STRIKE SuperEnsemble transforms a diverse set of independent tropical cyclone forecast aids into three transparent consensus techniques. It is built to reduce individual-model noise, counter persistent directional and intensity biases, and exploit the fact that different aids often make different errors.
STRIKE—short for Statistically-Tuned Real-time Integrated Kinematic Ensemble—turns diverse tropical cyclone guidance into one statistically refined, continuously verified forecast signal.
Begin with why combining independent guidance can be more stable than relying on a single aid, how disagreement is handled, and what STRK and STRR represent, along with the AI-only STRA technique now in development.
Continue to the techniques →Continue directly into basin- and horizon-specific weighting, historical bias estimation, robust screening, spherical averaging, issuance controls, and leakage-resistant replay.
Continue to the methodology →The core idea is simple: strong forecast aids do not fail in exactly the same way. When their errors differ in direction and magnitude, a disciplined statistical blend can let those errors offset one another while retaining the shared forecast signal.
Use eligible track and intensity aids that are actually present for the storm and cycle.
Reduce recurring along-track, cross-track, and intensity tendencies supported by historical samples.
Use robust statistics to reject members that sit too far outside the current guidance cluster.
Renormalize available weights and cap any one contributor so the product remains a true consensus.
Create 12-hour track and intensity points through 120 hours, with a full machine-readable audit trail.
STRK is the adaptive operational consensus, STRR isolates the effect of raw performance weighting, and STRA extends the framework to an AI-only contributor set now being developed for 2027.
The adaptive operational consensus. Historical performance, availability, walk-forward adoption, and learned member corrections shape its basin- and horizon-specific weight tables.
A cumulative raw-performance-weighted control. It uses the same historical sample, common frozen bias field, and robust production builder as STRK, but removes baseline shrink and the walk-forward adoption gate so the effect of the weighting philosophy can be isolated.
A corrected consensus built exclusively from eligible artificial-intelligence forecast techniques. Development and training will use AI-guidance performance collected during the 2026 tropical-cyclone season.
A model that runs too fast can partially offset one that runs too slow. A right-of-track tendency can partially offset a left-of-track tendency. The blend is most valuable when its contributors are skillful but not perfectly correlated.
Each point represents a different forecast aid. Their departures from truth are substantial, but they are not aligned in the same direction.
After correction, screening, and weighting, the retained field can locate a more stable center than any arbitrary single member.
Explore how STRIKE learns from completed seasons, processes each forecast cycle, and preserves a valid walk-forward test.
STRIKE is built from configured, independent forecast aids present in the current storm cycle. Existing consensus products are excluded from the member pool so the system does not recursively blend a blend. Individual GEFS members are also excluded from the STRIKE member set.
A model can be excellent at track and mediocre at intensity, or vice versa. STRIKE therefore resolves two independent weight tables and two independent correction systems at each forecast point.
Weight and bias behavior is organized by basin, component, and forecast horizon. Central Pacific cases map into the East Pacific profile.
This structure reflects the practical reality that member skill and error character can change between basins and across the forecast period.
The target is complementary information, not a popularity vote. Consensus aids already containing several models are kept out of the construction pool, while eligible deterministic and ensemble-mean aids can contribute according to the configured tables. This preserves clearer attribution and reduces hidden double counting.
Each training case is indexed by storm, initialization cycle, and forecast hour. Eligible member forecasts are matched to best-track truth, yielding track error, signed intensity error, along-track error, and cross-track error. Duplicate aid/case records are resolved before optimization.
The trainer uses only independent aids configured for the applicable basin, component, and horizon cell.
For each aid, recency-weighted mean absolute error is converted into a quality score. Availability contributes a smaller positive term so an aid that is skillful but rarely present does not receive the same practical influence as an aid that is both skillful and consistently usable.
For each held-out season, the candidate weights are trained only on earlier seasons, then compared with the baseline on the held-out year. A new table is adopted only when its aggregate walk-forward skill reaches the configured minimum and it beats the baseline in at least half of tested seasons.
Candidate files are generated for review; the trainer does not silently promote them to operations.
Track bias is estimated in a verifying-motion-relative coordinate frame: along-track and cross-track. Intensity bias is forecast maximum wind minus verifying maximum wind. Each tail of the historical error distribution can be trimmed before the weighted mean is calculated.
