In [3]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
pd.__version__ # need 0.14.0 for multiindex slicing
Out[3]:
'0.14.1'
In [4]:
o_raw = pd.read_table("overall_statistics_ksmall.txt")
v_raw = pd.read_table("variable_statistics_ksmall.txt")
# ncol: 48602, lev: 30 (88x), ilev: 31 (9x), 2D: 101x
#N_c = 48602 # for all variables, horizontal stacking
#N_d = 3008 # for all variables, horizontal stacking
N_c = 3008 # for all variables, vertical stacking
#N_c = 88*30 # for 3D variables, vertical stacking
N_d = 48602 # for all variables, vertical stacking
#N_c = 88 # for 3D variables, vertical stacking (ncol & lev distributed)
#N_d = 30*48602 # for 3D variables, vertical stacking (ncol & lev distributed)
In [5]:
o = o_raw.set_index(["K","M","STATISTIC"]).loc[:,"VALUE"].unstack()
v = v_raw.set_index(["K","M","STATISTIC","VARIABLE"]).loc[:,"VALUE"].unstack().unstack()
In [6]:
vi_raw = pd.read_table("variable_information.txt")
vi = vi_raw.set_index(["VARIABLE","INFO"]).unstack().loc[:,"VALUE"]
vi["levels"] = vi["levels"].astype("int")
vi.columns.name = ""
In [7]:
%pylab inline
Populating the interactive namespace from numpy and matplotlib
In [8]:
original_size = N_c * N_d
compressed_size = lambda K, M: N_d + N_c * K + N_d * M + N_c * K * M
M_max = lambda K: N_c * N_d / (N_d + K * N_c) - 1
plt.plot(arange(1,201), M_max(arange(1, 201)))
Out[8]:
[<matplotlib.lines.Line2D at 0x7f53dec34e80>]
In [9]:
o["compression_ratio_fixed"] = compressed_size(array(o.index.get_level_values("K")),array(o.index.get_level_values("M"))) / original_size
o.loc[:,"compression_ratio_fixed"].unstack("K")
Out[9]:
K
1
2
3
4
5
6
7
8
9
10
M
10
0.003883
0.004110
0.004336
0.004562
0.004789
0.005015
0.005241
0.005468
0.005694
0.005920
20
0.007413
0.007846
0.008278
0.008710
0.009142
0.009574
0.010006
0.010438
0.010870
0.011302
30
0.010944
0.011582
0.012219
0.012857
0.013495
0.014133
0.014771
0.015409
0.016046
0.016684
40
0.014474
0.015317
0.016161
0.017005
0.017848
0.018692
0.019535
0.020379
0.021223
0.022066
50
0.018004
0.019053
0.020103
0.021152
0.022201
0.023251
0.024300
0.025350
0.026399
0.027448
60
0.021534
0.022789
0.024045
0.025300
0.026555
0.027810
0.029065
0.030320
0.031575
0.032830
70
0.025065
0.026525
0.027986
0.029447
0.030908
0.032369
0.033830
0.035290
0.036751
0.038212
80
0.028595
0.030261
0.031928
0.033595
0.035261
0.036928
0.038594
0.040261
0.041928
0.043594
90
0.032125
0.033997
0.035870
0.037742
0.039614
0.041487
0.043359
0.045231
0.047104
0.048976
100
0.035655
0.037733
0.039811
0.041890
0.043968
0.046046
0.048124
0.050202
0.052280
0.054358
110
0.039185
0.041469
0.043753
0.046037
0.048321
0.050605
0.052889
0.055172
0.057456
0.059740
120
0.042716
0.045205
0.047695
0.050185
0.052674
0.055164
0.057653
0.060143
0.062633
0.065122
130
0.046246
0.048941
0.051637
0.054332
0.057027
0.059723
0.062418
0.065113
0.067809
0.070504
140
0.049776
0.052677
0.055578
0.058479
0.061381
0.064282
0.067183
0.070084
0.072985
0.075886
150
0.053306
0.056413
0.059520
0.062627
0.065734
0.068841
0.071948
0.075054
0.078161
0.081268
160
0.056837
0.060149
0.063462
0.066774
0.070087
0.073400
0.076712
0.080025
0.083338
0.086650
170
0.060367
0.063885
0.067404
0.070922
0.074440
0.077959
0.081477
0.084995
0.088514
0.092032
180
0.063897
0.067621
0.071345
0.075069
0.078794
0.082518
0.086242
0.089966
0.093690
0.097414
190
0.067427
0.071357
0.075287
0.079217
0.083147
0.087077
0.091006
0.094936
0.098866
0.102796
200
0.070957
0.075093
0.079229
0.083364
0.087500
0.091636
0.095771
0.099907
0.104042
0.108178
In [10]:
o.loc[:,"L_final"].unstack("K").plot()
Out[10]:
<matplotlib.axes.AxesSubplot at 0x7f53dec045c0>
In [11]:
o.loc[:,"iterations"].unstack("K")
Out[11]:
K
1
2
3
4
5
6
7
8
9
10
M
10
1
25
23
34
82
65
62
69
45
52
20
1
25
23
34
82
65
62
69
45
52
30
1
25
23
34
82
65
62
69
45
52
40
1
25
23
34
82
65
62
69
45
52
50
1
25
23
34
82
65
62
69
45
52
60
1
25
23
34
82
65
62
69
45
52
70
1
25
23
34
82
65
62
69
45
52
80
1
25
23
34
82
65
62
69
45
52
90
1
25
23
34
82
65
62
69
45
52
100
1
25
23
34
82
65
62
69
45
52
110
1
25
23
34
82
65
62
69
45
52
120
1
25
23
34
82
65
62
69
45
52
130
1
25
23
34
82
65
62
69
45
52
140
1
25
23
34
82
65
62
69
45
52
150
1
25
23
34
82
65
62
69
45
52
160
1
25
23
34
82
65
62
69
45
52
170
1
25
23
34
82
65
62
69
45
52
180
1
25
23
34
82
65
62
69
45
52
190
1
25
23
34
82
65
62
69
45
52
200
1
25
23
34
82
65
62
69
45
52
In [12]:
o.loc[:,"compression_ratio_fixed"].unstack("K")
Out[12]:
K
1
2
3
4
5
6
7
8
9
10
M
10
0.003883
0.004110
0.004336
0.004562
0.004789
0.005015
0.005241
0.005468
0.005694
0.005920
20
0.007413
0.007846
0.008278
0.008710
0.009142
0.009574
0.010006
0.010438
0.010870
0.011302
30
0.010944
0.011582
0.012219
0.012857
0.013495
0.014133
0.014771
0.015409
0.016046
0.016684
40
0.014474
0.015317
0.016161
0.017005
0.017848
0.018692
0.019535
0.020379
0.021223
0.022066
50
0.018004
0.019053
0.020103
0.021152
0.022201
0.023251
0.024300
0.025350
0.026399
0.027448
60
0.021534
0.022789
0.024045
0.025300
0.026555
0.027810
0.029065
0.030320
0.031575
0.032830
70
0.025065
0.026525
0.027986
0.029447
0.030908
0.032369
0.033830
0.035290
0.036751
0.038212
80
0.028595
0.030261
0.031928
0.033595
0.035261
0.036928
0.038594
0.040261
0.041928
0.043594
90
0.032125
0.033997
0.035870
0.037742
0.039614
0.041487
0.043359
0.045231
0.047104
0.048976
100
0.035655
0.037733
0.039811
0.041890
0.043968
0.046046
0.048124
0.050202
0.052280
0.054358
110
0.039185
0.041469
0.043753
0.046037
0.048321
0.050605
0.052889
0.055172
0.057456
0.059740
120
0.042716
0.045205
0.047695
0.050185
0.052674
0.055164
0.057653
0.060143
0.062633
0.065122
130
0.046246
0.048941
0.051637
0.054332
0.057027
0.059723
0.062418
0.065113
0.067809
0.070504
140
0.049776
0.052677
0.055578
0.058479
0.061381
0.064282
0.067183
0.070084
0.072985
0.075886
150
0.053306
0.056413
0.059520
0.062627
0.065734
0.068841
0.071948
0.075054
0.078161
0.081268
160
0.056837
0.060149
0.063462
0.066774
0.070087
0.073400
0.076712
0.080025
0.083338
0.086650
170
0.060367
0.063885
0.067404
0.070922
0.074440
0.077959
0.081477
0.084995
0.088514
0.092032
180
0.063897
0.067621
0.071345
0.075069
0.078794
0.082518
0.086242
0.089966
0.093690
0.097414
190
0.067427
0.071357
0.075287
0.079217
0.083147
0.087077
0.091006
0.094936
0.098866
0.102796
200
0.070957
0.075093
0.079229
0.083364
0.087500
0.091636
0.095771
0.099907
0.104042
0.108178
In [13]:
o.loc[:,"lanczos_max"].unstack("K")
Out[13]:
K
1
2
3
4
5
6
7
8
9
10
M
10
0
0
0
0
0
0
0
0
0
0
20
0
0
0
0
0
0
0
0
0
0
30
0
0
0
0
0
0
0
0
0
0
40
0
0
0
0
0
0
0
0
0
0
50
0
0
0
0
0
0
0
0
0
0
60
0
0
0
0
0
0
0
0
0
0
70
0
0
0
0
0
0
0
0
0
0
80
0
0
0
0
0
0
0
0
0
0
90
0
0
0
0
0
0
0
0
0
0
100
0
0
0
0
0
0
0
0
0
0
110
0
0
0
0
0
0
0
0
0
0
120
0
0
0
0
0
0
0
0
0
0
130
0
0
0
0
0
0
0
0
0
0
140
0
0
0
0
0
0
0
0
0
0
150
0
0
0
0
0
0
0
0
0
0
160
0
0
0
0
0
0
0
0
0
0
170
0
0
0
0
0
0
0
0
0
0
180
0
0
0
0
0
0
0
0
0
0
190
0
0
0
0
0
0
0
0
0
0
200
0
0
0
0
0
0
0
0
0
0
In [14]:
o.loc[:,"lanczos_max_converged"].unstack("K")
Out[14]:
K
1
2
3
4
5
6
7
8
9
10
M
10
0
0
0
0
0
0
0
0
0
0
20
0
0
0
0
0
0
0
0
0
0
30
0
0
0
0
0
0
0
0
0
0
40
0
0
0
0
0
0
0
0
0
0
50
0
0
0
0
0
0
0
0
0
0
60
0
0
0
0
0
0
0
0
0
0
70
0
0
0
0
0
0
0
0
0
0
80
0
0
0
0
0
0
0
0
0
0
90
0
0
0
0
0
0
0
0
0
0
100
0
0
0
0
0
0
0
0
0
0
110
0
0
0
0
0
0
0
0
0
0
120
0
0
0
0
0
0
0
0
0
0
130
0
0
0
0
0
0
0
0
0
0
140
0
0
0
0
0
0
0
0
0
0
150
0
0
0
0
0
0
0
0
0
0
160
0
0
0
0
0
0
0
0
0
0
170
0
0
0
0
0
0
0
0
0
0
180
0
0
0
0
0
0
0
0
0
0
190
0
0
0
0
0
0
0
0
0
0
200
0
0
0
0
0
0
0
0
0
0
In [15]:
o.loc[:,"lanczos_mean"].unstack("K")
Out[15]:
K
1
2
3
4
5
6
7
8
9
10
M
10
0
0
0
0
0
0
0
0
0
0
20
0
0
0
0
0
0
0
0
0
0
30
0
0
0
0
0
0
0
0
0
0
40
0
0
0
0
0
0
0
0
0
0
50
0
0
0
0
0
0
0
0
0
0
60
0
0
0
0
0
0
0
0
0
0
70
0
0
0
0
0
0
0
0
0
0
80
0
0
0
0
0
0
0
0
0
0
90
0
0
0
0
0
0
0
0
0
0
100
0
0
0
0
0
0
0
0
0
0
110
0
0
0
0
0
0
0
0
0
0
120
0
0
0
0
0
0
0
0
0
0
130
0
0
0
0
0
0
0
0
0
0
140
0
0
0
0
0
0
0
0
0
0
150
0
0
0
0
0
0
0
0
0
0
160
0
0
0
0
0
0
0
0
0
0
170
0
0
0
0
0
0
0
0
0
0
180
0
0
0
0
0
0
0
0
0
0
190
0
0
0
0
0
0
0
0
0
0
200
0
0
0
0
0
0
0
0
0
0
In [16]:
o.loc[:,"lanczos_mean_converged"].unstack("K")
Out[16]:
K
1
2
3
4
5
6
7
8
9
10
M
10
0
0
0
0
0
0
0
0
0
0
20
0
0
0
0
0
0
0
0
0
0
30
0
0
0
0
0
0
0
0
0
0
40
0
0
0
0
0
0
0
0
0
0
50
0
0
0
0
0
0
0
0
0
0
60
0
0
0
