hydro_opendata.data.minio模块¶

整合minio中的数据读写

era5-land数据¶

In [1]:

from hydro_opendata.data.minio import Era5L
import numpy as np
import geopandas as gpd

from hydro_opendata.data.minio import Era5L import numpy as np import geopandas as gpd

In [2]:

era5 = Era5L()

era5 = Era5L()

主要通过catalog和reader两个属性对象进行数据操作¶

In [3]:

# dir(era5.catalog), dir(era5.reader)

print(era5.catalog.spatial_resolution)

# dir(era5.catalog), dir(era5.reader) print(era5.catalog.spatial_resolution)

0.1 x 0.1; Native resolution is 9 km.

获取流域范围数据¶

In [4]:

aoi = gpd.read_file('basin.geojson')
aoi

aoi = gpd.read_file('basin.geojson') aoi

Out[4]:

	id	geometry
0	0	MULTIPOLYGON (((122.44241 39.80139, 122.39342 ...

利用catalog获取era5数据的数据集¶

In [5]:

era5.catalog.datasets

era5.catalog.datasets

Out[5]:

{'wis': {'start_time': numpy.datetime64('2015-07-01T00:00:00'),
  'end_time': numpy.datetime64('2021-12-31T23:00:00'),
  'bbox': [115, 38, 136, 54]}}

• 利用catalog.search()方法搜索era5数据的数据集，可指定时间范围和空间范围

In [6]:

e = era5.catalog.search(aoi=aoi)
e

e = era5.catalog.search(aoi=aoi) e

Out[6]:

	id	dataset	start_time	end_time	geometry
0	era5-land	wis	2015-07-01T00:00:00	2021-12-31T23:00:00	POLYGON ((122.39342 39.81027, 122.31744 39.833...

利用reader读取era5数据¶

• 通过指定四至范围读取era5数据

In [7]:

bbox=(121,39,123,40)
start_time=np.datetime64("2021-06-01T00:00:00.000000000")
end_time=np.datetime64("2021-06-30T23:00:00.000000000")

e1 = era5.reader.open_dataset(data_variables=['Total precipitation'], start_time=start_time, end_time=end_time, dataset='wis', bbox=bbox)
e1

bbox=(121,39,123,40) start_time=np.datetime64("2021-06-01T00:00:00.000000000") end_time=np.datetime64("2021-06-30T23:00:00.000000000") e1 = era5.reader.open_dataset(data_variables=['Total precipitation'], start_time=start_time, end_time=end_time, dataset='wis', bbox=bbox) e1

/home/zhujianfeng/.conda/envs/dev-env/lib/python3.10/site-packages/xarray/backends/plugins.py:80: RuntimeWarning: Engine 'cfgrib' loading failed:
Cannot find the ecCodes library
  warnings.warn(f"Engine {name!r} loading failed:\n{ex}", RuntimeWarning)

Out[7]:

<xarray.Dataset>
Dimensions:  (time: 720, lon: 21, lat: 11)
Coordinates:
  * lat      (lat) float32 39.0 39.1 39.2 39.3 39.4 ... 39.6 39.7 39.8 39.9 40.0
  * lon      (lon) float32 121.0 121.1 121.2 121.3 ... 122.7 122.8 122.9 123.0
  * time     (time) datetime64[ns] 2021-06-01 ... 2021-06-30T23:00:00
Data variables:
    tp       (time, lon, lat) float32 dask.array<chunksize=(24, 21, 11), meta=np.ndarray>
Attributes:
    Conventions:  CF-1.6
    history:      2022-07-21 12:13:40 GMT by grib_to_netcdf-2.25.1: /opt/ecmw...

xarray.Dataset

• Dimensions:
  • time: 720
  • lon: 21
  • lat: 11
• Coordinates: (3)
  • lat
    (lat)
    float32
    39.0 39.1 39.2 ... 39.8 39.9 40.0

    long_name :

    latitude

    units :

    degrees_north

    array([39. , 39.1, 39.2, 39.3, 39.4, 39.5, 39.6, 39.7, 39.8, 39.9, 40. ],
          dtype=float32)

