Read variables from a model along the trajectories of drifters.

import xarray as xr
import numpy as np
import cf_xarray as _
import pyproj
import trajan as ta
import matplotlib.pyplot as plt

Open drifter dataset from a CSV file

ds = ta.read_csv('bug05_pos.csv.xz',
                 lon='Longitude',
                 lat='Latitude',
                 time='Time',
                 name='Device')
print(ds)

2024-12-16 16:26:16 fv-az1766-447 trajan.accessor[2121] DEBUG Detecting trajectory dimension
2024-12-16 16:26:16 fv-az1766-447 trajan.accessor[2121] DEBUG No trajectory_id attribute/variable found, trying to identify by name.
2024-12-16 16:26:16 fv-az1766-447 trajan.accessor[2121] DEBUG Detecting time-variable for "obs"..
2024-12-16 16:26:16 fv-az1766-447 trajan.accessor[2121] DEBUG Detected obs-dim: obs, detected time-variable: time.
2024-12-16 16:26:16 fv-az1766-447 trajan.accessor[2121] DEBUG Detected un-structured (2D) trajectory dataset
2024-12-16 16:26:16 fv-az1766-447 trajan.traj2d[2121] DEBUG Condensing 844 observations.
2024-12-16 16:26:16 fv-az1766-447 trajan.traj2d[2121] DEBUG Condensed observations from: 844 to 844
2024-12-16 16:26:16 fv-az1766-447 trajan.accessor[2121] DEBUG Detecting trajectory dimension
2024-12-16 16:26:16 fv-az1766-447 trajan.accessor[2121] DEBUG No trajectory_id attribute/variable found, trying to identify by name.
2024-12-16 16:26:16 fv-az1766-447 trajan.accessor[2121] DEBUG Detecting time-variable for "obs"..
2024-12-16 16:26:16 fv-az1766-447 trajan.accessor[2121] DEBUG Detected obs-dim: obs, detected time-variable: time.
2024-12-16 16:26:16 fv-az1766-447 trajan.accessor[2121] DEBUG Detected un-structured (2D) trajectory dataset
2024-12-16 16:26:16 fv-az1766-447 trajan.traj[2121] DEBUG No grid-mapping specified, checking if coordinates are lon/lat..
2024-12-16 16:26:16 fv-az1766-447 trajan.traj[2121] DEBUG No grid-mapping specified, checking if coordinates are lon/lat..
2024-12-16 16:26:16 fv-az1766-447 trajan.traj[2121] DEBUG No grid-mapping specified, checking if coordinates are lon/lat..
2024-12-16 16:26:16 fv-az1766-447 trajan.traj[2121] DEBUG No grid-mapping specified, checking if coordinates are lon/lat..
<xarray.Dataset> Size: 27kB
Dimensions:     (trajectory: 1, obs: 844)
Coordinates:
  * trajectory  (trajectory) <U18 72B 'dev864475040536665'
  * obs         (obs) int64 7kB 0 1 2 3 4 5 6 7 ... 837 838 839 840 841 842 843
Data variables:
    time        (trajectory, obs) datetime64[ns] 7kB 2022-05-10T12:56:16.5000...
    lon         (trajectory, obs) float64 7kB 5.332 5.332 5.332 ... 5.25 5.25
    lat         (trajectory, obs) float64 7kB 60.38 60.38 60.38 ... 60.45 60.45
Attributes:
    Conventions:          CF-1.10
    featureType:          trajectory
    geospatial_lat_min:   60.38380600000001
    geospatial_lat_max:   60.45141000000001
    geospatial_lon_min:   5.244493666666667
    geospatial_lon_max:   5.33242
    time_coverage_start:  2020-01-01T00:00:07.250000
    time_coverage_end:    2022-05-20T11:03:46.120000

Open the Norkyst800 model

nk = xr.open_dataset(
    'https://thredds.met.no/thredds/dodsC/sea/norkyst800m/1h/aggregate_be')

Use cf-xarray to get the CRS

gm = nk.cf['grid_mapping']
nk_crs = pyproj.CRS.from_cf(gm.attrs)
print(nk_crs)

2024-12-16 16:26:25 fv-az1766-447 pyproj[2121] DEBUG PROJ_ERROR: proj_create: several objects matching this name: Krovak (Greenwich), Equal Earth Greenwich, Laborde Grid (Greenwich), Modified Krovak (Greenwich), Krovak East North (Greenwich), Modified Krovak East North (Greenwich), ...
{"$schema": "https://proj.org/schemas/v0.2/projjson.schema.json", "type": "ProjectedCRS", "name": "undefined", "base_crs": {"$schema": "https://proj.org/schemas/v0.7/projjson.schema.json", "type": "GeographicCRS", "name": "undefined", "datum": {"type": "GeodeticReferenceFrame", "name": "undefined", "ellipsoid": {"name": "undefined", "semi_major_axis": 6378137, "semi_minor_axis": 6356752.3142}}, "coordinate_system": {"subtype": "ellipsoidal", "axis": [{"name": "Longitude", "abbreviation": "lon", "direction": "east", "unit": "degree"}, {"name": "Latitude", "abbreviation": "lat", "direction": "north", "unit": "degree"}]}}, "conversion": {"$schema": "https://proj.org/schemas/v0.7/projjson.schema.json", "type": "Conversion", "name": "unknown", "method": {"name": "Polar Stereographic (variant B)", "id": {"authority": "EPSG", "code": 9829}}, "parameters": [{"name": "Latitude of standard parallel", "value": 60, "unit": "degree", "id": {"authority": "EPSG", "code": 8832}}, {"name": "Longitude of origin", "value": 70, "unit": "degree", "id": {"authority": "EPSG", "code": 8833}}, {"name": "False easting", "value": 3192800, "unit": "metre", "id": {"authority": "EPSG", "code": 8806}}, {"name": "False northing", "value": 1784000, "unit": "metre", "id": {"authority": "EPSG", "code": 8807}}]}, "coordinate_system": {"$schema": "https://proj.org/schemas/v0.7/projjson.schema.json", "type": "CoordinateSystem", "subtype": "Cartesian", "axis": [{"name": "Easting", "abbreviation": "E", "direction": "east", "unit": "metre"}, {"name": "Northing", "abbreviation": "N", "direction": "north", "unit": "metre"}]}}

