Notebook

Developing `make_ww3_current_file` Worker¶

Code experiments and verification for the make_ww3_current_file worker.

wwatch3 requires the ocean current forcing file that is input to ww3_prnc to have a structure similar this file:

netcdf GSL5km_2009_CUR {
dimensions:
    longitude = 150 ;
    latitude = 236 ;
    time = UNLIMITED ; // (8760 currently)
variables:
    double time(time) ;
        time:long_name = "Time" ;
        time:time_origin = "2009-01-01 01:00:00" ;
        time:delta_t = "0000-00-00 01:00:00" ;
        time:units = "days since 2009-01-01T01:00:00Z" ;
    double latitude(latitude, longitude) ;
        latitude:long_name = "Latitude" ;
        latitude:units = "degrees_north" ;
    double longitude(latitude, longitude) ;
        longitude:long_name = "Longitude" ;
        longitude:units = "degrees_east" ;
    double uas(time, latitude, longitude) ;
        uas:standard_name = "uas" ;
        uas:long_name = "East component of current" ;
        uas:units = "m s-1" ;
    double vas(time, latitude, longitude) ;
        vas:standard_name = "vas" ;
        vas:long_name = "North component of current" ;
        vas:units = "m s-1" ;

(Example courtesy of Caroline Sevigny at Université du Québec à Rimouski)

The worker needs to:

use time_counter from grid_U.nc or grid_V.nc file,

and rename it to time

use nav_lon and the mesh_mask file,

and rename them to longitude and latitude

unstagger the top grid cell

(nominally 0.5 m depth) values of the vozocrtx and vomecrty variables from grid_U.nc and grid_V.nc files on to the nav_lon and nav_lat of the mesh_mask file

rotate the unstaggered velocity component arrays from NEMO grid orientation to NS map orientation
rename the unstaggered and rotated velocity component arrays to u_current and v_current

to produce an netCDF4 file with a structure similar to above that ww3_prnc will accept.

In [65]:

import arrow
import cmocean
import matplotlib.pyplot as plt
import numpy as np
import numpy.ma as ma
import pandas as pd
import xarray as xr

from salishsea_tools import viz_tools

In [2]:

%matplotlib inline

In [3]:

mesh_mask = xr.open_dataset('/results/nowcast-sys/NEMO-forcing/grid/mesh_mask_downbyone2.nc')
mesh_mask

Out[3]:

<xarray.Dataset>
Dimensions:       (t: 1, x: 398, y: 898, z: 40)
Dimensions without coordinates: t, x, y, z
Data variables:
    e1f           (t, y, x) float64 413.6 424.0 431.4 437.0 441.4 445.0 ...
    e1t           (t, y, x) float64 397.1 415.8 426.5 433.6 438.9 443.0 ...
    e1u           (t, y, x) float64 406.5 421.1 430.0 436.2 440.9 444.7 ...
    e1v           (t, y, x) float64 407.5 419.7 428.3 434.5 439.4 443.4 ...
    e2f           (t, y, x) float64 455.0 455.0 455.0 455.0 455.0 455.0 ...
    e2t           (t, y, x) float64 455.2 455.1 455.1 455.1 455.1 455.1 ...
    e2u           (t, y, x) float64 455.2 455.1 455.1 455.1 455.1 455.1 ...
    e2v           (t, y, x) float64 455.0 455.0 455.0 455.0 455.0 455.0 ...
    e3t_0         (t, z, y, x) float64 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 ...
    e3t_1d        (t, z) float64 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.001 1.002 ...
    e3u_0         (t, z, y, x) float64 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 ...
    e3v_0         (t, z, y, x) float64 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 ...
    e3w_0         (t, z, y, x) float64 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 ...
    e3w_1d        (t, z) float64 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.001 1.001 ...
    ff            (t, y, x) float64 0.0001064 0.0001064 0.0001064 0.0001064 ...
    fmask         (t, z, y, x) int8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...
    fmaskutil     (t, y, x) int8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...
    gdept_0       (t, z, y, x) float32 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 ...
    gdept_1d      (t, z) float64 0.5 1.5 2.5 3.5 4.5 5.5 6.5 7.501 8.501 ...
    gdepu         (t, z, y, x) float32 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
    gdepv         (t, z, y, x) float32 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
    gdepw_0       (t, z, y, x) float32 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
    gdepw_1d      (t, z) float64 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.001 9.002 ...
    glamf         (t, y, x) float32 -123.428 -123.423 -123.418 -123.412 ...
    glamt         (t, y, x) float32 -123.429 -123.424 -123.419 -123.413 ...
    glamu         (t, y, x) float32 -123.427 -123.421 -123.416 -123.411 ...
    glamv         (t, y, x) float32 -123.431 -123.425 -123.42 -123.415 ...
    gphif         (t, y, x) float32 46.8622 46.8641 46.8661 46.868 46.8699 ...
    gphit         (t, y, x) float32 46.8597 46.8615 46.8634 46.8653 46.8672 ...
    gphiu         (t, y, x) float32 46.8606 46.8625 46.8644 46.8662 46.8681 ...
    gphiv         (t, y, x) float32 46.8612 46.8632 46.8651 46.867 46.8689 ...
    isfdraft      (t, y, x) float32 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
    mbathy        (t, y, x) int16 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...
    misf          (t, y, x) int16 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...
    nav_lat       (y, x) float32 46.8597 46.8615 46.8634 46.8653 46.8672 ...
    nav_lev       (z) float32 0.5 1.5 2.50001 3.50003 4.50007 5.50015 ...
    nav_lon       (y, x) float32 -123.429 -123.424 -123.419 -123.413 ...
    time_counter  (t) float64 0.0
    tmask         (t, z, y, x) int8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...
    tmaskutil     (t, y, x) int8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...
    umask         (t, z, y, x) int8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...
    umaskutil     (t, y, x) int8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...
    vmask         (t, z, y, x) int8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...
    vmaskutil     (t, y, x) int8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...
Attributes:
    file_name: NEMO-forcing/grid/mesh_mask_downbyone2.nc
    TimeStamp: 19/06/2016 14:06:46 -0700
    history: Sun Jun 19 14:06:59 2016: ncks -4 -L4 -O mesh_mask.nc mesh_mask.nc
[2016-11-14 16:25] Added metadata to variable in preparation for creation of ERDDAP datasets.
    NCO: "4.5.2"
    Conventions: CF-1.6
    title: SalishSeaCast NEMO downonegrid2 Bathymetry Mesh Mask
    institution: Dept of Earth, Ocean & Atmospheric Sciences, University of British Columbia
    source: NEMO-3.6 Salish Sea configuration
    references: https://salishsea.eos.ubc.ca/erddap/info/
https://bitbucket.org/salishsea/nemo-forcing/src/tip/grid/bathy_meter_downbyone2.nc

In [4]:

u_nemo = xr.open_dataset('/results/SalishSea/nowcast-blue/11apr17/SalishSea_1h_20170411_20170411_grid_U.nc')
u_nemo

Out[4]:

<xarray.Dataset>
Dimensions:               (axis_nbounds: 2, depthu: 40, nvertex: 4, time_counter: 24, x: 398, y: 898)
Coordinates:
  * depthu                (depthu) float32 0.5 1.5 2.50001 3.50003 4.50007 ...
    nav_lat               (y, x) float32 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
    nav_lon               (y, x) float32 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
    time_centered         (time_counter) datetime64[ns] 2017-04-11T00:30:00 ...
  * time_counter          (time_counter) datetime64[ns] 2017-04-11T00:30:00 ...
Dimensions without coordinates: axis_nbounds, nvertex, x, y
Data variables:
    area                  (y, x) float32 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
    bounds_lat            (y, x, nvertex) float32 0.0 0.0 0.0 0.0 0.0 0.0 ...
    bounds_lon            (y, x, nvertex) float32 0.0 0.0 0.0 0.0 0.0 0.0 ...
    depthu_bounds         (depthu, axis_nbounds) float32 0.0 1.0 1.0 2.00001 ...
    time_centered_bounds  (time_counter, axis_nbounds) float64 3.701e+09 ...
    time_counter_bounds   (time_counter, axis_nbounds) float64 3.701e+09 ...
    vozocrtx              (time_counter, depthu, y, x) float64 nan nan nan ...
Attributes:
    name: SalishSea_1h_20170411_20170411
    description: ocean U grid variables
    title: ocean U grid variables
    Conventions: CF-1.5
    production: An IPSL model
    timeStamp: 2017-Apr-11 12:00:39 PDT
    history: Tue Apr 11 12:23:27 2017: ncks -4 -L4 -O SalishSea_1h_20170411_20170411_grid_U.nc SalishSea_1h_20170411_20170411_grid_U.nc
    NCO: 4.4.2

In [5]:

u_nemo.vozocrtx[:, 0, ...]

Out[5]:

<xarray.DataArray 'vozocrtx' (time_counter: 24, y: 898, x: 398)>
[8577696 values with dtype=float64]
Coordinates:
    depthu         float32 0.5
    nav_lat        (y, x) float32 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
    nav_lon        (y, x) float32 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
    time_centered  (time_counter) datetime64[ns] 2017-04-11T00:30:00 ...
  * time_counter   (time_counter) datetime64[ns] 2017-04-11T00:30:00 ...
Dimensions without coordinates: y, x
Attributes:
    standard_name: sea_water_x_velocity
    long_name: ocean current along i-axis
    units: m/s
    online_operation: average
    interval_operation: 40 s
    interval_write: 1 h
    cell_methods: time: mean (interval: 40 s)
    cell_measures: area: area

In [6]:

v_nemo = xr.open_dataset('/results/SalishSea/nowcast-blue/11apr17/SalishSea_1h_20170411_20170411_grid_V.nc')
v_nemo

Out[6]:

<xarray.Dataset>
Dimensions:               (axis_nbounds: 2, depthv: 40, nvertex: 4, time_counter: 24, x: 398, y: 898)
Coordinates:
  * depthv                (depthv) float32 0.5 1.5 2.50001 3.50003 4.50007 ...
    nav_lat               (y, x) float32 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
    nav_lon               (y, x) float32 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
    time_centered         (time_counter) datetime64[ns] 2017-04-11T00:30:00 ...
  * time_counter          (time_counter) datetime64[ns] 2017-04-11T00:30:00 ...
Dimensions without coordinates: axis_nbounds, nvertex, x, y
Data variables:
    area                  (y, x) float32 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
    bounds_lat            (y, x, nvertex) float32 0.0 0.0 0.0 0.0 0.0 0.0 ...
    bounds_lon            (y, x, nvertex) float32 0.0 0.0 0.0 0.0 0.0 0.0 ...
    depthv_bounds         (depthv, axis_nbounds) float32 0.0 1.0 1.0 2.00001 ...
    time_centered_bounds  (time_counter, axis_nbounds) float64 3.701e+09 ...
    time_counter_bounds   (time_counter, axis_nbounds) float64 3.701e+09 ...
    vomecrty              (time_counter, depthv, y, x) float64 nan nan nan ...
Attributes:
    name: SalishSea_1h_20170411_20170411
    description: ocean V grid variables
    title: ocean V grid variables
    Conventions: CF-1.5
    production: An IPSL model
    timeStamp: 2017-Apr-11 12:00:39 PDT
    history: Tue Apr 11 12:23:29 2017: ncks -4 -L4 -O SalishSea_1h_20170411_20170411_grid_V.nc SalishSea_1h_20170411_20170411_grid_V.nc
    NCO: 4.4.2

In [7]:

v_nemo.vomecrty[:, 0, ...]