0
0
0
0
0
0
0
70
0
0
0
0
0
0
0
0
0
0
80
0
0
0
0
0
0
0
0
0
0
90
0
0
0
0
0
0
0
0
0
0
100
0
0
0
0
0
0
0
0
0
0
110
0
0
0
0
0
0
0
0
0
0
120
0
0
0
0
0
0
0
0
0
0
130
0
0
0
0
0
0
0
0
0
0
140
0
0
0
0
0
0
0
0
0
0
150
0
0
0
0
0
0
0
0
0
0
160
0
0
0
0
0
0
0
0
0
0
170
0
0
0
0
0
0
0
0
0
0
180
0
0
0
0
0
0
0
0
0
0
190
0
0
0
0
0
0
0
0
0
0
200
0
0
0
0
0
0
0
0
0
0
In [17]:
o.loc[:,"lanczos_min"].unstack("K")
Out[17]:
K
1
2
3
4
5
6
7
8
9
10
M
10
0
0
0
0
0
0
0
0
0
0
20
0
0
0
0
0
0
0
0
0
0
30
0
0
0
0
0
0
0
0
0
0
40
0
0
0
0
0
0
0
0
0
0
50
0
0
0
0
0
0
0
0
0
0
60
0
0
0
0
0
0
0
0
0
0
70
0
0
0
0
0
0
0
0
0
0
80
0
0
0
0
0
0
0
0
0
0
90
0
0
0
0
0
0
0
0
0
0
100
0
0
0
0
0
0
0
0
0
0
110
0
0
0
0
0
0
0
0
0
0
120
0
0
0
0
0
0
0
0
0
0
130
0
0
0
0
0
0
0
0
0
0
140
0
0
0
0
0
0
0
0
0
0
150
0
0
0
0
0
0
0
0
0
0
160
0
0
0
0
0
0
0
0
0
0
170
0
0
0
0
0
0
0
0
0
0
180
0
0
0
0
0
0
0
0
0
0
190
0
0
0
0
0
0
0
0
0
0
200
0
0
0
0
0
0
0
0
0
0
In [18]:
o.loc[:,"lanczos_min_converged"].unstack("K")
Out[18]:
K
1
2
3
4
5
6
7
8
9
10
M
10
0
0
0
0
0
0
0
0
0
0
20
0
0
0
0
0
0
0
0
0
0
30
0
0
0
0
0
0
0
0
0
0
40
0
0
0
0
0
0
0
0
0
0
50
0
0
0
0
0
0
0
0
0
0
60
0
0
0
0
0
0
0
0
0
0
70
0
0
0
0
0
0
0
0
0
0
80
0
0
0
0
0
0
0
0
0
0
90
0
0
0
0
0
0
0
0
0
0
100
0
0
0
0
0
0
0
0
0
0
110
0
0
0
0
0
0
0
0
0
0
120
0
0
0
0
0
0
0
0
0
0
130
0
0
0
0
0
0
0
0
0
0
140
0
0
0
0
0
0
0
0
0
0
150
0
0
0
0
0
0
0
0
0
0
160
0
0
0
0
0
0
0
0
0
0
170
0
0
0
0
0
0
0
0
0
0
180
0
0
0
0
0
0
0
0
0
0
190
0
0
0
0
0
0
0
0
0
0
200
0
0
0
0
0
0
0
0
0
0
In [19]:
o.loc[:,"time_input"].unstack("K")
Out[19]:
K
1
2
3
4
5
6
7
8
9
10
M
10
42.97990
9.94910
15.50650
16.26390
16.33430
16.42610
13.73750
11.08370
8.88494
15.87870
20
43.31800
8.69896
14.32550
17.71960
17.01740
9.21970
15.57390
11.12010
8.92646
11.58560
30
41.07940
8.88053
15.71720
8.21382
16.41150
8.49662
15.67260
11.05070
8.59100
11.87890
40
40.13400
9.01983
15.58890
8.22454
15.52080
9.21106
10.85090
11.05080
9.71850
16.04650
50
40.56170
10.52100
15.71000
10.51600
16.31130
8.52261
10.74600
11.07480
8.90190
7.39747
60
39.79010
12.31600
9.63148
10.28730
17.42060
15.77360
15.70030
11.04200
8.61949
7.59818
70
40.01110
8.88096
9.61642
9.03971
16.44650
10.94510
12.67260
11.06410
16.98880
9.00765
80
40.06760
8.98192
9.24652
8.77061
17.27780
7.56561
12.17040
10.85300
11.80230
9.24272
90
39.88680
15.57000
9.40810
17.18210
16.87550
7.56736
8.71712
10.92020
11.97840
16.61890
100
41.97040
15.58590
9.39768
16.72030
16.27970
9.90925
8.66113
10.87130
15.79540
15.62000
110
39.90300
15.16340
9.38203
16.58550
16.37800
8.67999
13.32640
10.89030
16.72940
16.48130
120
10.08230
15.04360
9.33654
16.33400
15.96070
8.71561
12.22330
11.05160
12.07700
16.58180
130
9.94383
14.61300
11.95540
41.06550
9.10636
13.86890
15.67910
11.04560
12.13590
16.31410
140
10.09190
15.80480
8.57795
15.93490
8.91187
7.78253
8.85068
10.99800
16.23130
16.13240
150
9.89135
15.56120
8.65707
17.03530
15.79020
8.41629
8.85053
11.05280
7.57112
15.18230
160
10.06290
15.58560
8.46989
16.20200
15.29000
16.50920
16.92730
11.07280
7.46672
15.81580
170
10.48760
15.60910
9.98515
16.91990
16.80620
15.87950
8.64900
15.52910
8.61146
8.54116
180
10.06620
15.71460
8.39660
17.60180
16.71690
13.14440
8.85717
11.30130
8.61399
8.39741
190
9.91562
15.56150
17.18500
16.33050
16.05120
10.06690
16.76520
8.90050
13.72930
11.48390
200
9.86038
15.20310
17.43150
17.60460
15.69840
9.85547
16.25000
9.07867
15.14500
12.22830
In [20]:
o.loc[:,"time_solve"].unstack("K") / 60
Out[20]:
K
1
2
3
4
5
6
7
8
9
10
M
10
0.083017
0.533903
0.582618
0.815680
1.975067
1.697800
1.857783
2.319100
1.516485
1.953733
20
0.143521
0.605242
0.702655
0.919577
2.105850
1.827583
2.030150
2.499233
1.680750
2.125500
30
0.253987
0.778828
0.883903
1.104117
2.279583
2.014033
2.247583
2.743467
1.900367
2.418850
40
0.387565
0.915927
1.113708
1.330282
2.508250
2.217483
2.524767
3.076883
2.193517
2.724633
50
0.516195
1.069355
1.376312
1.585813
2.767900
2.537383
2.859900
3.504650
2.534433
3.209067
60
0.700507
1.396838
1.639628
1.846417
3.138933
2.860467
3.286517
3.919850
2.949400
3.713817
70
0.879533
1.614903
2.045217
2.178000
3.520133
3.302800
3.757383
4.456500
3.441767
4.271950
80
1.123257
1.915567
2.400533
2.649083
3.924133
3.714150
4.331600
5.117900
3.929083
4.862833
90
1.397782
2.238433
2.798983
3.062767
4.337550
4.154900
4.892067
5.740917
4.517000
5.564883
100
1.637312
2.563967
3.378117
3.478000
4.898133
4.739633
5.355083
6.315700
5.153283
6.226283
110
1.875217
2.919017
3.939083
4.009633
5.298350
5.229717
6.106200
7.084367
5.892183
7.128550
120
2.224200
3.510300
4.375900
4.818783
5.796133
5.794333
6.721917
8.130100
6.628650
7.974383
130
2.535400
3.896233
4.804600
4.873667
6.591483
6.278900
7.563233
8.892217
7.317000
8.743267
140
2.899283
4.275233
5.720117
5.585800
7.370467
7.078067
8.459467
9.699933
8.134467
9.791983
150
3.160817
4.845817
6.070700
6.270733
8.007517
7.759133
9.451250
10.707683
9.210817
11.063317
160
3.708233
5.492567
6.675500
6.777383
8.714017
8.770400
10.263917
11.792933
10.221867
12.066733
170
4.005117
5.686167
7.610800
7.696983
9.525933
9.433567
11.317400
12.968333
10.841850
13.189450
180
4.116850
6.392467
8.361500
8.293267
10.214050
10.452950
12.222883
13.952133
11.890767
14.461517
190
4.993733
6.999350
9.310950
8.884667
11.032817
11.159967
13.353917
15.261900
13.204033
15.455800
200
5.339817
7.765733
9.958267
9.901300
12.171550
12.298100
14.445067
16.904833
14.169083
16.771500
In [21]:
statistics_of_interest = ["rms_error","max_error","precisionbits","srr","correlation"]
In [22]:
v.mean(axis=0).unstack()[statistics_of_interest].join(vi)
Out[22]:
rms_error
max_error
precisionbits
srr
correlation
levels
name
VARIABLE
ABSORB
0.001467
0.217074
1.667108
3.295136
0.981809
30
Aerosol absorption
AEROD_v
0.004679
0.093269
3.252420
4.229692
0.990865
1
Total Aerosol Optical Depth in visible band
ANRAIN
0.006887
0.479828
0.264864
2.984493
0.985676
30
Average rain number conc
ANSNOW
0.004095
0.338459
0.857183
2.180191
0.967410
30
Average snow number conc
AODABS
0.004366
0.117032
2.807556
4.099596
0.990065
1
Aerosol absorption optical depth 550 nm
AODDUST1
0.006773
0.102084
2.823611
3.837607
0.989125
1
Aerosol optical depth 550 nm model 1 from dust
AODDUST3
0.004525
0.108660
2.990315
4.029360
0.989314
1
Aerosol optical depth 550 nm model 3 from dust
AODMODE1
0.007950
0.090349
3.033658
4.167597
0.992492
1
Aerosol optical depth 550 nm mode 1
AODMODE2
0.009033
0.274097
1.332021
2.626025
0.953269
1
Aerosol optical depth 550 nm mode 2
AODMODE3
0.004547
0.108180
3.001464
4.030117
0.989037
1
Aerosol optical depth 550 nm mode 3
AODVIS
0.004679
0.093269
3.252420
4.229692
0.990865
1
Aerosol optical depth 550 nm
AQRAIN
0.002916
0.218896
1.599774
3.074270
0.984777
30
Average rain mixing ratio
AQSNOW
0.003176
0.268212
1.257473
3.361745
0.990175
30
Average snow mixing ratio
AREI
0.007126
0.191995
1.770409
4.410668
0.996969
30
Average ice effective radius
AREL
0.006193
0.259059
1.234031
4.535346
0.997219
30
Average droplet effective radius
ATMEINT
0.006089
0.079286
3.301977
5.476003
0.997366
1
Vertically integrated total atmospheric energy
AWNC
0.005969
0.221180
1.421845
3.360092
0.988563
30
Average cloud water number conc
AWNI
0.004364
0.202741
1.725367
3.261343
0.987636
30
Average cloud ice number conc
BURDEN1
0.005692
0.086299
3.222393
4.193424
0.992325
1
Aerosol burden mode 1
BURDEN2
0.010478
0.285589
1.461173
2.792766
0.949315
1
Aerosol burden mode 2
BURDEN3
0.004366
0.136551
2.506415
3.759967
0.987204
1
Aerosol burden mode 3
CCN3
0.003192
0.186441
1.900958
4.206911
0.995308
30
CCN concentration at S=0.1%
CDNUMC
0.011280
0.149119
2.202505
3.464438
0.984600
1
Vertically-integrated droplet concentration
CLDFSNOW
0.005886
0.137916
2.151002
4.724054
0.998163
30
CLDFSNOW
CLDHGH
0.010871
0.095210
2.794603
4.194362
0.995272
1
Vertically-integrated high cloud
CLDICE
0.002553
0.120254
2.446547
3.498578
0.992023
30
Grid box averaged cloud ice amount
CLDLIQ
0.005084
0.235631
1.411061
3.458221
0.990317
30
Grid box averaged cloud liquid amount
CLDLOW
0.011214
0.184445
2.070033
5.089144
0.997546
1
Vertically-integrated low cloud
CLDMED
0.011042
0.191403
1.675948
4.229055
0.996759
1
Vertically-integrated mid-level cloud
CLDTOT
0.011067
0.104695
2.700284
4.632549
0.996781
1
Vertically-integrated total cloud
...
