tams.run()

tams.run() is a convenience function that does

1. tams.identify()

2. tams.track()

3. tams.classify()

4. aggregation and statistics

Using a selection of the example post-processed MPAS data, we investigate its outputs.

import xarray as xr

import tams

xr.set_options(display_expand_data=False)

Show code cell output

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<xarray.core.options.set_options at 0x7b41715f9bd0>

tams.run() expects certain variable names.

ds = tams.load_example_mpas().rename({"tb": "ctt", "precip": "pr"}).isel(time=slice(1, 13))
ds

<xarray.Dataset> Size: 6MB
Dimensions:  (time: 12, lat: 180, lon: 341)
Coordinates:
  * time     (time) datetime64[ns] 96B 2006-09-08T13:00:00 ... 2006-09-09
  * lon      (lon) float64 3kB 85.0 85.25 85.5 85.75 ... 169.2 169.5 169.8 170.0
  * lat      (lat) float64 1kB -4.875 -4.625 -4.375 -4.125 ... 39.38 39.62 39.88
Data variables:
    ctt      (time, lat, lon) float32 3MB 266.8 266.9 267.0 ... 262.9 261.2
    pr       (time, lat, lon) float32 3MB ...

xarray.Dataset

• Dimensions:
  • time: 12
  • lat: 180
  • lon: 341
• Coordinates: (3)
  • time
    (time)
    datetime64[ns]
    2006-09-08T13:00:00 ... 2006-09-09

    array(['2006-09-08T13:00:00.000000000', '2006-09-08T14:00:00.000000000',
           '2006-09-08T15:00:00.000000000', '2006-09-08T16:00:00.000000000',
           '2006-09-08T17:00:00.000000000', '2006-09-08T18:00:00.000000000',
           '2006-09-08T19:00:00.000000000', '2006-09-08T20:00:00.000000000',
           '2006-09-08T21:00:00.000000000', '2006-09-08T22:00:00.000000000',
           '2006-09-08T23:00:00.000000000', '2006-09-09T00:00:00.000000000'],
          dtype='datetime64[ns]')

  • lon
    (lon)
    float64
    85.0 85.25 85.5 ... 169.8 170.0

    long_name :

    Longitude

    units :

    degree_east

    array([ 85.  ,  85.25,  85.5 , ..., 169.5 , 169.75, 170.  ], shape=(341,))

  • lat
    (lat)
    float64
    -4.875 -4.625 ... 39.62 39.88

    standard_name :

    latitude

    long_name :

    Latitude

    units :

    degree_north

    axis :

    Y

    array([-4.875, -4.625, -4.375, -4.125, -3.875, -3.625, -3.375, -3.125, -2.875,
           -2.625, -2.375, -2.125, -1.875, -1.625, -1.375, -1.125, -0.875, -0.625,
           -0.375, -0.125,  0.125,  0.375,  0.625,  0.875,  1.125,  1.375,  1.625,
            1.875,  2.125,  2.375,  2.625,  2.875,  3.125,  3.375,  3.625,  3.875,
            4.125,  4.375,  4.625,  4.875,  5.125,  5.375,  5.625,  5.875,  6.125,
            6.375,  6.625,  6.875,  7.125,  7.375,  7.625,  7.875,  8.125,  8.375,
            8.625,  8.875,  9.125,  9.375,  9.625,  9.875, 10.125, 10.375, 10.625,
           10.875, 11.125, 11.375, 11.625, 11.875, 12.125, 12.375, 12.625, 12.875,
           13.125, 13.375, 13.625, 13.875, 14.125, 14.375, 14.625, 14.875, 15.125,
           15.375, 15.625, 15.875, 16.125, 16.375, 16.625, 16.875, 17.125, 17.375,
           17.625, 17.875, 18.125, 18.375, 18.625, 18.875, 19.125, 19.375, 19.625,
           19.875, 20.125, 20.375, 20.625, 20.875, 21.125, 21.375, 21.625, 21.875,
           22.125, 22.375, 22.625, 22.875, 23.125, 23.375, 23.625, 23.875, 24.125,
           24.375, 24.625, 24.875, 25.125, 25.375, 25.625, 25.875, 26.125, 26.375,
           26.625, 26.875, 27.125, 27.375, 27.625, 27.875, 28.125, 28.375, 28.625,
           28.875, 29.125, 29.375, 29.625, 29.875, 30.125, 30.375, 30.625, 30.875,
           31.125, 31.375, 31.625, 31.875, 32.125, 32.375, 32.625, 32.875, 33.125,
           33.375, 33.625, 33.875, 34.125, 34.375, 34.625, 34.875, 35.125, 35.375,
           35.625, 35.875, 36.125, 36.375, 36.625, 36.875, 37.125, 37.375, 37.625,
           37.875, 38.125, 38.375, 38.625, 38.875, 39.125, 39.375, 39.625, 39.875])

