The National Centre for Atmospheric Science General Metadata Standard (NCAS-GENERAL) version 2.1

sonde Document Version: 1.0 Vocabulary Version: 2.1 First released: November 2024 Repository Link: https://doi.org/10.5281/zenodo.14045913 Revision History. Date Version Comments Revised by Contact 5 November 2024 v1.0 Initial document generation J.Hampton joshua.hampton @ncas.ac.uk Table of contents: 1. Introduction 2. Data Processing Levels 3. Data Types 4. Nomenclature 5. Data Quality Flags 6. File Naming 7. Common 7.1. Metadata (Global Attributes) 7.2. Dimensions 7.3. Variables 8. Data Product Specific 8.1. File Name Variants 8.2. Metadata (Global Attributes) 8.3. Dimensions 8.4. Variables 1. Introduction The data product developed for NCAS that this document supports are designed following the FAIR (Findable Accessible Interoperable Reusable) principles of data management: “FAIR Guiding Principles for scientific data management and stewardship.’’ Wilkinson et al., 2016: https://doi.org/10.1038/sdata.2016.18. There are now substantial drivers for data providers to implement the FAIR principles. These include: 1. The G20 group of nations: At the 2016 Hangzhou summit, the G20 leaders issued a statement endorsing the application of FAIR principles to research. 2. Increased emphasis on data and its reusability at policy level in national government and science funding bodies. 3. Science publishers have a growing requirement for data DOI’s and data traceability. 4. The science community is expected to demonstrate data dissemination, usage, and deliver impact statements. The FAIR principles of data management and stewardship aim to make data: Findable: ● The first step in use and reuse data is to find them. ● Metadata and data should be easy to find for both humans and computers. ● Machine-readable metadata are essential for automatic discovery of datasets and services. ● This means: ○ Metadata are assigned a globally unique and eternally persistent identifier. ○ Data are described with rich metadata. ○ Metadata are registered or indexed in a searchable resource. ○ Metadata specify the data identifier. Accessible: ● Once the user finds the required data, they need to know how they can be accessed, possibly including authentication and authorisation. ● This means: ○ Metadata are retrievable by their identifier using a standardised communications protocol. ○ The protocol is open, free, and universally implementable. ○ The protocol allows for an authentication and authorization procedure, where necessary. ○ Metadata are accessible, even when the data are no longer available Interoperable: ● Data usually needs to be integrated with other data. ● Data needs to interoperate with applications or workflows for analysis, storage, and processing. ● This means: ○ Metadata uses a formal, accessible, shared, and broadly applicable language for knowledge representation. ○ Metadata use vocabularies that follow FAIR principles. ○ Metadata include qualified references to other metadata. Reusable: ● The ultimate goal of FAIR is to optimise the use and reuse of data. ● To achieve this, metadata and data should be well-described so that they can be replicated and/or combined in different settings. ● This means: ○ Metadata have a plurality of accurate and relevant attributes. ○ Metadata are released with a clear and accessible data usage licence. ○ Metadata are associated with their provenance. ○ Metadata meet domain-relevant community standards. NCAS (https://www.ncas.ac.uk/) is putting its own house inorder and applying the FAIR principles to the data it generates. Working with the data scientists at CEDA (https://www.ceda.ac.uk/), the UK archive for atmospheric and earth observation data, the NCAS-Observation team have: ● Defined data products for NCAS instrumentation. ● Uniquely named all the NCAS instrumentation. ● Developed an integrated repository structure for software and supporting documentation. ● Introduced a controlled vocabulary for instruments and data products. ● Introduced file standards that utilise the controlled vocabulary and are NetCDF4 - classic compliant. The team is also working to produce various software tools which enable easy access to the data in these files. These tools are open-source and can be accessed via the NCAS-Observations website (https://sites.google.com/ncas.ac.uk/ncasobservations/home). The site also provides access to supporting information. This document details what a user should expect to find in one of our data files. The first section of this document details the common file components and how they should be used: this includes how file names are constructed and the use of quality control flags. The second section details a common set of file level metadata (global and variable attributes and array dimensions) that appear in all files. i.e. irrespective of the data product. The final section details the data product specific metadata. Note that: 1. Files will never include metadata (dimensions, global or variable attributes) that are not detailed in the data product supporting document. 