...
...
...
...
...
...
...
WGUSTD
0.012423
0.189505
1.776587
3.312548
0.981893
1
wind gusts from turbulence
WSUB
0.004067
0.142639
2.296229
4.606386
0.997205
30
Diagnostic sub-grid vertical velocity
WTKE
0.004067
0.142639
2.296229
4.606386
0.997205
30
Standard deviation of updraft velocity
Z3
0.000677
0.014161
5.778648
9.406384
0.999991
30
Geopotential Height (above sea level)
bc_a1
0.003380
0.208501
1.699095
3.095448
0.978915
30
bc_a1 concentration
dgnd_a01
0.005143
0.146678
2.219933
4.862490
0.997793
30
dry dgnum, interstitial, mode 01
dgnd_a02
0.006322
0.114046
2.412585
5.062347
0.998209
30
dry dgnum, interstitial, mode 02
dgnd_a03
0.005722
0.124622
2.346163
4.133092
0.995825
30
dry dgnum, interstitial, mode 03
dgnw_a01
0.005321
0.138765
2.296636
5.520037
0.999123
30
wet dgnum, interstitial, mode 01
dgnw_a02
0.005406
0.103211
2.647615
5.495297
0.999194
30
wet dgnum, interstitial, mode 02
dgnw_a03
0.006635
0.169771
1.968442
5.248636
0.999030
30
wet dgnum, interstitial, mode 03
dst_a1
0.002306
0.210160
1.629192
3.269843
0.983426
30
dst_a1 concentration
dst_a1SF
0.004886
0.627142
-0.095978
0.859491
0.695058
1
dst_a1 dust surface emission
dst_a3
0.001856
0.207706
1.682591
3.092108
0.978208
30
dst_a3 concentration
dst_a3SF
0.004886
0.627142
-0.095978
0.859491
0.695058
1
dst_a3 dust surface emission
ncl_a1
0.004213
0.122508
2.304085
5.219200
0.998652
30
ncl_a1 concentration
ncl_a2
0.003811
0.117472
2.307051
4.961108
0.998177
30
ncl_a2 concentration
ncl_a3
0.004551
0.146352
1.965695
5.493614
0.999003
30
ncl_a3 concentration
num_a1
0.002818
0.288731
1.264885
2.985674
0.978747
30
num_a1 concentration
num_a2
0.004452
0.344714
0.911881
2.547357
0.962337
30
num_a2 concentration
num_a3
0.002258
0.208242
1.639729
3.395467
0.985971
30
num_a3 concentration
pom_a1
0.003114
0.314294
1.044180
2.888264
0.972639
30
pom_a1 concentration
so4_a1
0.005247
0.125397
2.400111
4.231615
0.993735
30
so4_a1 concentration
so4_a2
0.003561
0.419844
0.709966
2.132497
0.926999
30
so4_a2 concentration
so4_a3
0.004629
0.226972
1.506700
3.697342
0.984864
30
so4_a3 concentration
soa_a1
0.003675
0.143745
2.074490
3.570641
0.989913
30
soa_a1 concentration
soa_a2
0.004887
0.245735
1.441618
2.924345
0.975632
30
soa_a2 concentration
wat_a1
0.004999
0.245158
1.433154
4.345703
0.996745
30
aerosol water, interstitial, mode 01
wat_a2
0.002958
0.325752
1.096198
2.718148
0.971210
30
aerosol water, interstitial, mode 02
wat_a3
0.005317
0.193280
1.618580
4.742294
0.997950
30
aerosol water, interstitial, mode 03
186 rows × 7 columns
In [23]:
v.mean(axis=1,level="STATISTIC")[statistics_of_interest]
Out[23]:
STATISTIC
rms_error
max_error
precisionbits
srr
correlation
K
M
1
10
0.032613
0.558757
0.026621
1.554078
0.873119
20
0.022761
0.477184
0.322678
2.035921
0.919614
30
0.017676
0.421664
0.547743
2.384021
0.937093
40
0.014652
0.380159
0.719347
2.642628
0.950439
50
0.012387
0.333471
0.944553
2.897084
0.960944
60
0.010741
0.303019
1.116428
3.114140
0.966650
70
0.009491
0.270752
1.301510
3.293191
0.971755
80
0.008459
0.248603
1.445139
3.458535
0.975514
90
0.007528
0.226416
1.630676
3.627225
0.978230
100
0.006783
0.207941
1.770880
3.772235
0.980854
110
0.006155
0.194422
1.887100
3.906495
0.982478
120
0.005602
0.179617
2.028047
4.038655
0.984053
130
0.005078
0.166141
2.161475
4.168980
0.985168
140
0.004636
0.155347
2.260499
4.297271
0.986745
150
0.004224
0.143907
2.377828
4.412588
0.988654
160
0.003865
0.134175
2.482508
4.531365
0.989652
170
0.003598
0.127328
2.566201
4.628785
0.990111
180
0.003359
0.120923
2.637349
4.726216
0.990721
190
0.003136
0.114491
2.738418
4.827706
0.991189
200
0.002931
0.108098
2.841470
4.923266
0.991673
2
10
0.029116
0.541185
0.091786
1.711856
0.890421
20
0.019743
0.442116
0.427316
2.228669
0.932706
30
0.015137
0.383848
0.677909
2.599401
0.949513
40
0.012470
0.340357
0.872866
2.877093
0.961129
50
0.010400
0.288785
1.149991
3.154917
0.969615
60
0.009021
0.256475
1.359411
3.358547
0.974699
70
0.007842
0.231775
1.533854
3.556741
0.978073
80
0.006889
0.210270
1.687870
3.740784
0.980980
90
0.006124
0.190196
1.854390
3.906308
0.983257
100
0.005504
0.175505
1.981879
4.059663
0.984741
...
...
...
...
...
...
...
