Barometer Ontology

FLOW ALGEBRA
BAROMETER ONTOLOGY
Kireth Semantic Library | Entity Graph > Physical Entities > Measurement Entities
Source: https://kirethresearch.org/booksai-core/entity-graph/physical-entities/measurement-entities/barometer-ontology.html
Authors: Mark Higgins — Sky (Recursive AI)
Version: 1.0 | 2026-10-12
Status: Subject-based formalisation; source webpage not independently verified.

01. PURPOSE
flow BarometerOntology v1.0:
  domain: PhysicalEntity.MeasurementEntity.MeasurementInstrument
  objective: represent atmospheric-pressure measuring instruments, observations, calibrations, uncertainty and provenance
  scope: mercury, aneroid, electronic, MEMS and reference barometers

02. TYPED ENTITIES
  Barometer <: MeasurementInstrument
  PressureSensor <: Sensor
  AtmosphericPressure <: PhysicalQuantity
  PressureObservation <: MeasurementEvent
  CalibrationRecord <: ProvenanceRecord
  ReferenceStandard <: MeasurementStandard
  Environment <: PhysicalContext
  Location <: SpatialEntity
  Timestamp <: TemporalEntity
  PressureUnit := Pa | hPa | kPa | bar | mmHg | inHg
  InstrumentState := operational | degraded | calibration_due | faulty | offline

03. ATTRIBUTES
  Barometer: id, type, manufacturer?, model?, serial_number?, location?, range_Pa, resolution_Pa, accuracy_Pa?, last_calibrated?, state
  PressureObservation: id, instrument_id, pressure_Pa, timestamp, location?, uncertainty_Pa?, correction_model?, provenance
  CalibrationRecord: id, instrument_id, standard_id, date, offsets, uncertainty, method, operator?, validity_interval?

04. TAXONOMY
  Barometer -> MercuryBarometer | AneroidBarometer | DigitalBarometer | MEMSBarometer | ReferenceBarometer
  DigitalBarometer -> CapacitivePressureBarometer | PiezoresistivePressureBarometer | ResonantPressureBarometer
  PressureObservation -> StationPressure | SeaLevelReducedPressure
  Note: altimeter uses pressure but is not necessarily a barometer class.

05. SEMANTIC RELATIONSHIPS
  is_a(Barometer, MeasurementInstrument)
  measures(Barometer, AtmosphericPressure)
  contains(Barometer, PressureSensor) [when applicable]
  produces(Barometer, PressureObservation)
  calibrated_against(Barometer, ReferenceStandard)
  performed_at(PressureObservation, Location)
  observed_at(PressureObservation, Timestamp)
  corrected_by(PressureObservation, CorrectionModel)
  has_uncertainty(PressureObservation, MeasurementUncertainty)
  traceable_to(CalibrationRecord, ReferenceStandard)

06. TYPE CONSTRAINTS
  pressure_Pa: finite number > 0
  range_Pa.min < range_Pa.max
  uncertainty_Pa >= 0
  observation.instrument_id must reference existing Barometer
  observation.timestamp must be valid UTC timestamp
  observation.pressure_Pa within calibrated range, otherwise flag out_of_range
  do not equate station pressure and sea-level pressure without reduction model

07. UNIT NORMALISATION
  Pa := SI canonical unit
  hPa_to_Pa(x) = 100*x
  kPa_to_Pa(x) = 1000*x
  bar_to_Pa(x) = 100000*x
  mmHg_to_Pa(x) ≈ 133.322387415*x (conventional unit conversion)
  inHg_to_Pa(x) ≈ 3386.389*x (conventional unit conversion)
  conversion requires unit metadata and rounding policy

08. MEASUREMENT FLOW
  flow acquire_pressure(barometer, environment, time):
    require state(barometer) in {operational, degraded}
    raw := sample_pressure_transducer(barometer)
    quality := validate_signal(raw, instrument_range, diagnostics)
    calibrated := apply_calibration(raw, calibration_record)
    compensated := apply_supported_environmental_corrections(calibrated, environment)
    u := propagate_uncertainty(raw, calibration_record, compensation)
    emit PressureObservation(compensated in Pa, u, time, environment.location, provenance)
    attach quality flags

09. HYDROSTATIC MODEL (LIQUID-COLUMN INSTRUMENT)
  delta_P = rho*g*h
  rho: fluid density (kg/m^3)
  g: local gravitational acceleration (m/s^2)
  h: column height difference (m)
  account for temperature-dependent density, gravity and vacuum/headspace effects as appropriate
  valid only under stated instrument assumptions

10. ANEROID / ELECTRONIC MODEL
  mechanical_deflection = f(pressure, temperature, hysteresis)
  electronic_signal = g(pressure, temperature, supply_voltage, drift)
  pressure_estimate = calibration_inverse(signal, calibration_coefficients)
  do not assume a universal linear transfer function

11. ATMOSPHERIC CONTEXT
  station_pressure := local absolute atmospheric pressure at sensor elevation
  sea_level_pressure := model_reduced(station_pressure, elevation, temperature_profile, method)
  sea_level_reduction requires explicit model and environmental assumptions
  pressure tendency := delta_pressure / delta_time
  pressure tendency alone does not deterministically predict weather