Out[7]:

<xarray.DataArray 'vomecrty' (time_counter: 24, y: 898, x: 398)>
[8577696 values with dtype=float64]
Coordinates:
    depthv         float32 0.5
    nav_lat        (y, x) float32 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
    nav_lon        (y, x) float32 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
    time_centered  (time_counter) datetime64[ns] 2017-04-11T00:30:00 ...
  * time_counter   (time_counter) datetime64[ns] 2017-04-11T00:30:00 ...
Dimensions without coordinates: y, x
Attributes:
    standard_name: sea_water_y_velocity
    long_name: ocean current along j-axis
    units: m/s
    online_operation: average
    interval_operation: 40 s
    interval_write: 1 h
    cell_methods: time: mean (interval: 40 s)
    cell_measures: area: area

In [8]:

u_unstaggered, v_unstaggered = viz_tools.unstagger(u_nemo.vozocrtx[:, 0, ...], v_nemo.vomecrty[:, 0, ...])
for coord in ('nav_lat', 'nav_lon', 'time_centered'):
    del u_unstaggered.coords[coord]
    del v_unstaggered.coords[coord]
del u_unstaggered.coords['depthu']
del v_unstaggered.coords['depthv']
u_unstaggered, v_unstaggered

Out[8]:

(<xarray.DataArray 'vozocrtx' (time_counter: 24, y: 897, x: 397)>
 array([[[ nan,  nan, ...,  nan,  nan],
         [ nan,  nan, ...,  nan,  nan],
         ..., 
         [  0.,   0., ...,  nan,  nan],
         [  0.,   0., ...,  nan,  nan]],
 
        [[ nan,  nan, ...,  nan,  nan],
         [ nan,  nan, ...,  nan,  nan],
         ..., 
         [  0.,   0., ...,  nan,  nan],
         [  0.,   0., ...,  nan,  nan]],
 
        ..., 
        [[ nan,  nan, ...,  nan,  nan],
         [ nan,  nan, ...,  nan,  nan],
         ..., 
         [  0.,   0., ...,  nan,  nan],
         [  0.,   0., ...,  nan,  nan]],
 
        [[ nan,  nan, ...,  nan,  nan],
         [ nan,  nan, ...,  nan,  nan],
         ..., 
         [  0.,   0., ...,  nan,  nan],
         [  0.,   0., ...,  nan,  nan]]])
 Coordinates:
   * time_counter  (time_counter) datetime64[ns] 2017-04-11T00:30:00 ...
 Dimensions without coordinates: y, x,
 <xarray.DataArray 'vomecrty' (time_counter: 24, y: 897, x: 397)>
 array([[[ nan,  nan, ...,  nan,  nan],
         [ nan,  nan, ...,  nan,  nan],
         ..., 
         [  0.,   0., ...,  nan,  nan],
         [  0.,   0., ...,  nan,  nan]],
 
        [[ nan,  nan, ...,  nan,  nan],
         [ nan,  nan, ...,  nan,  nan],
         ..., 
         [  0.,   0., ...,  nan,  nan],
         [  0.,   0., ...,  nan,  nan]],
 
        ..., 
        [[ nan,  nan, ...,  nan,  nan],
         [ nan,  nan, ...,  nan,  nan],
         ..., 
         [  0.,   0., ...,  nan,  nan],
         [  0.,   0., ...,  nan,  nan]],
 
        [[ nan,  nan, ...,  nan,  nan],
         [ nan,  nan, ...,  nan,  nan],
         ..., 
         [  0.,   0., ...,  nan,  nan],
         [  0.,   0., ...,  nan,  nan]]])
 Coordinates:
   * time_counter  (time_counter) datetime64[ns] 2017-04-11T00:30:00 ...
 Dimensions without coordinates: y, x)

In [9]:

u_current, v_current = viz_tools.rotate_vel(u_unstaggered, v_unstaggered)

