Closed adrfantini closed 5 years ago
Currently, we can read this file as a curvilinear grid, but when read it as a regular GDAL file with read_stars
there is no information about the georeference, at all.
stars
is not alone here:
> r = readGDAL("hmr_big.nc")
hmr_big.nc has GDAL driver netCDF
and has 1361 rows and 1286 columns
Warning message:
In readGDAL("hmr_big.nc") : GeoTransform values not available
so in order to test this, please provide a netcdf file that at least gives a longlat grid, not just a matrix, when read through GDAL. Or give the key to this (offset, delta for x and y). As far as I understand, we shouldn't read these from the long and lat matrices, because they would contain the non-obtran transformed coordinates (ie the curvilinear grid).
Ok, I'll test this out and find a proper example in a few days.
Here you can find a time slice of an example netCDF file from one EURO-CORDEX model. The grid is a rotated one, defined as:
int rotated_latitude_longitude ;
rotated_latitude_longitude:grid_mapping_name = "rotated_latitude_longitude" ;
rotated_latitude_longitude:grid_north_pole_latitude = 39.25 ;
rotated_latitude_longitude:grid_north_pole_longitude = -162. ;
rotated_latitude_longitude:north_pole_grid_longitude = 0. ;
lat
and lon
arrays are included. The CRS is not indicated explicitly. GDAL cannot understand the SRS of the file.
Here is another similar example.
Can this help?
What would be the proj4string for these datasets?
In theory it should be the same as previously discussed in r-spatial/sf#651, so something along the lines of "+proj=ob_tran +o_proj=longlat +o_lon_p=-162 +o_lat_p=39.25 +lon_0=180 +to_meter=0.01745329"
This uses the forward projection trick also used in the issue you refer to.
library(stars)
# Loading required package: abind
# Loading required package: sf
# Linking to GEOS 3.6.2, GDAL 2.2.3, PROJ 4.9.3
(r = read_stars("example_ob_tran.nc"))
# stars object with 3 dimensions and 1 attribute
# attribute(s):
# example_ob_tran.nc
# Min. :247.0
# 1st Qu.:267.4
# Median :275.9
# Mean :274.5
# 3rd Qu.:282.6
# Max. :292.6
# dimension(s):
# from to offset delta refsys point values
# x 1 106 -28.43 0.44 NA NA NULL [x]
# y 1 103 21.89 -0.44 NA NA NULL [y]
# time 1 1 1979-01-16 12:00:00 NA POSIXct NA NULL
First the path using the lon
and lat
subdatasets to directly create a curvilinear grid:
lon = read_stars('NETCDF:"example_ob_tran2.nc":lon')
lat = read_stars('NETCDF:"example_ob_tran2.nc":lat')
rr = st_as_stars(r, curvilinear = list(x = lon[[1]], y = lat[[1]]))
rr
# stars object with 3 dimensions and 1 attribute
# attribute(s):
# example_ob_tran.nc
# Min. :247.0
# 1st Qu.:267.4
# Median :275.9
# Mean :274.5
# 3rd Qu.:282.6
# Max. :292.6
# dimension(s):
# from to offset delta refsys point
# x 1 106 NA NA +proj=longlat +datum=WGS8... NA
# y 1 103 NA NA +proj=longlat +datum=WGS8... NA
# time 1 1 1979-01-16 12:00:00 NA POSIXct NA
# values
# x [106x103] -44.1407 [°], ..., 64.404 [°] [x]
# y [106x103] 22.1994 [°], ..., 72.4199 [°] [y]
# time NULL
# curvilinear grid
plot(rr, axes = TRUE, border = NA)
Next compute the curvilinear grid parametrically from the shifted grid (
r
), using the forward trick:
obtran = "+proj=ob_tran +o_proj=longlat +o_lon_p=-162 +o_lat_p=39.25 +lon_0=180 +to_meter=0.01745329"
st_crs(r) = 4326
r.ll = st_transform(r, obtran)
st_crs(r.ll) = 4326
plot(r.ll, axes = TRUE, border = NA)
r.ll
# stars object with 3 dimensions and 1 attribute
# attribute(s):
# example_ob_tran.nc
# Min. :247.0
# 1st Qu.:267.4
# Median :275.9
# Mean :274.5
# 3rd Qu.:282.6
# Max. :292.6
# dimension(s):
# from to offset delta refsys point
# x 1 106 NA NA +proj=longlat +datum=WGS8... NA
# y 1 103 NA NA +proj=longlat +datum=WGS8... NA
# time 1 1 1979-01-16 12:00:00 NA POSIXct NA
# values
# x [106x103] -44.1407, ..., 64.404 [x]
# y [106x103] 22.1994, ..., 72.42 [y]
# time NULL
# curvilinear grid
st_bbox(rr)
# Units: [°]
# xmin ymin xmax ymax
# -44.14069 22.19937 64.40398 72.41995
st_bbox(r.ll)
# xmin ymin xmax ymax
# -44.14070 22.19937 64.40399 72.41996
I guess that now after #65 the another approach would be:
r2 = read_stars('example_ob_tran.nc', curvilinear=c('lon', 'lat'))
Is one approach more 'correct' than the other?
the obtran PROJ string does not come from the file, lon
and lat
do. Using only stuff that comes from the file feels easier. Having said that, the obtran parameters again may be somewhere in the metadata. lon
and lat
arrays only work when they are present.
