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

Hide code cell output

<xarray.core.options.set_options at 0x79e9f330b4d0>

tams.run() expects certain variable names.

ds = tams.data.open_example("mpas-regridded").rename({"tb": "ctt"}).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
  * lat      (lat) float64 1kB -4.875 -4.625 -4.375 -4.125 ... 39.38 39.62 39.88
  * lon      (lon) float64 3kB 85.0 85.25 85.5 85.75 ... 169.2 169.5 169.8 170.0
Data variables:
    ctt      (time, lat, lon) float32 3MB ...
    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]')

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

    long_name :

    latitude

    units :

    degrees_north

    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])

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

    long_name :

    longitude

    units :

    degrees_east

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

• Data variables: (2)
  • ctt
    (time, lat, lon)
    float32
    ...

    long_name :

    brightness temperature

    units :

    K

    [736560 values with dtype=float32]

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

    long_name :

    precipitation rate

    units :

    mm h-1

    [736560 values with dtype=float32]

tams.run() produces three output datasets.

%%time

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

Show code cell output

Hide code cell output

Starting `identify` 2026-06-02 14:07:27

[Parallel(n_jobs=-2)]: Done   1 tasks      | elapsed:    0.7s

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

[Parallel(n_jobs=-2)]: Done   7 tasks      | elapsed:    2.2s

[Parallel(n_jobs=-2)]: Done  12 tasks      | elapsed:    3.5s
[Parallel(n_jobs=-2)]: Done  12 out of  12 | elapsed:    3.5s finished

Starting `track` 2026-06-02 14:07:31

Starting `classify` 2026-06-02 14:07:31

Starting statistics calculations 2026-06-02 14:07:32
Starting CE aggregation (into MCS time series) 2026-06-02 14:07:32

Starting gridded data aggregation 2026-06-02 14:07:32

[Parallel(n_jobs=-2)]: Done   1 tasks      | elapsed:    0.3s

[Parallel(n_jobs=-2)]: Done   4 tasks      | elapsed:    1.3s

[Parallel(n_jobs=-2)]: Done   7 tasks      | elapsed:    2.0s

Computing stats for MCS summary dataset 2026-06-02 14:07:36
Done 2026-06-02 14:07:36
CPU times: user 7.98 s, sys: 303 ms, total: 8.28 s
Wall time: 8.3 s

[Parallel(n_jobs=-2)]: Done  12 tasks      | elapsed:    3.3s
[Parallel(n_jobs=-2)]: Done  12 out of  12 | elapsed:    3.3s 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

	geometry	area_km2	core	area_core_km2	time	mcs_id	mcs_class
0	POLYGON ((167.5 9.49188, 166.5 9.54, 166.07978...	25634.1184	POLYGON ((167.5 9.61125, 167.41158 9.625, 166....	11798.035271	2006-09-08 13:00:00	0	DSL
1	POLYGON ((152.5 8.79886, 152.4243 8.875, 152.2...	79372.537834	MULTIPOLYGON (((152.25 9.23253, 152.16346 9.37...	48340.476814	2006-09-08 13:00:00	1	DSL
2	POLYGON ((148.75 8.23715, 148.50395 8.375, 148...	14036.478097	POLYGON ((148.75 8.34082, 148.68898 8.375, 148...	7223.353767	2006-09-08 13:00:00	2	DSL
3	POLYGON ((147.25 7.01224, 147 7.01659, 146.954...	22263.09014	POLYGON ((147.75 7.1996, 147.25 7.23491, 147.1...	6159.967885	2006-09-08 13:00:00	3	DSL
4	POLYGON ((138.25 15.00363, 138.04338 15.125, 1...	10596.159009	POLYGON ((138.25 15.10322, 138.21292 15.125, 1...	5433.912228	2006-09-08 13:00:00	4	DSL
...	...	...	...	...	...	...	...
261	POLYGON ((117.25 13.78693, 117 13.81703, 115 1...	155497.732041	MULTIPOLYGON (((116.98605 14.125, 117.75 14.53...	15771.546674	2006-09-09 00:00:00	57	CCC
262	POLYGON ((111 2.19542, 109.91985 2.875, 109.75...	264162.134187	MULTIPOLYGON (((110.25 3.20253, 110.17251 3.37...	80937.089703	2006-09-09 00:00:00	58	CCC
263	POLYGON ((110.75 20.62436, 110.74361 20.625, 1...	95355.10757	MULTIPOLYGON (((111.5 20.85344, 111.30824 20.8...	48925.646734	2006-09-09 00:00:00	97	DSL
264	POLYGON ((101.75 17.48897, 101.5 17.59824, 101...	296457.481388	MULTIPOLYGON (((103.50533 18.625, 103.53368 19...	97831.694327	2006-09-09 00:00:00	97	DSL
265	POLYGON ((101.75 -1.71823, 101.25 -1.55739, 10...	732976.336208	MULTIPOLYGON (((99.32592 3.875, 99.25 4.11006,...	427573.34013	2006-09-09 00:00:00	100	DSL

