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Metadata Report for BODC Series Reference Number 1761282


Metadata Summary

Data Description

Data Category Meteorology -unspecified
Instrument Type
NameCategories
anemometer  anemometers
Vaisala PTB 210 digital barometer  meteorological packages
Kongsberg Seatex Seapath 320+ Precise Heading, Attitude and Positioning Sensor  Global Navigation Satellite System receivers; Differential Global Positioning System receivers; inertial navigation systems
Rotronic Hygromet MP402H temperature and humidity probe  meteorological packages
Kipp and Zonen SP LITE2 pyranometer  radiometers
Kipp and Zonen PSQ1 PAR Quantum Sensor  radiometers
Instrument Mounting research vessel
Originating Country United Kingdom
Originator Dr Rosie Chance
Originating Organization University of York Department of Chemistry
Processing Status banked
Online delivery of data Download available - Ocean Data View (ODV) format
Project(s) ACCACIA
Arctic Research Programme
 

Data Identifiers

Originator's Identifier JR20130713-PRODQXF_MET
BODC Series Reference 1761282
 

Time Co-ordinates(UT)

Start Time (yyyy-mm-dd hh:mm) 2013-07-13 10:31
End Time (yyyy-mm-dd hh:mm) 2013-08-16 10:35
Nominal Cycle Interval 60.0 seconds
 

Spatial Co-ordinates

Start Latitude 53.54250 N ( 53° 32.5' N )
End Latitude 83.32400 N ( 83° 19.4' N )
Start Longitude 20.70033 W ( 20° 42.0' W )
End Longitude 34.83600 E ( 34° 50.2' E )
Positional Uncertainty 0.0 to 0.01 n.miles
Minimum Sensor or Sampling Depth -20.0 m
Maximum Sensor or Sampling Depth -15.3 m
Minimum Sensor or Sampling Height -
Maximum Sensor or Sampling Height -
Sea Floor Depth -
Sea Floor Depth Source -
Sensor or Sampling Distribution Scattered at fixed depths - The sensors are scattered with respect to depth but each remains effectively at the same depth for the duration of the series
Sensor or Sampling Depth Datum Approximate - Depth is only approximate
Sea Floor Depth Datum -
 

Parameters

BODC CODERankUnitsTitle
AADYAA011DaysDate (time from 00:00 01/01/1760 to 00:00 UT on day)
AAFDZZ011DaysTime (time between 00:00 UT and timestamp)
ALATGP011DegreesLatitude north relative to WGS84 by unspecified GPS system
ALONGP011DegreesLongitude east relative to WGS84 by unspecified GPS system
CAPHTU011MillibarsPressure (measured variable) exerted by the atmosphere by barometer and expressed at measurement altitude
CAPHTU021MillibarsPressure (measured variable) exerted by the atmosphere by barometer (second sensor) and expressed at measurement altitude
CDTAZZ021Degrees CelsiusTemperature of the atmosphere by thermometer (second sensor)
CRELZZ021PercentRelative humidity of the atmosphere by second sensor
CSLRR1011Watts per square metreDownwelling vector irradiance as energy of electromagnetic radiation (solar (300-3000nm) wavelengths) in the atmosphere by pyranometer
CSLRR1021Watts per square metreDownwelling vector irradiance as energy of electromagnetic radiation (solar (300-3000nm) wavelengths) in the atmosphere by pyranometer (second sensor)
EWDASS011Degrees TrueDirection (from) of wind relative to True North {wind direction} in the atmosphere by in-situ anemometer
EWSBSS011Metres per secondSpeed of wind {wind speed} in the atmosphere by in-situ anemometer
IRRDSV011MicroEinsteins per square metre per secondDownwelling vector irradiance as photons of electromagnetic radiation (PAR wavelengths) in the atmosphere by cosine-collector radiometer
PARERXSD1MicroEinsteins per square metre per secondDownwelling vector irradiance as photons of electromagnetic radiation (PAR wavelengths) in the atmosphere by cosine-collector radiometer (second sensor)

Definition of Rank

  • Rank 1 is a one-dimensional parameter
  • Rank 2 is a two-dimensional parameter
  • Rank 0 is a one-dimensional parameter describing the second dimension of a two-dimensional parameter (e.g. bin depths for moored ADCP data)

Problem Reports

No Problem Report Found in the Database


Data Access Policy

Public domain data

These data have no specific confidentiality restrictions for users. However, users must acknowledge data sources as it is not ethical to publish data without proper attribution. Any publication or other output resulting from usage of the data should include an acknowledgment.