• Data variables: (2)
  • ctt
    (time, lat, lon)
    float32
    266.8 266.9 267.0 ... 262.9 261.2

    long_name :

    Brightness temperature

    units :

    K

    array([[[266.8233 , 266.8882 , 266.95795, ..., 268.88196, 269.06625,
             269.22308],
            [266.7498 , 266.91086, 266.94043, ..., 269.02643, 269.17902,
             269.28247],
            [266.86603, 267.0336 , 267.0265 , ..., 269.07828, 269.27002,
             269.40576],
            ...,
            [265.21896, 265.2382 , 265.0005 , ..., 265.66608, 265.7233 ,
             266.0509 ],
            [264.91156, 265.0018 , 264.59222, ..., 266.05298, 266.25204,
             266.3739 ],
            [264.79828, 264.50156, 263.52072, ..., 266.12006, 266.1328 ,
             266.4638 ]],
    
           [[266.18558, 266.23065, 265.9952 , ..., 268.7587 , 268.52835,
             269.0521 ],
            [265.47928, 266.09277, 266.58337, ..., 268.40442, 267.71396,
             268.22873],
            [266.18246, 266.36987, 266.6086 , ..., 268.21286, 269.1558 ,
             269.3137 ],
    ...
            [261.70346, 261.5307 , 261.31805, ..., 263.31296, 262.39337,
             264.088  ],
            [260.962  , 260.7507 , 260.56967, ..., 262.22348, 260.3174 ,
             262.5429 ],
            [260.73984, 260.55212, 260.43814, ..., 260.8113 , 260.98175,
             261.987  ]],
    
           [[254.09164, 261.57742, 264.79114, ..., 269.29813, 268.95905,
             268.8455 ],
            [229.90985, 249.84294, 262.9656 , ..., 269.4653 , 269.1423 ,
             269.0349 ],
            [221.7955 , 240.73265, 257.16742, ..., 269.66415, 269.26108,
             269.08923],
            ...,
            [261.9635 , 261.69196, 261.34564, ..., 262.25034, 263.14874,
             263.62305],
            [261.393  , 261.20203, 260.9976 , ..., 264.43652, 263.85138,
             262.72397],
            [261.20978, 261.0633 , 260.94617, ..., 262.57962, 262.91544,
             261.17606]]], shape=(12, 180, 341), dtype=float32)

  • pr
    (time, lat, lon)
    float32
    ...

    long_name :

    Precipitation rate

    units :

    mm h-1

    [736560 values with dtype=float32]

• Indexes: (3)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2006-09-08 13:00:00', '2006-09-08 14:00:00',
                   '2006-09-08 15:00:00', '2006-09-08 16:00:00',
                   '2006-09-08 17:00:00', '2006-09-08 18:00:00',
                   '2006-09-08 19:00:00', '2006-09-08 20:00:00',
                   '2006-09-08 21:00:00', '2006-09-08 22:00:00',
                   '2006-09-08 23:00:00', '2006-09-09 00:00:00'],
                  dtype='datetime64[ns]', name='time', freq=None))

  • lon
    PandasIndex

    PandasIndex(Index([  85.0,  85.25,   85.5,  85.75,   86.0,  86.25,   86.5,  86.75,   87.0,
            87.25,
           ...
           167.75,  168.0, 168.25,  168.5, 168.75,  169.0, 169.25,  169.5, 169.75,
            170.0],
          dtype='float64', name='lon', length=341))

  • lat
    PandasIndex

    PandasIndex(Index([-4.875, -4.625, -4.375, -4.125, -3.875, -3.625, -3.375, -3.125, -2.875,
           -2.625,
           ...
           37.625, 37.875, 38.125, 38.375, 38.625, 38.875, 39.125, 39.375, 39.625,
           39.875],
          dtype='float64', name='lat', length=180))

• Attributes: (0)

tams.run() produces three output datasets.

