===================
qa4sm-preprocessing
===================
.. image:: https://github.com/awst-austria/qa4sm-preprocessing/workflows/Automated%20Tests/badge.svg?branch=master&event=push
:target: https://github.com/awst-austria/qa4sm-preprocessing/actions
.. image:: https://coveralls.io/repos/awst-austria/qa4sm-preprocessing/badge.png?branch=master
:target: https://coveralls.io/r/awst-austria/qa4sm-preprocessing?branch=master
.. image:: https://badge.fury.io/py/qa4sm-preprocessing.svg
:target: https://badge.fury.io/py/qa4sm-preprocessing
This package contains functions to preprocess certain data before using them
in the QA4SM online validation framework.
ISMN FRM
========
This module contains the routine to assign FRM qualifications ISMN sensors.
The Quality Indicators (QIs) are based on a Triple Collocation run with
80% CI between ISMN (0-10 cm, "good" time stamps), ERA5-Land ("swvl1") and
ESA CCI SM v06.1 PASSIVE. See ``./docs/ismn_frm.rst`` for more details.
CGLS HR SSM SWI
===============
Read CGLS HR SSM and SWI images (1km sampling) in netcdf format and convert
them to time series.
The image reader allows reading/converting data for a spatial subset (bbox) only.
Time series are stored in 5*5 DEG cell files, i.e. there are `~250 000 time series`
stored in one single cell file.
Time series reading is done based on cell level. Up to 6 cells are loaded into
memory at a time. The ``read_area`` function allows reading multiple GPI time series
around one location at once (and optionally converting them into a single, averaged
series, to represent the mean SM for an area).
Necessary updates
-----------------
At the moment it is only possible to read a single variable. However, in order
to mask SM time series based in location quality flags, it is necessary to
read multiple parameters. When passing the averaged time series for an area
to `pytesmo` for validation, masking can not be done in `pytesmo`, but must be done
beforehand.
.. _pyscaffold-notes:
Note
====
This project has been set up using PyScaffold 4.0.2. For details and usage
information on PyScaffold see https://pyscaffold.org/.
Raw data
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"description": "===================\nqa4sm-preprocessing\n===================\n\n.. image:: https://github.com/awst-austria/qa4sm-preprocessing/workflows/Automated%20Tests/badge.svg?branch=master&event=push\n :target: https://github.com/awst-austria/qa4sm-preprocessing/actions\n\n.. image:: https://coveralls.io/repos/awst-austria/qa4sm-preprocessing/badge.png?branch=master\n :target: https://coveralls.io/r/awst-austria/qa4sm-preprocessing?branch=master\n\n.. image:: https://badge.fury.io/py/qa4sm-preprocessing.svg\n :target: https://badge.fury.io/py/qa4sm-preprocessing\n\n\nThis package contains functions to preprocess certain data before using them\nin the QA4SM online validation framework.\n\nISMN FRM\n========\n\nThis module contains the routine to assign FRM qualifications ISMN sensors.\nThe Quality Indicators (QIs) are based on a Triple Collocation run with\n80% CI between ISMN (0-10 cm, \"good\" time stamps), ERA5-Land (\"swvl1\") and\nESA CCI SM v06.1 PASSIVE. See ``./docs/ismn_frm.rst`` for more details.\n\n\nCGLS HR SSM SWI\n===============\n\nRead CGLS HR SSM and SWI images (1km sampling) in netcdf format and convert\nthem to time series.\nThe image reader allows reading/converting data for a spatial subset (bbox) only.\nTime series are stored in 5*5 DEG cell files, i.e. there are `~250 000 time series`\nstored in one single cell file.\n\nTime series reading is done based on cell level. Up to 6 cells are loaded into\nmemory at a time. The ``read_area`` function allows reading multiple GPI time series\naround one location at once (and optionally converting them into a single, averaged\nseries, to represent the mean SM for an area).\n\nNecessary updates\n-----------------\nAt the moment it is only possible to read a single variable. However, in order\nto mask SM time series based in location quality flags, it is necessary to\nread multiple parameters. When passing the averaged time series for an area\nto `pytesmo` for validation, masking can not be done in `pytesmo`, but must be done\nbeforehand.\n\n\n.. _pyscaffold-notes:\n\nNote\n====\n\nThis project has been set up using PyScaffold 4.0.2. For details and usage\ninformation on PyScaffold see https://pyscaffold.org/.\n",
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