The recommended acknowledgment is

"This study uses data from the data source/organisation/programme, provided by the British Oceanographic Data Centre and funded by the funding body."


Narrative Documents

ACCACIA RRS James Clark Ross JR20130713 (JR288) Underway Cruise Document

Cruise details

Dates 2013-07-13 — 2013-08-16
Principal Scientific Officer

Lucy Carpenter, University of York

Rosie Chance, University of York

Data supplied by Polar Data Centre, British Antarctic Survey

RRS James Clark Ross departed from TImmingham, United Kingdom on 13 July 2013 and docked in Dundee, United Kingdom on 16 August 2013.

This was the second cruise of the NERC funded project called Aerosol-Cloud Coupling and Climate interactions in the Arctic (ACCACIA). The aims of the cruise were to:

  • obtain surface based in-situ measurements of marine aerosol composition and properties and aerosol precursos gases (DMS, VOC, halocarbon)
  • Collect standard oceanographic measurements such as salinity, chlorophyll-a and nutrient concentrations
  • Conduct bubble tank experiments to develop a primary multicomponent seaspray aerosol flux parameterisation

The data provided to BODC includes the entire cruise period from 2013-07-13 — 2013-08-16. Date and time were supplied in UT. The span of the data covers all scientific activities.

Kipp & Zonen Photosynthetically Active Radiation Quantum Sensor PQS1

The PQS1 is an atmospheric radiometer designed to measure incident radiation at photosynthetically active radiation (PAR) wavelengths. It incorporates a diffuser with an excellent directional (cosine) response and a silicon photodiode detector.

If used in field research applications, the PQS1 can be connected with the METEON handheld display unit, which also as data-logging capability. For permanent installations, it can be connected to the LOGBOX SD data logger.

Specifications

Spectral range 400 to 700 nm (± 4 nm)
Sensitivity 4 to 10 µV µmol-1 m-2 s-1
Response time < 1 µs
Non linearity < 1% (0 to 10000 µV µmol-1 m-2 s-1)
Temperature dependence < -0.1% °C-1
Sensitivity change per year < 2%
Directional error < 3% (up to 80° zenith angle)
Field of view 180°
Operating temperature -30 to 70°C
Relative humidity 0 to 100 % RH

A link to the PQS1 specification sheet can be found here: PQS1 Spec sheet

Kipp and Zonen SP Lite and SP Lite2 Silicon Pyranometer

An atmospheric pyranometer that measures solar radiation over the range 400-1100 nm by means of a silicon photo-diode detector mounted in a diffuser. The sensor measures the radiation received over the entire hemisphere and the diffuser's sensitivity is proportional to the cosine of the angle of incidence of the incoming radiation. The photodiode creates a voltage output that is proportional to the incoming radiation. The SP Lite2 supersedes the SP Lite and features an improved sensitivity and faster response time than its predecessor.

Specifications

Specification SP Lite SP Lite2
Spectral range 400-1100 nm 400-1100 nm
Sensitivity 100 µV W-1 m-2 60 to 100 µV W-1 m-2
Response time < 1 s < 500 ns
Maximum irradiance 2000 W m-2 2000 W m-2
Operating temperature -30 to 70°C -30 to 70°C
Temperature dependence 0.15% °C-1 0.15% °C-1

Further details can be found in the manufacturer's specification sheets for the SP Lite and SP Lite2.

Rotronic Hygromet MP102H and MP402H temperature and humidity probes

This meteorological probe measures humidity and temperature with the plug-in HygroClip HC2-S3 sensor module, and can also be equipped with a signal conditioned Pt100 temperature probe.

The two models differ in that the MP102H produces a voltage output while the MP402H produces a current output. Other characteristics are common to both models.

The specification sheet can be accessed here Rotronic MP102H and MP402H.

