Processing fvcom files
process-fvcom.RmdIn this article we will demonstrate how to extract data from
fvcom .nc files using similar functions to
those for general .nc/.nc4 files.
Alternative Packages
Before we demonstrate the functionality available here, we would like
to make it clear that we have not built a package to deal with all
possible aspects of an fvcom file. If you are looking for
additional functionality then we would recommend one of the following
packages:
- the FVCOM package,
- the fvcom.tbx package.
Extracting Data from fvcom files
For the purpose of this example, we have collected the url for a
fvcom file and stored it in a separate .txt
file and so we can load the required packages, along with collecting the
file to be read in.
library(tidyverse)
library(satpoint)
fvcom_file <- read_lines("fvcom_links.txt")fvcom files have data constructed on a triangular mesh
and the stored variables are either provided in terms of the nodes that
form the bounds of each triangle or the centroid of triangle. In the
fvcom file these are denoted by the node and
nele dimensions respectively.
Within this package we have only built functionality to extract the
variables associated with the nodes. To see which variables are
available and whether they are recorded at the node or
nele level, we can use
nc_obj <- ncdf4::nc_open(fvcom_file)
collect_variables(nc_obj, nodes = TRUE)## # A tibble: 53 × 3
## name longname node_or_elem
## * <chr> <chr> <chr>
## 1 nprocs number of processors <NA>
## 2 partition partition nele
## 3 fvcom_mesh fvcom_mesh <NA>
## 4 lon nodal longitude node
## 5 lat nodal latitude node
## 6 lonc zonal longitude nele
## 7 latc zonal latitude nele
## 8 siglay_center Sigma Layers nele
## 9 siglev_center Sigma Levels nele
## 10 h_center Bathymetry nele
## # ℹ 43 more rowsTo extract the variable values themselves, we can choose one of the
variables above that are recorded at the node level and
run
extract_nodes_fvcom(nc_obj, nc_var_name = "precip")## # A tibble: 2,399,976 × 3
## value date_time node
## <dbl> <dttm> <int>
## 1 0.0000000150 2020-01-01 00:00:00 1
## 2 0.0000000162 2020-01-01 00:00:00 2
## 3 0.0000000172 2020-01-01 00:00:00 3
## 4 0.0000000182 2020-01-01 00:00:00 4
## 5 0.0000000192 2020-01-01 00:00:00 5
## 6 0.0000000195 2020-01-01 00:00:00 6
## 7 0.0000000198 2020-01-01 00:00:00 7
## 8 0.0000000202 2020-01-01 00:00:00 8
## 9 0.0000000205 2020-01-01 00:00:00 9
## 10 0.0000000199 2020-01-01 00:00:00 10
## # ℹ 2,399,966 more rowsSummarising the values across buffers
If we want to extract the data over specific areas, as we did in the
vignette("satpoint", package = "satpoint") vignette, we
first need to construct the buffers. Taking the same sites as
before:
sites <- tibble(
site = LETTERS[1:4],
longitude = c(-5.837704, -6.592514, -7.885055, -3.421410),
latitude = c(57.607846, 57.291280, 56.900022, 58.187849)
)
sites## # A tibble: 4 × 3
## site longitude latitude
## <chr> <dbl> <dbl>
## 1 A -5.84 57.6
## 2 B -6.59 57.3
## 3 C -7.89 56.9
## 4 D -3.42 58.2We can create buffers but as the fvcom file is stored at
a smaller scale, we want the buffers to be smaller.
buffers <- site_buffers(sites, x_coord = "longitude", y_coord = "latitude",
crs = 4326, buffers = c(2))
buffers## Simple feature collection with 4 features and 1 field
## Geometry type: POLYGON
## Dimension: XY
## Bounding box: xmin: -7.918481 ymin: 56.88169 xmax: -3.386795 ymax: 58.20619
## Geodetic CRS: WGS 84
## # A tibble: 4 × 2
## site geometry
## <chr> <POLYGON [°]>
## 1 A_2 ((-5.866424 57.59847, -5.866384 57.5983, -5.866304 57.59795, -5.865805 57.59797, -…
## 2 B_2 ((-6.615266 57.27812, -6.615221 57.27795, -6.614972 57.27796, -6.614927 57.27779, …
## 3 C_2 ((-7.907938 56.88706, -7.907885 56.88689, -7.907636 56.88691, -7.907583 56.88673, …
## 4 D_2 ((-3.446063 58.17525, -3.445816 58.17525, -3.445792 58.17508, -3.445545 58.17508, …We can now extract the data from our fvcom file and
average the values across the buffers
extract_site_grids_fvcom(fvcom_file, sites = buffers,
nc_crs = 4326, nc_var_name = "precip")## Extracting the coordinate names directly from the file## Warning in collect_geo_names(nc_obj, name_x, name_y): More than one possible match for
## coordinate names, the first match will be used.## Warning: attribute variables are assumed to be spatially constant throughout all
## geometries## Collecting variable from file.## Summarising variable across site## Simple feature collection with 72 features and 3 fields
## Geometry type: POLYGON
## Dimension: XY
## Bounding box: xmin: -7.918481 ymin: 56.88169 xmax: -5.803766 ymax: 57.62605
## Geodetic CRS: WGS 84
## # A tibble: 72 × 4
## site date_time precip geometry
## <chr> <dttm> <dbl> <POLYGON [°]>
## 1 A_2 2020-01-01 00:00:00 2.59e-16 ((-5.866424 57.59847, -5.866384 57.5983, -5.866304 5…
## 2 A_2 2020-01-01 01:00:00 5.04e-12 ((-5.866424 57.59847, -5.866384 57.5983, -5.866304 5…
## 3 A_2 2020-01-01 02:00:00 1.01e-11 ((-5.866424 57.59847, -5.866384 57.5983, -5.866304 5…
## 4 A_2 2020-01-01 03:00:00 1.51e-11 ((-5.866424 57.59847, -5.866384 57.5983, -5.866304 5…
## 5 A_2 2020-01-01 04:00:00 8.89e-10 ((-5.866424 57.59847, -5.866384 57.5983, -5.866304 5…
## 6 A_2 2020-01-01 05:00:00 1.76e- 9 ((-5.866424 57.59847, -5.866384 57.5983, -5.866304 5…
## 7 A_2 2020-01-01 06:00:00 2.64e- 9 ((-5.866424 57.59847, -5.866384 57.5983, -5.866304 5…
## 8 A_2 2020-01-01 07:00:00 3.65e- 9 ((-5.866424 57.59847, -5.866384 57.5983, -5.866304 5…
## 9 A_2 2020-01-01 08:00:00 4.66e- 9 ((-5.866424 57.59847, -5.866384 57.5983, -5.866304 5…
## 10 A_2 2020-01-01 09:00:00 5.68e- 9 ((-5.866424 57.59847, -5.866384 57.5983, -5.866304 5…
## # ℹ 62 more rows