2. If data for a common variable is not available then, these variables will be included but will contain only their designated _FillValue. 3. If data for a data product specific variable is not available then, rather than pad the file with variables containing only the _FillValue, that variable will be omitted. All the data files have the same file-level metadata irrespective of the instrument source. The file-level metadata components in each file are: ● Global attributes: including the metadata standards followed in the file, change log (history) and other useful, general information (e.g. licence, authors etc): ● Dimensions: information about the data array sizes. All data have a dimension of time ● Variables: attributes of each data variable included in the file, e.g. name(s), units Comment and feedback on the data file content, structure and supporting document is always welcome and appreciated. Please contact: barbara.b[email protected] 2. Data Processing Levels The NCAS-AMF standard defines the following data product levels and are used to indicate what post-processing has been performed. That is what additional processing has been performed over-and-above that done automatically by the instrument at the time of the original measurement. There are 4 processing levels defined as follows: ● Data Product Level 0 ○ Data is in the format native to the instrument - no processing has been applied. ●Data Product Level 1 ○ Post processing to parse data from native format to archive format - basic QC applied. ●Data Product Level 2 ○ Post processing to parse data from native format to archive format - basic QC applied and additional processing for motion correction, flow distortion, statistical appraisal, calibration. ● Data Product Level 3 ○ A data product that is derived from a data product of level 1 or 2 or their combination. The data to be found in these files will always be level 1 or above. Level 0 data is kept in deep storage and is only accessible through contact with the file author, 3. Data types All data conform to defined data types. Depending on the software used to interrogate the files data types may be given a different name to that used here. To be precise: Python3 name Definition Range byte 8-bit unsigned integer 0 to 255 int32 32-bit signed integer -2,147,483,648 to +2,147,483,647 int64 64-bit signed integer -9,223,372,036,854,775,808 to +9,223,372,036,854,775,80 7 float32 32-bit Single-precision floating-point -3.4E+38 to +3.4E+38 float64 64-bit Double-precision floating-point -1.7E+308 to +1.7E+308 Note the data type is not given as an explicit variable attribute. 4. Nomenclature The language and field name structuring used throughout this document and the files themselves, follows the CF conventions. In particular attributes and variable names are: ● All lowercase. ● Where the attribute or variable name comprises multiple parts these parts are separated by an underscore “_”. Full details of the CF conventions used can be found at (http://cfconventions.org/). All variables are defined in terms of their appropriate dimensions, the ordering of which is always temporal, spatial, other and the data type, float64, int32 for example: as pointed out in section 3 the data type definition is implicit - no type attribute is given in file and is provided here for reference. The standard_name attribute of a variable refers to the name that has been accepted, for that variable, into the CF conventions and not every variable has a standard_name. A useful tool for finding what standard_names have been accepted is http://cfconventions.org/Data/cf-standard-names/27/build/cf-standard-name-table.html. Where a standard_name is not available then this attribute is omitted from the variable definition. Even if a standard_name is not available then the long_name always is. There is no rule as to what form this should take but this attribute is used to provide a meaningful description of the variable. The units attribute provides the units the data is provided in. When there is a standard_name defined then an associated units attribute is also defined. Some variables have no units and in