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GEUS-Glaciology-and-Climate / ice_discharge

Greenland Ice Sheet solid ice discharge from 1986 through last month
https://doi.org/10.5194/essd-12-1367-2020
GNU Lesser General Public License v3.0
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Helheim 10 % drop on 2019-12-26 #16

Closed mankoff closed 4 years ago

mankoff commented 4 years ago

The following Org workbook demonstrates the error. It can also be seen graphically by looking at the Sentinel 1 velocity product from file IV_20191214_20200107.nc and one before or after, zoomed in near the Helheim gate. Pixels at the N edge of the glacier appear much slower.

* Helheim decline
:PROPERTIES:
:header-args:jupyter-python+: :session helheim :eval no-export
:END:

Last data point [2019-12-26 Thu] has a 10 % drop.

#+BEGIN_SRC jupyter-python :exports results :results raw drawer
import numpy as np
import pandas as pd

sector = pd.read_csv("./out/sector_D.csv", index_col=0, parse_dates=True)
hc = sector.columns[['HELHEIM' in _ for _ in sector.columns]].values[0]
sector.loc[sector.index.year >= 2019][hc]
#+END_SRC

#+RESULTS:
#+begin_example
Date
2019-01-12    32.003
2019-01-24    32.523
2019-02-05    32.588
2019-02-17    32.680
2019-03-01    33.028
2019-03-13    33.544
2019-03-25    33.494
2019-04-05    33.415
2019-04-18    33.661
2019-04-30    33.205
2019-05-12    33.411
2019-05-24    33.436
2019-06-05    33.347
2019-06-17    33.900
2019-06-29    34.478
2019-07-11    34.941
2019-07-23    35.942
2019-08-04    35.447
2019-08-16    35.432
2019-08-28    36.546
2019-09-09    36.386
2019-09-21    37.203
2019-10-03    36.858
2019-10-15    37.184
2019-10-27    36.341
2019-11-08    35.779
2019-11-20    35.570
2019-12-02    34.598
2019-12-14    33.921
2019-12-26    29.730
2020-01-07    34.539
Name: HELHEIMGLETSCHER, dtype: float64
#+end_example

Let's find it in the raw data

#+BEGIN_SRC jupyter-python :exports results :results raw drawer
meta = pd.read_csv("./out/gate_meta.csv")
gate_id = meta[meta['Mouginot_2019'] == hc]['gate'].values[0]
meta[meta['gate'] == gate_id].T
#+END_SRC

#+RESULTS:
|               |               171 |
|---------------+-------------------|
| gate          |               231 |
| mean_x        |            304157 |
| mean_y        |          -2576528 |
| lon           | -38.2680878986995 |
| lat           |  66.3773177878685 |
| n_pixels      |                36 |
| sector        |                63 |
| region        |                SE |
| Bjork_2015    |  Helheim Gletsjer |
| Mouginot_2019 |  HELHEIMGLETSCHER |

#+BEGIN_SRC jupyter-python :exports results :results raw drawer
dat = pd.read_csv("./tmp/dat_100_5000.csv")
dat = dat[dat['gates_gateID@gates_100_5000'] == gate_id]

col_id = ['vel_eff_2019_1' in _ for _ in dat.columns]
vel = dat.iloc[:,col_id]
vel.columns = [_[8:18] for _ in vel.columns]
col_id = ['err_eff_2019_1' in _ for _ in dat.columns]
err = dat.iloc[:,col_id]
err.columns = [_[8:18] for _ in err.columns]