%%time

ce, mcs, mcs_summary = tams.run(ds, parallel=True)

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Starting `identify` 2025-09-13 20:45:26

[Parallel(n_jobs=-2)]: Done   1 tasks      | elapsed:    0.5s

[Parallel(n_jobs=-2)]: Done   4 tasks      | elapsed:    0.8s

[Parallel(n_jobs=-2)]: Done   7 tasks      | elapsed:    1.0s

[Parallel(n_jobs=-2)]: Done  12 tasks      | elapsed:    1.5s
[Parallel(n_jobs=-2)]: Done  12 out of  12 | elapsed:    1.5s finished

Starting `track` 2025-09-13 20:45:27

Starting `classify` 2025-09-13 20:45:27

Starting statistics calculations 2025-09-13 20:45:28
Starting CE aggregation (into MCS time series) 2025-09-13 20:45:28

Starting gridded data aggregation 2025-09-13 20:45:28

[Parallel(n_jobs=-2)]: Done   1 tasks      | elapsed:    0.4s

[Parallel(n_jobs=-2)]: Done   4 tasks      | elapsed:    1.4s

[Parallel(n_jobs=-2)]: Done   7 tasks      | elapsed:    2.5s

Computing stats for MCS summary dataset 2025-09-13 20:45:32
Done 2025-09-13 20:45:32
CPU times: user 6.62 s, sys: 209 ms, total: 6.82 s
Wall time: 6.84 s

[Parallel(n_jobs=-2)]: Done  12 tasks      | elapsed:    4.3s
[Parallel(n_jobs=-2)]: Done  12 out of  12 | elapsed:    4.3s finished

CE dataset

Identified cloud elements (CEs). Tracking has added mcs_id; classification has added mcs_class.

Each row corresponds to a CE–time that belongs to an MCS. A certain MCS at a given time may include multiple CEs.

ce

	cs235	area_km2	area219_km2	cs219	time	mcs_id	mcs_class
0	POLYGON ((167.5 9.49188, 167.75 9.58423, 167.7...	25634.1184	11798.035271	MULTIPOLYGON (((167.5 9.61125, 167.53439 9.625...	2006-09-08 13:00:00	0	DSL
1	POLYGON ((152.5 8.79886, 152.61238 8.875, 154....	79372.537834	48340.476814	MULTIPOLYGON (((152.75 10.37642, 153 10.44407,...	2006-09-08 13:00:00	1	DSL
2	POLYGON ((148.75 8.23715, 149 8.27821, 149.109...	14036.478097	7223.353767	MULTIPOLYGON (((148.75 8.34082, 148.99758 8.37...	2006-09-08 13:00:00	2	DSL
3	POLYGON ((147.25 7.01224, 147.75 7.01643, 148 ...	22263.09014	6159.967885	MULTIPOLYGON (((147.75 7.1996, 148 7.32651, 14...	2006-09-08 13:00:00	3	DSL
4	POLYGON ((138.25 15.00363, 138.5 15.05098, 138...	10596.159009	5433.912228	MULTIPOLYGON (((138.25 15.10322, 138.33471 15....	2006-09-08 13:00:00	4	DSL
...	...	...	...	...	...	...	...
306	POLYGON ((101.75 17.48897, 103 17.52605, 103.2...	296457.481388	98134.338311	MULTIPOLYGON (((107.5 20.06175, 107.65073 20.1...	2006-09-09 00:00:00	98	DSL
307	POLYGON ((101.75 -1.71823, 102.25 -1.65082, 10...	732976.336208	429992.676965	MULTIPOLYGON (((108.25 3.023, 108.39196 3.125,...	2006-09-09 00:00:00	71	CCC
308	POLYGON ((94.75 0.87272, 94.78525 0.875, 95 0....	1535138.591424	772410.489496	MULTIPOLYGON (((95.75 6.2973, 95.858 6.375, 95...	2006-09-09 00:00:00	71	CCC
309	POLYGON ((97 22.14142, 97.25 22.17086, 97.75 2...	780231.600791	309818.201344	MULTIPOLYGON (((98.25 22.91528, 98.28253 23.12...	2006-09-09 00:00:00	101	DSL
310	POLYGON ((86.25 19.75351, 86.5 19.77476, 86.75...	53903.194943	20853.307518	MULTIPOLYGON (((87.25 21.32294, 87.5 21.33814,...	2006-09-09 00:00:00	95	DSL

