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Geopoints is the format used by Metview to handle spatially irregular data (e.g. observations) in a non-BUFR format. For a full list and details of functions and operators on geopoints, see Geopoints Functions.

Geopoints is an ASCII format which can be generated 'by hand', or could be the result of a Metview computation. For example, the Observation Filter icon can return a geopoints variable as the result of filtering BUFR data (see Observation filtering and plotting Example). Several geopoints variables can be contained within a Geopointset variable (currently supported by Macro and Python only, not by the user interface or the plotting).

The Metview Macro language has a number of functions available for the input, output, handling and operating on geopoints. Geopoints can be visualised with Metview and can be customised using symbol visual definitions - see Symbol Plotting. Metview can also convert between Geopoints and GRIB - see Geopoints To Grib and Grib To Geopoints.

The Geopoints file formats

A geopoints file is an ASCII file containing a header section and a data section consisting of several columns. There are some different 'flavours' of the format, described below.

The data elements that can be present in a geopoints file are the point coordinates (latitude and longitude), the level, date, time and value of either one or two parameters. A two-parameter geopoints file is considered by the plotting engine to contain the components of a vector quantity such as wind.

The format does not care about the alignment of columns, it just requires that there is at least one whitespace character between entries. The elements that must be present are an initial line tagged with the keyword #GEO, a line tagged with the keyword #DATA and the data points themselves. There can also be an optional section for meta-data, which must start with #METADATA - see section below for details. The lines in-between the header and the data sections are for human-readable information only and are not used in the interpretation of the file. All formats apart from the Standard format require an additional line in the header section to specify the format; this line must start with #FORMAT followed by the name of the format being used.

Note that a time should be expressed as HHMM; a time of 12 will be interpreted as 0012 , ie 00:12.

Standard (6-column) geopoints

This is the default format that Metview uses. This example shows a geopoints file containing dry bulb temperature at 2m (PARAMETER = 12004).

lat        long    level  date       time    value
36.15      -5.35     0   19970810    1200    300.9
34.58      32.98     0   19970810    1200    301.6
41.97      21.65     0   19970810    1200    299.4
45.03       7.73     0   19970810    1200    294
45.67       9.7      0   19970810    1200    302.2
44.43       9.93     0   19970810    1200    293.4


#FORMAT XYV (Compact format)

This format allows data to be specified with just three columns: X (longitude), Y (latitude) and V (value). The start of an example file would look like the following:

x/long y/lat value
-5.35  36.15  300.9
32.98  34.58  301.6
21.65  41.97  299.4

#FORMAT XY_VECTOR (XY Vector format)

This format allows two parameters to be stored as the components of a two-dimensional vector (for example uv wind components). The start of an example file would look like the following:

# lat    lon   height   date       time      u       v
80       0      0      20030617    1200   -4.9001  -8.3126
80       5.5    0      20030617    1200   -5.6628  -7.7252
80       11     0      20030617    1200   -6.4254  -7.13829


#FORMAT POLAR_VECTOR (Polar Vector format)

This format allows two parameters to be stored as the speed and direction of a two-dimensional vector, the direction being specified in degrees where zero is due South and angles increase clockwise. The start of an example file would look like the following:

# lat      lon     height     date       time   speed   direction
 50.97     6.05      0      20030614     1200     4       90
 41.97     21.65     0      20030614     1200     5       330
 35.85     14.48     0      20030614     1200     11      170

#FORMAT NCOLS (Multi-column format)

This format allows any number of parameters to be stored in a geopoints file. The #COLUMNS section is used to understand the columns, as they can be put in any order. The following column names are reserved and are treated specially: longitudeleveldatetimestnid. A column with a different name will be treated as a value column. The data should all be numeric, apart from stnid, which is stored as a string.

The start of an example file would look like the following:

latitude longitude  time date       t2     o3    td    rh
32.55   35.85  0600    20120218    273.9   35   280.3   75
31.72   35.98  1800    20120218    274.9   24   290.4   68
51.93   8.32   1200    20140218    278.9   28   300.5   34
41.1    20.82  1200    20150218    279.9   83   310.6   42

For Polar Vector geopoints, only the first value (speed) is considered during operations. For XY geopoints, both values are considered during most operations where it makes sense to do so. For the NCOLS format, all value columns are manipulated during operations.

Currently the level, date and time can only be used for filtering (or can be extracted into  Vector variables for other uses). They must be present in the file but you can specify any dummy value if you do not intend to use them.

Storing and retrieving meta-data

A geopoints file can have a section of meta-data key-value pairs in its header before the #DATA section, as illustrated here:

#lat	long	level	date	time	value
55.01   8.41    0.2     20130804  1200  294.4
54.33   8.60    0.2     20130804  1200  296.9

Here, four pieces of meta-data are stored. They can be set and queried in the Macro (or Python) language, like this:

data = read('geopoints_with_metadata.gpt')
md = metadata(data)



Meta-data can also be set by passing a definition like this:

gpt_new = set_metadata(gpt, (mykey1:'val1', mykey2: 5))

If geopoints variables contain meta-data and they are part of a geopointset, they can be filtered on their meta-data - see Geopointset for details.

Extracting and setting columns

There are two ways to extract columns of data from a geopoints variable.

  1. Use the functions provided, e.g.

    lats = latitudes(gpt)
    vals = values(gpt)
    rh   = values(gpt, 'rh') # assuming NCOLS format with a value column of name 'rh'
  2. Use column indexing, e.g.

    lats = gpt['latitude']
    vals = gpt['value']
    rh   = gpt['rh'] # assuming NCOLS format with a value column of name 'rh'

To assign values to a column, again there are 2 methods, but they have different behaviours:

  1. Use the set_ functions provided - these create new geopoints variables and do not modify the originals, e.g.
    gpt_new = set_latitudes(gpt, lats) # lats is a vector
  2. Use column indexing - this modifies the original geopoints variable and is therefore more efficient, e.g.
    gpt['latitude'] = lats # lats is a vector

Operations between geopoints and fieldsets

When you carry out an operation between geopoints and fieldset (or images) variables the result is another geopoints variable :

  • When operating with fieldsets, the values of the field(s) at the geopoints locations are calculated by interpolation and the resulting field values undergo the operation with the geopoints values
  • When combining with an image no interpolation takes place; the pixel values where the geopoints are located are extracted and these undergo the operation with the geopoints values
  • Unless otherwise stated in the operator or function description, only the first value of a two-valued geopoint is considered during a calculation

Combinations include algebraic operations, boolean operations and a number of functions. See  Geopoints Functions for details.

Missing values in geopoints

When you combine fieldset data with geopoints, you may end up with some missing values in your geopoints variable. These will have the value contained in the built-in global variable geo_missing_value. Any operation on a geopoints variable will bypass missing values (e.g. mean()) or retain them unaltered (e.g. max()); see individual function descriptions for more details.

In order to remove missing values from a geopoints variable, use the function remove_missing_values() as illustrated below:

  geo_clean = remove_missing_values (geo_source) 

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