these instances the units attribute is given a value “1” (string not numeric). Where a variable may have missing or padding values then these would be replaced by a NaN (Not-a-Number) placeholder. This is not allowed under the CF convention and a numeric value has to be used. The value of this numeric value is indicated by the attribute _FillValue. To improve automated visualisation the attributes valid_min and valid_max are used. These are the maximum and minimum values, in a given file, for that variable; the range excludes the value assigned to _FillValue attribute. This is a numeric value and is of the same data type as the data. Where a variable is a directly measured quantity then the attribute cell_methods is used. This indicates what the data represent. All the data is a time series of some sort so each data point may be a mean of a series measurements, the standard deviation of a series of measurements or it may be a point measurement. The cell_methods attribute would indicate this with “time: mean”, “time: standard_deviation”, and “time: point” respectively. Although some variables have a spatial dimension and in principle the cell-method can be used to indicate what has been done in the spatial dimension that is not utilised in this standard. The attribute coordinate is included to aid usability and given the value “latitude longitude”. This attribute indicates what is used as the spatial coordinate reference grid. These are the basic attributes you will find and where others arise they will be described locally. 5. Data Quality Flags The data provided will have had some level of processing performed upon: be that instrument or post processing averaging, motion correction, or the variable may be derived from such core variables. These concepts were introduced in section 3. The quality of the data is provided via the Data Quality Control Flag. This flag is a mask and represents the provider's considered opinion. Data users can apply the mask to the data or not - it is the user's choice. By taking this approach, the data provided is of greatest versatility. A file containing just one data quality flag will contain the variable qc_flag. Where a file contains more that on data quality flag variable the data quality flag named is structured as: qc_flag_<name> ■ qc_flag_temperature ■ qc_flag_relative_humidity ■ qc_flag_pressure ■ qc_flag_wind ■ qc_flag_radiation ■ qc_flag_precipitation Flag variables are always of data type byte and are defined such that they have the same dimensions as the variables they are associated with: there is a flag value associated with every data point. They all follow a standard structure with the following attributes: units Definition: Units of a variable’s content. Where a variable is unit less the value 1 is used. Example: 1 long_name Definition: Long descriptive name which is often used for labelling plots Example: Data Quality flag: Temperature flag_values Definition: Values the data flag can have Example: 0b, 1b, 2b, 3b flag_meanings Definition: How the flag should be interpreted Example: not_used good_data suspect_data_unspecified_instrument_performance_issues_contact_d ata_originator_for_more_information Suspect_data_time_stamp_error The flag_values attribute values must be stored in the netCDF file as an array of bytes, and the flag_meanings as a string with each meaning separated by a space. To reflect the fact that what affects data quality can vary, the flag_values and flag_meanings are not rigidly tied down. That is they may vary on a file-by-file basis. What does not vary is the structure and the usage: the qc_flag variable is structured and used so that for every flag_value there is a corresponding flag_meaning. In this standard we use an integer value in the range 0 to n (being of data type byte the maximum value of n is 255): ■ 0 is reserved for future use and is not used ■ 1 is always good data. Consider the variable air_temperature which has data: -20 -3 -2 -1 -2 -3 -2 -1 0 -1 0 2 3 4 2 3 20 4 3 2 While qc_flag_temperature has data: 3 1 2 1 1 1 1 1 1 1 1 1 1 2 1 1 3 2 1 1 The flag_values attribute is “0b, 1b, 2b, 3b” and the flag_meanings attribute gives: not_used good_data suspect_data_unspecified_instrument_performance_issues_contact_data_originator_for_mo re_information Bad_data_value_outside_instrument_measurement_range If the user wanted only to see “good” data (indicated by a qc_flag value of 1) all they would need to do would be to: 1. Make a copy of the variable data array 2. Set the value of the elements in the duplicate data array that correspond to elements on the qc_flag that have a value