311 rows × 7 columns

ce.info()

<class 'geopandas.geodataframe.GeoDataFrame'>
RangeIndex: 311 entries, 0 to 310
Data columns (total 7 columns):
 #   Column       Non-Null Count  Dtype         
---  ------       --------------  -----         
 0   cs235        311 non-null    geometry      
 1   area_km2     311 non-null    Float64       
 2   area219_km2  311 non-null    Float64       
 3   cs219        311 non-null    geometry      
 4   time         311 non-null    datetime64[ns]
 5   mcs_id       311 non-null    Int64         
 6   mcs_class    311 non-null    category      
dtypes: Float64(2), Int64(1), category(1), datetime64[ns](1), geometry(2)
memory usage: 16.0 KB

ce.area_km2.describe()

count             311.0
mean      322513.334878
std       454722.354567
min        10377.283677
25%         45083.40339
50%       158404.664835
75%       415329.825091
max      3252490.158201
Name: area_km2, dtype: Float64

MCS dataset

At each time, CEs with the same MCS ID are combined into one.

Some statistics of precip (pr) and cloud-top temperature (ctt) within the MCS areas are included.

Each row corresponds to an MCS–time.

mcs

	time	cs235	cs219	nce	area_km2	area219_km2	mcs_id	mcs_class	mean_pr	std_pr	npixel	mean_pr219	std_pr219	mean_ctt219	std_ctt219	npixel219
0	2006-09-08 13:00:00	MULTIPOLYGON (((167.5 9.49188, 167.75 9.58423,...	MULTIPOLYGON (((167.5 9.61125, 167.53439 9.625...	1	25634.1184	11798.035271	0	DSL	2.645099	0.927722	36	3.28087	0.533223	209.896072	4.906273	18
1	2006-09-08 13:00:00	MULTIPOLYGON (((152.5 8.79886, 152.61238 8.875...	MULTIPOLYGON (((152.75 10.37642, 153 10.44407,...	1	79372.537834	48340.476814	1	DSL	1.910169	1.426157	106	2.547689	1.304625	215.991379	16.687454	64
2	2006-09-08 13:00:00	MULTIPOLYGON (((148.75 8.23715, 149 8.27821, 1...	MULTIPOLYGON (((148.75 8.34082, 148.99758 8.37...	1	14036.478097	7223.353767	2	DSL	2.254961	0.982726	19	2.883151	0.706086	213.163315	4.442413	11
3	2006-09-08 13:00:00	MULTIPOLYGON (((147.25 7.01224, 147.75 7.01643...	MULTIPOLYGON (((147.75 7.1996, 148 7.32651, 14...	1	22263.09014	6159.967885	3	DSL	2.019073	0.856571	29	2.915771	0.337212	214.483337	2.475573	9
4	2006-09-08 13:00:00	MULTIPOLYGON (((138.25 15.00363, 138.5 15.0509...	MULTIPOLYGON (((138.25 15.10322, 138.33471 15....	1	10596.159009	5433.912228	4	DSL	1.95028	0.935729	15	2.571446	0.641791	211.823792	5.197964	8
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
266	2006-09-09 00:00:00	MULTIPOLYGON (((126 16.04305, 126.25 16.10036,...	MULTIPOLYGON (((127.75 19.60362, 127.75 19.603...	4	963155.281892	326920.015309	98	DSL	0.951342	1.145551	1243	1.412011	1.488934	214.811386	6.743108	440
267	2006-09-09 00:00:00	MULTIPOLYGON (((148.75 -2.21941, 149.5 -2.2141...	MULTIPOLYGON (((150.5 -0.67942, 150.55409 -0.6...	1	157397.412137	40993.731489	100	DSL	0.900998	0.597975	205	1.25952	0.619944	217.582062	4.004134	55
268	2006-09-09 00:00:00	MULTIPOLYGON (((97 22.14142, 97.25 22.17086, 9...	MULTIPOLYGON (((98.25 22.91528, 98.28253 23.12...	1	780231.600791	309818.201344	101	DSL	1.046286	2.042402	1002	1.905522	2.855528	216.48819	13.512681	393
269	2006-09-09 00:00:00	MULTIPOLYGON (((168.75 19.50717, 170 19.75475,...	MULTIPOLYGON (((169 22.58414, 169.03442 22.625...	1	724340.022743	12713.449719	102	DSL	1.05563	0.930567	931	1.742241	1.036302	218.198257	3.076819	24
270	2006-09-09 00:00:00	MULTIPOLYGON (((129.5 30.22046, 130 30.22383, ...	MULTIPOLYGON (((130 30.3112, 130.12126 30.375,...	1	59910.587417	22585.544931	103	DSL	1.321026	0.87163	80	1.65364	0.771776	216.026505	7.512138	33

