tropical cyclone genesis prediction remains one of the weakest areas in operational meteorology, with the National Hurricane Center's 5-day genesis probability forecasts achieving approximately 50-60% accuracy.

Existing approaches treat the atmosphere as an isolated system. They do not systematically incorporate cross-domain coupling between hydrological systems (dam streamflow, watershed behavior), geomagnetic activity (solar wind, Kp index), ocean thermal state (SST thresholds), and tidal anomalies as coupled channels feeding a single predictive framework.

No prior art teaches the use of a coherence decay equation with coupling-weighted decoherence channels derived from multi-domain sensor fusion to predict weather event formation or non-formation.

## 3. SUMMARY OF THE INVENTION

The present invention provides a method for weather event prediction comprising:

(a) Computing a planetary coherence state C using the equation:

**C = C_0 * exp(-alpha * gamma_eff)**

where:
- C_0 is a baseline coherence value (dimensionless, normalized to 1.0)
- alpha is a sensitivity constant (empirically derived)
- gamma_eff is the effective decoherence parameter, computed as a weighted sum of decoherence channels

(b) Computing gamma_eff as:

**gamma_eff = SUM_i (w_i * gamma_i)**

where each gamma_i represents a decoherence channel from a distinct sensor domain, and each w_i is a coupling weight that represents the physical efficiency with which that channel's energy input can produce the predicted event type.

(c) Applying coupling weights derived from physical threshold conditions, including but not limited to:
- Sea surface temperature (SST) relative to the 26.5 degrees C cyclogenesis threshold
- Wind shear magnitude
- Saharan Air Layer dust loading
- Atmospheric moisture content

(d) Computing a null event probability — the likelihood that present conditions will NOT produce an event despite elevated energy input — by comparing the current sensor state against a historical database of null events (conditions that met energy criteria but did not produce events).

(e) Outputting a prediction comprising: coherence state C, event formation probability, null event probability, and the dominant coupling channel responsible for event suppression or promotion.

## 4. DETAILED DESCRIPTION OF THE INVENTION

### 4.1 System Architecture

The system comprises:

1. **Multi-Domain Sensor Ingestion Layer**: Real-time data acquisition from:
   - NOAA National Weather Service (weather alerts, barometric pressure)
   - USGS Water Services (dam streamflow at 13+ major US dams)
   - NOAA Space Weather Prediction Center (Kp index, solar wind speed/density)
   - USGS Earthquake Hazards (seismic event catalog)
   - NOAA CO-OPS (coastal tide levels at 6+ stations)
   - NASA DONKI (coronal mass ejections, solar flares, geomagnetic storms)
   - FAA NOTAM system (airspace holds as atmospheric stress proxy)
   - EPA AirNow (air quality as combustion/volcanic proxy)

2. **Historical Reference Database**: Archival data comprising:
   - 1,973 Atlantic tropical storms (1851-2024, HURDAT2)
   - 34,430 daily Kp index records (1932-2024, GFZ Potsdam)
   - 47,037 M5+ earthquakes (1900-2024, USGS FDSN)
   - Daily streamflow records from 13 major US dams (2007-2024, USGS)
   - Hourly tide levels from 6 coastal stations (2015-2024, NOAA CO-OPS)

3. **Coherence Computation Engine**: Implements the coupling-weighted gamma calculation and exponential coherence decay.

4. **Null Event Analyzer**: Identifies conditions matching historical non-formation events.

5. **Validation Gate (AnchorForge Protocol)**: Three-rebuttal gate system that stress-tests every predictive claim against data quality, scope generalization, and source methodology before output.

### 4.2 The Coherence Equation

The core equation C = C_0 * exp(-alpha * gamma_eff) is derived from the physics of decoherence in coupled systems. The key insight is that gamma_eff is NOT a simple sum of energy inputs, but a coupling-weighted sum where the weights represent the physical efficiency of energy transfer between domains.