not equal to 1 to NaN. This will result in the temporary data variable looking like: NaN -3 NaN -1 -2 -3 -2 -1 0 -1 0 2 3 NaN 2 3 NaN NaN 3 2 If the user wanted to accept “suspect” data in addition to “good” data (indicated by a qc_flag value of 1 and ) all they would need to do would be to: 1. Make a copy of the variable data array 2. Set the value of the elements in the duplicate data array that correspond to elements on the qc_flag that have a value not equal to 1 or 2 to NaN. This will result in the temporary data variable looking like: NaN -3 -2 -1 -2 -3 -2 -1 0 -1 0 2 3 4 2 3 NaN 4 3 2 6. File Naming File names follow a defined structure and are built up of unique components. In building the file name components are separated by an underscore “_” while individual elements within a component are separated by a hyphen “-”. The basic structure is as follows and note the use of lower case: <instrument-name>_<platform-name>_<YYYY><MM><DD>-<HH><mm><SS>_<data-produ ct>_<option-1>_<option-2>_<option-3>_v<version>.nc where ● Instrument name: name of an instrument as registered with CEDA. The Instrument name is unique and in the CEDA archive it is linked to an instrument record that lists relevant information such as serial number. This leads to improved archive searching and hence data visibility and data traceability. To register a new NCAS instrument, visit https://github.com/ncasuk/ncas-data-instrument-vocabs and create a new issue. For all other instruments, contact CEDA at [email protected] ○ Example: ncas-aws-1 ● Platform name: CEDA abbreviation tied to the platform record for the where or on what (in the case of ships and aircraft) was the instrument deployed. These can be found by searching https://catalogue.ceda.ac.uk/?q=&sort_by=relevance&record_type=Platform, or contact [email protected] ○ Example: oden, faam, cvao ● Date & Time: YYYYMMDD-HHmmSS ○ Example: ■ for a file containing up to 1 year’s (YYYY) worth of data: 2016, ■ for a file containing up to 1 month’s (MM) worth of data: 201604, ■ for a file containing up to 1 day’s (DD) worth of data: 20160401, ■ for a file containing up to 1 hour’s (HH) worth of data: 20160401-09, ■ for a file containing up to 1 minute’s (mm) worth of data: 20160401-0950, ■ for a file starting at a specific time - for example launch time of soundings: 20160401-095059. ● Data product: name of the defined data product ○ Example: surface-met ● option1, option2, option3: these are optional extras providing more information to the user. Where this is applicable the file name variant will be discussed in detail in section 8.1. ○ Example: ■ option1: cas, cis, fixed, ppi, rhi, user-1, user-2, user-3, user-4, user-5, winds-ppi , high-range-mode , low-range-mode ■ option 2: co, cr, 10mins, 15mins ■ Option 3: standard or advanced ● version: version of the data set ○ Example: n.m: n - major revision integer, m - minor revision integer type: int32 units: 1 long_name: Year valid_min: smallest valid value of this variable in the file valid_max: largest valid value of this variable in the file month dimensions: time type: int32 units: 1 long_name: Month valid_min: smallest valid value of this variable in the file valid_max: largest valid value of this variable in the file day dimensions: time type: int32 units: 1 long_name: Day valid_min: smallest valid value of this variable in the file valid_max: largest valid value of this variable in the file hour dimensions: time type: int32 units: 1 long_name: Hour valid_min: smallest valid value of this variable in the file valid_max: largest valid value of this variable in the file minute dimensions: time type: int32 units: 1 long_name: Minute valid_min: smallest valid value of this variable in the file valid_max: largest valid value of this variable in the file second dimensions: time type: float32 units: 1 long_name: Second valid_min: smallest valid value of this variable in the file valid_max: largest valid value of this variable in the file Additional Common Variables - Stationary Platform latitude For the stationary platform this is single valued and is presented in this form to aid archiving and subsequent searching. dimensions: latitude type: float32 units: degrees_north standard_name: latitude long_name: Latitude longitude For the stationary platform this is single valued and is presented in this form to aid archiving and subsequent searching. dimensions: longitude type: float32 units: degrees_east standard_name: longitude long_name: Longitude Additional Common Variables - Moving Platform - Sea latitude For moving platforms the length of the variable is the same as time and