Allows to extract and interpolate specific locations
from grib files (see eupp_get_gridded).
Requires ecCodes to be installed.
eupp_interpolate_grib(
file,
at,
atname = NULL,
bilinear = TRUE,
wide = TRUE,
verbose = FALSE,
ignore_init = FALSE,
...
)character, name of the grib file from which the data should be extracted.
object of class sf or sfc containing
point locations to be extracted/interpolated.
NULL (default) or character length 1.
Is set (and the variable exists in at) it will be
appended to the returned object.
logical, defaults to TRUE. If set to
FALSE nearest neighbour interpolation is used.
logical, defaults to TRUE. If TRUE the
result is in a wide format, else long format.
logical, defaults to FALSE.
logical, defaults to FALSE; see 'Details' for more
information. Only has an effect when wide = TRUE.
currently unused.
When downloading the GRIB version 1 data sets using
eupp_get_gridded() a .rds file with the grib
inventory will be stored alongside with the GRIB file itself. If
prsent, this .rds file will be used to retrieve the meta information
(what the different fields of the GRIB file contain).
If not present, ecCodes must be installed and the index will be created
by calling grib_ls -j.
It then reads the grib file using read_stars
to read the data (in bands) and performs the interpolation using
st_extract.
Please note that at needs a valid coordinate reference
system (CRS) and must only contain POINT geometries for now.
wide: Brings the interpolated data to a wide format. Reduces the
amount of meta information. See also ignore_init below when working
with analysis data.
ignore_init: This option can be handy when processing analysis data.
Analysis data (ERA5 reanalysis) consists of variables coming from the analysis
directly (e.g., air temperature) and short-term predictions for certain parameters
such as precipitation. Thus, initialization date/time and time valid differs
between them resulting in a 'sparse' matrix if wide = TRUE. The argument
ignore_init allows to ignore initialization date/time and forecast step
to get a less (or non-)sparse data.frame. WARNING: When used with forecast data
the data may no longer be unique leading to overlapping data (warnings/errors).