In [18]:

fig, (ax0, ax1) = plt.subplots(1, 2, figsize=(18, 9))
u_current[0].plot(ax=ax0, cmap=cmocean.cm.speed)
ax0.set_title('u current at 0.5 m\n' + ax0.get_title())
v_current[0].plot(ax=ax1, cmap=cmocean.cm.speed)
ax1.set_title('v current at 0.5 m\n' + ax1.get_title())

Out[18]:

<matplotlib.text.Text at 0x7f91b756ecc0>

We need to create a new dataset with:

the time_counter coordinate renamed to time because that is the coordinate/dimension name that ww3_prnc expects
a latitude coordinate derived from the nav_lat variable because that is one of the variable names that ww3_prnc expects
a longitude coordinate derived from the nav_lon variable because that is one of the variable names that ww3_prnc expects
a u_wind variable with its time_counter coordinate renamed to time
a v_wind variable with its time_counter coordinate renamed to time

In [11]:

ds = xr.Dataset(
    data_vars={
        'u_current': u_current.rename({'time_counter': 'time'}),
        'v_current': v_current.rename({'time_counter': 'time'}),
    },
    coords={
        'time': u_nemo.time_counter.rename('time').rename({'time_counter': 'time'}),
        'latitude': mesh_mask.nav_lat[1:, 1:],
        'longitude': mesh_mask.nav_lon[1:, 1:],
    }
)
del ds.coords['time_centered']
ds

Out[11]:

<xarray.Dataset>
Dimensions:    (time: 24, x: 397, y: 897)
Coordinates:
  * time       (time) datetime64[ns] 2017-04-11T00:30:00 2017-04-11T01:30:00 ...
    latitude   (y, x) float32 46.8648 46.8668 46.8687 46.8706 46.8725 ...
    longitude  (y, x) float32 -123.427 -123.422 -123.416 -123.411 -123.406 ...
Dimensions without coordinates: x, y
Data variables:
    u_current  (time, y, x) float64 nan nan nan nan nan nan nan nan nan nan ...
    v_current  (time, y, x) float64 nan nan nan nan nan nan nan nan nan nan ...

The time_centered coordinate is deleted because it is an artifact from NEMO that has no relevance for wwatch3.

In [12]:

ds.to_netcdf('SoG_current_20170411.nc')

In [13]:

!/usr/bin/ncdump -cst SoG_current_20170411.nc

netcdf SoG_current_20170411 {
dimensions:
	time = 24 ;
	y = 897 ;
	x = 397 ;
variables:
	double time(time) ;
		time:_FillValue = NaN ; // NaN
		time:axis = "T" ;
		time:standard_name = "time" ;
		time:long_name = "Time axis" ;
		time:time_origin = "1900-01-01 00:00:00" ;
		time:bounds = "time_counter_bounds" ;
		time:units = "seconds since 1900-01-01" ;
		time:calendar = "gregorian" ;
		time:_Storage = "chunked" ;
		time:_ChunkSizes = 1 ;
		time:_DeflateLevel = 4 ;
		time:_Shuffle = "true" ;
		time:_Endianness = "little" ;
	double u_current(time, y, x) ;
		u_current:_FillValue = NaN ;
		u_current:coordinates = "longitude latitude" ;
		u_current:_Storage = "contiguous" ;
		u_current:_Endianness = "little" ;
	double v_current(time, y, x) ;
		v_current:_FillValue = NaN ;
		v_current:coordinates = "longitude latitude" ;
		v_current:_Storage = "contiguous" ;
		v_current:_Endianness = "little" ;
	float latitude(y, x) ;
		latitude:_FillValue = NaNf ;
		latitude:_Storage = "chunked" ;
		latitude:_ChunkSizes = 897, 397 ;
		latitude:_DeflateLevel = 4 ;
		latitude:_Shuffle = "true" ;
		latitude:_Endianness = "little" ;
	float longitude(y, x) ;
		longitude:_FillValue = NaNf ;
		longitude:_Storage = "chunked" ;
		longitude:_ChunkSizes = 897, 397 ;
		longitude:_DeflateLevel = 4 ;
		longitude:_Shuffle = "true" ;
		longitude:_Endianness = "little" ;