vel[vel.columns[-6:]]
#+END_SRC

#+RESULTS:
|      | 2019_10_27 | 2019_11_08 | 2019_11_20 | 2019_12_02 | 2019_12_14 | 2019_12_26 |
|------+------------+------------+------------+------------+------------+------------|
| 4008 |          0 |          0 |    3.59353 |    6.37899 |    8.73327 |    5.36361 |
| 4009 |          0 |          0 |    6070.31 |    5907.67 |    5636.05 |    829.695 |
| 4010 |          0 |          0 |    6070.31 |    5907.67 |    5636.05 |    829.695 |
| 4011 |          0 |          0 |    6422.81 |    6232.66 |    6049.39 |    1211.98 |
| 4012 |          0 |          0 |    6312.86 |    5996.95 |    5898.27 |    1689.72 |
| 4013 |          0 |          0 |    6615.85 |    6289.21 |    6309.13 |    2509.64 |
| 4014 |          0 |          0 |    6838.14 |    6765.23 |    6147.08 |    6401.22 |
| 4015 |          0 |          0 |    6838.14 |    6765.23 |    6147.08 |    6401.22 |
| 4016 |          0 |          0 |    6638.59 |     6313.4 |    6475.88 |    6064.98 |
| 4017 |    8286.74 |    8114.79 |    7987.67 |    7878.28 |    7861.25 |    7774.74 |
| 4018 |    8286.74 |    8114.79 |    7987.67 |    7878.28 |    7861.25 |    7774.74 |
| 4019 |    8549.14 |    8331.16 |    8154.72 |       8090 |    7988.51 |    7936.16 |
| 4020 |    8549.14 |    8331.16 |    8154.72 |       8090 |    7988.51 |    7936.16 |
| 4021 |    8549.14 |    8331.16 |    8154.72 |       8090 |    7988.51 |    7936.16 |
| 4022 |    9213.67 |    8848.32 |    8753.04 |    8675.07 |    8582.07 |    8592.01 |
| 4023 |    8721.11 |    8466.87 |    8390.01 |    8255.41 |    8177.78 |    8192.03 |
| 4024 |    9119.38 |    8882.95 |    8701.69 |    8660.64 |     8547.2 |    8423.07 |
| 4025 |    9119.38 |    8882.95 |    8701.69 |    8660.64 |     8547.2 |    8423.07 |
| 4026 |    9119.38 |    8882.95 |    8701.69 |    8660.64 |     8547.2 |    8423.07 |
| 4027 |    9116.25 |    8879.54 |    8717.64 |    8598.36 |    8499.84 |    8451.84 |
| 4028 |    9116.25 |    8879.54 |    8717.64 |    8598.36 |    8499.84 |    8451.84 |
| 4029 |    8872.64 |    8610.32 |    8453.23 |    8392.89 |    8407.33 |    8306.79 |
| 4030 |    8872.64 |    8610.32 |    8453.23 |    8392.89 |    8407.33 |    8306.79 |
| 4031 |    8722.84 |    8425.22 |    8256.52 |    8251.52 |    8205.33 |    8182.11 |
| 4032 |    8211.32 |    7042.34 |    7925.64 |    7507.21 |    7008.91 |    7688.17 |
| 4033 |    8211.32 |    7042.34 |    7925.64 |    7507.21 |    7008.91 |    7688.17 |
| 4034 |    5180.69 |    2579.19 |    5577.93 |    5595.76 |    5208.05 |    4644.75 |
| 4035 |    5180.69 |    2579.19 |    5577.93 |    5595.76 |    5208.05 |    4644.75 |
| 4036 |    5180.69 |    2579.19 |    5577.93 |    5595.76 |    5208.05 |    4644.75 |
| 4037 |    5611.34 |    5716.22 |    5504.59 |    4990.52 |    4917.02 |    5809.17 |
| 4038 |       4337 |    4450.64 |    4233.21 |    3826.61 |    4161.36 |    4575.45 |
| 4039 |    835.603 |    1348.97 |    3503.86 |     151.47 |    497.261 |    113.531 |
| 4040 |    835.603 |    1348.97 |    3503.86 |     151.47 |    497.261 |    113.531 |
| 4041 |    835.603 |    1348.97 |    3503.86 |     151.47 |    497.261 |    113.531 |
| 4042 |    7.34474 |    11.9245 |    23.5171 |    2.01749 |   0.626358 |    45.1899 |
| 4043 |    7.34474 |    11.9245 |    23.5171 |    2.01749 |   0.626358 |    45.1899 |

#+CAPTION: Table showing velocities at Helheim gate pixels.
#+CAPTION: It appears the 2019_12_26 slowdown is due to the top right corner of this table.