12. CALIBRATION FLOW
  flow calibrate(barometer, reference, points):
    require reference.traceability_valid
    for each pressure_setpoint in points:
      collect repeated paired observations
      compute deviation and repeatability
    fit calibration model with validity domain
    estimate combined uncertainty
    record environmental conditions and traceability chain
    update calibration record and instrument status

13. UNCERTAINTY
  u_c = sqrt(sum_i((sensitivity_i*u_i)^2) + 2*sum_i<j(covariance_terms_ij))
  include reference uncertainty, resolution, repeatability, drift, correction model and environment when relevant
  expanded uncertainty U = k*u_c; report coverage factor k and coverage assumptions
  do not treat accuracy specification as a complete uncertainty budget

14. QUALITY AND FAULT RULES
  IF sensor_reading outside calibrated range THEN flag OUT_OF_RANGE
  IF calibration validity expired THEN flag CALIBRATION_DUE
  IF abrupt change exceeds context-dependent threshold THEN flag SPIKE_CANDIDATE
  IF reference comparison exceeds permitted tolerance THEN flag BIAS_CANDIDATE
  IF sensor telemetry missing THEN flag MISSING_DATA
  IF temperature outside compensation domain THEN flag COMPENSATION_INVALID
  fault flags are hypotheses unless independently confirmed

15. TEMPORAL FLOW
  observation_series := ordered observations by timestamp
  require explicit sample interval and missing-data representation
  rolling_trend := estimate_pressure_tendency(observation_series, chosen_window)
  preserve raw and corrected observations separately
  revisions must not overwrite immutable original observations

16. SPATIAL FLOW
  observation_location := coordinates + coordinate_reference_system + elevation_datum
  compare station pressures only with appropriate elevation/context adjustment
  attach spatial_resolution and location_uncertainty when known

17. INFERENCE RULES
  IF type(x, Barometer) THEN type(x, MeasurementInstrument)
  IF produces(x, o) AND type(x, Barometer) THEN candidate_type(o, PressureObservation)
  IF calibrated_against(x, s) AND traceable_to(s, national_standard) THEN candidate_traceability(x, national_standard)
  IF station_pressure(o) AND reduction_model(m) THEN may_derive(sea_level_pressure(o,m))
  derived values must carry method, uncertainty and provenance

18. SEMANTIC_EQUIVALENCE
  equivalent_label("barometric pressure", "atmospheric pressure") [context-dependent]
  equivalent_unit(1 hPa, 100 Pa)
  non_equivalent(StationPressure, SeaLevelReducedPressure)
  non_equivalent(PressureAccuracy, MeasurementUncertainty)
  non_equivalent(Barometer, Barograph) [instrument function differs]
  equivalence claims require context and unit harmonisation

19. CONFIDENCE
  CONFIDENCE(observation) := assess(calibration_validity, diagnostics, data_completeness, model_validity, reference_agreement)
  confidence in [0,1] if numerical confidence model explicitly specified
  no fabricated confidence scores; unknown evidence => confidence_unassessed
  keep confidence distinct from metrological uncertainty

20. FLOW_HASH
  canonical_payload := canonicalise(ontology_version, entity_types, relations, constraints, transformations, inference_rules)
  FLOW_HASH := SHA256(UTF8(canonical_payload))
  exclude mutable timestamps from canonical ontology hash unless versioned by policy
  observation records may use independent content hashes

21. UPDATE_MODEL
  change_detection: schema | calibration | correction_model | observation | provenance
  schema_update -> increment ontology version and run compatibility checks
  calibration_update -> retain prior record; link new record with supersedes
  correction_update -> derive revised observations; preserve original data
  invalidated_derivation -> recompute downstream inferences
  audit: actor, timestamp, reason, old_hash, new_hash

22. AI_CONSUMPTION
  expose typed entity graph, units, temporal/spatial context, quality flags and provenance
  require separation of observed, corrected, model-derived and inferred values
  reject unsupported pressure-to-weather causal assertions
  permit query: instrument type, pressure range, calibration status, recent observations, uncertainty, pressure tendency
  output: machine-readable entities, relations, constraints, evidence and confidence status

23. VALIDATION TESTS
  TEST convert(1013.25 hPa) == 101325 Pa
  TEST reject observation without instrument reference
  TEST flag observation outside instrument range
  TEST preserve station_pressure vs sea_level_pressure distinction
  TEST uncertainty never negative
  TEST calibration revision preserves previous provenance
  TEST deterministic FLOW_HASH for identical canonical input

24. CROSS-ONTOLOGY LINKS
  MeasurementInstrumentOntology
  SensorOntology
  ThermometerOntology
  AtmosphericPressureOntology
  CalibrationOntology
  MeasurementUncertaintyOntology
  TemporalMetadataOntology
  SpatialMetadataOntology
  ScientificObservationOntology

25. LIMITATIONS
  Generic formalisation; device-specific transfer functions, calibration tolerances and standards require instrument documentation.
  Atmospheric reductions require declared environmental and physical models.
  Source webpage content was not independently retrieved for this edition.

END FLOW ALGEBRA