the axis value of Y is incorporated to aid subsequent usage. The cell_method attribute indicates averaging that has taken place to produce the value. This is given only for the time axis: value can be point, mean, standard_deviation. dimensions: latitude type: float32 units: degrees_north axis: Y standard_name: latitude long_name: Latitude _FillValue: -1e+20 valid_min: smallest valid value of this variable in the file valid_max: largest valid value of this variable in the file cell_method: time: point longitude For moving platforms the length of the variable is the same as time and the axis value of Y is incorporated to aid subsequent usage. The cell_method attribute indicates averaging that has taken place to produce the value. This is given only for the time axis: value can be point, mean, standard_deviation. dimensions: longitude type: float32 units: degrees_east axis: X standard_name: longitude long_name: Longitude _FillValue: -1e+20 valid_min: smallest valid value of this variable in the file valid_max: largest valid value of this variable in the file cell_method: time: point platform_course This describes the motion and orientation of the vehicle from which observations were. The platform course is the direction in which the platform is travelling (not necessarily the same as the direction in which it is pointing) dimensions: time type: float32 units: degree standard_name: platform_course long_name: Direction in which the platform is travelling _FillValue: -1e+20 valid_min: smallest valid value of this variable in the file valid_max: largest valid value of this variable in the file cell_method: time: mean coordinate: latitude longitude platform_orientation The platform orientation is the direction in which the "front" or longitudinal axis of the platform is pointing. dimensions: time type: float32 units: degree standard_name: platform_orientation long_name: Direction in which the platform is pointing _FillValue: -1e+20 valid_min: smallest valid value of this variable in the file valid_max: largest valid value of this variable in the file cell_method: time: mean coordinate: latitude longitude platform_speed_wrt_ground The platform speed with respect to ground is relative to the solid Earth beneath it, i.e. the sea floor for a ship. It is often called the "ground speed" of the platform dimensions: time type: float32 units: m s-1 standard_name: platform_speed_wrt_ground long_name: Platform speed with respect to ground _FillValue: -1e+20 valid_min: smallest valid value of this variable in the file valid_max: largest valid value of this variable in the file cell_method: time: mean coordinate: latitude longitude instrument_pitch_angle This is the pitch angle of the instrument: depending on exactly how the instrument was mounted to the platform the motion of the instrument may be independent of the platform motion. In aviation terms the pitch axis has its origin at the centre of gravity and is directed to the right, parallel to a line drawn from wingtip to wingtip. Motion about this axis is called pitch. A positive pitching motion raises the nose of the aircraft and lowers the tail. dimensions: time type: float32 units: degree long_name: Instrument Pitch Angle _FillValue: -1e+20 valid_min: smallest valid value of this variable in the file valid_max: largest valid value of this variable in the file cell_method: time: mean coordinate: latitude longitude instrument_pitch_rate This indicates the rate at which the pitch angle is changing dimensions: time type: float32 units: degree s-1 long_name: Instrument Pitch Angle Rate of Change _FillValue: -1e+20 valid_min: smallest valid value of this variable in the file valid_max: largest valid value of this variable in the file cell_method: time: mean coordinate: latitude longitude instrument_pitch_minimum This is the minimum pitch angle measured during the sample averaging period. dimensions: time type: float32 units: degree long_name: Instrument Pitch Angle Minimum _FillValue: -1e+20 valid_min: smallest valid value of this variable in the file valid_max: largest valid value of this variable in the file cell_method: time: minimum coordinate: latitude longitude instrument_pitch_maximum This is the maximum pitch angle measured during the sample averaging period. dimensions: time type: float32 units: degree long_name: Instrument Pitch Angle Maximum _FillValue: -1e+20 valid_min: smallest valid value of this variable in the file valid_max: largest valid value of this variable in the file cell_method: time: maximum coordinate: latitude longitude instrument_pitch_standard_deviation This is the standard deviation of