Let's make a table to explore the errors. Something like:
|          | Date                   |
|----------+------------------------|
| pixel_id | Velocity (err) [err %] |

#+BEGIN_SRC jupyter-python :exports results :results raw drawer
df = vel.round(1).astype(np.str)
df = df + ' ('
df = df + err.round(1).astype(np.str)
df = df + ')'
ratio = (err/vel * 100).round(1).fillna(0).astype(np.str)
df = df + ' ['
df = df + ratio
df = df + '%]'
df
df[df.columns[-3:]].head(15)
#+END_SRC

#+RESULTS:
|      | 2019_12_02            | 2019_12_14            | 2019_12_26           |
|------+-----------------------+-----------------------+----------------------|
| 4008 | 6.4 (2.1) [33.3%]     | 8.7 (1.8) [20.8%]     | 5.4 (1.2) [23.1%]    |
| 4009 | 5907.7 (87.3) [1.5%]  | 5636.1 (94.5) [1.7%]  | 829.7 (7.8) [0.9%]   |
| 4010 | 5907.7 (87.3) [1.5%]  | 5636.1 (94.5) [1.7%]  | 829.7 (7.8) [0.9%]   |
| 4011 | 6232.7 (134.4) [2.2%] | 6049.4 (145.7) [2.4%] | 1212.0 (25.8) [2.1%] |
| 4012 | 5997.0 (114.4) [1.9%] | 5898.3 (81.3) [1.4%]  | 1689.7 (12.4) [0.7%] |
| 4013 | 6289.2 (170.7) [2.7%] | 6309.1 (129.3) [2.1%] | 2509.6 (35.9) [1.4%] |
| 4014 | 6765.2 (136.6) [2.0%] | 6147.1 (78.2) [1.3%]  | 6401.2 (55.3) [0.9%] |
| 4015 | 6765.2 (136.6) [2.0%] | 6147.1 (78.2) [1.3%]  | 6401.2 (55.3) [0.9%] |
| 4016 | 6313.4 (78.3) [1.2%]  | 6475.9 (77.7) [1.2%]  | 6065.0 (43.8) [0.7%] |
| 4017 | 7878.3 (41.9) [0.5%]  | 7861.3 (46.5) [0.6%]  | 7774.7 (41.2) [0.5%] |
| 4018 | 7878.3 (41.9) [0.5%]  | 7861.3 (46.5) [0.6%]  | 7774.7 (41.2) [0.5%] |
| 4019 | 8090.0 (59.5) [0.7%]  | 7988.5 (46.3) [0.6%]  | 7936.2 (32.4) [0.4%] |
| 4020 | 8090.0 (59.5) [0.7%]  | 7988.5 (46.3) [0.6%]  | 7936.2 (32.4) [0.4%] |
| 4021 | 8090.0 (59.5) [0.7%]  | 7988.5 (46.3) [0.6%]  | 7936.2 (32.4) [0.4%] |
| 4022 | 8675.1 (79.0) [0.9%]  | 8582.1 (75.2) [0.9%]  | 8592.0 (62.3) [0.7%] |

#+CAPTION: Published error product does not capture these bad pixels.
#+CAPTION: *NOTE*: Published error product is not useful. There is no way we know velocity to <1 %.

In summary - velocity drop seems non-physical, but is not captured in the error product. Perhaps an outlier filtering algorithm could be implemented to catch this type of issue.

mankoff commented 4 years ago

Fixed in 0332e9e, or at least no longer visible in the time series, so presumably the filter caught it.