Metocean altimetry track comparison for global wave model¶
This notebook will demonstrate the use of ModelSkill on a larger dataset containing more than 9 million satellite track observation points.
Note: requires running the download.ipynb first!
In [1]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import modelskill as ms
from matplotlib_inline.backend_inline import set_matplotlib_formats
set_matplotlib_formats('png')
Big data¶
Run the download.ipynb first
In [2]:
fn = '../data/SW_gwm_3a_extracted_2018.dfs0'
mr = ms.ModelResult(fn, name='GWM', item='Sign. Wave Height', gtype="track")
mr
Out[2]:
<TrackModelResult> 'GWM' (n_points: 9141126)
In [3]:
o1 = ms.TrackObservation('../data/altimetry_3a_2018_filter1.dfs0', item=2, name='3a')
In [4]:
cmp = ms.compare(o1, mr)[0] # compare returns a list of comparison objects, here we only have one
In [5]:
cmp.sel(end='2018-1-15').skill()
Out[5]:
| n | bias | rmse | urmse | mae | cc | si | r2 | |
|---|---|---|---|---|---|---|---|---|
| observation | ||||||||
| 3a | 372399 | -0.475266 | 0.633188 | 0.418388 | 0.510794 | 0.940363 | 0.124046 | 0.719939 |
In [6]:
cmp.skill()
Out[6]:
| n | bias | rmse | urmse | mae | cc | si | r2 | |
|---|---|---|---|---|---|---|---|---|
| observation | ||||||||
| 3a | 9111762 | -0.490248 | 0.647898 | 0.42359 | 0.521119 | 0.942401 | 0.123508 | 0.719179 |
Spatial skill¶
Spatial skill with 1 deg bins and default bin edges.
In [7]:
ss = cmp.spatial_skill(metrics=['bias'], bins=(np.arange(-180,180,1), np.arange(-90,90,1)), n_min=20)
Add attrs and plot
In [8]:
ss.ds['bias'].attrs = dict(long_name="Bias of significant wave height, Hm0",units="m")
ss.ds['n'].attrs = dict(long_name="N of significant wave height",units="-")
fig, axes = plt.subplots(ncols=1, nrows=2, figsize = (8, 10))
ss.plot('n', ax=axes[0])
ss.plot('bias', ax=axes[1]);
Multiple bins - spatial skill for wave height¶
Use all_df to obtain and df argument to pass customized data back to comparer.
In [9]:
all_df = cmp.to_dataframe()
mean_val = all_df[['mod_val','obs_val']].mean(axis=1)
all_df['val_cat'] = pd.cut(mean_val,[0,2,5,np.inf],labels=["Hm0[m]=[0, 2)","Hm0[m]=[2, 5)","Hm0[m]=[5, inf)"])
all_df.head()
Out[9]:
| x | y | obs_val | mod_val | model | observation | val_cat | |
|---|---|---|---|---|---|---|---|
| time | |||||||
| 2018-01-01 00:00:00 | -33.706020 | 23.181158 | 2.611 | 2.292599 | GWM | 3a | Hm0[m]=[2, 5) |
| 2018-01-01 00:00:01 | -33.720741 | 23.240074 | 2.608 | 2.292612 | GWM | 3a | Hm0[m]=[2, 5) |
| 2018-01-01 00:00:02 | -33.735474 | 23.298990 | 2.518 | 2.292624 | GWM | 3a | Hm0[m]=[2, 5) |
| 2018-01-01 00:00:03 | -33.750214 | 23.357904 | 2.729 | 2.292637 | GWM | 3a | Hm0[m]=[2, 5) |
| 2018-01-01 00:00:04 | -33.764965 | 23.416819 | 2.593 | 2.292650 | GWM | 3a | Hm0[m]=[2, 5) |
In [10]:
cmp.data["val_cat"] = all_df["val_cat"]
In [11]:
ss = cmp.spatial_skill(by=["val_cat"], metrics=["bias"], bins=(np.arange(-180,180,5), np.arange(-90,90,5)), n_min=20)
In [12]:
ss.ds['bias'].attrs = dict(long_name="Bias of significant wave height, Hm0", units="m")
ss.ds['n'].attrs = dict(long_name="N of significant wave height", units="-")
ss.ds['val_cat'].attrs = dict(long_name="Range of sign. wave height, Hm0", units="m")
In [13]:
ss.plot('n', figsize=(12,4));
In [14]:
ss.plot('bias', figsize=(12,4));