271 rows × 16 columns

mcs.info()

<class 'geopandas.geodataframe.GeoDataFrame'>
RangeIndex: 271 entries, 0 to 270
Data columns (total 16 columns):
 #   Column       Non-Null Count  Dtype         
---  ------       --------------  -----         
 0   time         271 non-null    datetime64[ns]
 1   cs235        271 non-null    geometry      
 2   cs219        271 non-null    geometry      
 3   nce          271 non-null    Int64         
 4   area_km2     271 non-null    Float64       
 5   area219_km2  271 non-null    Float64       
 6   mcs_id       271 non-null    Int64         
 7   mcs_class    271 non-null    category      
 8   mean_pr      271 non-null    Float64       
 9   std_pr       271 non-null    Float64       
 10  npixel       271 non-null    Int64         
 11  mean_pr219   271 non-null    Float64       
 12  std_pr219    271 non-null    Float64       
 13  mean_ctt219  271 non-null    Float64       
 14  std_ctt219   271 non-null    Float64       
 15  npixel219    271 non-null    Int64         
dtypes: Float64(8), Int64(4), category(1), datetime64[ns](1), geometry(2)
memory usage: 35.4 KB

MCS summary dataset

No more shapes (just first and last centroid points).

Each row corresponds to an MCS, including some statistics for the track such as the MCS’s duration and initial and final coordinates.

mcs_summary

	first_time	last_time	duration	mcs_id	mcs_class	mean_mean_pr	mean_mean_pr219	mean_mean_ctt219	mean_std_ctt219	mean_area_km2	mean_area219_km2	mean_nce	first_centroid	last_centroid	distance_km
0	2006-09-08 13:00:00	2006-09-08 13:00:00	0 days 01:00:00	0	DSL	2.645099	3.28087	209.896072	4.906273	25634.1184	11798.035271	1.0	POINT (166.99291 10.21816)	POINT (166.99291 10.21816)	0.0
1	2006-09-08 13:00:00	2006-09-08 13:00:00	0 days 01:00:00	1	DSL	1.910169	2.547689	215.991379	16.687454	79372.537834	48340.476814	1.0	POINT (152.90791 10.97758)	POINT (152.90791 10.97758)	0.0
2	2006-09-08 13:00:00	2006-09-08 13:00:00	0 days 01:00:00	2	DSL	2.254961	2.883151	213.163315	4.442413	14036.478097	7223.353767	1.0	POINT (148.77232 8.90816)	POINT (148.77232 8.90816)	0.0
3	2006-09-08 13:00:00	2006-09-08 13:00:00	0 days 01:00:00	3	DSL	2.019073	2.915771	214.483337	2.475573	22263.09014	6159.967885	1.0	POINT (147.51589 7.72076)	POINT (147.51589 7.72076)	0.0
4	2006-09-08 13:00:00	2006-09-08 13:00:00	0 days 01:00:00	4	DSL	1.95028	2.571446	211.823792	5.197964	10596.159009	5433.912228	1.0	POINT (137.91402 15.61447)	POINT (137.91402 15.61447)	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
99	2006-09-08 22:00:00	2006-09-08 22:00:00	0 days 01:00:00	99	DSL	1.65246	1.845998	217.693146	5.018254	27287.757427	13371.649506	1.0	POINT (104.65093 -0.2743)	POINT (104.65093 -0.2743)	0.0
100	2006-09-08 23:00:00	2006-09-09 00:00:00	0 days 02:00:00	100	DSL	0.933822	1.233324	216.869774	2.780729	131313.619017	24776.843726	1.0	POINT (149.0416 -0.65489)	POINT (148.86278 -0.27059)	47.185759
101	2006-09-08 23:00:00	2006-09-09 00:00:00	0 days 02:00:00	101	DSL	1.131009	2.072433	215.648209	13.630206	715452.49923	304477.598491	1.0	POINT (91.68003 25.6258)	POINT (92.35389 25.77116)	76.739478
102	2006-09-09 00:00:00	2006-09-09 00:00:00	0 days 01:00:00	102	DSL	1.05563	1.742241	218.198257	3.076819	724340.022743	12713.449719	1.0	POINT (165.95451 29.77248)	POINT (165.95451 29.77248)	0.0
103	2006-09-09 00:00:00	2006-09-09 00:00:00	0 days 01:00:00	103	DSL	1.321026	1.65364	216.026505	7.512138	59910.587417	22585.544931	1.0	POINT (129.22112 31.61942)	POINT (129.22112 31.61942)	0.0