Specifications

Start up time 3 s (typical)
Data refresh time 1 s (typical)
Humidity range 0 to 100% RH
Humidity accuracy 0.8% RH
Temperature range -40 to 80°C
Temperature accuracy 0.1°C
Maximum air velocity ar probe 20 m s-1
User configurable limits -999 to 9999 engineering units
HC2-S3 Probe material Polycarbonate
Probe dust filter Polyethylene

Vaisala PTB210 Digital Barometer

The basic specifications for this pressure sensor are as follows:

  • Manufacturer: Vaisala
  • Type: Silicon capacitive sensor
  • Model: PTB210
  • Range: 900 - 1100 hPa
  • Output: 0-5VDC
  • Total Accuracy (20°C): ±0.30hPa
  • Operating temperature: -40 to +60 deg C
  • Weight: 110g
  • Certification Ingress Protection: IP65

Further details can be found in the manufacturer's specification sheet.

Kongsberg Seatex Seapath 320+ Precise Heading, Attitude and Positioning Sensor

The Seapath 320+ is a navigational system that combines two Global Navigation Satellite System (GNSS) receivers with a MRU 5+ inertial sensor to provide high resolution and accuracy positional data. The inertial sensor employs linear accelerometers and unique microelectromechanical systems (MEMS)-type angular rate gyros to provide 0.01 RMS pitch and roll accuracy. The GNSS receivers can use multiple satellite constellations (GPS, GLONASS and Galileo, when available), and combine data from these to improve heading and positional measurements. In case of missing data from one GNSS receiver, the other receiver provides position and velocity, and the inertial sensor provides heading from its internal rate sensors.

The main characteristics are presented below, and the specification sheet can be accessed here Kongsberg Seatex Seapath 320+ .

Specifications

Heading accuracy

0.04° RMS (4m baseline)

0.065° RMS (2.5 baseline)

Roll and pitch accuracy 0.02° RMS for ± 5° amplitude
Scale factor error in roll, pitch and heading 0.05% RMS
Heave accuracy (real time) 5 cm or 5%, whichever is highest
Heave accuracy (delayed signal) 2 cm or 2%, whichever is highest
Heave motion periods (real time) 1 to 20 seconds
Heave motion periods (delayed signal) 1 to 50 seconds
Position accuracy (DGPS/DGlonass) 1 m (95% CEP)
Position accuracy (SBAS) 1 m (95% CEP)
Position accuracy (with RTK corrections) 0.2 m (95% CEP)
Velocity accuracy 0.07 m s-1 (95% CEP)
Data update rate Up to 100Hz

RRS James Clark Ross (JR288) ACCACIA Underway Meteorology Document

Content of data series

Parameter Units Parameter code Comments
Latitude Degrees (+ve N) ALATGP01 -
Longitude degrees (+ve E) ALONGP01 -
Atmospheric pressure mbar CAPHZZ01 Primary sensor
Atmospheric pressure mbar CAPHZZ02 Secondary sensor
Air temperature °C CDTAZZ01 Secondary sensor
Relative humidity % CRELZZ02 Secondary sensor
PAR µE m-2 s-1 IRRDSV01 Primary sensor
PAR µE m-2 s-1 PARERXSD Secondary sensor
TIR W m-2 CSLRR101 Primary sensor
TIR W m-2 CSLRR102 Secondary sensor
Absolute wind direction degrees EWDASO01 Corrected for ship's heading and speed
Absolute wind speed m s-1 EWSBSO01 Corrected for ship's heading and speed

Instrumentation

The meteorological suite of sensors were located on the meteorological, with all instruments located 20m above sea level, except the anemometer, which is at 22.5m above sea level and has an orientation of 0° on the bow.

Manufacturer Model Serial number Comments
Ultrasonic anemometer     Anemometer
Rotronic MPH402H-050300 60599556/60599558 Air Temperature and Relative Humidity
Vaisala PTB210 Class B V145002/V145003 Air Pressure
Kipp and Zonen SPLite2 112993/112992 TIR
Kipp and Zonen Parlite Quantum 110127/110126 PAR

Data Processing Procedures

Originator's Data Processing

A number of meteorological parameters (air temperature, pressure, relative humidity, wind speed and direction) were obtained from the different instruments installed on board the ship.