// global attributes:
		:_NCProperties = "version=1|netcdflibversion=4.4.1|hdf5libversion=1.8.17" ;
		:_SuperblockVersion = 0 ;
		:_IsNetcdf4 = 1 ;
		:_Format = "netCDF-4" ;
data:

 time = "2017-04-11 00:30", "2017-04-11 01:30", "2017-04-11 02:30", 
    "2017-04-11 03:30", "2017-04-11 04:30", "2017-04-11 05:30", 
    "2017-04-11 06:30", "2017-04-11 07:30", "2017-04-11 08:30", 
    "2017-04-11 09:30", "2017-04-11 10:30", "2017-04-11 11:30", 
    "2017-04-11 12:30", "2017-04-11 13:30", "2017-04-11 14:30", 
    "2017-04-11 15:30", "2017-04-11 16:30", "2017-04-11 17:30", 
    "2017-04-11 18:30", "2017-04-11 19:30", "2017-04-11 20:30", 
    "2017-04-11 21:30", "2017-04-11 22:30", "2017-04-11 23:30" ;
}

With a ww3_prnc.inp file like:

$ WAVEWATCH III NETCDF Field preprocessor input ww3_prnc_wind.inp
$
$ Forcing type, grid type, time in file, header 
   'WND' 'LL' T T
$
$ Dimension variable names
  x y
$
$ Wind component variable names
  u_current v_current
$
$ Forcing source file path/name
$ File is produced by make_ww3_current_file worker
  'wind/SoG_current_20170121.nc'

ww3_prnc will process files like the above into current.ww3 files, and ww3_shel will accept them.

Here's a comparison of the current speeds from /results/SalishSea/nowcast-blue/18apr17/SalishSea_1d_20170418_20170418_grid_[UV].nc and those in the output from a wwatch3 run that used them as forcing:

In [53]:

nemo = xr.open_dataset('../../analysis-doug/notebooks/SoG-waves/SoG_current_20170419.nc')
wwf = xr.open_dataset('../../analysis-doug/notebooks/SoG-waves/SoG_ww3_fields_20170419_20170421.nc')

In [80]:

time = 12
fig, axs = plt.subplots(1, 2, figsize=(18, 9))
mesh = axs[0].pcolormesh(
    nemo.longitude, nemo.latitude, ma.masked_invalid(np.sqrt(nemo.u_current[time]**2 + nemo.v_current[time]**2)),
    cmap=cmocean.cm.speed,
)
cbar = fig.colorbar(mesh, ax=axs[0])
cbar.set_label('0.5m Depth Current Speed [m/s]')
axs[0].set_title(f'NEMO Currents Input to ww3_prnc\nat {pd.to_datetime(nemo.time[time].values)} UTC')

axs[1].pcolormesh(
    # time*2+1 because wwf starts on the hour and has 30min interval vs. nemo 60min intervals on the half-hour
    wwf.longitude, wwf.latitude, ma.masked_invalid(np.sqrt(wwf.ucur[time*2+1]**2 + wwf.vcur[time*2+1]**2)),
    cmap=cmocean.cm.speed,
)
cbar = fig.colorbar(mesh, ax=axs[1])
cbar.set_label('Current Speed [m/s]')
axs[1].set_title(f'Currents Output from ww3_shel\nat {pd.to_datetime(wwf.time[time*2+1].values)} UTC')

axs[0].set_xlim(axs[1].get_xlim())
axs[0].set_ylim(axs[1].get_ylim())

Out[80]:

(48.0, 50.970001220703125)

In [ ]:

Developing make_ww3_current_file Worker¶

Developing `make_ww3_current_file` Worker¶