Common Weather Data Mistakes and Fixes

Symptom: suspiciously dry periods or cold spikes

A blank entry or missing-value sentinel is not a measurement. Converting missing precipitation to zero makes an incomplete month look dry. Treating a negative sentinel as a real temperature can create an absurd cold record. Fix the parser first: map documented missing markers to missing values, retain quality flags, and count valid observations independently for each element.

In a hypothetical example, an otherwise complete month has one unavailable rainfall day. The sum of the other days is a partial total, not proof that the missing day was dry. Report the missing count and apply a predeclared completeness rule. The NOAA GHCN-Daily page links to format and flag documentation; use those definitions instead of guessing from the appearance of a file.

Symptom: an event appears on the wrong date

UTC and local time can assign the same hour to different calendar days. Station observation days may also differ from midnight-to-midnight aggregation. In a hypothetical timing example, rain recorded at 23:00 UTC belongs to the next local date at a fixed UTC+2 offset. Grouping the unconverted timestamp under its UTC date changes the daily story without changing the underlying weather.

Use a named time zone where appropriate, not a single fixed offset for an entire year that crosses daylight-saving changes. Verify interval meaning as well as clock time. The Open-Meteo historical documentation distinguishes instantaneous fields from precipitation accumulated over the preceding hour. An interval ending at midnight is not interchangeable with one beginning there. Test boundaries manually before summarizing a large download.

Symptom: plausible values describing the wrong variable

Read the returned unit metadata. A temperature unit switch can make a series look dramatically warmer, and wind in meters per second cannot be compared numerically with kilometers per hour. Convert values once, in a documented step. In an illustrative conversion, 10 m/s equals 36 km/h. For temperature departures, use the scale factor only when converting Celsius differences to Fahrenheit; the absolute-temperature offset does not apply.

Rain, total precipitation, and snowfall depth are different elements. Total precipitation includes the water equivalent of frozen precipitation; snowfall depth describes accumulated snow thickness under the product definition. Do not add snowfall centimeters to rainfall millimeters. Likewise, wind speed and gust are not synonyms, and a modeled apparent temperature is not automatically a NOAA heat index. Correct labels are part of data processing, not decoration.

Symptom: averages or totals change unexpectedly

Check the denominator. Averaging twelve monthly means equally gives a different weighting from averaging all valid daily values. Neither should be silently substituted for the other. Summing daily rainfall gives an accumulation; averaging those daily amounts gives a rate per sampled day. A chart labeled “monthly rainfall” should identify which quantity it shows.

Duplicate timestamps can inflate rainfall sums and threshold counts. Cumulative precipitation fields can also be overcounted if each running total is summed as though it were a fresh interval amount. Determine whether a field is cumulative and how it resets before deriving increments. Negative differences at resets require documented handling, not automatic deletion or arbitrary clipping.

In an illustrative cumulative example, successive totals of 2, 5, and 9 mm represent increments of 2, 3, and 4 mm from a zero start. Summing the running totals yields 16 mm, while the actual accumulation in this hypothetical sequence is 9 mm. This is an example of a field type, not a claim that a particular API returns those cumulative values.

Symptom: a remarkable ranking or abrupt climate step

A value can rank first in a short sample without being an official record. Check period length, season, missing candidates, ties, and the selected statistic. Ranking a July temperature against all annual temperatures answers a different question from ranking it among Julys. Record the comparison set and qualify the headline as “within this sample” where appropriate.

An abrupt change may follow station relocation, a sensor replacement, a different observing schedule, or a source switch. The ECMWF ERA5 resource provides reanalysis background, while Open-Meteo documents model choices for consistent historical analysis. Joining a station series to a grid series does not automatically create one homogeneous climate record. Keep provenance for each segment and investigate the join.

Do not “repair” every suspicious value by replacing it with a nearby average. That erases possible extremes and creates undocumented synthetic data. Preserve the original, flag the concern, compare supporting evidence, and report whether the issue was resolved. Quality assurance should be auditable even when it cannot determine the correct value.

Finish troubleshooting with a small test table covering missing values, unit conversions, duplicate times, midnight boundaries, and aggregation. Recompute the affected headline after the fix and compare it with the prior result. If only the wording was wrong, correct the wording; if arithmetic was wrong, correct both the calculation and every conclusion derived from it.

Troubleshooting questions

Tools and related reading

Cross-check the date lookup and comparison tool. Read research workflow, ranking interpretation, and historical accuracy.

References

• NOAA: GHCN-Daily — quality information.
• Open-Meteo: Historical Weather API — units and intervals.
• ECMWF: ERA5 — source background.

About the author

The WeatherRecall editorial project provides educational synthesis of cited documentation. No original field research or named expert credentials are claimed. All numerical examples are hypothetical diagnostic cases, not findings about actual observations.