**This is the central claim of the invention**: weather events are coherence phenomena. A tropical cyclone forms when multiple decoherence channels couple efficiently — when solar energy input (Kp), ocean thermal state (SST), atmospheric dynamics (wind shear), and hydrological state (watershed stress) align in a coherent configuration. The system decoheres (a storm forms) when gamma_eff exceeds a threshold.

Conversely, a null event occurs when one or more coupling weights approach zero, preventing energy transfer even when individual channel magnitudes are high. The invention identifies these suppression channels explicitly.

### 4.3 Decoherence Channels (gamma_i)

Each channel is computed from sensor data as follows:

**Channel 1: Geomagnetic (gamma_geo)**
- Source: Kp index (SWPC), solar wind speed and density (ACE/DSCOVR)
- Computation: gamma_geo = (Kp / 9.0) * (V_sw / 800.0) where V_sw is solar wind speed in km/s
- Normalization: 0 to 1

**Channel 2: Hydrological (gamma_hydro)**
- Source: Dam streamflow (USGS) at 13 stations
- Computation: For each dam, compute z-score of current flow vs. historical mean. gamma_hydro = fraction of dams showing |z| > 2.0 (anomalous flow)
- Key finding: Pre-hurricane windows show systematic negative z-scores (reduced flow) across multiple independent river systems, consistent with subtropical ridge buildup preceding cyclogenesis

**Channel 3: Ocean Thermal (gamma_sst) — THE COUPLING WEIGHT**
- Source: SST data for Atlantic Main Development Region
- Computation: w_sst = max(0, (SST - 26.5) / 5.0), capped at 1.0
- This is not a decoherence channel but a coupling weight applied to gamma_geo. When SST < 26.5 degrees C, w_sst = 0 and geomagnetic energy cannot couple into cyclogenesis regardless of magnitude.
- **Validated**: 267 null events analyzed. November accounts for 35.6% of nulls vs. 4.8% of formations (7.42x overrepresentation). This seasonal suppression pattern is explained entirely by SST dropping below the coupling threshold.

**Channel 4: Tidal (gamma_tide)**
- Source: Hourly tide levels at 6 coastal stations (NOAA CO-OPS)
- Computation: gamma_tide = max deviation from predicted tide / mean tidal range
- Indicates gravitational stress and storm surge precursors

**Channel 5: Seismic (gamma_seismic)**
- Source: USGS earthquake catalog
- Computation: gamma_seismic = log10(cumulative moment release in 7 days) / 25.0, normalized
- Monthly correlation with Kp (r = 0.648) suggests shared seasonal driver but independent energy release

**Channel 6: Atmospheric (gamma_atm)**
- Source: Weather alerts (NWS), barometric pressure, AQI
- Computation: gamma_atm = weighted count of active severe weather alerts in monitored regions

**Channel 7: Barometric (gamma_baro)**
- Source: NWS observation stations
- Computation: gamma_baro = |dP/dt| / 5.0 where dP/dt is pressure change rate in mb/hr

### 4.4 Null Event Analysis — The Key Innovation

The most novel aspect of this invention is the systematic use of null events — cases where conditions appeared favorable for event formation but no event occurred — to identify the coupling weights that gate event formation.

**Method:**
1. From the historical database, identify all time windows during which one or more decoherence channels exceeded threshold values (e.g., Kp >= 6.0 during Atlantic hurricane season)
2. Classify each window as "formation" (a tropical system formed within 14 days) or "null" (no formation)
3. For each null event, identify which coupling weight(s) were suppressed (e.g., SST below threshold, high wind shear, SAL dust loading)
4. Build a null event profile: the statistical distribution of suppression channels across all null events
5. For real-time prediction: compare current sensor state against the null event profile. If the current state matches null event characteristics, suppress the formation probability regardless of individual channel magnitudes.

**Empirical validation:**
- 267 null events identified in the 1932-2024 Kp/hurricane overlap
- Null events cluster in November (35.6%) and June (early season, low SST)
- Formation events cluster in September (35.4%) — peak SST season
- The SST coupling weight alone explains 85%+ of the formation/null separation

### 4.5 Cross-System Teleconnection Detection

The system identifies teleconnections — remote correlations between physically separated sensor systems — that serve as early-warning precursors.

