Limit weather claims to the recorded evidence

A missing field is an evidence boundary

All three saved precipitationLast3Hours values are null. The correct reading is that this field supplies no usable amount for that interval in these records. Replacing null with 0 mm would change missing information into a numerical observation that the source did not provide.

A text description such as Clear or Cloudy does not repair that gap. It does not establish an accumulated three-hour precipitation total. Nor does an RH below 100% prove that no precipitation occurred elsewhere in the atmosphere or earlier in the interval.

Make three paper columns: recorded value, missing value and inference. Place the precipitation nulls in the second, not the first. Write a proposed rainfall claim in the third and name the observations required to assess it.

Keep pressure products in their own categories

The API field named barometricPressure contains 103284.85 Pa for KJFK and 101490.06 Pa for KSEA; it is null for KDEN. All seaLevelPressure entries are null. The available field name alone does not establish that its numbers are true station pressures, sea-level-reduced pressures or altimeter settings.

You can convert an available number from Pa to hPa by dividing by 100, but a unit conversion does not determine its meteorological definition. Preserve the API field name and uncertainty about comparability. Do not substitute it for a missing seaLevelPressure value or derive a horizontal pressure gradient from these records.

A pressure-gradient comparison would require appropriate, consistently defined pressure quantities, spatial context and suitable timing, along with method metadata. The lesson does not perform that analysis. The NWS API documentation provides the service and field framework; interpreting an observational product still requires its definition and provenance. NWS API documentation.

Preserve quality-control information without inventing a grade

Raw qualityControl codes are retained beside the source quantities. They should not be relabeled as a percentage certainty or an independently verified pass. The lesson has not audited the station instruments or reproduced the upstream quality-control process.

The same caution applies to an empty rawMessage. The saved records contain no formal raw METAR string to decode. A plausible-looking reconstruction would be new authored content, not preserved evidence. An honest audit keeps the absence visible.

These records are not a climate sample

KJFK, KDEN and KSEA were deliberately selected and observed at different UTC times. Their temperatures, humidity and wind values describe those specific records. They cannot establish a city’s typical climate, a seasonal average or a long-term trend.

Even a simultaneous set of three stations would not by itself establish those broader claims. A climatological question needs an appropriate longer record, sampling coverage and consistent methods. A causal question needs attention to competing explanations, not merely a difference between selected values.

Compare KJFK and KSEA, then reverse the pair. The signed differences reverse, but the amount of observational evidence is unchanged. Repeating the same selection many times does not create additional independent samples or strengthen a climate inference.

Build a reviewable claim ledger

Write one supported statement, such as the stored KJFK temperature exceeding the stored KSEA temperature by 7°C at their respective times. Add the identifiers and timestamps. Then write three stronger statements: a typical climate ranking, a precipitation-total comparison and a sea-level pressure-gradient claim.

For each stronger statement, identify the missing data or definition. Replace an unsupported conclusion with a targeted evidence request: a consistent long-term record, an actual interval precipitation observation, or verified pressure-product metadata and comparable readings. The purpose is to make progress on the scientific question rather than declaring all comparisons useless.

The snapshot remains useful for practicing unit conversions, source tracing, missingness and conditional humidity calculations. Its limits matter because they determine which conclusions those practices support.

Transfer: review an invented zero

A dashboard fills missing three-hour precipitation with zero and averages the three filled values. It reports that no rain fell at any station. What went wrong, and what can be concluded from the original fields?

Check the weather-evidence transfer

The dashboard replaced missing values with invented measurements. An average of those replacements does not establish rainfall. The original fields provide no usable three-hour amounts for the selected records; appropriate precipitation observations are needed before calculating or comparing totals.