Only cells meeting the minimum sample requirement become eligible. Operational correction subtracts the eligible coefficient, multiplied by a component/horizon application strength, from the member forecast before consensus construction.
The first available season begins from one fixed equal-weight seed for the historical control framework. After each season is complete, the next season is built only from information that would have existed at that boundary: STRK is cumulatively re-estimated with its adopted and shrunk performance method, while STRR is cumulatively re-estimated from raw performance with zero baseline shrink and no adoption gate.
A common cumulative bias field is estimated from completed prior seasons and frozen for the replay techniques during the next season. This keeps the comparison focused on weighting behavior rather than giving one technique a different correction history.
The issuance gate checks the ranked track contributors in short, medium, and long horizons. Missing top-five track contributors receive a longer grace period than missing additional contributors. Intensity availability does not block issuance.
A member with a clean, contiguous track that legitimately ends before a later horizon is marked reduced-horizon and excluded from that later attendance gate, rather than treated as late.
At each nonzero tau, the builder resolves the basin/component/horizon coefficient for each contributor. Track correction translates signed along/cross bias into an earth-relative displacement using the member’s local heading. Intensity correction subtracts the shrunk signed bias and constrains the result to a physically bounded range.
Tau zero remains an anchor and is not bias-corrected.
The builder finds a robust center from median latitude and longitude, measures each corrected contributor’s great-circle distance from that center, and computes a median absolute deviation. The rejection threshold is the larger of 2.75 × MAD or a lead-time-dependent floor that expands from 175 n mi early to 625 n mi at 120 hours.
The same 2.75 × MAD structure is used around the median intensity, with a minimum rejection threshold of 25 kt.
If fewer than five contributors survive, that component is not published for the tau.
Configured weights are renormalized across the retained contributors. If any contributor would exceed 45% after missing members or outliers are removed, iterative water-filling caps that member and redistributes its excess proportionally among the others.
Each issued technique contains points every 12 hours through 120 hours. The archive records resolved weight tables, correction details, rejected and retained members, normalization decisions, readiness status, source hashes, coefficient hashes, and final product hashes.
Once issued for a cycle, the package can be locked rather than silently changing as late source data arrives.
The expensive work is historical analysis performed periodically. Live construction is compact numerical processing over a small set of forecast points—well suited to a modest virtual machine rather than specialized hardware.
STRIKE’s live workload consists primarily of dictionary lookups, robust summary statistics, great-circle calculations, small weighted reductions, and JSON serialization. It does not require a numerical weather prediction model, GPU cluster, or large in-memory inference service.
A standard virtual machine can execute the cycle builder because the system consumes existing forecast aids rather than integrating the atmosphere itself. Historical training uses tabular arrays and can also run on commodity CPU resources.
Verification is presented in two layers: a finalized 2019–2025 cumulative walk-forward replay and the provisional live season. Historical scorecards use NHC-style homogeneous comparisons at each standard forecast period, so every displayed aid in a column is evaluated on the same storm, cycle, and forecast-hour cases.
STRK and STRR are evaluated through a true walk-forward replay. Training advances only after each season ends, preventing future information from improving past forecasts.
Across 112 season, basin, and forecast-hour rankings, STRK finished in the automated top five 99 times, while STRR did so 93 times. STRR recorded the most first-place finishes with 17, illustrating the upside of raw performance weighting in favorable seasons.
The replay does not support a claim that one weighting approach always wins. Raw-performance STRR produces the greatest upside in favorable seasons, while STRK remains highly stable, placing in the automated top five in 88% of track rankings.
STRK is placed alongside EMNI, OFCI, and FSSE using cases available to both techniques. Mean errors and relative differences are shown together so the scale of each result remains visible. Each comparator has its own verification universe.
OFCL is the NHC official forecast, not a peer automated model aid. It remains visible in the scorecard because it is operationally important, while the placement findings above use the automated-aid peer rank so guidance can be compared on a more like-for-like basis.
Positive differences indicate lower STRK error. Negative differences indicate higher STRK error and are shown neutrally. Values that round to 0.0% are reported as a tie. EMNI and OFCI use embedded pairwise records; FSSE is aggregated from the homogeneous seasonal scorecard. Results from different comparator cards are not directly comparable.