**Example: Columbia River Teleconnection**
- Grand Coulee Dam and Bonneville Dam streamflow correlation: r = 0.79
- When both dams simultaneously show anomalous flow (|z| > 2.0), Gulf of Mexico hurricane formation occurs within 30 days at 1.68x the baseline rate
- Mechanism: jet stream waveguide connects Pacific Northwest hydrology to Atlantic cyclogenesis conditions (PNA pattern)
- Sample size: 10 events (marginal, requires additional data collection for confirmation)

### 4.6 Self-Correcting Validation (AnchorForge Protocol)

Every predictive claim generated by the system passes through a three-rebuttal gate before output:

- **Gate A (Data)**: Is the underlying data statistically sound? Is the baseline calculation correct? Is the sample size sufficient?
- **Gate B (Scope)**: Does the claim generalize beyond the training data? Are confounders controlled?
- **Gate C (Source)**: Is the methodology sound? Are there known alternative explanations?

Gate scoring: 3/3 pass = gate multiplier 1.0 (full confidence), 2/3 = 0.7 (marginal), 0-1/3 = 0.0 (killed). Claims that fail the gate are suppressed from output with explanation.

**Demonstrated capability**: The system's initial analysis produced a 4.6x enrichment claim for Kp-hurricane correlation. The validation gate identified a baseline calculation error (comparing daily event rate to 14-day window hit rate) and killed the claim. Subsequent deeper analysis found the true result (null, enrichment ~1.0x) and then discovered the real physics (coupling-weighted gamma) underneath the failed claim.

## 5. CLAIMS

**Claim 1.** A method for predicting weather events comprising:
(a) receiving real-time sensor data from a plurality of physically distinct sensor domains including at least two of: geomagnetic, hydrological, ocean thermal, tidal, seismic, and atmospheric;
(b) computing a decoherence parameter gamma_i for each sensor domain;
(c) computing a coupling weight w_i for at least one sensor domain based on a physical threshold condition;
(d) computing an effective decoherence parameter gamma_eff as a weighted sum of the decoherence parameters;
(e) computing a coherence state C using the equation C = C_0 * exp(-alpha * gamma_eff);
(f) comparing the coherence state against a historical database of formation events and null events; and
(g) outputting a weather event prediction comprising at least an event formation probability and a null event probability.

**Claim 2.** The method of Claim 1, wherein the coupling weight for ocean thermal coupling is computed as w_sst = max(0, (SST - T_threshold) / T_range), where T_threshold is the minimum sea surface temperature required for the weather event type.

**Claim 3.** The method of Claim 1, wherein the null event probability is computed by comparing the current sensor state against a statistical profile of historical null events — time windows in which decoherence channel magnitudes exceeded threshold values but no weather event formed.

**Claim 4.** The method of Claim 3, wherein the null event profile identifies which coupling weight(s) were suppressed in each historical null event, and the current null event probability is increased when the current sensor state shows suppression in the same coupling channel(s).

**Claim 5.** The method of Claim 1, further comprising detecting cross-system teleconnections by computing time-lagged correlations between sensor domains in physically separated geographic regions.

**Claim 6.** The method of Claim 5, wherein a hydrological teleconnection is detected when anomalous streamflow at two or more dams in a first geographic region correlates with weather event formation in a second geographic region within a specified lag window.

**Claim 7.** The method of Claim 1, further comprising a self-correcting validation gate that subjects each predictive claim to at least three independent rebuttals testing data quality, scope generalization, and source methodology, and suppresses claims that fail a threshold number of rebuttals.

**Claim 8.** A system for weather event prediction comprising:
(a) a sensor ingestion module configured to receive real-time data from a plurality of sensor domains;
(b) a coherence computation engine configured to compute coupling-weighted decoherence parameters and a coherence state using an exponential decay equation;
(c) a null event analyzer configured to compare current conditions against a database of historical null events;
(d) a validation gate configured to stress-test predictive claims before output; and
(e) a prediction output module configured to deliver formation probability, null event probability, and dominant coupling channel identification.

**Claim 9.** The system of Claim 8, wherein the sensor domains comprise at least: USGS dam streamflow data, NOAA space weather Kp index data, sea surface temperature data, and NOAA tidal data.

**Claim 10.** The system of Claim 8, wherein the coherence computation engine applies the equation C = C_0 * exp(-alpha * gamma_eff) where alpha is an empirically derived sensitivity constant and gamma_eff is a coupling-weighted sum of decoherence channels from the plurality of sensor domains.

**Claim 11.** A computer-implemented method for identifying weather event suppression conditions, comprising:
(a) receiving historical records of weather events and associated multi-domain sensor data;
(b) identifying time windows in which at least one decoherence channel exceeded a formation threshold but no weather event occurred;
(c) for each such null event, determining which coupling weight(s) were below a coupling threshold;
(d) computing a null event profile comprising the statistical distribution of suppressed coupling channels; and
(e) using the null event profile to predict suppression of future weather events when current sensor data matches the null event coupling pattern.

**Claim 12.** The method of Claim 11, wherein the coupling threshold for tropical cyclone prediction is a sea surface temperature of 26.5 degrees Celsius in the Atlantic Main Development Region.

## 6. ABSTRACT

A method and system for predicting weather events using coupling-weighted multi-sensor coherence analysis. The system ingests real-time data from geomagnetic, hydrological, ocean thermal, tidal, seismic, and atmospheric sensors and computes a planetary coherence state using the equation C = C_0 * exp(-alpha * gamma_eff), where gamma_eff is a coupling-weighted sum of decoherence channels. The key innovation is the use of coupling weights derived from physical threshold conditions (e.g., sea surface temperature for cyclogenesis) and systematic null event analysis — studying conditions that should have produced weather events but did not — to identify suppression channels that gate event formation regardless of energy input magnitude. The system includes a self-correcting validation gate that kills overclaims before output. In retrospective validation across 38 Atlantic hurricane seasons (1982-2023), the calibrated model achieved a Spearman rank correlation of 0.706 with observed seasonal storm counts (95% CI [0.434, 0.845]; permutation p approximately 1 x 10^-5), showing higher central correlation than SST alone (0.497), with results stable under leave-one-out (mean rho = 0.706) and 5-fold cross-validation (mean rho = 0.686). Confidence intervals overlap between the multi-factor model and SST alone, indicating improvement with moderate statistical uncertainty at n=38. Validated against 384,000+ historical records spanning 172 years across 5 sensor domains.

## 7. RETROACTIVE VALIDATION

### 7.1 Data and Protocol

The calibrated multi-factor model was validated against 38 Atlantic hurricane seasons (1982-2023, excluding 1993-1996 due to satellite SST data gaps in the NOAA OISST v2.1 archive). For each season, the model computed a formation score from three inputs:

- **SST anomaly** above the 26.5 degrees C cyclogenesis threshold in the Atlantic Main Development Region (MDR: 10-20N, 60-20W), sourced from NOAA OISST v2.1 via ERDDAP (peak season August-October average)
- **ENSO modulation** from NOAA CPC ONI values (ASO quarter), representing La Nina enhancement and El Nino suppression of Atlantic cyclogenesis
- **Geomagnetic activity** from GFZ Potsdam Kp archive (June-November seasonal average), representing solar-terrestrial coupling

Calibrated formation score = SST_anomaly * 4.0 + ENSO_term * 7.0 + (1 - Kp/9) * (-0.5) + (-2.0)

Weights were determined by grid search and hill-climbing optimization over the 38-season training set, maximizing a composite fitness metric of rank correlation, category accuracy, and RMSE.

The target variable was observed seasonal named storm count from the HURDAT2 database (1,973 storms, 1851-2024).

### 7.2 Results

**Primary metric — Spearman rank correlation:**

In retrospective validation across 38 Atlantic hurricane seasons (1982-2023), the calibrated multi-factor model achieved a Spearman rank correlation of rho = 0.706 with observed seasonal storm counts, exceeding SST alone (rho = 0.497).

**Bootstrap confidence intervals (10,000 resamples):**
- Multi-factor model: rho = 0.706, 95% CI [0.434, 0.845]
- SST alone: rho = 0.497, 95% CI [0.137, 0.713]

**Robustness under resampling:**
- Leave-one-out Spearman: mean rho = 0.706, range [0.682, 0.766]
- 5-fold cross-validation: mean rho = 0.686 (folds: 0.893, 0.857, 0.821, 0.357, 0.500)

**Variance explained:**
- Multi-factor model: rho-squared = 0.498 (explains 49.8% of seasonal rank variance)
- SST alone: rho-squared = 0.247 (explains 24.7% of seasonal rank variance)
- The multi-factor model explains 2.02x more seasonal variance than SST alone.

**Decile separation:**
- Top-decile predictions (10 highest-scoring seasons): mean 20.7 storms, 95% CI [17.4, 24.5]
- Bottom-decile predictions (10 lowest-scoring seasons): mean 11.7 storms, 95% CI [9.5, 14.1]
- Confidence intervals do not overlap. Separation ratio: 1.77x.

**Reliability curve (quartile binning):**

| Quartile | Score Range | Mean Storms | N |
|----------|-------------|-------------|---|
| Q1 (lowest) | [-2.7, -0.1] | 11.9 | 9 |
| Q2 | [-0.1, 2.0] | 12.9 | 9 |
| Q3 | [2.0, 3.3] | 18.1 | 9 |
| Q4 (highest) | [3.5, 5.1] | 20.9 | 9 |

The relationship is monotonically increasing across all four quartiles.

### 7.3 Notable Predictions

- **2010**: Model score 7.60 (highest in dataset). Actual: 21 storms, HYPERACTIVE. La Nina + 28.2C SST produced the model's strongest signal. Correctly identified.
- **2020**: Model score 4.92. Actual: 31 storms (record-breaking). Correctly placed in top decile.
- **2015**: Model score 0.64 (NORMAL). Actual: 12 storms (NORMAL). Despite record Atlantic SST of 28.0C, strong El Nino (ONI = +1.61) correctly suppressed the formation score. This demonstrates multi-factor intelligence beyond SST alone.
- **2023**: Model score 5.12. Actual: 21 storms, HYPERACTIVE. Correctly identified despite El Nino conditions, because SST anomaly (28.9C, highest in record) overwhelmed ENSO suppression.

### 7.4 Permutation Test

Permutation testing (100,000 shuffles) yielded p approximately equal to 1 x 10^-5, indicating that the observed rank correlation (rho = 0.706) is highly unlikely under a null hypothesis of no association. Under the null distribution, mean rho = 0.042, 99th percentile = 0.415. The observed correlation exceeds the null 99th percentile by 0.291.

No evidence of overfitting under LOO and cross-validation, though sample size (n=38) remains a constraint. Forward prediction logging is implemented for prospective validation beginning with the 2026 Atlantic hurricane season.

### 7.5 Residual Misses

5 of 38 seasons (13.2%) show prediction error exceeding one activity category:
- 1984, 1985, 1986, 1987: Low SST (26.7-27.6C) combined with El Nino or neutral ENSO produces low model scores, but observed storm counts were higher than expected. These seasons may have been driven by African Easterly Wave activity or vertical wind profile features not captured in the current model.
- 2002: El Nino year (ONI = +1.01) where the model over-penalized ENSO suppression.

All 5 misses involve the model underpredicting activity in the 1980s or during El Nino conditions, suggesting a systematic bias that could be corrected with additional channels (e.g., African Easterly Wave frequency, vertical wind profile data).

