docs: correct documentation mistakes and typos (#165)

* docs: correct documentation mistakes and typos

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docs/building/introduction.rst

@@ -10,7 +10,7 @@ file, which is a YAML file that describes sources of meteorological
 fields as well as the operations to perform on them, before they are
 written to a zarr file. The input of the process is a range of dates and
 some options to control the layout of the output. Statistics will be
-computed as the dataset is build, and stored in the metadata, with other
+computed as the dataset is built, and stored in the metadata, with other
 information such as the the locations of the grid points, the list of
 variables, etc.
 
@@ -24,35 +24,35 @@ variables, etc.
 
 date
    Throughout this document, the term `date` refers to a date and time,
-   not just a date. A training dataset is covers a continuous range of
+   not just a date. A training dataset covers a continuous range of
    dates with a given frequency. Missing dates are still part of the
-   dataset, but the data are missing and marked as such using NaNs.
-   Dates are always in UTC, and refer to date at which the data is
-   valid. For accumulations and fluxes, that would be the end of the
-   accumulation period.
+   dataset, but missing data are marked as such using NaNs. Dates are
+   always in UTC, and refer to date at which the data is valid. For
+   accumulations and fluxes, that would be the end of the accumulation
+   period.
 
 variable
-   A `variable` is meteorological parameter, such as temperature, wind,
-   etc. Multilevel parameters are treated as separate variables, one for
-   each level. For example, temperature at 850 hPa and temperature at
-   500 hPa will be treated as two separate variables (`t_850` and
-   `t_500`).
+   A `variable` is a meteorological parameter, such as temperature,
+   wind, etc. Multilevel parameters are treated as separate variables,
+   one for each level. For example, temperature at 850 hPa and
+   temperature at 500 hPa will be treated as two separate variables
+   (`t_850` and `t_500`).
 
 field
-   A `field` is a variable at a given date. It is represented by a array
-   of values at each grid point.
+   A `field` is a variable at a given date. It is represented by an
+   array of values at each grid point.
 
 source
-   The `source` is a software component that given a list of dates and
-   variables will return the corresponding fields. A example of source
+   The `source` is a software component that, given a list of dates and
+   variables will return the corresponding fields. An example of source
    is ECMWF's MARS archive, a collection of GRIB or NetCDF files, a
    database, etc. See :ref:`sources` for more information.
 
 filter
    A `filter` is a software component that takes as input the output of
-   a source or the output of another filter can modify the fields and/or
-   their metadata. For example, typical filters are interpolations,
-   renaming of variables, etc. See :ref:`filters` for more information.
+   a source or another filter and can modify the fields and/or their
+   metadata. For example, typical filters are interpolations, renaming
+   of variables, etc. See :ref:`filters` for more information.
 
 ************
  Operations
@@ -62,19 +62,20 @@ In order to build a training dataset, sources and filters are combined
 using the following operations:
 
 join
-   The join is the process of combining several sources data. Each
-   source is expected to provide different variables at the same dates.
+   The join is the process of combining several sources of data. Each
+   source is expected to provide different variables for the same of
+   dates.
 
 pipe
    The pipe is the process of transforming fields using filters. The
-   first step of a pipe is typically a source, a join or another pipe.
-   The following steps are filters.
+   first step of a pipe is typically a source, a join, or another pipe.
+   This can subsequently followed by more filters.
 
 concat
    The concatenation is the process of combining different sets of
-   operation that handle different dates. This is typically used to
-   build a dataset that spans several years, when the several sources
-   are involved, each providing a different period.
+   operations that handle different dates. This is typically used to
+   build a dataset that spans several years, when several sources are
+   involved, each providing data for different period.
 
 Each operation is considered as a :ref:`source <sources>`, therefore
 operations can be combined to build complex datasets.
@@ -87,7 +88,7 @@ First recipe
 ============
 
 The simplest `recipe` file must contain a ``dates`` section and an
-``input`` section. The latter must contain a `source` In that case, the
+``input`` section. The latter must contain a `source`. In that case, the
 source is ``mars``
 
 .. literalinclude:: yaml/building1.yaml
@@ -132,15 +133,15 @@ This will build the following dataset:
 Adding some forcing variables
 =============================
 
-When training a data-driven models, some forcing variables may be
+When training a data-driven model, some forcing variables may be
 required such as the solar radiation, the time of day, the day in the
 year, etc.
 
-These are provided by the ``forcings`` source. In that example, we add a
-few of them. The `template` option is used to point to another source,
-in that case the first instance of ``mars``. This source is used to get
-information about the grid points, as some of the forcing variables are
-grid dependent.
+These are provided by the ``forcings`` source. Let us add a few of them
+to the above example. The `template` option is used to point to another
+source, in that case the first instance of ``mars``. This source is used
+to get information about the grid points, as some of the forcing
+variables are grid dependent.
 
 .. literalinclude:: yaml/building3.yaml
    :language: yaml