the pitch angle sample in the sample averaging period and indicates the “steadiness of the motion around this axis. dimensions: time type: float32 units: degree long_name: Instrument Pitch Angle Standard Deviation _FillValue: -1e+20 valid_min: smallest valid value of this variable in the file valid_max: largest valid value of this variable in the file cell_method: time: standard_deviation coordinate: latitude longitude instrument_roll_angle This is the roll angle of the instrument: depending on exactly how the instrument was mounted to the platform the motion of the instrument may be independent of the platform motion. In aviation terms the roll axis has its origin at the centre of gravity and is directed forward, parallel to the fuselage reference line. Motion about this axis is called roll. A positive rolling motion lifts the left wing and lowers the right wing. dimensions: time type: float32 units: degree long_name: Instrument Roll Angle _FillValue: -1e+20 valid_min: smallest valid value of this variable in the file valid_max: largest valid value of this variable in the file cell_method: time: mean coordinate: latitude longitude instrument_roll_rate This indicates the rate at which the pitch angle is changing dimensions: time type: float32 units: degree s-1 long_name: Instrument Roll Angle Rate of Change _FillValue: -1e+20 valid_min: smallest valid value of this variable in the file valid_max: largest valid value of this variable in the file cell_method: time: mean coordinate: latitude longitude instrument_roll_minimum This is the minimum roll angle measured during the sample averaging period. dimensions: time type: float32 units: degree long_name: Instrument Roll Angle Minimum _FillValue: -1e+20 valid_min: smallest valid value of this variable in the file valid_max: largest valid value of this variable in the file cell_method: time: minimum coordinate: latitude longitude instrument_roll_maximum This is the maximum roll angle measured during the sample averaging period. dimensions: time type: float32 units: degree long_name: Instrument Roll Angle Maximum _FillValue: -1e+20 valid_min: smallest valid value of this variable in the file valid_max: largest valid value of this variable in the file cell_method: time: maximum coordinate: latitude longitude instrument_roll_standard_deviation This is the standard deviation of the pitch angle sample in the sample averaging period and indicates the “steadiness of the motion around this axis. dimensions: time type: float32 units: degree long_name: Instrument Roll Angle Standard Deviation _FillValue: -1e+20 valid_min: smallest valid value of this variable in the file valid_max: largest valid value of this variable in the file cell_method: time: standard_deviation coordinate: latitude longitude instrument_yaw_angle This is the yaw angle of the instrument: depending on exactly how the instrument was mounted to the platform the motion of the instrument may be independent of the platform motion. In aviation terms the yaw axis has its origin at the centre of gravity and is directed towards the bottom of the aircraft, perpendicular to the wings and to the fuselage reference line. A positive yawing motion moves the nose of the aircraft to the right. dimensions: time type: float32 units: degree long_name: Instrument Yaw Angle _FillValue: -1e+20 valid_min: smallest valid value of this variable in the file valid_max: largest valid value of this variable in the file cell_method: time: mean coordinate: latitude longitude instrument_yaw_rate This indicates the rate at which the pitch angle is changing dimensions: time type: float32 units: degree s-1 long_name: Instrument Yaw Angle Rate of Change _FillValue: -1e+20 valid_min: smallest valid value of this variable in the file valid_max: largest valid value of this variable in the file cell_method: time: mean coordinate: latitude longitude instrument_yaw_minimum This is the minimum yaw angle measured during the sample averaging period. dimensions: time type: float32 units: degree long_name: Instrument Yaw Angle Minimum _FillValue: -1e+20 valid_min: smallest valid value of this variable in the file valid_max: largest valid value of this variable in the file cell_method: time: minimum coordinate: latitude longitude instrument_yaw_maximum This is the maximum yaw angle measured during the sample averaging period. dimensions: time type: float32 units: degree long_name: Instrument Yaw Angle Maximum _FillValue: -1e+20 valid_min: smallest valid value of this variable in the file valid_max: largest valid value of this variable in the file cell_method: time: maximum coordinate: latitude