9
110
0.002782
0.094374
2.797765
5.013485
0.996318
120
0.002512
0.082843
2.946283
5.152317
0.997680
130
0.002274
0.074714
3.098169
5.292548
0.998644
140
0.002081
0.068202
3.246845
5.417917
0.998906
150
0.001917
0.062985
3.362109
5.533344
0.999038
160
0.001772
0.058264
3.490930
5.643580
0.999168
170
0.001635
0.052899
3.638873
5.755664
0.999277
180
0.001525
0.049159
3.756998
5.854614
0.999349
190
0.001429
0.046101
3.860802
5.946728
0.999413
200
0.001334
0.042846
3.970926
6.043406
0.999467
10
10
0.018575
0.423196
0.474407
2.327416
0.944117
20
0.011957
0.332279
0.882937
2.945056
0.968691
30
0.008868
0.275008
1.207679
3.374942
0.977232
40
0.007049
0.227183
1.532244
3.704354
0.982189
50
0.005750
0.191070
1.819766
3.999339
0.985279
60
0.004828
0.165970
2.042113
4.240312
0.988533
70
0.004145
0.143698
2.262235
4.459335
0.991887
80
0.003604
0.125297
2.457762
4.651643
0.993959
90
0.003168
0.110530
2.640131
4.828030
0.995349
100
0.002814
0.099847
2.781825
4.990386
0.996665
110
0.002509
0.089355
2.934708
5.149199
0.997753
120
0.002257
0.076193
3.119781
5.296329
0.998078
130
0.002044
0.068117
3.288772
5.434620
0.998301
140
0.001865
0.061987
3.436399
5.562975
0.998488
150
0.001707
0.056384
3.599720
5.689856
0.998629
160
0.001572
0.051557
3.740192
5.806754
0.998754
170
0.001458
0.047120
3.856658
5.915552
0.998937
180
0.001354
0.043330
3.963846
6.020019
0.999090
190
0.001264
0.039919
4.069947
6.119300
0.999217
200
0.001179
0.036685
4.171292
6.217852
0.999405
200 rows × 5 columns
In [24]:
v.mean(axis=0).unstack()[statistics_of_interest].sort("rms_error").head(20).join(vi)
v.mean(axis=0).unstack()[statistics_of_interest + ["min_original", "min_reconstructed", "max_original"]].sort("rms_error").head(20).join(vi)
v.loc(axis=0)[10,200].unstack()[statistics_of_interest].sort("rms_error").head(20).join(vi)
Out[24]:
rms_error
max_error
precisionbits
srr
correlation
levels
name
VARIABLE
Z3
0.000093
0.001825
8.09788
11.48360
1.000000
30
Geopotential Height (above sea level)
SL
0.000187
0.004193
6.89782
10.15920
1.000000
30
Liquid water static energy
SLV
0.000187
0.004150
6.91255
10.15060
1.000000
30
Liq wat virtual static energy
ABSORB
0.000347
0.051824
3.27025
4.95593
0.999481
30
Aerosol absorption
UFLX
0.000376
0.045725
3.45087
3.62299
0.996701
31
Zonal momentum flux
SNOWHICE
0.000386
0.008948
5.80428
8.26936
0.999995
1
Water equivalent snow depth
EXTINCT
0.000392
0.048715
3.35948
5.41888
0.999727
30
Aerosol extinction
dst_a3
0.000459
0.037725
3.72834
4.72287
0.999283
30
dst_a3 concentration
T
0.000492
0.013920
5.16672
9.19041
0.999999
30
Temperature
LHFLX
0.000511
0.007705
6.01995
8.69611
0.999997
1
Surface latent heat flux
num_a3
0.000511
0.039075
3.67762
5.15034
0.999603
30
num_a3 concentration
QFLX
0.000513
0.007708
6.01934
8.69396
0.999997
1
Surface water flux
dst_a1
0.000531
0.040611
3.62199
5.00558
0.999515
30
dst_a1 concentration
FSNT
0.000548
0.005119
6.60992
8.78762
0.999997
1
Net solar flux at top of model
FSNTOA
0.000551
0.005012
6.64054
8.77884
0.999997
1
Net solar flux at top of atmosphere
FSNS
0.000573
0.008808
5.82698
8.63233
0.999997
1
Net solar flux at surface
VFLX
0.000573
0.044507
3.48981
3.28978
0.994758
31
Meridional momentm flux
TSMN
0.000577
0.006990
6.16045
8.16407
0.999994
1
Minimum surface temperature over output period
TS
0.000577
0.006990
6.16045
8.16407
0.999994
1
Surface temperature (radiative)
TSMX
0.000577
0.006990
6.16045
8.16407
0.999994
1
Maximum surface temperature over output period
In [25]:
v.mean(axis=0).unstack()[statistics_of_interest].sort("max_error").head(20).join(vi)
v.loc(axis=0)[10,200].unstack()[statistics_of_interest].sort("max_error").head(20).join(vi)
Out[25]:
rms_error
max_error
precisionbits
srr
correlation
levels
name
VARIABLE
Z3
0.000093
0.001825
8.09788
11.48360
1.000000
30
Geopotential Height (above sea level)
SLV
0.000187
0.004150
6.91255
10.15060
1.000000
30
Liq wat virtual static energy
SL
0.000187
0.004193
6.89782
10.15920
1.000000
30
Liquid water static energy
FSNTOA
0.000551
0.005012
6.64054
8.77884
0.999997
1
Net solar flux at top of atmosphere
FSNT
0.000548
0.005119
6.60992
8.78762
0.999997
1
Net solar flux at top of model
TREFHT
0.000698
0.006678
6.22644
7.93538
0.999992
1
Reference height temperature
TSMX
0.000577
0.006990
6.16045
8.16407
0.999994
1
Maximum surface temperature over output period
TSMN
0.000577
0.006990
6.16045
8.16407
0.999994
1
Minimum surface temperature over output period
TS
0.000577
0.006990
6.16045
8.16407
0.999994
1
Surface temperature (radiative)
FLUTC
0.000668
0.007469
6.06482
7.97354
0.999992
1
Clearsky upwelling longwave flux at top of model
FLNTC
0.000669
0.007475
6.06361
7.97172
0.999992
1
Clearsky net longwave flux at top of model
LHFLX
0.000511
0.007705
6.01995
8.69611
0.999997
1
Surface latent heat flux
QFLX
0.000513
0.007708
6.01934
8.69396
0.999997
1
Surface water flux
SRFRAD
0.000633
0.008004
5.96512
8.37901
0.999995
1
Net radiative flux at surface
FSNS
0.000573
0.008808
5.82698
8.63233
0.999997
1
Net solar flux at surface
SNOWHICE
0.000386
0.008948
5.80428
8.26936
0.999995
1
Water equivalent snow depth
SOLIN
0.001105
0.009228
5.75970
8.02962
0.999993
1
Solar insolation
FLUT
0.001242
0.010120
5.62666
7.13715
0.999975
1
Upwelling longwave flux at top of model
FLNT
0.001245
0.010166
5.62016
7.13376
0.999975
1
Net longwave flux at top of model
FLDS
0.000942
0.010468
5.57788
7.62895
0.999987
1
Downwelling longwave flux at surface
In [26]:
#v.loc(axis=0)[8:10,:]#.mean(axis=0).unstack()[statistics_of_interest].sort("precisionbits", ascending=False).head(20).join(vi)
v.loc(axis=0)[8,170].unstack()[statistics_of_interest].sort("precisionbits", ascending=False).head(20).join(vi)
Out[26]:
rms_error
max_error
precisionbits
srr
correlation
levels
name
VARIABLE
Z3
0.000131
0.003796
7.04134
10.99390
1.000000
30
Geopotential Height (above sea level)
TSMX
0.000772
0.008110
5.94603
7.74317
0.999989
1
Maximum surface temperature over output period
TSMN
0.000772
0.008110
5.94603
7.74317
0.999989
1
Minimum surface temperature over output period
TS
0.000772
0.008110
5.94603
7.74317
0.999989
1
Surface temperature (radiative)
SL
0.000256
0.008121
5.94406
9.70447
0.999999
30
Liquid water static energy
SLV
0.000256
0.008123
5.94381
9.69628
0.999999
30
Liq wat virtual static energy
FLUTC
0.000880
0.009109
5.77850
7.57660
0.999986
1
Clearsky upwelling longwave flux at top of model
FLNTC
0.000880
0.009172
5.76849
7.57549
0.999986
1
Clearsky net longwave flux at top of model
TREFHT
0.000874
0.009189
5.76585
7.61042
0.999987
1
Reference height temperature
QFLX
0.000783
0.010223
5.61197
8.08239
0.999993
1
Surface water flux
LHFLX
0.000783
0.010325
5.59771
8.08133
0.999993
1
Surface latent heat flux
TROP_Z
0.001536
0.013820
5.17709
7.33894
0.999981
1
Tropopause Height
FLDS
0.001150
0.014327
5.12512
7.34209
0.999981
1
Downwelling longwave flux at surface
FLUT
0.001656
0.016283
4.94048
6.72229
0.999955
1
Upwelling longwave flux at top of model
FLNT
0.001658
0.016320
4.93724
6.71986
0.999955
1
Net longwave flux at top of model
AODVIS
0.001036
0.016544
4.91755
5.75488
0.999829
1
Aerosol optical depth 550 nm
AEROD_v
0.001036
0.016544
4.91755
5.75488
0.999829
1
Total Aerosol Optical Depth in visible band
BURDEN1
0.001373
0.017326
4.85088
5.67473
0.999808
1
Aerosol burden mode 1
AODMODE1
0.002187
0.017587
4.82932
5.49059
0.999753
1
Aerosol optical depth 550 nm mode 1
TMQ
0.001359
0.017731
4.81756
7.49127
0.999985
1
Total (vertically integrated) precipitable water
In [27]:
#v.mean(axis=0).unstack()[statistics_of_interest].sort("rms_error", ascending=False).head(20).join(vi)
v.loc(axis=0)[10,200].unstack()[statistics_of_interest].sort("rms_error", ascending=False).head(20).join(vi)
Out[27]:
rms_error
max_error
precisionbits
srr
correlation
levels
name
VARIABLE
SSTSFMBL
0.002599
0.062668
2.99611
6.28976
0.999918
1
Mobilization flux at surface
CLDMED
0.002491
0.030366
4.04140
6.07505
0.999890
1
Vertically-integrated mid-level cloud
PSL
0.002299
0.024323
4.36156
6.29992
0.999919
1
Sea level pressure
FREQZM
0.002256
0.022684
4.46221
6.60402
0.999947
1
Fractional occurance of ZM convection
FREQS
0.002167
0.051397
3.28217
6.26081
0.999915
30
Fractional occurance of snow
TOT_ICLD_VISTAU
0.002156
0.137734
1.86004
4.22326
0.998566
30
Total in-cloud extinction visible sw optical d...
PRECSL
0.002120
0.077733
2.68533
5.59529
0.999786
1
Large-scale (stable) snow rate (water equivalent)
AODMODE2
0.002118
0.048138
3.37669
4.27573
0.998666
1
Aerosol optical depth 550 nm mode 2
PRECSC
0.002112
0.145466
1.78125
4.80423
0.999359
1
Convective snow rate (water equivalent)
CLDHGH
0.002105
0.022417
4.47926
6.13657
0.999899
1
Vertically-integrated high cloud
FICE
0.002078
0.080303
2.63840
7.15372
0.999975
30
Fractional ice content within cloud
ANRAIN
0.002065
0.118444
2.07772
4.51513
0.999043
30
Average rain number conc
SSAVIS
0.002043
0.019640
4.67005
6.58229
0.999946
1
Aerosol singel-scatter albedo
CDNUMC
0.002041
0.028572
4.12927
5.44676
0.999737
1
Vertically-integrated droplet concentration
TAUX
0.002005
0.037994
3.71808
5.60743
0.999790
1
Zonal surface stress
T850
0.002000
0.023772
4.39457
6.26535
0.999915
1
Temperature at 850 mbar pressure surface
PRECSH
0.001989
0.054360
3.20131
5.06256
0.999552
1
Shallow Convection precipitation rate
CLOUD
0.001950
0.038775
3.68873
6.15884
0.999902
30
Cloud fraction
AREI
0.001916
0.039118
3.67602
5.96171
0.999871
30
Average ice effective radius
BURDEN2
0.001903
0.030185
4.05005
4.68276
0.999242
1
Aerosol burden mode 2
In [28]:
#v.mean(axis=0).unstack()[statistics_of_interest].sort("max_error", ascending=False).head(20).join(vi)
v.loc(axis=0)[10,200].unstack()[statistics_of_interest].sort("max_error", ascending=False).head(20).join(vi)
Out[28]:
rms_error
max_error
precisionbits
srr
correlation
levels
name
VARIABLE
H2SO4
0.001470
0.190855
1.38945
4.13879
0.998387
30
H2SO4 concentration
PRECSC
0.002112
0.145466
1.78125
4.80423
0.999359
1
Convective snow rate (water equivalent)
TOT_ICLD_VISTAU
0.002156
0.137734
1.86004
4.22326
0.998566
30
Total in-cloud extinction visible sw optical d...
