Quality control

Observation quality control annotates suspicious physics; it does not rewrite measurements or drop reports. Request it on weather.observations(), inspect the bitfield, and choose the filtering policy that fits your model.

from mostlyright import weather

reports = weather.observations(
    "KNYC",
    "2025-01-06",
    "2025-01-08",
    quality_control=True,
)

clean = reports[reports["obs_qc_status"] == 0]
flagged = reports[reports["obs_qc_status"] != 0]

quality_control is the 2.0 keyword. The old qc= name is gone.

One column, five rules

obs_qc_status is an integer bitfield. Zero means that none of the registered rules fired. A set bit means that rule flagged the row.

Bit	Rule id	Flags when
0	temp_c.out_of_range	Temperature is outside [-89, 57] °C
1	dewpoint_c.exceeds_temp	Dewpoint is above temperature
2	wind_speed_ms.negative	Wind speed is negative
3	wind_direction_degrees.out_of_range	Wind direction is outside [0, 360]
4	sea_level_pressure_hpa.out_of_range	Sea-level pressure is outside [870, 1085] hPa

Existing bit positions are stable. New rules append a new bit so stored values keep their meaning.

The separate qc_field column is the upstream source’s quality marker. It is not the SDK physics bitfield.

Test individual rules

TEMP_RANGE = 1 << 0
DEWPOINT = 1 << 1

status = reports["obs_qc_status"].fillna(0).astype(int)

bad_temperature = reports[(status & TEMP_RANGE) != 0]

# Accept a dewpoint warning but reject every other rule.
other_rules = status & ~DEWPOINT
usable = reports[other_rules == 0]

The returned measurements remain unchanged. This lets you audit a sensor failure, change your filtering threshold later, or re-run training without re-fetching.

QC and settlement labels

QC applies to observation features, not settlement truth. weather.training_table() keeps its daily_summary_* labels tied to the venue’s settlement source.

For exact settlement replay, preserve the label as reported even when an observation feature looks suspicious. For model training, record the policy that decided whether to keep, mask, or transform a flagged feature.

Other QC vocabularies

Not every domain uses the observation bitfield:

• CWOP and satellite rows use qc_status values such as clean, flagged, or suspect.
• NWP forecast rows carry model-aware qc_status.
• qc_field is raw source metadata on METAR/SPECI rows.

Do not compare these columns as though they were the same scale.

See also

• Weather observations: request per-report rows
• Observation schema: measurement and provenance fields
• Sources & provenance: why QC never changes source identity
• API reference: domains and signatures