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A credible public forecast system should explain its scope, controls, and limitations as clearly as its strengths.
STRIKE does not simulate the atmosphere. It statistically combines and corrects existing tropical cyclone guidance using finalized historical verification and cycle-specific availability.
STRIKE is an experimental decision-support and research product. Official forecasts, watches, warnings, and public safety instructions remain the responsibility of the appropriate meteorological agencies.
An operationally motivated statistical model developed to support decisions involving forecast uncertainty, employee safety, infrastructure exposure, and continuity of operations.
STRIKE SuperEnsemble is an independently developed statistical tropical-cyclone consensus system created by Adam Hazell, an engineer in the radio-frequency and broadcast field.
The system originated from the need to evaluate forecast risk across a geographically distributed network of broadcast infrastructure. Operational decisions included prioritizing site preparation, positioning technical personnel and recovery resources, protecting employee safety, and maintaining continuity at facilities representing multimillion-dollar infrastructure investments.
This application required more than a single deterministic forecast or a binary interpretation of the forecast cone. The relevant decision space included model disagreement, track uncertainty, expected intensity, forecast timing, site exposure, and the comparative consequences of underestimating or overestimating risk at different locations.
STRIKE applies an engineering and statistical framework to that problem. It evaluates multiple forecast aids using historical verification, separates performance by forecast horizon and basin where supported, applies defined weighting and correction procedures, and generates consensus tracks and intensities from the available guidance.
The development of STRIKE was informed by prior work creating internal weather-monitoring and decision-support applications for technical operations. That experience included the integration of meteorological datasets, development of site-specific risk displays, and conversion of complex forecast information into operationally useful guidance.
The model is designed so that its inputs, calculations, and verification results can be inspected rather than treated as an opaque forecast product.
AI-assisted development tools were used extensively during software construction and evaluation. Their role included accelerating code development, processing historical archives, constructing tests, comparing model configurations, and supporting backtesting and hindcasting.
The forecast methodology itself remains deterministic and reproducible: defined inputs are processed through documented statistical procedures to produce the published output. AI tools accelerated development and analysis; they do not independently generate STRIKE forecasts.
The next stage of the STRIKE SuperEnsemble is focused on independent evaluation, improved access to upstream forecast aids, and collaboration with the meteorological community. The objective is not simply to demonstrate favorable retrospective results, but to determine whether the methodology remains useful, stable, and operationally defensible under real-time forecasting conditions.
Research access to forecast aids at or near native model availability would allow the STRIKE SuperEnsemble to operate before those records appear in the public A-deck. Earlier availability is necessary for genuine real-time testing within the same decision window faced by operational forecasters.
The highest-priority need is ECMWF deterministic tropical-cyclone guidance, preferably EMXI, together with other major numerical and AI-derived aids that are not consistently available through public real-time datasets.
Annual verification of STRIKE SuperEnsemble ATCF records is sought for storms in which the system produced real-time forecasts. Evaluation using NHC procedures and case definitions would permit fair comparison with official forecasts and recognized operational aids.
The purpose is to establish an external performance record based only on forecasts issued before the verifying outcome was known, separate from internal hindcasting and development results.
Technical review by tropical-cyclone researchers and operational forecasters would provide scrutiny of the STRIKE SuperEnsemble training design, weighting methodology, bias correction, case selection, and safeguards against information leakage.
A longer-term objective is experimental evaluation within the NHC forecast-development process. Consideration for inclusion should follow only if independent real-time verification demonstrates stable and measurable value beyond existing guidance.
A further research question is whether a verified superensemble can improve public interpretation of tropical-cyclone guidance. Individual model tracks and intensity forecasts can fluctuate sharply between cycles, drawing attention to short-term movement rather than the broader forecast signal.
The STRIKE SuperEnsemble is designed to reduce independent forecast errors through statistical consensus and may provide a more stable representation of track and intensity evolution. Any public-facing use should remain supplemental to official NHC products and should be evaluated for both forecast performance and communication effectiveness.
Straightforward answers about the methodology, forecast process, and performance record.
Explore current verification, understand the forecast approach, or review the methodology behind STRK, STRR, and the developing STRA technique.