DSTSFMBL
0.001454
0.136540
1.87260
2.42167
0.982428
1
Mobilization flux at surface
dst_a1SF
0.001454
0.136540
1.87260
2.42167
0.982428
1
dst_a1 dust surface emission
dst_a3SF
0.001454
0.136540
1.87260
2.42167
0.982428
1
dst_a3 dust surface emission
ANRAIN
0.002065
0.118444
2.07772
4.51513
0.999043
30
Average rain number conc
QC
0.001253
0.109353
2.19294
5.66345
0.999805
30
Q tendency - shallow convection LW export
DMS
0.000813
0.102966
2.27976
6.07825
0.999890
30
DMS concentration
CMFDQR
0.001215
0.084831
2.55926
5.09065
0.999569
30
Q tendency - shallow convection rainout
ICWMR
0.001392
0.082268
2.60352
5.87033
0.999854
30
Prognostic in-cloud water mixing ratio
FICE
0.002078
0.080303
2.63840
7.15372
0.999975
30
Fractional ice content within cloud
PRECSL
0.002120
0.077733
2.68533
5.59529
0.999786
1
Large-scale (stable) snow rate (water equivalent)
wat_a2
0.000766
0.075513
2.72713
4.33234
0.998767
30
aerosol water, interstitial, mode 02
so4_a2
0.000856
0.071282
2.81032
3.83542
0.997543
30
so4_a2 concentration
AREL
0.001548
0.071075
2.81450
6.15519
0.999902
30
Average droplet effective radius
ICIMR
0.001259
0.070788
2.82035
5.20809
0.999634
30
Prognostic in-cloud ice mixing ratio
ANSNOW
0.001686
0.070543
2.82536
3.36209
0.995260
30
Average snow number conc
CMFMCDZM
0.001409
0.066729
2.90554
5.67453
0.999808
31
Convection mass flux from ZM deep
SO2
0.000983
0.065008
2.94325
4.54293
0.999079
30
SO2 concentration
In [29]:
# good variables
v.loc(axis=1)[("VT","V","Z3"),statistics_of_interest]
Out[29]:
VARIABLE
V
VT
Z3
STATISTIC
correlation
max_error
precisionbits
rms_error
srr
correlation
max_error
precisionbits
rms_error
srr
correlation
max_error
precisionbits
rms_error
srr
K
M
1
10
0.759554
0.320530
0.641468
0.032731
0.620503
0.771228
0.311654
0.681982
0.034773
0.651632
0.999724
0.090627
2.46391
0.006284
5.41227
20
0.939839
0.263703
0.923015
0.017191
1.549550
0.939986
0.253047
0.982523
0.018639
1.551250
0.999890
0.050173
3.31695
0.003960
6.07840
30
0.959991
0.268408
0.897502
0.014092
1.836340
0.961757
0.251442
0.991700
0.014962
1.868250
0.999963
0.022637
4.46516
0.002298
6.86389
40
0.975119
0.238931
1.065340
0.011156
2.173420
0.976241
0.223306
1.162910
0.011837
2.206320
0.999978
0.020145
4.63341
0.001795
7.21998
50
0.982843
0.187085
1.418230
0.009281
2.438760
0.983566
0.172454
1.535710
0.009863
2.469520
0.999988
0.014424
5.11535
0.001310
7.67483
60
0.988409
0.174232
1.520920
0.007639
2.719650
0.988806
0.194202
1.364370
0.008151
2.744630
0.999993
0.013711
5.18849
0.000975
8.09981
70
0.991538
0.163582
1.611920
0.006533
2.945430
0.991852
0.178074
1.489450
0.006959
2.972610
0.999995
0.013992
5.15926
0.000843
8.30956
80
0.993840
0.146122
1.774760
0.005577
3.173600
0.993966
0.157166
1.669640
0.005992
3.188540
0.999996
0.013241
5.23886
0.000744
8.48994
90
0.995141
0.106122
2.236210
0.004955
3.344290
0.995184
0.110362
2.179690
0.005355
3.350760
0.999998
0.007202
6.11745
0.000597
8.80894
100
0.996077
0.101506
2.300360
0.004453
3.498310
0.996068
0.103150
2.277190
0.004839
3.496770
0.999998
0.006912
6.17669
0.000534
8.96871
110
0.996675
0.089815
2.476890
0.004100
3.617320
0.996695
0.089859
2.476190
0.004437
3.621870
0.999998
0.005892
6.40714
0.000483
9.11338
120
0.997084
0.083291
2.585700
0.003840
3.711940
0.997108
0.082948
2.591650
0.004152
3.717870
0.999999
0.005536
6.49681
0.000441
9.24373
130
0.997536
0.072485
2.786170
0.003531
3.833180
0.997563
0.075215
2.732830
0.003811
3.841210
0.999999
0.005233
6.57819
0.000399
9.38973
140
0.997869
0.066151
2.918100
0.003283
3.937990
0.997911
0.069813
2.840360
0.003529
3.952110
0.999999
0.005101
6.61512
0.000364
9.52295
150
0.998157
0.066450
2.911580
0.003054
4.042600
0.998210
0.066712
2.905900
0.003267
4.063500
0.999999
0.004958
6.65592
0.000335
9.64386
160
0.998353
0.064536
2.953760
0.002887
4.123530
0.998402
0.067213
2.895130
0.003087
4.145230
0.999999
0.004690
6.73611
0.000309
9.75640
170
0.998502
0.064745
2.949080
0.002753
4.191940
0.998545
0.065323
2.936280
0.002946
4.212860
0.999999
0.004549
6.78029
0.000292
9.84042
180
0.998681
0.062756
2.994100
0.002584
4.283770
0.998735
0.062751
2.994220
0.002747
4.313620
0.999999
0.004437
6.81630
0.000275
9.92714
190
0.998850
0.057343
3.124250
0.002413
4.382360
0.998895
0.055635
3.167860
0.002567
4.411400
1.000000
0.004366
6.83947
0.000250
10.06400
200
0.998977
0.054628
3.194210
0.002275
4.467070
0.999024
0.055729
3.165430
0.002413
4.500780
1.000000
0.004104
6.92888
0.000230
10.18420
2
10
0.828131
0.279177
0.840749
0.028207
0.835124
0.830367
0.306200
0.707454
0.030439
0.843688
0.999780
0.087599
2.51294
0.005609
5.57619
20
0.948576
0.264394
0.919238
0.015929
1.659500
0.949386
0.249217
1.004530
0.017159
1.670640
0.999944
0.051610
3.27621
0.002840
6.55827
30
0.970886
0.223072
1.164420
0.012054
2.061640
0.972108
0.212997
1.231100
0.012812
2.092130
0.999976
0.042172
3.56758
0.001864
7.16559
40
0.981364
0.186594
1.422020
0.009670
2.379620
0.981894
0.174893
1.515450
0.010348
2.400240
0.999986
0.032357
3.94977
0.001405
7.57340
50
0.988615
0.160850
1.636210
0.007572
2.732480
0.988912
0.174811
1.516130
0.008112
2.751420
0.999993
0.028359
4.14005
0.001026
8.02685
60
0.992338
0.140735
1.828950
0.006217
3.016800
0.992501
0.147790
1.758380
0.006677
3.032230
0.999995
0.017476
4.83848
0.000880
8.24898
70
0.994569
0.106140
2.235960
0.005237
3.264280
0.994592
0.111549
2.164250
0.005674
3.267270
0.999997
0.014754
5.08277
0.000703
8.57239
80
0.995860
0.094937
2.396890
0.004574
3.459530
0.995837
0.099517
2.328920
0.004980
3.455520
0.999997
0.015572
5.00486
0.000616
8.76409
90
0.996790
0.076823
2.702330
0.004029
3.642780
0.996771
0.085668
2.545100
0.004386
3.638490
0.999998
0.012530
5.31844
0.000533
8.97106
100
0.997377
0.072244
2.790980
0.003642
3.788300
0.997370
0.075306
2.731090
0.003959
3.786240
0.999998
0.012194
5.35765
0.000484
9.11030
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
9
110
0.999259
0.040171
3.637690
0.001937
4.699160
0.999249
0.037627
3.732100
0.002116
4.690010
1.000000
0.005735
6.44598
0.000226
10.20760
120
0.999380
0.031648
3.981730
0.001772
4.827430
0.999373
0.029946
4.061490
0.001935
4.819320
1.000000
0.005295
6.56127
0.000206
10.34540
130
0.999468
0.030439
4.037950
0.001642
4.937930
0.999461
0.029962
4.060730
0.001793
4.928880
1.000000
0.004524
6.78831
0.000186
10.49330
140
0.999550
0.029633
4.076630
0.001509
5.059320
0.999547
0.028917
4.111960
0.001644
5.054040
1.000000
0.003860
7.01734
0.000169
10.63250
150
0.999616
0.025663
4.284160
0.001394
5.174160
0.999614
0.024657
4.341830
0.001518
5.169490
1.000000
0.003367
7.21443
0.000154
10.75840
160
0.999672
0.024224
4.367390
0.001289
5.287170
0.999670
0.023745
4.396240
0.001403
5.283260
1.000000
0.002772
7.49461
0.000139
10.90840
170
0.999714
0.021305
4.552670
0.001204
5.385300
0.999712
0.019384
4.688980
0.001310
5.382090
1.000000
0.002503
7.64214
0.000131
10.99800
180
0.999752
0.019196
4.703050
0.001121
5.488490
0.999750
0.017616
4.826950
0.001220
5.484260
1.000000
0.002164
7.85212
0.000120
11.11970
190
0.999782
0.017925
4.801860
0.001051
5.581670
0.999781
0.017569
4.830810
0.001144
5.577930
1.000000
0.002007
7.96039
0.000110
11.24430
200
0.999811
0.014866
5.071860
0.000978
5.685620
0.999810
0.014415
5.116290
0.001064
5.682640
1.000000
0.001893
8.04499
0.000102
11.35870
10
10
0.938356
0.219047
1.190680
0.017395
1.532530
0.937673
0.245176
1.028110
0.018984
1.524840
0.999965
0.050638
3.30363
0.002227
6.90862
20
0.980932
0.128987
1.954700
0.009780
2.363250
0.980456
0.145464
1.781270
0.010747
2.345640
0.999989
0.027260
4.19706
0.001264
7.72586
30
0.991326
0.085235
2.552410
0.006614
2.927680
0.991277
0.088651
2.495710
0.007199
2.923630
0.999995
0.024174
4.37043
0.000821
8.34938
40
0.994860
0.073429
2.767500
0.005096
3.303830
0.994780
0.074613
2.744430
0.005574
3.292780
0.999997
0.019051
4.71397
0.000639
8.70910
50
0.996679
0.056184
3.153700
0.004098
3.618300
0.996609
0.063071
2.986870
0.004495
3.603220
0.999998
0.013844
5.17463
0.000515
9.02239
60
0.997705
0.048836
3.355910
0.003408
3.884390
0.997663
0.051763
3.271940
0.003733
3.871370
0.999999
0.010995
5.50702
0.000429
9.28605
70
0.998351
0.041459
3.592180
0.002889
4.122550
0.998313
0.046638
3.422350
0.003172
4.106350
0.999999
0.009853
5.66527
0.000344
9.60491
80
0.998746
0.039205
3.672810
0.002520
4.319950
0.998719
0.041360
3.595630
0.002764
4.304850
0.999999
0.005462
6.51638
0.000287
9.86289
90
0.999021
0.036553
3.773880
0.002226
4.498400
0.999006
0.039500
3.662010
0.002436
4.487260
1.000000
0.004720
6.72692
0.000255
10.03280
100
0.999215
0.036223
3.786950
0.001994
4.657700
0.999203
0.039248
3.671250
0.002180
4.646930
1.000000
0.004663
6.74462
0.000229
10.18750
110
0.999360
0.032390
3.948300
0.001800
4.805020
0.999353
0.034504
3.857110
0.001965
4.797270
1.000000
0.004514
6.79134
0.000200
10.38310
120
0.999468
0.025774
4.277930
0.001641
4.938570
0.999464
0.027569
4.180820
0.001789
4.932490
1.000000
0.004357
6.84233
0.000179
10.54510
130
0.999558
0.023009
4.441670
0.001497
5.071420
0.999554
0.026040
4.263130
0.001630
5.066280
1.000000
0.003736
7.06434
0.000162
10.68880
140
0.999621
0.019998
4.644020
0.001386
5.182670
0.999619
0.023862
4.389130
0.001507
5.179980
1.000000
0.003555
7.13587
0.000147
10.83130
150
0.999676
0.018024
4.793960
0.001281
5.295760
0.999676
0.021087
4.567490
0.001390
5.296720
1.000000
0.003101
7.33307
0.000135
10.95510
160
0.999727
0.016863
4.890000
0.001176
5.419450
0.999727
0.018639
4.745550
0.001276
5.419800
1.000000
0.002720
7.52227
0.000126
11.05260
170
0.999766
0.016409
4.929370
0.001089
5.529700
0.999766
0.019072
4.712400
0.001182
5.530120
1.000000
0.002374
7.71851
0.000117
11.15940
180
0.999797
0.015076
5.051600
0.001013
5.634640