266 rows × 7 columns

ce.info()

<class 'geopandas.geodataframe.GeoDataFrame'>
RangeIndex: 266 entries, 0 to 265
Data columns (total 7 columns):
 #   Column         Non-Null Count  Dtype         
---  ------         --------------  -----         
 0   geometry       266 non-null    geometry      
 1   area_km2       266 non-null    Float64       
 2   core           266 non-null    geometry      
 3   area_core_km2  266 non-null    Float64       
 4   time           266 non-null    datetime64[ns]
 5   mcs_id         266 non-null    Int64         
 6   mcs_class      266 non-null    category      
dtypes: Float64(2), Int64(1), category(1), datetime64[ns](1), geometry(2)
memory usage: 13.8 KB

ce.area_km2.describe()

count             266.0
mean      299646.526759
std       435244.136045
min        10377.283677
25%        38009.793516
50%       138391.312977
75%       375933.734702
max      2801305.079679
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	geometry	core	nce	area_km2	area_core_km2	mcs_id	mcs_class	mean_pr	std_pr	npixel	mean_pr_core	std_pr_core	mean_ctt_core	std_ctt_core	npixel_core
0	2006-09-08 13:00:00	MULTIPOLYGON (((167.5 9.49188, 166.5 9.54, 166...	MULTIPOLYGON (((167.5 9.61125, 167.41158 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, 152.4243 8.875,...	MULTIPOLYGON (((152.25 9.23253, 152.16346 9.37...	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, 148.50395 8.37...	MULTIPOLYGON (((148.75 8.34082, 148.68898 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 7.01659, 1...	MULTIPOLYGON (((147.75 7.1996, 147.25 7.23491,...	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, 138.04338 15....	MULTIPOLYGON (((138.25 15.10322, 138.21292 15....	1	10596.159009	5433.912228	4	DSL	1.95028	0.935729	15	2.571446	0.641791	211.823792	5.197964	8
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
236	2006-09-09 00:00:00	MULTIPOLYGON (((126 16.04305, 125.25 16.32695,...	MULTIPOLYGON (((125.25 18.29594, 125.06833 18....	4	963155.281892	326617.371325	97	DSL	0.951342	1.145551	1243	1.409412	1.486175	214.83139	6.734364	442
237	2006-09-09 00:00:00	MULTIPOLYGON (((148.75 -2.21941, 147.5 -1.9227...	MULTIPOLYGON (((150.44311 -0.625, 150.5 -0.537...	1	157397.412137	40993.731489	99	DSL	0.900998	0.597975	205	1.25952	0.619944	217.582062	4.004134	55
238	2006-09-09 00:00:00	MULTIPOLYGON (((101.75 -1.71823, 101.25 -1.557...	MULTIPOLYGON (((99.32592 3.875, 99.25 4.11006,...	1	732976.336208	427573.34013	100	DSL	0.769442	0.954339	947	1.12346	1.051847	217.626099	13.392982	558
239	2006-09-09 00:00:00	MULTIPOLYGON (((155.5 -4.77577, 155 -4.77092, ...	MULTIPOLYGON (((153.5 -1.46794, 153.57218 -1.6...	1	489014.692674	149373.297305	103	DSL	0.846575	0.948353	628	1.271607	1.048026	216.019394	4.94757	202
240	2006-09-09 00:00:00	MULTIPOLYGON (((129.5 30.22046, 128.75 30.3713...	MULTIPOLYGON (((129.89756 30.375, 130 30.5994,...	1	59910.587417	22585.544931	104	DSL	1.321026	0.87163	80	1.65364	0.771776	216.026505	7.512138	33

241 rows × 16 columns

mcs.info()