Grid the drifter dataset to the timesteps of the model.

times = nk.sel(time=slice('2022-05-10', '2022-05-20')).time.values
ds = ds.traj.gridtime(times)
ds = ds.dropna('time', how='all')

print(ds)

2024-12-16 16:26:25 fv-az1766-447 trajan.accessor[2121] DEBUG Detecting trajectory dimension
2024-12-16 16:26:25 fv-az1766-447 trajan.accessor[2121] DEBUG Detecting time-variable for "obs"..
2024-12-16 16:26:25 fv-az1766-447 trajan.accessor[2121] DEBUG Detected obs-dim: obs, detected time-variable: time.
2024-12-16 16:26:25 fv-az1766-447 trajan.accessor[2121] DEBUG Detected un-structured (2D) trajectory dataset
<xarray.Dataset> Size: 6kB
Dimensions:     (trajectory: 1, time: 239)
Coordinates:
  * time        (time) datetime64[ns] 2kB 2022-05-10T13:00:00 ... 2022-05-20T...
  * trajectory  (trajectory) <U18 72B 'dev864475040536665'
Data variables:
    lon         (trajectory, time) float64 2kB 5.332 5.332 5.332 ... 5.249 5.25
    lat         (trajectory, time) float64 2kB 60.38 60.38 60.38 ... 60.44 60.45
Attributes:
    Conventions:          CF-1.10
    featureType:          trajectory
    geospatial_lat_min:   60.38380600000001
    geospatial_lat_max:   60.45141000000001
    geospatial_lon_min:   5.244493666666667
    geospatial_lon_max:   5.33242
    time_coverage_start:  2020-01-01T00:00:07.250000
    time_coverage_end:    2022-05-20T11:03:46.120000

Transform the drifter dataset to the CRS of the model

dst = ds.traj.transform(nk_crs)

2024-12-16 16:26:25 fv-az1766-447 trajan.accessor[2121] DEBUG Detecting trajectory dimension
2024-12-16 16:26:25 fv-az1766-447 trajan.accessor[2121] DEBUG Detected obs-dim: time, detected time-variable: time.
2024-12-16 16:26:25 fv-az1766-447 trajan.accessor[2121] DEBUG Detected structured (1D) trajectory dataset
2024-12-16 16:26:25 fv-az1766-447 trajan.traj[2121] DEBUG No grid-mapping specified, checking if coordinates are lon/lat..
2024-12-16 16:26:25 fv-az1766-447 trajan.accessor[2121] DEBUG Detecting trajectory dimension
2024-12-16 16:26:25 fv-az1766-447 trajan.accessor[2121] DEBUG Detected obs-dim: time, detected time-variable: time.
2024-12-16 16:26:25 fv-az1766-447 trajan.accessor[2121] DEBUG Detected structured (1D) trajectory dataset

Extract the values of a variable for the trajectory

temp = nk.isel(depth=0).sel(time=dst.time,
                            X=dst.x,
                            Y=dst.y,
                            method='nearest').temperature
print(temp)

<xarray.DataArray 'temperature' (time: 239, trajectory: 1)> Size: 956B
[239 values with dtype=float32]
Coordinates:
    X           (trajectory, time) float64 2kB 3.416e+05 3.416e+05 ... 3.464e+05
    Y           (trajectory, time) float64 2kB 4.344e+05 4.344e+05 ... 4.416e+05
    depth       float64 8B 0.0
  * time        (time) datetime64[ns] 2kB 2022-05-10T13:00:00 ... 2022-05-20T...
    lat         (trajectory, time) float64 2kB 60.38 60.38 60.38 ... 60.44 60.45
    lon         (trajectory, time) float64 2kB 5.33 5.33 5.33 ... 5.243 5.249
  * trajectory  (trajectory) <U18 72B 'dev864475040536665'
Attributes:
    units:          Celsius
    time:           ocean_time
    grid:           grid
    location:       face
    field:          temperature, scalar, series
    grid_mapping:   projection_stere
    long_name:      Sea water potential temperature
    standard_name:  sea_water_potential_temperature
    _ChunkSizes:    [   1    1   17 2602]

Notice that the lat and lon variables from Norkyst match the original lat and lon from the dataset.

plt.figure()
temp.plot()
plt.show()