0.999797
0.017163
4.864520
0.001100
5.633670
1.000000
0.001990
7.97330
0.000109
11.26720
190
0.999824
0.013711
5.188570
0.000945
5.734670
0.999823
0.015473
5.014100
0.001027
5.732990
1.000000
0.001856
8.07381
0.000101
11.37100
200
0.999845
0.012854
5.281600
0.000886
5.827290
0.999844
0.015292
5.031090
0.000964
5.824980
1.000000
0.001825
8.09788
0.000093
11.48360
200 rows × 15 columns
In [30]:
# bad variables
v.loc(axis=1)[("soa_a2","CMFDQR","TOT_ICLD_VISTAU"),statistics_of_interest]
Out[30]:
VARIABLE
CMFDQR
TOT_ICLD_VISTAU
soa_a2
STATISTIC
correlation
max_error
precisionbits
rms_error
srr
correlation
max_error
precisionbits
rms_error
srr
correlation
max_error
precisionbits
rms_error
srr
K
M
1
10
0.935128
0.831227
-0.733315
0.014663
1.49691
0.870440
0.863806
-0.788780
0.019822
1.02246
0.809418
0.838487
-0.745860
0.016882
0.767995
20
0.959301
0.800885
-0.679667
0.011686
1.82427
0.911693
0.857936
-0.778943
0.016545
1.28324
0.840783
0.769293
-0.621606
0.015564
0.885305
30
0.975111
0.751592
-0.588022
0.009176
2.17321
0.926705
0.845402
-0.757710
0.015132
1.41200
0.857900
0.758325
-0.600888
0.014772
0.960673
40
0.979653
0.717059
-0.520163
0.008306
2.31689
0.937850
0.820705
-0.714935
0.013974
1.52682
0.889726
0.613881
-0.296031
0.013124
1.131330
50
0.982807
0.708659
-0.503164
0.007641
2.43723
0.942555
0.787982
-0.656234
0.013451
1.58186
0.920090
0.550637
-0.139174
0.011261
1.352150
60
0.987623
0.659105
-0.398581
0.006491
2.67258
0.946721
0.774796
-0.631888
0.012968
1.63462
0.929854
0.514995
-0.042630
0.010578
1.442500
70
0.989639
0.623518
-0.318504
0.005942
2.80009
0.949942
0.759654
-0.603415
0.012580
1.67842
0.950426
0.413015
0.275734
0.008940
1.685230
80
0.990371
0.589952
-0.238671
0.005729
2.85270
0.952737
0.753228
-0.591158
0.012233
1.71882
0.971575
0.278263
0.845477
0.006806
2.078670
90
0.992180
0.569403
-0.187521
0.005165
3.00216
0.954899
0.736276
-0.558320
0.011956
1.75180
0.977740
0.249791
1.001210
0.006032
2.252790
100
0.992780
0.554584
-0.149479
0.004964
3.05947
0.957160
0.679558
-0.442668
0.011660
1.78806
0.980667
0.244702
1.030900
0.005626
2.353410
110
0.993953
0.542598
-0.117956
0.004544
3.18701
0.960097
0.666384
-0.414425
0.011261
1.83822
0.985863
0.153256
1.705980
0.004817
2.577280
120
0.994932
0.519463
-0.055093
0.004161
3.31402
0.962402
0.627323
-0.327280
0.010938
1.88028
0.987739
0.146190
1.774080
0.004488
2.679300
130
0.995421
0.501562
-0.004500
0.003956
3.38702
0.965034
0.606819
-0.279338
0.010555
1.93168
0.989478
0.143896
1.796900
0.004160
2.789020
140
0.995905
0.488652
0.033122
0.003741
3.46749
0.968775
0.567734
-0.183287
0.009984
2.01193
0.990887
0.139391
1.842790
0.003872
2.892220
150
0.996262
0.482382
0.051751
0.003575
3.53320
0.974092
0.504621
-0.013273
0.009106
2.14463
0.991439
0.140358
1.832820
0.003754
2.937070
160
0.996585
0.462576
0.112237
0.003417
3.59812
0.978702
0.461445
0.115769
0.008266
2.28428
0.992045
0.136778
1.870100
0.003619
2.989810
170
0.996834
0.455777
0.133599
0.003290
3.65281
0.982388
0.439786
0.185125
0.007524
2.42005
0.992472
0.135538
1.883230
0.003521
3.029450
180
0.997039
0.451629
0.146791
0.003182
3.70087
0.984406
0.435556
0.199071
0.007083
2.50707
0.992804
0.132499
1.915940
0.003443
3.061940
190
0.997455
0.418412
0.257004
0.002951
3.81006
0.985725
0.434351
0.203067
0.006779
2.57034
0.993454
0.128531
1.959810
0.003284
3.129920
200
0.997613
0.414881
0.269230
0.002858
3.85611
0.987671
0.390035
0.358325
0.006304
2.67536
0.993719
0.123898
2.012780
0.003217
3.159640
2
10
0.949138
0.813521
-0.702251
0.013031
1.66721
0.892951
0.863536
-0.788329
0.018126
1.15151
0.829051
0.777192
-0.636344
0.016076
0.838632
20
0.970152
0.783346
-0.647722
0.010036
2.04395
0.924748
0.858885
-0.780537
0.015325
1.39373
0.866331
0.704763
-0.495210
0.014359
1.001530
30
0.978628
0.712064
-0.510078
0.008510
2.28182
0.936154
0.823370
-0.719613
0.014157
1.50804
0.888868
0.622119
-0.315262
0.013172
1.126070
40
0.984223
0.675823
-0.434717
0.007322
2.49874
0.943942
0.785738
-0.652121
0.013293
1.59897
0.911623
0.570566
-0.190466
0.011817
1.282700
50
0.988058
0.639539
-0.355105
0.006377
2.69820
0.947997
0.774591
-0.631507
0.012816
1.65164
0.936417
0.527587
-0.077480
0.010088
1.510920
60
0.989998
0.589637
-0.237900
0.005839
2.82538
0.951813
0.759592
-0.603296
0.012349
1.70520
0.964057
0.433893
0.204589
0.007638
1.912160
70
0.991834
0.564117
-0.174067
0.005278
2.97102
0.954354
0.755894
-0.596256
0.012027
1.74334
0.973692
0.355067
0.493838
0.006551
2.133730
80
0.993477
0.551282
-0.140863
0.004719
3.13244
0.957142
0.717281
-0.520611
0.011662
1.78777
0.977930
0.317480
0.655262
0.006007
2.258880
90
0.994357
0.520672
-0.058447
0.004390
3.23675
0.959912
0.665832
-0.413229
0.011287
1.83495
0.985386
0.227607
1.135380
0.004897
2.553540
100
0.995071
0.509298
-0.026581
0.004104
3.33399
0.962418
0.635019
-0.344871
0.010935
1.88059
0.987838
0.210362
1.249060
0.004470
2.685130
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
9
110
0.998554
0.218972
1.191180
0.002225
4.21749
0.988178
0.328525
0.605926
0.006173
2.70548
0.996508
0.184582
1.437660
0.002400
3.582180
120
0.998742
0.201739
1.309440
0.002075
4.31796
0.990398
0.258623
0.951077
0.005567
2.85468
0.996982
0.172486
1.535450
0.002232
3.687190
130
0.998910
0.191200
1.386850
0.001931
4.42140
0.992188
0.208113
1.264560
0.005023
3.00289
0.997426
0.154138
1.697710
0.002061
3.801830
140
0.999043
0.179265
1.479830
0.001810
4.51480
0.993591
0.202066
1.307100
0.004551
3.14519
0.997753
0.150105
1.735960
0.001926
3.899840
150
0.999151
0.172699
1.533670
0.001705
4.60150
0.994595
0.192531
1.376840
0.004181
3.26774
0.998005
0.129837
1.945220
0.001815
3.985360
160
0.999232
0.164834
1.600910
0.001622
4.67314
0.995574
0.174653
1.517440
0.003784
3.41146
0.998251
0.118750
2.074000
0.001700
4.080190
170
0.999312
0.152390
1.714160
0.001535
4.75262
0.996306
0.162890
1.618030
0.003458
3.54167
0.998439
0.105631
2.242890
0.001606
4.162170
180
0.999376
0.149995
1.737020
0.001462
4.82340
0.996901
0.138058
1.856660
0.003167
3.66821
0.998599
0.096334
2.375810
0.001521
4.240100
190
0.999432
0.137113
1.866560
0.001394
4.89147
0.997501
0.133189
1.908450
0.002845
3.82300
0.998737
0.091018
2.457700
0.001445
4.314710
200
0.999489
0.130206
1.941130
0.001322
4.96784
0.997828
0.131054
1.931760
0.002652
3.92428
0.998885
0.084151
2.570880
0.001357
4.404870
10
10
0.975266
0.656360
-0.392559
0.009147
2.17766
0.934351
0.809331
-0.694803
0.014349
1.48861
0.888527
0.636792
-0.348894
0.013191
1.123980
20
0.987531
0.450154
0.151508
0.006515
2.66725
0.950615
0.683761
-0.451565
0.012498
1.68793
0.936494
0.486705
0.038882
0.010082
1.511760
30
0.992846
0.331755
0.591809
0.004942
3.06606
0.956897
0.626803
-0.326083
0.011695
1.78374
0.957920
0.441695
0.178879
0.008252
1.800690
40
0.995034
0.328510
0.605993
0.004119
3.32862
0.962673
0.604202
-0.273102
0.010899
1.88541
0.978414
0.351872
0.506876
0.005941
2.274710
50
0.996231
0.312111
0.679869
0.003590
3.52714
0.966214
0.531391
-0.087846
0.010378
1.95600
0.986403
0.324261
0.624772
0.004725
2.605180
60
0.997088
0.225824
1.146730
0.003156
3.71297
0.971063
0.493956
0.017545
0.009617
2.06598
0.990641
0.197598
1.339360
0.003924
2.873100
70
0.997613
0.212422
1.235000
0.002858
3.85615
0.976182
0.460543
0.118591
0.008736
2.20453
0.992708
0.179101
1.481150
0.003465
3.052410
80
0.998039
0.193055
1.372920
0.002591
3.99764
0.981019
0.431799
0.211568
0.007808
2.36653
0.994246
0.170009
1.556320
0.003080
3.222660
90
0.998317
0.165206
1.597660
0.002400
4.10818
0.984843
0.433422
0.206156
0.006984
2.52741
0.995506
0.151083
1.726580
0.002723
3.400510
100
0.998574
0.157242
1.668940
0.002210
4.22719
0.989046
0.407289
0.295876
0.005944
2.76014
0.996304
0.136256
1.875610
0.002469
3.541280
110
0.998762
0.151223
1.725250
0.002058
4.32957
0.991780
0.354304
0.496941
0.005152
2.96625
0.996789
0.130101
1.942300
0.002302
3.642480
120
0.998908
0.139937
1.837150
0.001933
4.41989
0.993248
0.287216
0.799792
0.004671
3.10768
0.997275
0.117338
2.091260
0.002121
3.760730
130
0.999023
0.133830
1.901530
0.001829
4.49977
0.994443
0.255457
0.968850
0.004239
3.24770
0.997625
0.107586
2.216440
0.001980
3.859720
140
0.999141
0.126625
1.981370
0.001715
4.59246
0.995369
0.224625
1.154410
0.003871
3.37894
0.997948
0.087988
2.506540
0.001841
3.965120
150
0.999239
0.117768
2.085980
0.001614
4.68056
0.996077
0.205802
1.280670
0.003563
3.49840
0.998235
0.071001
2.816020
0.001707
4.073580
160
0.999323
0.110741
2.174740
0.001522
4.76488
0.996824
0.194294
1.363690
0.003207
3.65036
0.998482
0.066228
2.916420
0.001584
4.182120
170
0.999396
0.107175
2.221960
0.001438
4.84703
0.997409
0.172039
1.539200
0.002897
3.79717
0.998656
0.064385
2.957130
0.001490
4.270160
180
0.999467
0.100571
2.313720
0.001352
4.93646
0.997909
0.142146
1.814560
0.002602
3.95164
0.998834
0.061054
3.033760
0.001388
4.372540
190
0.999523
0.091884
2.444040
0.001279
5.01650
0.998248
0.139551
1.841140
0.002383
4.07898
0.998981
0.057560
3.118790
0.001297
4.469850
200
0.999569
0.084831
2.559260
0.001215
5.09065
0.998566
0.137734
1.860040
0.002156
4.22326
0.999096
0.055853
3.162220
0.001222
4.555950
200 rows × 15 columns
In [31]:
# fewer scenarios (K=10 only)
v.loc(axis=0)[10,:].mean(axis=0).unstack()[statistics_of_interest].sort("max_error").tail(60).join(vi)
Out[31]:
rms_error
max_error
precisionbits
srr
correlation
levels
name
VARIABLE
DTV
0.002307
0.150761
2.312318
3.777210
0.993982
30
T vertical diffusion
AREI
0.005116
0.150780
2.295831
4.860082
0.998484
30
Average ice effective radius
IWC
0.003250
0.151053
2.127201
4.416385
0.996918
30
Grid box average ice water content
PRECL
0.004623
0.151348
2.053304
3.470388
0.990067
1
Large-scale (stable) precipitation rate (liq +...