<class 'geopandas.geodataframe.GeoDataFrame'>
RangeIndex: 241 entries, 0 to 240
Data columns (total 16 columns):
 #   Column         Non-Null Count  Dtype         
---  ------         --------------  -----         
 0   time           241 non-null    datetime64[ns]
 1   geometry       241 non-null    geometry      
 2   core           241 non-null    geometry      
 3   nce            241 non-null    Int64         
 4   area_km2       241 non-null    Float64       
 5   area_core_km2  241 non-null    Float64       
 6   mcs_id         241 non-null    Int64         
 7   mcs_class      241 non-null    category      
 8   mean_pr        241 non-null    Float64       
 9   std_pr         241 non-null    Float64       
 10  npixel         241 non-null    Int64         
 11  mean_pr_core   241 non-null    Float64       
 12  std_pr_core    241 non-null    Float64       
 13  mean_ctt_core  241 non-null    Float64       
 14  std_ctt_core   241 non-null    Float64       
 15  npixel_core    241 non-null    Int64         
dtypes: Float64(8), Int64(4), category(1), datetime64[ns](1), geometry(2)
memory usage: 31.6 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_pr_core	mean_mean_ctt_core	mean_std_ctt_core	mean_area_km2	mean_area_core_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
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
100	2006-09-08 23:00:00	2006-09-09 00:00:00	0 days 02:00:00	100	DSL	0.811172	1.199069	216.89772	13.187364	731530.027472	413782.578921	1.0	POINT (103.48369 2.7195)	POINT (103.56398 2.63599)	12.896688
101	2006-09-08 23:00:00	2006-09-08 23:00:00	0 days 01:00:00	101	DSL	0.420176	0.500188	216.85463	1.26252	35653.26044	4362.490837	1.0	POINT (101.19661 11.42259)	POINT (101.19661 11.42259)	0.0
102	2006-09-08 23:00:00	2006-09-08 23:00:00	0 days 01:00:00	102	DSL	0.418313	0.44796	216.057571	2.313324	59905.773641	13963.960685	1.0	POINT (96.17502 16.12416)	POINT (96.17502 16.12416)	0.0
103	2006-09-09 00:00:00	2006-09-09 00:00:00	0 days 01:00:00	103	DSL	0.846575	1.271607	216.019394	4.94757	489014.692674	149373.297305	1.0	POINT (155.5229 -0.60289)	POINT (155.5229 -0.60289)	0.0
104	2006-09-09 00:00:00	2006-09-09 00:00:00	0 days 01:00:00	104	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

105 rows × 15 columns

mcs_summary.info()

<class 'geopandas.geodataframe.GeoDataFrame'>
RangeIndex: 105 entries, 0 to 104
Data columns (total 15 columns):
 #   Column              Non-Null Count  Dtype          
---  ------              --------------  -----          
 0   first_time          105 non-null    datetime64[ns] 
 1   last_time           105 non-null    datetime64[ns] 
 2   duration            105 non-null    timedelta64[ns]
 3   mcs_id              105 non-null    Int64          
 4   mcs_class           105 non-null    category       
 5   mean_mean_pr        105 non-null    Float64        
 6   mean_mean_pr_core   105 non-null    Float64        
 7   mean_mean_ctt_core  105 non-null    Float64        
 8   mean_std_ctt_core   105 non-null    Float64        
 9   mean_area_km2       105 non-null    Float64        
 10  mean_area_core_km2  105 non-null    Float64        
 11  mean_nce            105 non-null    Float64        
 12  first_centroid      105 non-null    geometry       
 13  last_centroid       105 non-null    geometry       
 14  distance_km         105 non-null    Float64        
dtypes: Float64(8), Int64(1), category(1), datetime64[ns](2), geometry(2), timedelta64[ns](1)
memory usage: 12.8 KB

mcs_summary.describe()

	first_time	last_time	duration	mcs_id	mean_mean_pr	mean_mean_pr_core	mean_mean_ctt_core	mean_std_ctt_core	mean_area_km2	mean_area_core_km2	mean_nce	distance_km
count	105	105	105	105.0	105.0	105.0	105.0	105.0	105.0	105.0	105.0	105.0
mean	2006-09-08 16:31:25.714285824	2006-09-08 17:49:08.571428608	0 days 02:17:42.857142857	52.0	1.138997	1.590076	213.928635	6.939896	194988.950333	79454.696398	1.049501	51.122709
min	2006-09-08 13:00:00	2006-09-08 13:00:00	0 days 01:00:00	0.0	0.187491	0.44796	205.555122	0.343413	10596.159009	4362.490837	1.0	0.0
25%	2006-09-08 14:00:00	2006-09-08 15:00:00	0 days 01:00:00	26.0	0.770348	1.062927	211.345551	3.913308	27856.409327	9287.723999	1.0	0.0
50%	2006-09-08 16:00:00	2006-09-08 16:00:00	0 days 01:00:00	52.0	1.063115	1.36649	214.644318	6.444789	63671.779856	17972.930837	1.0	0.0
75%	2006-09-08 18:00:00	2006-09-08 21:00:00	0 days 03:00:00	78.0	1.407423	1.845998	216.561127	9.052417	197646.566652	76259.805545	1.0	45.682202
max	2006-09-09 00:00:00	2006-09-09 00:00:00	0 days 10:00:00	104.0	3.069273	6.575438	222.615143	20.648977	1782531.771679	808963.602143	2.666667	750.167907
std	NaN	NaN	0 days 02:11:00.676927481	30.454885	0.542146	0.86736	3.482926	4.062472	328631.541102	139784.626896	0.205075	116.360032