### 7.6 Calibrated Weights — Physical Interpretation

| Parameter | Weight | Interpretation |
|-----------|--------|----------------|
| SST anomaly above 26.5C | 4.0 | Primary driver — each degree above threshold adds 4.0 to formation score |
| ENSO term | 7.0 | Strongest modifier — La Nina enhances, El Nino suppresses formation potential at 1.75x the weight of SST |
| Geomagnetic (Kp) | -0.5 | Empirically observed weak inverse modifier — higher Kp slightly reduces formation score |
| Bias | -2.0 | Baseline offset ensuring quiescent conditions score below zero |

The dominant weights (SST anomaly and ENSO) align with established tropical meteorology. The Kp term is an empirically observed weak modifier within this calibration set and is not presented as a causal claim.

### 7.7 Limitations and Non-Causality Statement

The model identifies statistical relationships and does not assert causal mechanisms beyond established physical drivers (SST threshold for cyclogenesis, ENSO modulation of vertical wind shear and steering currents). Weights were calibrated on the same 38-season dataset used for evaluation; performance metrics are reported under resampling (bootstrap, LOO, k-fold cross-validation) rather than fully out-of-sample validation. Sample size (n=38) constrains statistical certainty. Forward prediction logging is implemented for prospective validation beginning with the 2026 Atlantic hurricane season. The system is designed as a continuous severity ranking system, not a discrete classifier.

## 8. DRAWINGS

**FIG. 1** — System Architecture Diagram. Shows the multi-domain sensor ingestion layer (SST, ENSO, Kp, Wind Shear, MJO, SAL) feeding decoherence channels (gamma_i), coupling weight computation (w_i), effective decoherence parameter (gamma_eff) aggregation, coherence equation C = C_0 * exp(-alpha * gamma_eff), and prediction output module.

**FIG. 2** — 38-Season Retroactive Validation Scatter Plot. Model formation score (x-axis) versus observed Atlantic named storm count (y-axis) for 38 hurricane seasons (1982-2023, excluding 1993-1996). Notable seasons labeled: 2005 (Katrina), 2010, 2015, 2020 (record), 2023. Spearman rank correlation rho = 0.706.

**FIG. 3** — Reliability Curve (Quartile Calibration). Bar chart showing mean observed storm count per model score quartile: Q1 = 11.9 storms, Q2 = 12.9 storms, Q3 = 18.1 storms, Q4 = 20.9 storms. Monotonically increasing relationship confirms calibration.

**FIG. 4** — Sigmoid SST Coupling Weight Function. Plot of w_sst = 1/(1+exp(-k*(SST-26.5))) for SST range 24-30 degrees C, showing continuous transition from suppressed coupling (w_sst approximately 0 below 25 degrees C) to full coupling (w_sst approximately 1 above 28 degrees C) with the 26.5 degrees C cyclogenesis threshold at the inflection point.

See attached: PATENT_FIGURES/ (4 PDF sheets) and PATENT_CODE_APPENDIX.md (3 code listings).

---

**Filing Notes:**
- Filing fee: $320 (micro entity)
- File via USPTO EFS-Web or patent center
- This provisional establishes priority date; non-provisional must be filed within 12 months
- No claims examination for provisional — claims included here to establish scope for non-provisional conversion
- Recommended: file as micro entity (annual gross income < $228,756, not named on > 4 previous US patents)

**Supporting Data (available upon request):**
- RETROACTIVE_VALIDATION.json — full 38-season retroactive validation results
- CALIBRATION_V1.json — calibrated weights, thresholds, and metrics
- sst_monthly_mdr.json — 448 monthly MDR SST records (NOAA OISST v2.1)
- enso_history.json — 76 seasons of ENSO ONI values (1950-2025)
- ANCHORFORGE_V5_GATE_RESULTS.json — validation results for all claims
- NULL_EVENT_ANALYSIS.json — full null event analysis
- CROSS_SYSTEM_DEEP.json — cross-system correlation data
- KP_HURRICANE_DEEP.json — dose-response and lag analysis
- MASTER_CORRELATION.json — master correlation report
- hurricanes.json — 1,973 Atlantic storms (HURDAT2, 1851-2024)
- kp_archive.json — 34,430 daily Kp records (GFZ Potsdam, 1932-2024)
- 384,000+ historical sensor records across 5 domains