I am having issues with curvilinear
for this file with rotated grid.
fn = 'historical.nc'
fn %>% read_stars(sub = 'Q100', curvilinear=c('lon', 'lat'))
#Q100, trying to read file: netCDF:NETCDF:"/home/clima-archive4-b/afantini/regcm_simulations/EURO-CORDEX/change/italy_change/mrro/faster_way/data_hadgem/yearmax/historical.nc":Q100:lon
#Error in CPL_read_gdal(as.character(x), as.character(options), as.character(driver), :
file not found
fn %>% read_ncdf(var = 'Q100', curvilinear=c('lon', 'lat'))
#Error: length(d) == length(dim(x[[1]])) is not TRUE
lon = fn %>% read_ncdf(var = 'lon', quiet = TRUE)
lat = fn %>% read_ncdf(var = 'lat', quiet = TRUE)
ll = setNames(c(lon, lat), c("x", "y"))
fn %>% read_ncdf(var = 'Q100', quiet = TRUE) %>% st_as_stars(curvilinear = ll)
#Error: length(d) == length(dim(x[[1]])) is not TRUE
Can you reproduce this?
fn = 'historical.nc'
lon = fn %>% read_ncdf(var = 'lon', quiet = TRUE)
lat = fn %>% read_ncdf(var = 'lat', quiet = TRUE)
lo = st_set_dimensions(lon, names = c("x", "y"))
la = st_set_dimensions(lat, names = c("x", "y"))
ll = setNames(c(lo, la), c("x", "y"))
x = fn %>% read_stars(sub = 'Q100', quiet = TRUE) %>% st_as_stars(curvilinear = ll)
Any suggestions how we can make this easier?
And, any idea what went wrong?
Shouldn't we be able to read this by just doing fn %>% read_stars(sub = 'Q100', curvilinear = c('lon', 'lat'))
?
If you read lat
and lon
with read_stars
instead of read_ncdf
, it plots fine. Alternatively, I assume you could read everything with read_ncdf
, but I am getting a Error: length(d) == length(dim(x[[1]])) is not TRUE
if trying to plot or display x = fn %>% read_ncdf(var = 'Q100', quiet = TRUE) %>% st_as_stars(curvilinear = ll)
Thanks for sorting out! @mdsumner this may be related that in lon and lat both x and y dimensions have a negative delta (cellsize)?
Is that the GDAL default? I remember it would always be upside down if the "default" transform was applied, where offsets both zero and deltas both 1. It was the wrong orientation for a simple png image. I'll explore
First time I see a negative x delta! It is simply what the netcdf gdal driver communicates; I guess it comes from the ordering of the array in the netcdf file which is, afaict, not prescribed.
fn = 'historical.nc'
fn %>% read_stars(sub = 'Q100', curvilinear = c('lon', 'lat')) %>% plot
now works.
Mh, not for me with GDAL 2.2.2. I am getting:
Q100, trying to read file: netCDF:NETCDF:"/dev/shm/afantini/historical.nc":Q100:lon
Error in CPL_read_gdal(as.character(x), as.character(options), as.character(driver), :
file not found
In addition: Warning message:
In CPL_read_gdal(as.character(x), as.character(options), as.character(driver), :
GDAL Error 1: Failed to parse NETCDF: prefix string into expected 2, 3 or 4 fields.