ABSORB
0.001092
0.152000
2.139774
3.718342
0.989966
30
Aerosol absorption
WGUSTD
0.009387
0.159573
1.925118
3.728114
0.989039
1
wind gusts from turbulence
FREQS
0.005249
0.159683
1.967627
5.261795
0.999213
30
Fractional occurance of snow
TAUX
0.008240
0.159833
1.989205
3.969613
0.994015
1
Zonal surface stress
VFLX
0.001319
0.160396
2.087766
2.340672
0.957608
31
Meridional momentm flux
CMFMCDZM
0.003645
0.161406
1.775917
4.605288
0.997919
31
Convection mass flux from ZM deep
so4_a3
0.003247
0.161805
2.031945
4.184827
0.992704
30
so4_a3 concentration
SSTSFMBL
0.009603
0.162602
1.930210
4.790970
0.998109
1
Mobilization flux at surface
SNOWHICE
0.009109
0.162854
2.919806
5.138699
0.991865
1
Water equivalent snow depth
AWNC
0.004605
0.163289
1.911400
3.748893
0.992979
30
Average cloud water number conc
bc_a1
0.002457
0.164768
2.133224
3.576152
0.988670
30
bc_a1 concentration
CMFDT
0.003099
0.165926
1.906378
4.703353
0.997948
30
T tendency - shallow convection
LANDFRAC
0.008297
0.166796
2.678521
7.021870
0.999360
1
Fraction of sfc area covered by land
OCNFRAC
0.008611
0.167786
2.671528
7.049564
0.999306
1
Fraction of sfc area covered by ocean
AQRAIN
0.002229
0.168214
1.927235
3.469130
0.991019
30
Average rain mixing ratio
UFLX
0.000897
0.170230
2.142051
2.644936
0.970718
31
Zonal momentum flux
ICLDIWP
0.002988
0.171661
1.949385
4.001875
0.994231
30
In-cloud ice water path
ICWMR
0.003727
0.174587
1.688543
4.755901
0.998301
30
Prognostic in-cloud water mixing ratio
TKE
0.002680
0.179692
2.130382
4.504809
0.997176
31
Turbulent Kinetic Energy
TAUTMSX
0.007790
0.183793
1.994213
2.856892
0.957881
1
Zonal turbulent mountain surface stress
wat_a1
0.003682
0.184100
1.894613
4.781895
0.998221
30
aerosol water, interstitial, mode 01
KVH
0.003475
0.185180
1.922911
3.994893
0.995400
31
Vertical diffusion diffusivities (heat/moisture)
CLDLIQ
0.003772
0.186238
1.902282
3.883411
0.994689
30
Grid box averaged cloud liquid amount
VD01
0.002724
0.189762
1.989764
4.075565
0.995490
30
Vertical diffusion of Q
QTFLX
0.003661
0.190431
2.182217
5.542273
0.999126
31
Total water flux
KVM
0.003637
0.190924
1.801932
3.935414
0.995265
31
Vertical diffusion diffusivities (momentum)
soa_a2
0.003739
0.194723
1.787021
3.327927
0.984954
30
soa_a2 concentration
LCLOUD
0.005653
0.195806
1.811231
4.820936
0.998143
30
Liquid cloud fraction
PRECSL
0.008072
0.195993
1.623321
4.113386
0.994463
1
Large-scale (stable) snow rate (water equivalent)
FICE
0.007421
0.197044
1.609409
5.828861
0.999287
30
Fractional ice content within cloud
PRECSH
0.006537
0.200311
1.681084
3.690425
0.992156
1
Shallow Convection precipitation rate
num_a1
0.002130
0.203107
1.824452
3.414676
0.987545
30
num_a1 concentration
ICIMR
0.003666
0.203327
1.586450
4.012357
0.994773
30
Prognostic in-cloud ice mixing ratio
AODMODE2
0.006635
0.206758
1.836645
3.055206
0.974747
1
Aerosol optical depth 550 nm mode 2
AQSNOW
0.002395
0.207676
1.689907
3.789387
0.994127
30
Average snow mixing ratio
CMFDQR
0.002874
0.209861
1.495143
4.092331
0.996444
30
Q tendency - shallow convection rainout
NUMLIQ
0.004362
0.225002
1.485313
3.372489
0.989077
30
Grid box averaged cloud liquid number
AREL
0.004691
0.225738
1.407114
4.920433
0.998450
30
Average droplet effective radius
pom_a1
0.002310
0.227821
1.581035
3.353629
0.984126
30
pom_a1 concentration
QC
0.003460
0.236336
1.287794
4.520361
0.997582
30
Q tendency - shallow convection LW export
wat_a2
0.002197
0.256026
1.405818
3.147286
0.983557
30
aerosol water, interstitial, mode 02
PRECSC
0.008153
0.257288
1.140838
3.252799
0.984395
1
Convective snow rate (water equivalent)
ANSNOW
0.003332
0.257889
1.360512
2.492806
0.978072
30
Average snow number conc
DMS
0.002985
0.264619
1.119987
4.697205
0.997218
30
DMS concentration
num_a2
0.003229
0.277273
1.313120
3.042506
0.978477
30
num_a2 concentration
SNOWHLND
0.009510
0.326186
1.105382
5.449907
0.994402
1
Water equivalent snow depth
BURDEN2
0.007675
0.329139
1.421354
3.239291
0.971717
1
Aerosol burden mode 2
TOT_ICLD_VISTAU
0.006682
0.379783
0.606781
2.831333
0.981639
30
Total in-cloud extinction visible sw optical d...
ANRAIN
0.005135
0.406939
0.512524
3.407987
0.992012
30
Average rain number conc
LND_MBL
0.011713
0.427956
0.977802
2.695587
0.845516
1
Soil erodibility factor
SO2
0.003376
0.455712
0.674063
3.174208
0.980712
30
SO2 concentration
so4_a2
0.002666
0.470166
0.716732
2.563449
0.957498
30
so4_a2 concentration
dst_a1SF
0.004019
0.495278
0.244394
1.143793
0.790318
1
dst_a1 dust surface emission
DSTSFMBL
0.004019
0.495278
0.244394
1.143793
0.790318
1
Mobilization flux at surface
dst_a3SF
0.004019
0.495278
0.244394
1.143793
0.790318
1
dst_a3 dust surface emission
H2SO4
0.004325
0.564690
-0.029436
2.891796
0.978089
30
H2SO4 concentration
In [32]:
v.mean(axis=1,level="STATISTIC")[statistics_of_interest].sort("rms_error").head(20).join(o.loc[:,"compression_ratio"])
Out[32]:
rms_error
max_error
precisionbits
srr
correlation
compression_ratio
K
M
10
200
0.001179
0.036685
4.171292
6.217852
0.999405
0.706582
190
0.001264
0.039919
4.069947
6.119300
0.999217
0.671280
8
200
0.001315
0.042373
3.899866
6.057898
0.999315
0.565332
9
200
0.001334
0.042846
3.970926
6.043406
0.999467
0.635957
10
180
0.001354
0.043330
3.963846
6.020019
0.999090
0.635978
7
200
0.001389
0.043791
3.849059
5.979769
0.999246
0.494707
8
190
0.001410
0.045703
3.794118
5.958123
0.999195
0.537091
9
190
0.001429
0.046101
3.860802
5.946728
0.999413
0.604185
10
170
0.001458
0.047120
3.856658
5.915552
0.998937
0.600676
7
190
0.001485
0.047639
3.743508
5.884144
0.999109
0.469996
8
180
0.001516
0.050718
3.671740
5.858091
0.998975
0.508849
9
180
0.001525
0.049159
3.756998
5.854614
0.999349
0.572413
10
160
0.001572
0.051557
3.740192
5.806754
0.998754
0.565374
7
180
0.001595
0.052499
3.614048
5.781628
0.998938
0.445284
6
200
0.001599
0.052599
3.722570
5.787851
0.998655
0.424082
9
170
0.001635
0.052899
3.638873
5.755664
0.999277
0.540641
8
170
0.001635
0.055154
3.557428
5.751932
0.998804
0.480607
5
200
0.001681
0.054493
3.670439
5.713097
0.998802
0.353457
10
150
0.001707
0.056384
3.599720
5.689856
0.998629
0.530071
6
190
0.001713
0.056721
3.610284
5.689755
0.998554
0.402901
In [33]:
v.mean(axis=1,level="STATISTIC")[statistics_of_interest].sort("max_error").head(20).join(o.loc[:,"compression_ratio"])
Out[33]:
rms_error
max_error
precisionbits
srr
correlation
compression_ratio
K
M
10
200
0.001179
0.036685
4.171292
6.217852
0.999405
0.706582
190
0.001264
0.039919
4.069947
6.119300
0.999217
0.671280
8
200
0.001315
0.042373
3.899866
6.057898
0.999315
0.565332
9
200
0.001334
0.042846
3.970926
6.043406
0.999467
0.635957
10
180
0.001354
0.043330
3.963846
6.020019
0.999090
0.635978
7
200
0.001389
0.043791
3.849059
5.979769
0.999246
0.494707
8
190
0.001410
0.045703
3.794118
5.958123
0.999195
0.537091
9
190
0.001429
0.046101
3.860802
5.946728
0.999413
0.604185
10
170
0.001458
0.047120
3.856658
5.915552
0.998937
0.600676
7
190
0.001485
0.047639
3.743508
5.884144
0.999109
0.469996
9
180
0.001525
0.049159
3.756998
5.854614
0.999349
0.572413
8
180
0.001516
0.050718
3.671740
5.858091
0.998975
0.508849
10
160
0.001572
0.051557
3.740192
5.806754
0.998754
0.565374
7
180
0.001595
0.052499
3.614048
5.781628
0.998938
0.445284
6
200
0.001599
0.052599
3.722570
5.787851
0.998655
0.424082
9
170
0.001635
0.052899
3.638873
5.755664
0.999277
0.540641
5
200
0.001681
0.054493
3.670439
5.713097
0.998802
0.353457
8
170
0.001635
0.055154
3.557428
5.751932
0.998804
0.480607
10
150
0.001707
0.056384
3.599720
5.689856
0.998629
0.530071
6
190
0.001713
0.056721
3.610284
5.689755
0.998554
0.402901
In [34]:
v.mean(axis=1,level="STATISTIC")[statistics_of_interest].sort("rms_error", ascending=False).head(20).join(o.loc[:,"compression_ratio"])
Out[34]:
rms_error
max_error
precisionbits
srr
correlation
compression_ratio
K
M
1
10
0.032613
0.558757
0.026621
1.554078
0.873119
0.003883
2
10
0.029116
0.541185
0.091786
1.711856
0.890421