File delivered to BODC
Filename Content description Format Interval Start date/time (UTC) End date/time (UTC)
anemometer.ACO Wind speed and direction data .ACO 1 second 12/07/2013 08:59:44 hours 16/08/2013 11:10:00 hours
oceanlogger.ACO Meteorology and Surface Hydrography data .ACO 5 seconds 13/07/2013 11:17:38 hours 16/08/2013 11:09:56 hours

BODC Data Processing

Data were banked at BODC following standard data banking procedures, including checking meteorological channels for improbable values, working out absolute wind speed and direction, and screening the data for anomalous values. The originator's variables were mapped to appropriate BODC parameter codes as follows:

anemometer.ACO
Originator's variable Originator's units Description BODC Code BODC Units Unit conversion Comments
anemometer-wind_speed knots Wind speed (relative to moving platform) in the atmosphere by in-situ anemometer ERWSSS01 m/s *0.51444  
anemometer-wind_dir degrees Wind direction (relative to moving platform) in the atmosphere by in-situ anemometer ERWDSS01 degrees   0° on the bow
oceanlogger.ACO
Originator's variable Originator's units Description BODC Code BODC Units Unit conversion
oceanlogger-airtemp1 degrees C Temperature of the atmosphere by thermometer CDTAZZ01 degrees C  
oceanlogger-airtemp2 degrees C Temperature of the atmosphere by thermometer (second sensor) CDTAZZ02 degrees C  
oceanlogger-humidity1 % Relative humidity of the atmosphere CRELZZ01 %  
oceanlogger-humidity2 % Relative humidity of the atmosphere by second sensor CRELZZ02 %  
oceanlogger-baro1 hPa Pressure (measured variable) exerted by the atmosphere by barometer and expressed at measurement altitude CAPHTU01 mbar 1 hPa = 1 mbar
oceanlogger-baro2 hPa Pressure (measured variable) exerted by the atmosphere by barometer (second sensor) and expressed at measurement altitude CAPHTU02 mbar 1 hPa = 1 mbar
oceanlogger-par1 µmol s-1 m-2 Downwelling vector irradiance as photons (PAR wavelengths) in the atmosphere by cosine-collector radiometer IRRDSV01 µE m-2 m-1 equivalent units
oceanlogger-par2 µmol s-1 m-2 Downwelling vector irradiance as photons (PAR wavelengths) in the atmosphere by cosine-collector radiometer (second sensor) PARERXSD µE m-2 m-1 equivalent units
oceanlogger-tir1 W m-2 Downwelling vector irradiance as energy (solar (300-3000nm) wavelengths) in the atmosphere by pyranometer CSLRR101 W m-2 -
oceanlogger-tir2 W m-2 Downwelling vector irradiance as energy (solar (300-3000nm) wavelengths) in the atmosphere by pyranometer (second sensor) CSLRR102 W m-2 -

Other parameters were sent in the oceanlogger file (sea surface temperature, chlorphyll, transmittance, conductivity and salinity) but they will be dealt with in the Surface underway processinf document.

All data expressed at measurement altitude. All the reformatted data were visualised using the in-house EDSERPLO software. Suspect data were marked by adding an appropriate quality control flag, missing data by both setting the data to an appropriate value and setting the quality control flag.

Absolute wind speed and direction

Relative wind speed and direction were corrected for the ship's heading and speed using the gyrocompass heading, ship velocities (calculated at BODC from the main positional channels) and an anemometer orientation of 0° on the bow.

Air temperature and Relative humidity

During screening it was clear that the primary sensors (CDTAZZ01 and CRELZZ01) hadn't logged any data. These channels were completely null and are not included in the final file. CRELZZ02 exhibits periods with fairly constant data, but these weren't considered suspect, therefore no flags were applied.

PAR and TIR

All negative values identified on both channels were flagged M as they are due to the instrument's own radiation.


Project Information

ACCACIA- Aerosol Cloud Coupling and Climate Interactions in the Arctic

The ACCACIA project is a £2.05 million component of the Natural Environment Research Council (NERC) Arctic Research programme (ARP) running from 2012 to 2016. The aim of ACCACIA is to collect data using both airplanes and ships based in the Svalbard archipelago near the margin of permanent Arctic sea ice cover and study fluxes of solar and infrared radiation above and below the clouds, the vertical structure of the low-level atmosphere and how aerosol concentration levels change with the seasons and with the extent of sea-ice cover. The results from this research will inform not only cutting-edge modelling of the global climate system and predictions for future climatic change, but also more immediate weather forecasts for mid-to-high-latitude locations such as the UK.

Background

Over most of the globe low clouds act to cool the surface since they reflect sunlight; over the arctic the highly reflective ice surface reduces the significance of cloud reflectivity, and the absorption of infrared radiation by cloud water droplets becomes the dominant effect - this acts to trap heat below cloud, warming the surface. Although climate models generally show a strong greenhouse warming effect in the Arctic, they also disagree with each other more in the Arctic than anywhere else, producing a wider range of possible future climate conditions. The models also tend not to be able to reproduce current Arctic climate conditions very accurately. This large uncertainty in models of the Arctic climate results primarily from poor representation of physical processes within the models, and some unique and particularly challenging conditions. The largest single source of uncertainty is the representation of clouds. The models use simple representations of cloud properties that were developed from observations in mid latitude or tropical cloud systems - very different conditions from those that exist in the Arctic.

The main activities of this project are to make airborne in situ measurements of cloud microphysical properties, the vertical structure of the boundary layer and aerosol properties, and the fluxes of solar and infra red radiation above, below, and within cloud. It will also measure the production rates and properties of aerosol at the surface and their variability with season and extent of sea ice cover. These measurements will be used, along with a range of numerical models of aerosol and cloud processes, and atmospheric dynamics to evaluate the interactions between sea ice extent, aerosol production and cloud properties. New and improved descriptions of these processes suitable for use within climate models will be developed, tested, and implemented within the MetOffice climate model HadGEM. The ability of the current MetOffice models to reproduce the observed Arctic cloud and boundary layer properties will be tested and the impact of the new parameterization schemes evaluated. Finally we will undertake a series of climate simulations to examine how future climate will evolve, and the feedbacks between warming of the Arctic, melting of sea ice, production of aerosol, and the properties of clouds evaluated.

Participants

Dr Ian Brooks is the Lead Investigator and Professor Lucy Carpenter and Dr Amélie Kirchgaessner are Co-Investigators. The project is made up of collaborations with universities and research organisations including:

  • British Antarctic Survey
  • University of East Anglia
  • University of Leeds
  • University of Manchester
  • University of York

Fieldwork and data collection

The ACCACIA project involves the collection of data in the Svalbard archipelago near the margin of permanent sea ice cover using research vessels as well as the BAS MASIN and FAAM BAe146 aircrafts. The project consisted of two cruises, as detailed below:

Cruise identifier Research ship Cruise dates
Lance ACCACIA RV Lance 15 March 2013 - 31 March 2013
JR20130713 (JR288) RRS James Clark Ross 13 July 2013 - 16 August 2013

The aims of the research cruises are to make surface based in-situ measurements of marine aerosol composition and its properties, and aerosol precursor gases including DMS, VOCs and halocarbons. Trace gases were measured in air and water, and high volume aerosol samples were collected for off-line characterisation of organic composition. Ambient aerosol measurements and bubble tank experiments were conducted to characterise aerosol physical and chemical properties using a suite of instrumentation by the Manchester group. Together with black carbon/soot optical measurements and CCN measurements made as function of particle size and super-saturation, these will be used as input in cloud microphysical models to investigate their influence on aerosol-cloud feedback sensitivity whereas bubble tank results will be used to develop a primary multicomponent sea-spray aerosol flux parameterisation.


Arctic Research Programme

The Arctic Research Programme (ARP) is a £15m, five year (2010-2015) research programme funded by the Natural Environment Research Council (NERC). The aim of the programme is to improve our capability to predict changes in the Arctic, particularly over timescales of months to decades, including regional impacts and the potential for feedbacks on the global Earth System.

Background

The Arctic is a region of higher than average climate change and is predicted to remain so, the main evidences of this rapid climate change are the loss of summer sea ice, the thawing of permafrost (perennially frozen earth), melting of land ice, including ice sheets and glaciers, and the changing physical environment of Arctic ecosystems.

The Arctic represents a critical region for global environmental change and one where the UK has significant strategic interests. Understanding the drivers and feedbacks of this change, and predicting its scale and rate on timescales from months to decades, represents a major and urgent global scientific challenge of great societal importance.

The Arctic Programme will focus on four linked scientific objectives:

  • Understanding and attributing the current rapid changes in the Arctic
  • Quantifying processes leading to Arctic methane and carbon dioxide release
  • Reducing uncertainty in Arctic climate and associated regional biogeochemistry predictions
  • Assessing the likely risks of submarine hazards associated with rapid Arctic climate change

Deliverables from this programme will include:

  • New or improved models for process studies
  • Improved parameterisation of Arctic processes
  • Improved capabilities for predicting changes in the Arctic
  • Interpretation of current Arctic climate change and its implications for policy makers and Arctic communities

To achieve these objectives and deliverables, the Arctic Research Programme will aim to harness and co-ordinate UK scientific expertise and facilities in these areas, and link these to other international efforts. Fieldwork is expected to be highly interdisciplinary, potentially involving campaigns on land and ice stations, from ships, aircraft and satellites. Work on understanding longer term change in the Arctic will involve sediment cores and sampling on land. The use of a range of numerical models leading to improved predictability will be a vital element of this programme. Process-level understanding developed through observation-based work will be used to improve model components, and these will be used to test the impact of the processes on large-scale predictions. Clearly, integrated and innovative research across all science areas will be needed to achieve the programme's objectives.

Further details can be found on the ARP website.

Participants

34 different partner institutions are involved with the ARP. These are:

  • University of Aberdeen
  • Bangor University
  • British Antarctic Survey
  • British Geological Survey
  • Centre for Ecology and Hydrology
  • University of Cambridge
  • University of Dundee
  • Durham University
  • University of East Anglia
  • University of Leeds
  • Loughborough University
  • University of Manchester
  • National Centre for Atmospheric Science
  • National Oceanography Centre
  • University of Nottingham
  • Office of Naval Research
  • University of Oxford
  • University of Portsmouth
  • University of Reading
  • Royal Holloway, University of London
  • University of Sheffield
  • University of Southampton
  • University of Stirling
  • University of Sussex
  • University College London
  • University of Ulster
  • University of York
  • Natural Environment Research Council
  • Department for Energy and Climate Change
  • Department for Environment, Food and Rural Affairs
  • Foreign and Commonwealth Office
  • Met Office
  • Royal Navy
  • Living with Environmental Change

Research details

Overall 15 projects have been funded through ARP:

  • ACCACIA- Aerosol Cloud Coupling and Climate Interactions in the Arctic
  • APPOSITE- Arctic Predictability and Prediction on Seasonal to Inter-annual Timescales
  • CYCLOPS- Carbon Cycling Linkages to Permafrost Systems
  • HYDRA- Hydrological Controls on Carbon Cycling and Greenhouse Gas Budgets
  • LAC- Lakes and the Arctic Carbon Cycle
  • Landslide-Tsunami
  • MAMM- Methane and other Greenhouse Gases in the Arctic: Measurements, Process Studies and Modelling
  • SEATS- Submarine Estimates of Arctic Turbulence Spectra
  • TEA-COSI- The Environment of the Arctic: Climate, Ocean and Sea-Ice
  • Arctic methane hydrates and climate change
  • Canadian Archipelago Oceanography
  • Effects of a warming climate on the key organic carbon cycle processes in the Eurasian Arctic
  • Is the Arctic methane budget changing?
  • MIZ- The role to atmospheric, ice and oceanic interactions in the marginal ice zone
  • MIZ-WAVE- Wave-ice interaction and the marginal ice zone

Data Activity or Cruise Information

Cruise

Cruise Name JR20130713 (JR288)
Departure Date 2013-07-13
Arrival Date 2013-08-16
Principal Scientist(s)Lucy J Carpenter (University of York Department of Chemistry), Rosie Chance (University of York Department of Chemistry)
Ship RRS James Clark Ross

Complete Cruise Metadata Report is available here


Fixed Station Information


No Fixed Station Information held for the Series


BODC Quality Control Flags

The following single character qualifying flags may be associated with one or more individual parameters with a data cycle:

Flag Description
Blank Unqualified
< Below detection limit
> In excess of quoted value
A Taxonomic flag for affinis (aff.)
B Beginning of CTD Down/Up Cast
C Taxonomic flag for confer (cf.)
D Thermometric depth
E End of CTD Down/Up Cast
G Non-taxonomic biological characteristic uncertainty
H Extrapolated value
I Taxonomic flag for single species (sp.)
K Improbable value - unknown quality control source
L Improbable value - originator's quality control
M Improbable value - BODC quality control
N Null value
O Improbable value - user quality control
P Trace/calm
Q Indeterminate
R Replacement value
S Estimated value
T Interpolated value
U Uncalibrated
W Control value
X Excessive difference

SeaDataNet Quality Control Flags

The following single character qualifying flags may be associated with one or more individual parameters with a data cycle:

Flag Description
0 no quality control
1 good value
2 probably good value
3 probably bad value
4 bad value
5 changed value
6 value below detection
7 value in excess
8 interpolated value
9 missing value
A value phenomenon uncertain
B nominal value
Q value below limit of quantification