Bias Score Frequency Hit

Compute forecast/observed event frequency bias from explicit binary table; overforecast bias is not hit accuracy or probability calibration.

Description

Frequency bias compares how often an event was forecast with how often it occurred.

Frequency bias compares how often an event was forecast with how often it occurred. A value above one indicates overforecast frequency, independent of whether individual event forecasts were correct.^[1]

Compute forecast/observed event frequency bias from explicit binary table; overforecast bias is not hit accuracy or probability calibration.

When to use Bias Score Frequency Hit

Use this tool to compute forecast/observed event frequency bias from explicit binary table; overforecast bias is not hit accuracy or probability calibration.

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

How to use Bias Score Frequency Hit 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 frequencyBiasScore, observedEventCount, forecastEventCount. 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 contingency counts with positive total and at least one observed event. Return bias and the two event counts; zero forecast events can validly return zero bias.

bias = (hits + false alarms)/(hits + misses)

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

Output field	Meaning	Value type
frequencyBiasScore	Frequency bias score	number
observedEventCount	Observed event count	number
forecastEventCount	Forecast event count	number

json

{
  "input": {
    "hits": 30,
    "misses": 10,
    "falseAlarms": 20,
    "correctNegatives": 40
  },
  "output": {
    "frequencyBiasScore": 1.25,
    "observedEventCount": 40,
    "forecastEventCount": 50
  }
}

Limitations and assumptions

The ratio does not measure hit accuracy or probability calibration. No observed events leaves its denominator undefined.

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 False Alarm Ratio Precip Verification: Compute observed false alarms/(hits+false alarms), not false-positive rate over observed nonevents; no-event forecasts leave ratio undefined. For a complementary task, use 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.

References

Bias Score Frequency Hit — tool.io

Similar or alternative tools

False Alarm Ratio Precip Verification
Compute observed false alarms/(hits+false alarms), not false-positive rate over observed nonevents; no-event forecasts leave ratio undefined.
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.