0.007434
3
10
0.026023
0.520275
0.141566
1.857496
0.909598
0.010985
4
10
0.024421
0.487644
0.225357
1.945359
0.919464
0.014536
5
10
0.023227
0.484181
0.247338
2.015131
0.924582
0.018086
6
10
0.022947
0.476629
0.261784
2.034291
0.926251
0.021637
1
20
0.022761
0.477184
0.322678
2.035921
0.919614
0.007413
7
10
0.020720
0.447893
0.399521
2.175731
0.936636
0.025188
8
10
0.019766
0.444562
0.392137
2.239239
0.938765
0.028739
2
20
0.019743
0.442116
0.427316
2.228669
0.932706
0.014494
9
10
0.019230
0.440454
0.421433
2.280314
0.940855
0.032290
10
10
0.018575
0.423196
0.474407
2.327416
0.944117
0.035840
1
30
0.017676
0.421664
0.547743
2.384021
0.937093
0.010944
3
20
0.017391
0.426849
0.480582
2.400904
0.943077
0.021576
4
20
0.016387
0.400637
0.583792
2.499455
0.949668
0.028657
5
20
0.015510
0.382905
0.652881
2.571205
0.953374
0.035737
2
30
0.015137
0.383848
0.677909
2.599401
0.949513
0.021555
6
20
0.014901
0.363810
0.732395
2.635758
0.957521
0.042819
1
40
0.014652
0.380159
0.719347
2.642628
0.950439
0.014474
7
20
0.013745
0.353876
0.805793
2.757408
0.962853
0.049900
In [35]:
v.mean(axis=1,level="STATISTIC")[statistics_of_interest].sort("max_error", ascending=False).head(20).join(o.loc[:,"compression_ratio"])
Out[35]:
rms_error
max_error
precisionbits
srr
correlation
compression_ratio
K
M
1
10
0.032613
0.558757
0.026621
1.554078
0.873119
0.003883
2
10
0.029116
0.541185
0.091786
1.711856
0.890421
0.007434
3
10
0.026023
0.520275
0.141566
1.857496
0.909598
0.010985
4
10
0.024421
0.487644
0.225357
1.945359
0.919464
0.014536
5
10
0.023227
0.484181
0.247338
2.015131
0.924582
0.018086
1
20
0.022761
0.477184
0.322678
2.035921
0.919614
0.007413
6
10
0.022947
0.476629
0.261784
2.034291
0.926251
0.021637
7
10
0.020720
0.447893
0.399521
2.175731
0.936636
0.025188
8
10
0.019766
0.444562
0.392137
2.239239
0.938765
0.028739
2
20
0.019743
0.442116
0.427316
2.228669
0.932706
0.014494
9
10
0.019230
0.440454
0.421433
2.280314
0.940855
0.032290
3
20
0.017391
0.426849
0.480582
2.400904
0.943077
0.021576
10
10
0.018575
0.423196
0.474407
2.327416
0.944117
0.035840
1
30
0.017676
0.421664
0.547743
2.384021
0.937093
0.010944
4
20
0.016387
0.400637
0.583792
2.499455
0.949668
0.028657
2
30
0.015137
0.383848
0.677909
2.599401
0.949513
0.021555
5
20
0.015510
0.382905
0.652881
2.571205
0.953374
0.035737
1
40
0.014652
0.380159
0.719347
2.642628
0.950439
0.014474
3
30
0.013379
0.366781
0.735514
2.767690
0.958665
0.032166
6
20
0.014901
0.363810
0.732395
2.635758
0.957521
0.042819
In [70]:
# error vs compression ratio, one line per K
grouped_data = v.loc(axis=0)[5:10,:].mean(axis=1,level="STATISTIC")[statistics_of_interest].join(o.loc[:,"compression_ratio_fixed"]).reset_index().groupby("K")
for key,grp in grouped_data:
plt.plot(grp["compression_ratio_fixed"],grp["rms_error"],label="K = " + str(key))
plt.legend()
plt.xlabel("compression ratio")
plt.ylabel("mean rms error")
plt.title("error vs compression ratio, by K")
plt.xlim((0.08,0.11))
plt.ylim((0.001,0.002))
Out[70]:
(0.001, 0.002)
In [68]:
# compression ratio vs time per solve, one line per K
grouped_data = o.loc(axis=0)[7:10,:].reset_index().groupby("K")
for key,grp in grouped_data:
plt.plot(grp["compression_ratio_fixed"],grp["time_solve"],label="K = " + str(key))
plt.legend(loc=2)
plt.xlabel("compression ratio")
plt.ylabel("time to solve")
plt.title("error vs compression ratio, by K")
#plt.xlim((0.10,0.14))
#plt.ylim((0.0008,0.0012))
Out[68]:
<matplotlib.text.Text at 0x7f53ddefcf98>
In [37]:
# error vs compression ratio, one line per M
grouped_data = v.loc(axis=0)[:,:].mean(axis=1,level="STATISTIC")[statistics_of_interest].join(o.loc[:,"compression_ratio_fixed"]).reset_index().groupby("M")
for key,grp in grouped_data:
plt.plot(grp["compression_ratio_fixed"],grp["rms_error"],label="M = " + str(key))
#print(grp)
#plt.legend()
plt.xlabel("compression ratio")
plt.ylabel("mean rms error")
plt.title("error vs compression ratio, by M")
Out[37]:
<matplotlib.text.Text at 0x7f53de96f320>
In [38]:
# 3D variables only
variables_3D = list(vi[vi.levels == 30].index)
grouped_3D = v.loc[:,variables_3D].mean(axis=1,level="STATISTIC")[statistics_of_interest].join(o.loc[:,"compression_ratio_fixed"]).reset_index().groupby("K")
for key,grp in grouped_3D:
plt.plot(grp["compression_ratio_fixed"],grp["rms_error"],label="K = " + str(key))
plt.legend()
Out[38]:
<matplotlib.legend.Legend at 0x7f53de96bda0>
In [39]:
grouped_single = v.loc[:,"VT"][statistics_of_interest].join(o.loc[:,"compression_ratio_fixed"]).reset_index().groupby("K")
for key,grp in grouped_single:
plt.plot(grp["compression_ratio_fixed"],grp["rms_error"],label="K = " + str(key))
plt.legend()
Out[39]:
<matplotlib.legend.Legend at 0x7f53dea72f28>
In [40]:
t = v.loc(axis=0)[8,200].unstack().sort("max_error", ascending=False).join(vi)["levels"].reset_index().reset_index()
t[t.levels < 10].hist("index")
Out[40]:
array([[<matplotlib.axes.AxesSubplot object at 0x7f53de9d6c18>]], dtype=object)
In [41]:
t[t.levels>=30].hist("index")
Out[41]:
array([[<matplotlib.axes.AxesSubplot object at 0x7f53de7177f0>]], dtype=object)
In [42]:
#o.reset_index().plot(x="M", y="time_solve")
# 3D variables only
grouped_time = o.reset_index().groupby("K")
for key,grp in grouped_time:
plt.plot(grp["M"],grp["time_solve"],label="K = " + str(key))
plt.legend(loc=2)
Out[42]:
<matplotlib.legend.Legend at 0x7f53de690a20>
In [52]:
v.loc(axis=1)[["U","FSDSC","Z3","CCN3"],"rms_error"].join(o["compression_ratio_fixed"]).loc(axis=0)[8,:]
v.loc(axis=1)[["U","FSDSC","Z3","CCN3"],"correlation"].join(o["compression_ratio_fixed"]).loc(axis=0)[8,:]
Out[52]:
(CCN3, correlation)
(FSDSC, correlation)
(U, correlation)
(Z3, correlation)
compression_ratio_fixed
K
M
8
10
0.973654
0.991845
0.986003
0.999938
0.005468
20
0.989047
0.997210
0.995316
0.999985
0.010438
30
0.994398
0.998522
0.998008
0.999994
0.015409
40
0.996443
0.999339
0.998933
0.999996
0.020379
50
0.997627
0.999555
0.999318
0.999998
0.025350
60
0.998179
0.999720
0.999524
0.999998
0.030320
70
0.998578
0.999826
0.999651
0.999999
0.035290
80
0.998885
0.999866
0.999736
0.999999
0.040261
90
0.999091
0.999900
0.999788
0.999999
0.045231
100
0.999230
0.999924
0.999831
1.000000
0.050202
110
0.999335
0.999935
0.999859
1.000000
0.055172
120
0.999443
0.999951
0.999883
1.000000
0.060143
130
0.999514
0.999963
0.999905
1.000000
0.065113
140
0.999594
0.999971
0.999920
1.000000
0.070084
150
0.999642
0.999975
0.999932
1.000000
0.075054
160
0.999683
0.999980
0.999941
1.000000
0.080025
170
0.999718
0.999983
0.999949
1.000000
0.084995
180
0.999754
0.999986
0.999955
1.000000
0.089966
190
0.999781
0.999987
0.999961
1.000000
0.094936
200
0.999805
0.999989
0.999965
1.000000
0.099907
In [51]:
v.loc(axis=1)[["U","FSDSC","Z3","CCN3"],"max_error"].join(o["compression_ratio_fixed"]).loc(axis=0)[8,:]
Out[51]:
(CCN3, max_error)
(FSDSC, max_error)
(U, max_error)
(Z3, max_error)
compression_ratio_fixed
K
M
8
10
0.549114
0.300649
0.326978
0.060395
0.005468
20
0.435776
0.111562
0.195177
0.025157
0.010438
30
0.250617
0.102046
0.132702
0.021455
0.015409
40
0.222734
0.095497
0.137346
0.018558
0.020379
50
0.158119
0.073507
0.110762
0.017540
0.025350
60
0.147850
0.061602
0.085000
0.016874
0.030320
70
0.136870
0.055606
0.066489
0.014166
0.035290
80
0.098520
0.059387
0.061466
0.013852
0.040261
90
0.090614
0.062971
0.038670
0.011644
0.045231
100
0.085407
0.061753
0.038410
0.011237
0.050202
110
0.072485
0.057276
0.037132
0.009493
0.055172
120
0.070625
0.058673
0.032301
0.007653
0.060143
130
0.067034
0.056381
0.029827
0.006939
0.065113
140
0.065346
0.057903
0.026102
0.006592
0.070084
150
0.063502
0.055686
0.023904
0.005716
0.075054
160
0.056206
0.052997
0.022932
0.004736
0.080025
170
0.053030
0.051606
0.022263
0.003796
0.084995
180
0.049188
0.046691
0.021437
0.003730
0.089966
190
0.045225
0.040633
0.018439
0.003705
0.094936
200
0.045484
0.038110
0.018905
0.003679
0.099907
Content source: eth-cscs/compression
Similar notebooks: