Forecast Skill Heidke Score

Compute binary Heidke skill against marginal chance agreement on explicit observed contingency counts; undefined degenerate chance is rejected, no independence confidence interval or operational forecast quality certification.

Description

Heidke skill measures categorical agreement relative to agreement expected from contingency-table margins.

Heidke skill measures categorical agreement relative to agreement expected from contingency-table margins. Chance adjustment can yield negative skill even when raw accuracy appears substantial.^[1]

When to use Forecast Skill Heidke Score

Use this tool to compute binary Heidke skill against marginal chance agreement on explicit observed contingency counts; undefined degenerate chance is rejected, no independence confidence interval or operational forecast quality certification.

Prepare the observed sounding, cloud records or forecast verification data. Check pressure and height coordinates, wind conventions and the stated units.

How to use Forecast Skill Heidke Score and understand its results

Prepare the observed sounding, cloud records or forecast verification data. Check pressure and height coordinates, wind conventions and the stated units.
Enter hits, misses, falseAlarms, correctNegatives using the field table and calculation rules, then run the tool.
Read heidkeSkillScore, observedAccuracy, chanceAgreement. The explanation below defines the units, signs, ordering and any flags or placeholder values.
If the tool reports an input issue, correct the values and run it again. Apply the stated assumptions when interpreting a successfully returned result.

Input field	Meaning	Value type; see rules below	Requirement
hits	Hits	integer	Required
misses	Misses	integer	Required
falseAlarms	False alarms	integer	Required
correctNegatives	Correct negatives	integer	Required

Supply nonnegative integer counts and a positive total. Compute raw accuracy and marginal chance agreement; use the algebraic table expression for skill to avoid subtracting rounded near-unit chance. A zero skill denominator is an input error.

HSS = 2(hits × correct negatives − misses × false alarms)/[(hits+misses)(misses+correct negatives)+(hits+false alarms)(false alarms+correct negatives)]

Units, indexing, boundary conditions and exceptional cases are defined in the calculation rules above.

Output field	Meaning	Value type
heidkeSkillScore	Heidke skill score	number
observedAccuracy	Observed accuracy	number
chanceAgreement	Chance agreement	number

json

{
  "input": {
    "hits": 30,
    "misses": 10,
    "falseAlarms": 20,
    "correctNegatives": 40
  },
  "output": {
    "heidkeSkillScore": 0.4,
    "observedAccuracy": 0.7,
    "chanceAgreement": 0.5
  }
}

Limitations and assumptions

No independence assumption, confidence interval or forecast-quality certification is supplied.

Numeric inputs and returned numeric values must be finite; an unrepresentable returned value produces an error. Missing required fields, extra fields, wrong value types and violations of the calculation rules are input errors.

Alternative or Complementary methods

Use Equitable Threat Score ETS: Compute observed binary ETS after marginal random-hit correction, retaining possible negative skill; unlike raw CSI or HSS, correct negatives change chance hits but not event union directly. For a complementary task, use Bias Score Frequency Hit: Compute forecast/observed event frequency bias from explicit binary table; overforecast bias is not hit accuracy or probability calibration.

References

Forecast Skill Heidke Score — tool.io

Similar or alternative tools

Equitable Threat Score ETS
Compute observed binary ETS after marginal random-hit correction, retaining possible negative skill; unlike raw CSI or HSS, correct negatives change chance hits but not event union directly.
Bias Score Frequency Hit
Compute forecast/observed event frequency bias from explicit binary table; overforecast bias is not hit accuracy or probability calibration.