104 rows × 15 columns

mcs_summary.info()

<class 'geopandas.geodataframe.GeoDataFrame'>
RangeIndex: 104 entries, 0 to 103
Data columns (total 15 columns):
 #   Column            Non-Null Count  Dtype          
---  ------            --------------  -----          
 0   first_time        104 non-null    datetime64[ns] 
 1   last_time         104 non-null    datetime64[ns] 
 2   duration          104 non-null    timedelta64[ns]
 3   mcs_id            104 non-null    Int64          
 4   mcs_class         104 non-null    category       
 5   mean_mean_pr      104 non-null    Float64        
 6   mean_mean_pr219   104 non-null    Float64        
 7   mean_mean_ctt219  104 non-null    Float64        
 8   mean_std_ctt219   104 non-null    Float64        
 9   mean_area_km2     104 non-null    Float64        
 10  mean_area219_km2  104 non-null    Float64        
 11  mean_nce          104 non-null    Float64        
 12  first_centroid    104 non-null    geometry       
 13  last_centroid     104 non-null    geometry       
 14  distance_km       104 non-null    Float64        
dtypes: Float64(8), Int64(1), category(1), datetime64[ns](2), geometry(2), timedelta64[ns](1)
memory usage: 12.6 KB

mcs_summary.describe()

	first_time	last_time	duration	mcs_id	mean_mean_pr	mean_mean_pr219	mean_mean_ctt219	mean_std_ctt219	mean_area_km2	mean_area219_km2	mean_nce	distance_km
count	104	104	104	104.0	104.0	104.0	104.0	104.0	104.0	104.0	104.0	104.0
mean	2006-09-08 16:23:04.615384576	2006-09-08 17:59:25.384615424	0 days 02:36:20.769230769	51.5	1.156704	1.61739	213.8578	7.218748	208890.559483	83853.57485	1.066083	58.185853
min	2006-09-08 13:00:00	2006-09-08 13:00:00	0 days 01:00:00	0.0	0.187491	0.456384	205.555122	0.343413	10596.159009	4474.283814	1.0	0.0
25%	2006-09-08 14:00:00	2006-09-08 15:00:00	0 days 01:00:00	25.75	0.770805	1.092501	211.329475	4.331841	28062.533802	9114.303342	1.0	0.0
50%	2006-09-08 15:30:00	2006-09-08 17:00:00	0 days 01:00:00	51.5	1.059372	1.393241	214.508239	6.731265	65245.319034	18828.285517	1.0	0.0
75%	2006-09-08 18:00:00	2006-09-08 21:00:00	0 days 03:00:00	77.25	1.45971	1.95973	216.562508	9.324257	207862.252969	76796.45252	1.0	53.377108
max	2006-09-09 00:00:00	2006-09-09 00:00:00	0 days 10:00:00	103.0	3.069273	6.575438	222.817734	20.814117	2000734.41701	812164.938688	2.666667	750.167907
std	NaN	NaN	0 days 02:30:49.401384730	30.166206	0.547035	0.857315	3.514747	4.020769	351372.669445	150007.990804	0.226198	117.328995