2.2.2 gdalinfo
of the file:
Driver: netCDF/Network Common Data Format
Files: historical.nc
Size is 512, 512
Coordinate System is `'
Metadata:
NC_GLOBAL#absorption_emission_time_step_in_hours=18
NC_GLOBAL#asselin_filter_nu=0.0625
NC_GLOBAL#atmosphere_time_step_in_seconds=24
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NC_GLOBAL#boundary_nspgd=40
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NC_GLOBAL#c3s_disclaimer=This data has been produced in the context of the
C3S_34b_Lot1 and Lot 2 projects (PRINCIPLES/CORDEX4CDS) as a data
provider for the Climate Data Store within the Copernicus Climate Change
Service (C3S - https://climate.copernicus.eu/). While abiding by the
highest scientific and technical standards, ECMWF cannot warrant that
any information provided by the C3S will be entirely free from errors or
omissions or that such errors or omissions can or will be rectified
entirely. This applies to data from projects that continue to be
developed, but are made publicly available for the purpose of feedback
and testing. Some data or metadata may have been created or structured
in files or formats that are not error-free. ECMWF accepts no
responsibility with regard to such problems incurred as a result of
using this data (see http://climate.copernicus.eu/disclaimer-privacy for
the full disclaimer)
NC_GLOBAL#CDI=Climate Data Interface version 1.9.5 (http://mpimet.mpg.de/cdi)
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NC_GLOBAL#grid_size_in_meters=12000
NC_GLOBAL#history=2019-01-21 15:54:12 CET : created by create_Qx_regcm.R ; Mon Jan 21 15:19:58 2019: cdo yearmax data_hadgem/historical.nc data_hadgem/yearmax/historical.nc
Mon Jan 21 15:17:30 2019: cdo -L sellonlatbox,6.5,19,36,48 -selyear,1976/2005 /home/clima-archive4/CORDEX2/EUR-11/ICTP-RegCM4-6/MOHC-HadGEM2-ES/historical//mrro_EUR-11_MOHC-HadGEM2-ES_historical_r1i1p1_ICTP-RegCM4-6_v1_day_1970060112-2006010112.nc data_hadgem/historical.nc
Thu Jan 10 10:18:03 2019: cdo -O -z zip remap,/home/netapp-clima/users/jciarlo/scripts/postproc_cordex/sftlf_EUR-11_ICHEC-EC-EARTH_rcp45_r3i1p1_DMI-HIRHAM5_v1_fx.nc,../../../011grid_CORDEX_weights.nc mrro_EUR-11_MOHC-HadGEM2-ES_historical_r1i1p1_ICTP-RegCM4-6_v1_day_1970060112-1980010112.nc mrro_EUR-11_MOHC-HadGEM2-ES_historical_r1i1p1_ICTP-RegCM4-6_v1_day_1970060112-1980010112.nc_remap.nc
2017-10-31 16:25:30 : Created by RegCM RegCM Model program
NC_GLOBAL#ICTP_version_note=Archived on model native grid
NC_GLOBAL#institute_id=ICTP
NC_GLOBAL#institution=International Centre for Theoretical Physics
NC_GLOBAL#ipcc_scenario_code=HISTORICAL
NC_GLOBAL#lake_model_activated=0
NC_GLOBAL#landsurface_model=clm4.5
NC_GLOBAL#large_scale_cloud_fraction_scheme=0
NC_GLOBAL#lateral_boundary_condition_scheme=5
NC_GLOBAL#latitude_of_projection_origin=48
NC_GLOBAL#longitude_of_projection_origin=9.75
NC_GLOBAL#model_icbc_data_source=HA_85
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NC_GLOBAL#model_is_restarted=No
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NC_GLOBAL#model_simulation_end=1970-09-01 00:00:00 UTC
NC_GLOBAL#model_simulation_initial_start=1970-06-01 00:00:00 UTC
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NC_GLOBAL#model_sst_data_source=HA_85
NC_GLOBAL#moisture_scheme=1
NC_GLOBAL#ncdf4_version=1.16
NC_GLOBAL#note=The domain is larger than EUR-11
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NC_GLOBAL#_NCProperties=version=1|netcdflibversion=4.6.1|hdf5libversion=1.10.1
Subdatasets:
SUBDATASET_1_NAME=NETCDF:"historical.nc":time_bnds
SUBDATASET_1_DESC=[30x2] time_bnds (64-bit floating-point)
SUBDATASET_2_NAME=NETCDF:"historical.nc":lon
SUBDATASET_2_DESC=[114x94] longitude (64-bit floating-point)
SUBDATASET_3_NAME=NETCDF:"historical.nc":lat
SUBDATASET_3_DESC=[114x94] latitude (64-bit floating-point)
SUBDATASET_4_NAME=NETCDF:"historical.nc":mrro
SUBDATASET_4_DESC=[30x114x94] runoff_flux (32-bit floating-point)
SUBDATASET_5_NAME=NETCDF:"historical.nc":Q100
SUBDATASET_5_DESC=[114x94] Q100 (32-bit floating-point)
Corner Coordinates:
Upper Left ( 0.0, 0.0)
Lower Left ( 0.0, 512.0)
Upper Right ( 512.0, 0.0)
Lower Right ( 512.0, 512.0)
Center ( 256.0, 256.0)
What happens if you add driver = NULL
to the read_stars
command?
Same error.
This is after installing the branch with
remotes::install_github("r-spatial/stars", "values")
right?
