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You can build a small, testable cyclone-impact forecasting prototype from 61 data points, but the number alone says nothing about whether it can forecast reliably. First define what each point represents, what “impact” means, where and when it applies, and how far ahead the forecast is meant to look. Then establish a simple baseline and evaluate it in a way that keeps records from the same storm together.
Define the forecasting problem before choosing a model
Write down the project’s specification before fitting anything. The title does not establish the makeup of the 61 records or the intended forecast, so these are decisions to resolve rather than assumptions to make.
- Unit of observation: Is each row a distinct storm, a time step along a storm track, a location, or an aggregation?
- Target: Specify the impact outcome and how it is measured. Wind exposure, rainfall, flooding, damage, and population affected are different targets; one cannot stand in for another without a defensible definition.
- Geography and period: Name the basin or area and the dates represented. The scope determines whether the data sources and any evaluation can support the intended use.
- Forecast horizon: State how far ahead the model predicts and when its input information would be available. A prediction of an outcome at a specified lead time is more precise than a general claim to forecast “impact.”
- Intended decision: Explain who would use the forecast and what decision it could inform. A prototype for exploration is not an operational warning system.
Record whether multiple rows belong to the same storm. If so, those records are related observations, not 61 independent examples.
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Use IBTrACS for storm history, not as an assumed impact-label source
NOAA’s National Centers for Environmental Information describes the International Best Track Archive for Climate Stewardship (IBTrACS) as “the most complete global collection of tropical cyclones available.” The archive merges best-track information from multiple agencies, including storm locations and intensity. It can help identify storms and construct historical track or intensity features, but it does not, by itself, establish the project’s impact labels. See the NOAA IBTrACS project page.
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Before joining IBTrACS to the 61 records, check that the storm identifiers, basin, dates, variables, and units align. NOAA lists IBTrACS v04r01 and provides data in CSV, NetCDF, and shapefile formats. The project page says the archive is usually updated three times each week—typically Sunday, Tuesday, and Thursday—so record the version and the date you accessed it to make a dataset build reproducible. See NOAA’s IBTrACS project page.
Check consistency and missingness in the storm records
IBTrACS is not perfectly uniform across agencies or eras. NOAA notes that agencies use different practices for reporting maximum sustained winds, and that some historical systems—especially from before satellite observations—are absent. A wind value may also have been inferred from a known storm category; NOAA advises caution where wind quality is not well identified. Preserve agency-specific source fields where available, document units and wind averaging periods, and inspect missingness rather than treating every value as directly comparable. These limitations matter particularly when combining basins or comparing long periods. NOAA’s official metadata record for IBTrACS version 4.01 lists a publication date of 2019-02-15; consult the current project page for the version and files you use, rather than treating that metadata date as the release date of every current file. See NOAA’s IBTrACS metadata record.
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Use wind-exposure data only when the target and coverage fit
NOAA’s Tropical Cyclone Wind Exposure dataset is a possible contextual source for a wind-exposure project, not a general archive of damage or human impacts. NOAA describes an approach that intersects storm tracks with wind-intensity areas and calculates return intervals. Its listed coverage is 1988–2022 for the North Atlantic and Eastern Pacific, and 2001–2022 for the Western Pacific. Confirm that the geography, dates, and exposure outcome match your project before using it; do not treat it as a label source for flooding, damage, or people affected. See NOAA’s Tropical Cyclone Wind Exposure dataset record.
Build a cautious baseline with 61 points
Once the target, input features, and forecast horizon are explicit, begin with a transparent baseline rather than a complex model. The baseline should make clear what information the forecast uses and what a more elaborate model would need to improve on. Keep the model small relative to the amount and structure of the data, and report the number of storms represented—not just the number of rows.
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Choose a baseline appropriate to the target. For a numeric outcome, a simple reference prediction can show whether a model adds useful predictive information; for a categorical outcome, compare against a straightforward rule or class-frequency reference. These are methodological starting points, not claims that any particular approach will perform well on this dataset. Do not select a model or claim likely accuracy before the target and evaluation design are defined.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Evaluate by storm, not just by row
If several of the 61 records come from the same storm, a random row-level split can place closely related observations in both training and test sets. That can make performance appear stronger than it would be on storms the model has not seen. Where the data structure allows it, keep each storm’s observations together when splitting data for evaluation. Grouping by storm is methodological guidance for avoiding leakage; it is not a NOAA finding.
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With only 61 independent cases, even a storm-grouped test set will be small, and its score may be uncertain. Describe how many storms are in each split and report uncertainty around performance. If the goal is to generalize to other basins, places, or future seasons, a small collection of cases from one scope does not establish that broader performance; independent validation data would be needed.
Document the limits of what the result can support
Publish the data definition alongside any result: what one point means, how the impact label was measured, which storms and dates are included, the basin or geography, the forecast lead time, and how records were divided for evaluation. For sources such as IBTrACS, also note the version and access date, relevant agency fields, units, and known missingness. This makes it possible to distinguish a limited prototype from a forecast that has been shown to work for a particular operational decision.
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