Metocean MIKE21SW DutchCoast¶

Validate MIKE 21 Spectral wave model for the Dutch coast.

Including saving comparison collection to disk and loading again.

In [1]:

import os
from matplotlib_inline.backend_inline import set_matplotlib_formats
set_matplotlib_formats('png')
import modelskill as ms

In [2]:

fn = '../tests/testdata/SW/HKZN_local_2017_DutchCoast.dfsu'
mr = ms.model_result(fn, name='HKZN_local', item=0)
mr.data

Out[2]:

Dfsu2D
number of elements: 958
number of nodes: 570
projection: LONG/LAT
number of items: 15
time: 23 steps with dt=10800.0s
      2017-10-27 00:00:00 -- 2017-10-29 18:00:00

Define observations¶

In [3]:

o1 = ms.PointObservation('../tests/testdata/SW/HKNA_Hm0.dfs0', item=0, x=4.2420, y=52.6887, name="HKNA")
o2 = ms.PointObservation("../tests/testdata/SW/eur_Hm0.dfs0", item=0, x=3.2760, y=51.9990, name="EPL")

In [4]:

o1.plot.hist();

In [5]:

o1.plot(); 

In [6]:

o3 = ms.TrackObservation("../tests/testdata/SW/Alti_c2_Dutch.dfs0", item=3, name="c2")
o3.data.head()

Out[6]:

<xarray.Dataset>
Dimensions:  (time: 5)
Coordinates:
  * time     (time) datetime64[ns] 2017-10-27T12:52:52.337000 ... 2017-10-27T...
    x        (time) float64 2.423 2.414 2.405 2.396 2.387
    y        (time) float64 51.25 51.31 51.37 51.42 51.48
    z        float64 nan
Data variables:
    c2       (time) float64 1.638 1.383 1.791 1.7 1.8
Attributes:
    gtype:    track

In [7]:

ms.plotting.spatial_overview([o1, o2, o3], mr);

In [8]:

ms.plotting.temporal_coverage([o1, o2, o3], mr);

In [9]:

ms.plotting.temporal_coverage([o1, o2, o3], mr, limit_to_model_period=False);

Compare observations and model result¶

In [10]:

cc = ms.match([o1, o2, o3], mr)

Save the comparison to a file¶

Extracting model results¶ can be a time-consuming process. Therefore, it is recommended to save the results to a file. This can be done using the save method of the comparison object.

Each comparer can be saved as a NetCDF file, and a ComparerCollection can be saved as a zip file, preferably with the extension .msk.

In [11]:

cc.save("SW_DutchCoast.msk")

In [12]:

cc2 = ms.load("SW_DutchCoast.msk")
cc2

Out[12]:

<ComparerCollection>
Comparer: c2
Comparer: EPL
Comparer: HKNA

In [13]:

cc.skill().style()

Out[13]:

	n	bias	rmse	urmse	mae	cc	si	r2
observation
EPL	67	-0.067	0.224	0.213	0.189	0.970	0.083	0.933
HKNA	386	-0.194	0.352	0.293	0.252	0.971	0.094	0.905
c2	113	-0.001	0.352	0.352	0.295	0.974	0.128	0.900

In [14]:

cc["c2"].skill(metrics="mean_absolute_error")

Out[14]:

	n	mean_absolute_error
observation
c2	113	0.294585

In [15]:

cc["HKNA"].plot.timeseries(figsize=(10,5));

In [16]:

cc["HKNA"].plot.timeseries(width=1000, backend="plotly")

In [17]:

cc["EPL"].plot.scatter(figsize=(8,8), show_hist=True); 

In [18]:

cc["EPL"].plot.scatter(show_hist=True, backend='plotly', width=600, height=600); #,xlim=[-1,11])

In [19]:

cc["c2"].plot.hist(bins=20);

In [20]:

cc["HKNA"].plot.scatter(bins=0.25, cmap="viridis");

Clean up¶

In [21]:

os.remove("SW_DutchCoast.msk")