Red Sky At Night Sailor Delight Validity Percent

Actual matched local red-sky forecast accuracy for a predefined rain outcome and lead time. Same cohort and independent observations required; no universal sailor-delight validity percentage or causal weather explanation.

Description

Testing a weather saying requires paired recorded predictions and independently verified outcomes.

Testing a weather saying requires paired recorded predictions and independently verified outcomes.^[1]

Accuracy over known submitted cases describes that sample and differs from a validated future-weather probability. Use this tool to calculate red sky at night sailor delight validity percent from the supplied measurements and assumptions.

When to use Red Sky At Night Sailor Delight Validity Percent

Use this tool to calculate red sky at night sailor delight validity percent from the supplied measurements and assumptions. Identify the community, narrative version, source edition and interpretation context; prepare any annotations independently.

How to use Red Sky At Night Sailor Delight Validity Percent and understand its results

Identify the community, narrative version, source edition and interpretation context; prepare any annotations independently.
Enter independentlyVerifiedRainOutcomeFlags, recordedRedSkyPredictionFlags, pairedObservationKnownFlags, using the field meanings and rules below, then run the tool.
Read result.correctKnownCount, result.incorrectKnownCount, result.unknownCount, result.observedKnownAccuracy. Interpret the fields using their units, signs and evidence conventions below.
If input validation fails, correct the reported field and run again. Before applying the result, check reference scope, model assumptions and any missing evidence or placeholder values.

Input field	Meaning	Value type	Requirement
independentlyVerifiedRainOutcomeFlags	Independently verified rain outcome flags; binary 0/1 under the definitions below; caller assertion or measurement requiring independent evidence	list of number	Required
recordedRedSkyPredictionFlags	Recorded red sky prediction flags; binary 0/1 under the definitions below	list of number	Required
pairedObservationKnownFlags	Paired observation known flags; binary 1=known, 0=unknown, in the order described below	list of number	Required

Enter nonempty aligned independently verified rain outcomes, recorded predictions and paired-known flags, all binary 0/1. Require at least one known pair. Count exact prediction/outcome equality only among known pairs, return correct, incorrect and unknown counts, and divide correct by known count. observedKnownAccuracy is a fraction, despite the title's percent wording; no confusion-matrix class metrics, new prediction or validation population is supplied.

correct=Σknown_i×[actual_i=predicted_i]; incorrect=Σknown−correct; unknown=n−Σknown; accuracy=correct/Σknown

Use the exact units, boundary rules, ordering and missing-evidence conventions above.

Output field	Meaning	Value type
result	Structured calculation or selected reference, with fields listed below	object
result.correctKnownCount	Correct Known Count; follow the units, sign, ordering and evidence convention stated above	number
result.incorrectKnownCount	Incorrect Known Count; follow the units, sign, ordering and evidence convention stated above	number
result.unknownCount	Unknown Count; follow the units, sign, ordering and evidence convention stated above	number
result.observedKnownAccuracy	Correct known pairs divided by all known pairs; a fraction, multiply by 100 for percent	number

json

{
  "input": {
    "independentlyVerifiedRainOutcomeFlags": [
      0,
      1,
      0
    ],
    "recordedRedSkyPredictionFlags": [
      0,
      0,
      0
    ],
    "pairedObservationKnownFlags": [
      1,
      1,
      1
    ]
  },
  "output": {
    "result": {
      "correctKnownCount": 2,
      "incorrectKnownCount": 1,
      "unknownCount": 0,
      "observedKnownAccuracy": 0.6666666666666666
    }
  }
}

Limitations and assumptions

Accuracy over known submitted cases describes that sample and differs from a validated future-weather probability. Interpretation depends on the units, sampling scope and exact model described above.

All listed fields require explicit values. Unexpected fields, incorrect types, nonfinite numbers and sparse evidence arrays fail validation; the specific bounds and alignment requirements are stated above. Extremely large inputs or outputs can also be rejected. An input error is distinct from a successful result reporting false, no matches or missing evidence.

Alternative or Complementary methods

Use Weather Lore Almanac Prediction Catalog Lookup for this related task: Weather sayings compress observations into memorable predictions or advice.

Use Pinecone Open Close Humidity Forecast Mechanism Lookup when its separate inputs and assumptions fit the question: Pinecone scales can move passively in response to surrounding moisture.

References

Red Sky At Night Sailor Delight Validity Percent — tool.io

Similar or alternative tools

Weather Lore Almanac Prediction Catalog Lookup
Weather sayings compress observations into memorable predictions or advice.
Pinecone Open Close Humidity Forecast Mechanism Lookup
Pinecone scales can move passively in response to surrounding moisture.