longitude instrument_yaw_standard_deviation This is the standard deviation of the pitch angle sample in the sample averaging period and indicates the “steadiness of the motion around this axis. dimensions: time type: float32 units: degree long_name: Instrument Yaw Angle Standard Deviation _FillValue: -1e+20 valid_min: smallest valid value of this variable in the file valid_max: largest valid value of this variable in the file cell_method: time: standard_deviation coordinate: latitude longitude Additional Common Variables - Moving Platform - Air In addition to the variables introduced in Additional Common Variables - Moving Platform - Sea, data from aircraft deployments have the following additional variable. altitude Altitude is the (geometric) height above the geoid, which is the reference geopotential surface. The geoid used is WGS84. The axis value of Z is incorporated to aid subsequent usage. The cell_method attribute indicates averaging that has taken place to produce the value. This is given only for the time axis: value can be point, mean, standard_deviation. dimensions: time type: float32 units: m standard_name: altitude long_name: Geometric height above geoid (WGS84). _FillValue: -1e+20 axis: Z valid_min: smallest valid value of this variable in the file valid_max: largest valid value of this variable in the file cell_method: time: mean coordinate: latitude longitude 8. Data Product Specific The sonde data product is associated with the ncas-rapidsonde-1, ncas-rapidsonde-2, ncas-rapidsonde-1, ncas-rapidsonde-2 instruments. The variables in this data product are: ● Altitude ● Latitude ● Longitude ● Air Pressure ● Air Temperature ● Relative Humidity ● Wind speed ● Wind From Direction ● Upward Balloon Velocity ● Elapsed Time Where a given variable has been accepted by the CF community (indicated by the presence of a standard_name) it can be found in the standard name tables: http://cfconventions.org/Data/cf-standard-names/27/build/cf-standard-name-table.html 8.1. File Name Variants There are no file naming variants associated with this data product: options 1-3 in the file name structure are not used. 8.2. Metadata (Global Attributes) There are no additional metadata associated with this data product. 8.3. Dimensions There are no additional dimensions associated with this data product. 8.4. Variables In addition to the variables introduced in section 7 this data product has the additional product specific variables. Note: 1. Where a variable has no units this is indicated by assigning a value of “1” to the “units” attribute. 2. If data for a data product specific variable is not available then, rather than pad the file with variables containing only the _FillValue, that variable will be omitted. 3. Where no CF standard_name has been registered for the variable this attribute is omitted. altitude Altitude is the (geometric) height above the geoid, which is the reference geopotential surface. The geoid is similar to mean sea level. It is height above the geoid of the centre of a measurement range gate and hence is the sum of the platform_height global attribute and the distance of the centre of the measurement range gate vertically above the instrument. This variable has no cell_method as it is a function of the instrument rather than what is being measured. dimensions: time type: float32 units: m standard_name: altitude long_name: Geometric height above geoid (WGS84). _FillValue: -1.00E+20 axis: Z valid_min: smallest valid value of this variable in the file valid_max: largest valid value of this variable in the file cell_methods: time: point latitude Latitude is positive northward; its units of degree_north (or equivalent) indicate this explicitly. In a latitude-longitude system defined with respect to a rotated North Pole, the standard name of grid_latitude should be used instead of latitude. Grid latitude is positive in the grid-northward direction, but its units should be plain degree. dimensions: time type: float32 units: degrees_north standard_name: latitude long_name: Latitude _FillValue: -1.00E+20 axis: Y valid_min: smallest valid value of this variable in the file valid_max: largest valid value of this variable in the file cell_methods: time: point longitude Longitude is positive eastward; its units of degree_east (or equivalent) indicate this explicitly. In a latitude-longitude system defined with respect to a rotated North Pole, the standard name of grid_longitude should be used instead of longitude. Grid longitude is positive in the grid-eastward direction, but its units should be plain degree. dimensions: time type: float32 units: degrees_east standard_name: longitude long_name: Longitude _FillValue: -1.00E+20 axis: X valid_min: smallest valid value of this variable in the file valid_max: largest valid value of this variable in the file cell_methods: time: point air_pressure Air pressure is the atmospheric pressure at a location in Earth's Atmosphere and is directly proportional to the mass of air over that location. This is a static pressure, that is there is no dynamic component. This is different to surface pressure which assumes that the sensor is at the Earth’s surface. dimensions: time type: float32 units: hPa standard_name: air_pressure long_name: Air Pressure. _FillValue: -1.00E+20 valid_min: smallest valid value of this variable in the file valid_max: largest valid value of this variable in the file cell_methods: time: point coordinates: latitude longitude altitude air_temperature Air temperature is the bulk temperature of the air, not the surface (skin) temperature. dimensions: time type: float32 units: K standard_name: air_temperature long_name: Air Temperature. _FillValue: -1.00E+20 valid_min: smallest valid value of this variable in the file valid_max: largest valid value of this variable in the file cell_methods: time: point coordinates: latitude longitude altitude relative_humidity Relative humidity is the ratio of the partial pressure of water vapour to the equilibrium vapour pressure of water at a given temperature. Relative humidity depends on temperature and the pressure of the system of interest. The same amount of water vapour results in higher relative humidity in cool air than warm air. A related parameter is the dew point. dimensions: time type: float32 units: % standard_name: relative_humidity long_name: Relative Humidity. _FillValue: -1.00E+20 valid_min: smallest valid value of this variable in the file valid_max: largest valid value of this variable in the file cell_methods: time: point coordinates: latitude longitude altitude wind_speed Speed is the magnitude of velocity. Wind is defined as a two-dimensional (horizontal) air velocity vector, with no vertical component. (Vertical motion in the atmosphere has the standard name upward_air_velocity.) The wind speed is the magnitude of the wind velocity. dimensions: time type: float32 units: m s-1 standard_name: wind_speed long_name: Wind Speed _FillValue: -1.00E+20 valid_min: smallest valid value of this variable in the file valid_max: largest valid value of this variable in the file cell_methods: time: point coordinates: latitude longitude altitude wind_from_direction Wind is defined as a two-dimensional (horizontal) air velocity vector, with no vertical component. (Vertical motion in the atmosphere has the standard name upward_air_velocity.) In meteorological reports, the direction of the wind vector is usually (but not always) given as the direction from which it is blowing (wind_from_direction) (westerly, northerly, etc.). dimensions: time type: float32 units: degree standard_name: wind_from_direction long_name: Wind From Direction _FillValue: -1.00E+20 valid_min: smallest valid value of this variable in the file valid_max: largest valid value of this variable in the file cell_methods: time: point coordinates: latitude longitude altitude upward_balloon_velocity The velocity at which the balloon rises in the atmosphere. This is a function of the payload, the amount of gas and the impact of atmospheric winds. The upward motion of the balloon causes the aspiration of the sonde sensors. dimensions: time type: float32 units: m s-1 long_name: Balloon Ascent Rate _FillValue: -1.00E+20 valid_min: smallest valid value of this variable in the file valid_max: largest valid value of this variable in the file cell_methods: time: point coordinates: latitude longitude altitude elapsed_time The number of seconds that has elapsed since the balloon was launched. dimensions: time type: float32 units: s long_name: Elapsed Time _FillValue: -1.00E+20 valid_min: smallest valid value of this variable in the file valid_max: largest valid value of this variable in the file qc_flag This control flag indicates the quality atmospheric sounding. The number of values will be at least 2: 0 or 1, and no more than 256. The attribute flag_values is an array of bytes giving a list of the values used in context of this qc_flag: 0 is never used, 1 always indicates good data. The attribute flag_meanings indicates what each value means and how a data point with this flag value should be interpreted. There is a flag value and meaning for every data point and how this flag mask can be applied can be found in section 5 of this document.