Climate Normals Anomaly Deviation Calculator
Actual season/time-matched climate-normal signed deviations, not climate trend or anomaly attribution. Caller independently establishes matching units, evidence, method edition and applicability; output is not compliance, diagnosis, work-safety approval, geological identity or validated hazard prediction.
Description
A climate anomaly is the difference between an observation and a matched normal.
A climate anomaly is the difference between an observation and a matched normal.1
The baseline period, variable and month or other interval must agree before subtraction.
When to use Climate Normals Anomaly Deviation Calculator
Use this tool to calculate or review the supplied observations under the stated rules. Establish the observation method, units, averaging period and applicable reference or calibration.
How to use Climate Normals Anomaly Deviation Calculator and understand its results
- Establish the observation method, units, averaging period and applicable reference or calibration.
- Enter observedMonthlyValues, callerMatchedNormals, using the field meanings and rules below, then run the tool.
- Read signedAnomalies, meanSignedAnomaly, using the output meanings and calculation rules below.
- Correct any reported input issue and run again. Before using a successful result, check the stated units, signs, reference scope and observation coverage.
| Input field | Meaning | Value type | Requirement |
|---|---|---|---|
| observedMonthlyValues | Observed monthly values | list of number | Required |
| callerMatchedNormals | Caller matched normals | list of number | Required |
Use nonempty aligned finite observations and matched normals. Subtract each normal from its observation in original order and return those anomalies and their unweighted arithmetic mean. The implementation does not require twelve entries.
anomaly_i=observed_i−normal_i; meanAnomaly=Σ anomaly_i/N Use the matching field units and the exact boundary, ordering and reference rules above.
| Output field | Meaning | Value type |
|---|---|---|
| signedAnomalies | Signed anomalies; preserve the subtraction order and sign defined in the mechanics | list of number |
| meanSignedAnomaly | Mean signed anomaly; preserve the subtraction order and sign defined in the mechanics | number |
{
"input": {
"observedMonthlyValues": [
12,
8
],
"callerMatchedNormals": [
10,
10
]
},
"output": {
"signedAnomalies": [
2,
-2
],
"meanSignedAnomaly": 0
}
}Limitations and assumptions
Match the variable and interval for each pair. The average is not time-weighted and does not estimate trend, significance or baseline uncertainty.
Missing required fields, unknown fields, wrong types and violated rules return input issues. Numeric values and outputs must be finite. Lists and matrices must be dense, without missing cells. Numeric lists allow at most 10000 entries; ordinary string lists allow at most 128 strings of 128 UTF-16 units each; numeric matrices allow at most 128 rows and 128 columns, subject to narrower requirements above.
Alternative or Complementary methods
For a related step, use Record vs Normal Spread Z Checker: A standardized anomaly expresses an observation's departure from a reference mean in units of reference standard deviation. For a complementary comparison, use Koppen Climate Classification Code Lookup: Köppen codes summarize climates under a specified classification scheme.
References
Similar or alternative tools
- Record vs Normal Spread Z Checker
A standardized anomaly expresses an observation's departure from a reference mean in units of reference standard deviation.
- Koppen Climate Classification Code Lookup
Köppen codes summarize climates under a specified classification scheme.