Dang! Sorry, I missed that. Yep, can confirm it's working if using that branch. I believe we can close this?
The Error: length(d) == length(dim(x[[1]])) is not TRUE
with read_ncdf
persists but maybe that's material for another issue?
Why don't we get the full time series in the stars object?
Ah, sorry, that only concerns the mrro
variable.
Ah, see the damned 360-day calendar issue right there.
I noticed - time for its own temporal reference system. Did you mention there was an R package to convert back and forth between calendar time and 360-day/year model time?
@edzer where do you see a negative x? I see default geotransform on time, and typical +x/-y on the rest
# xoffset, xdelta, yskew, yres, xskew, ydelta (internal GDAL geotransform arrangement)
$time
[1] 0 1 0 0 0 1
$lon
[1] -9.51 0.11 0.00 -2.20 0.00 -0.11
$lat
[1] -9.51 0.11 0.00 -2.20 0.00 -0.11
$mrro
[1] -9.51 0.11 0.00 -2.20 0.00 -0.11
$Q100
[1] -9.51 0.11 0.00 -2.20 0.00 -0.11
in gdalinfo terms
Driver: netCDF/Network Common Data Format
Files: historical.nc
Size is 94, 114
Coordinate System is `'
Origin = (-9.509999923167690,-2.200000113090583)
Pixel Size = (0.109999998923271,-0.110000002700671)
But, I'd assume to ignore the geotransform in curvilinear case anyway - right? (GDAL may or may not override the default, depending on vicarious heuristics). Here using raster (divorced from how I tackle this in ocean model data - ignoring the extent/crs, but analogous) and using the quadmesh grid/base plot:
f <- "historical.nc"
r <- raster::raster(f, varname = "mrro")
coords <- raster::brick(raster::raster(f, varname = "lon"),
raster::raster(f, varname = "lat"))
library(quadmesh)
mesh_plot(r, coords = coords)
maps::map(add = TRUE)
I noticed - time for its own temporal reference system. Did you mention there was an R package to convert back and forth between calendar time and 360-day/year model time?
Yup, PCICt
!
@mdsumner I'm sorry, I mixed up the offset and delta colums!
I tried to recreate the figure you made but failed and got this:
are packages CRAN versions?
Oh! Yes, so easy to mix up - worldfile order, GDAL order, raster extent order ...
Yes both raster/quadmesh are CRAN versions.
Hmm, I'm seeing that error in the netcdf pathway - so I'll investigate that(!):
x <- read_ncdf(fn, curvilinear = c("lon", "lat"))
x
#Error in dim.stars(x) : length(d) == length(dim(x[[1]])) is not TRUE
First shot at PCICt support:
library(stars)
# Loading required package: abind
# Loading required package: sf
# Linking to GEOS 3.7.0, GDAL 2.3.2, PROJ 5.2.0
fn = 'historical.nc'
(xx = read_stars(fn, sub = 'mrro', curvilinear = c("lon", "lat")))
# lon,
# lat,
# mrro,
# stars object with 3 dimensions and 1 attribute
# attribute(s):
# historical.nc":mrro [kg/m^2/s]
# Min. :0
# 1st Qu.:0
# Median :0
# Mean :0
# 3rd Qu.:0
# Max. :0
# NA's :173400
# dimension(s):
# from to offset delta refsys point
# x 1 94 NA NA +proj=longlat +datum=WGS8... NA
# y 1 114 NA NA +proj=longlat +datum=WGS8... NA
# time 1 30 1976-07-01 360 days PCICt NA
# values
# x [94x114] 3.91253 [°],...,19.1685 [°] [x]
# y [94x114] 35.3469 [°],...,48.495 [°] [y]
# time NULL
# curvilinear grid
plot(xx[,,,1:12])
# Warning messages:
# 1: In seq(from = x$offset + (x$from - 1 + where) * x$delta, by = x$delta, :
# Incompatible methods ("+.PCICt", "Ops.difftime") for "+"
# 2: In seq(from = x$offset + (x$from - 1 + where) * x$delta, by = x$delta, :
# Incompatible methods ("+.PCICt", "Ops.difftime") for "+"
Look at the labels:
Wonderful! Should probably continue this in #29
I have a
netcdf
file (link) of which I know the proper CRS, but the CRS is not encoded in the file itself:+proj=ob_tran +o_proj=longlat +o_lat_p=29.0 +o_lon_p=0.0 +lon_0=15.0 +a=6367470 +b=6367470
stars
, however, refuses to use it:This is very similar to what happened in r-spatial/sf#651 and has to do, as far as I understand it, with limited support in GDAL for the transformation
ob_tran
.In the case of
sf
, a workaround usinglwgeom
was possible. Is there anything similar that can be done forstars
?