  • lon
    (lon)
    float32
    121.0 121.1 121.2 ... 122.9 123.0

    long_name :

    longitude

    units :

    degrees_east

    array([121. , 121.1, 121.2, 121.3, 121.4, 121.5, 121.6, 121.7, 121.8, 121.9,
           122. , 122.1, 122.2, 122.3, 122.4, 122.5, 122.6, 122.7, 122.8, 122.9,
           123. ], dtype=float32)

  • time
    (time)
    datetime64[ns]
    2021-06-01 ... 2021-06-30T23:00:00

    long_name :

    time

    array(['2021-06-01T00:00:00.000000000', '2021-06-01T01:00:00.000000000',
           '2021-06-01T02:00:00.000000000', ..., '2021-06-30T21:00:00.000000000',
           '2021-06-30T22:00:00.000000000', '2021-06-30T23:00:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (1)
  • tp
    (time, lon, lat)
    float32
    dask.array<chunksize=(24, 21, 11), meta=np.ndarray>

    long_name :

    Total precipitation

    units :

    m

    Array Chunk Bytes 649.69 kiB 21.66 kiB Shape (720, 21, 11) (24, 21, 11) Dask graph 30 chunks in 6 graph layers Data type float32 numpy.ndarray

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Index([              39.0, 39.099998474121094,  39.20000076293945,
            39.29999923706055, 39.400001525878906,               39.5,
           39.599998474121094,  39.70000076293945,  39.79999923706055,
           39.900001525878906,               40.0],
          dtype='float32', name='lat'))

  • lon
    PandasIndex

    PandasIndex(Index([             121.0,  121.0999984741211, 121.19999694824219,
           121.30000305175781,  121.4000015258789,              121.5,
            121.5999984741211, 121.69999694824219, 121.80000305175781,
            121.9000015258789,              122.0,  122.0999984741211,
           122.19999694824219, 122.30000305175781,  122.4000015258789,
                        122.5,  122.5999984741211, 122.69999694824219,
           122.80000305175781,  122.9000015258789,              123.0],
          dtype='float32', name='lon'))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2021-06-01 00:00:00', '2021-06-01 01:00:00',
                   '2021-06-01 02:00:00', '2021-06-01 03:00:00',
                   '2021-06-01 04:00:00', '2021-06-01 05:00:00',
                   '2021-06-01 06:00:00', '2021-06-01 07:00:00',
                   '2021-06-01 08:00:00', '2021-06-01 09:00:00',
                   ...
                   '2021-06-30 14:00:00', '2021-06-30 15:00:00',
                   '2021-06-30 16:00:00', '2021-06-30 17:00:00',
                   '2021-06-30 18:00:00', '2021-06-30 19:00:00',
                   '2021-06-30 20:00:00', '2021-06-30 21:00:00',
                   '2021-06-30 22:00:00', '2021-06-30 23:00:00'],
                  dtype='datetime64[ns]', name='time', length=720, freq=None))

• Attributes: (2)

  Conventions :

  CF-1.6

  history :

  2022-07-21 12:13:40 GMT by grib_to_netcdf-2.25.1: /opt/ecmwf/mars-client/bin/grib_to_netcdf.bin -S param -o /cache/data3/adaptor.mars.internal-1658405615.0302866-15351-15-83c8a3b2-abf2-46ba-a14c-ec308c207ff8.nc /cache/tmp/83c8a3b2-abf2-46ba-a14c-ec308c207ff8-adaptor.mars.internal-1658405415.5680676-15351-27-tmp.grib

• 通过矢量数据文件读取

In [8]:

shp = 'basin.geojson'
e2 = era5.reader.from_shp(data_variables=['Total precipitation'], start_time=start_time, end_time=end_time, dataset='wis', shp=shp)
e2

shp = 'basin.geojson' e2 = era5.reader.from_shp(data_variables=['Total precipitation'], start_time=start_time, end_time=end_time, dataset='wis', shp=shp) e2

Out[8]:

<xarray.Dataset>
Dimensions:  (time: 720, lon: 6, lat: 5)
Coordinates:
  * lat      (lat) float32 39.8 39.9 40.0 40.1 40.2
  * lon      (lon) float32 122.3 122.4 122.5 122.6 122.7 122.8
  * time     (time) datetime64[ns] 2021-06-01 ... 2021-06-30T23:00:00
Data variables:
    tp       (time, lon, lat) float32 dask.array<chunksize=(24, 6, 5), meta=np.ndarray>
Attributes:
    Conventions:  CF-1.6
    history:      2022-07-21 12:13:40 GMT by grib_to_netcdf-2.25.1: /opt/ecmw...

xarray.Dataset

• Dimensions:
  • time: 720
  • lon: 6
  • lat: 5
• Coordinates: (3)
  • lat
    (lat)
    float32
    39.8 39.9 40.0 40.1 40.2

    long_name :

    latitude

    units :

    degrees_north

    array([39.8, 39.9, 40. , 40.1, 40.2], dtype=float32)

  • lon
    (lon)
    float32
    122.3 122.4 122.5 122.6 122.7 122.8

    long_name :

    longitude

    units :

    degrees_east

    array([122.3, 122.4, 122.5, 122.6, 122.7, 122.8], dtype=float32)

  • time
    (time)
    datetime64[ns]
    2021-06-01 ... 2021-06-30T23:00:00

    long_name :

    time

    array(['2021-06-01T00:00:00.000000000', '2021-06-01T01:00:00.000000000',
           '2021-06-01T02:00:00.000000000', ..., '2021-06-30T21:00:00.000000000',
           '2021-06-30T22:00:00.000000000', '2021-06-30T23:00:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (1)
  • tp
    (time, lon, lat)
    float32
    dask.array<chunksize=(24, 6, 5), meta=np.ndarray>

    long_name :

    Total precipitation

    units :

    m

    Array Chunk Bytes 84.38 kiB 2.81 kiB Shape (720, 6, 5) (24, 6, 5) Dask graph 30 chunks in 6 graph layers Data type float32 numpy.ndarray

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Index([ 39.79999923706055, 39.900001525878906,               40.0,
           40.099998474121094,  40.20000076293945],
          dtype='float32', name='lat'))

  • lon
    PandasIndex

    PandasIndex(Index([122.30000305175781,  122.4000015258789,              122.5,
            122.5999984741211, 122.69999694824219, 122.80000305175781],
          dtype='float32', name='lon'))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2021-06-01 00:00:00', '2021-06-01 01:00:00',
                   '2021-06-01 02:00:00', '2021-06-01 03:00:00',
                   '2021-06-01 04:00:00', '2021-06-01 05:00:00',
                   '2021-06-01 06:00:00', '2021-06-01 07:00:00',
                   '2021-06-01 08:00:00', '2021-06-01 09:00:00',
                   ...
                   '2021-06-30 14:00:00', '2021-06-30 15:00:00',
                   '2021-06-30 16:00:00', '2021-06-30 17:00:00',
                   '2021-06-30 18:00:00', '2021-06-30 19:00:00',
                   '2021-06-30 20:00:00', '2021-06-30 21:00:00',
                   '2021-06-30 22:00:00', '2021-06-30 23:00:00'],
                  dtype='datetime64[ns]', name='time', length=720, freq=None))

• Attributes: (2)

  Conventions :

  CF-1.6

  history :

  2022-07-21 12:13:40 GMT by grib_to_netcdf-2.25.1: /opt/ecmwf/mars-client/bin/grib_to_netcdf.bin -S param -o /cache/data3/adaptor.mars.internal-1658405615.0302866-15351-15-83c8a3b2-abf2-46ba-a14c-ec308c207ff8.nc /cache/tmp/83c8a3b2-abf2-46ba-a14c-ec308c207ff8-adaptor.mars.internal-1658405415.5680676-15351-27-tmp.grib

• 通过已有aoi对象读取

In [9]:

e3 = era5.reader.from_aoi(data_variables=['Total precipitation'], start_time=start_time, end_time=end_time, dataset='wis', aoi=aoi)
e3

e3 = era5.reader.from_aoi(data_variables=['Total precipitation'], start_time=start_time, end_time=end_time, dataset='wis', aoi=aoi) e3

Out[9]:

<xarray.Dataset>
Dimensions:  (time: 720, lon: 6, lat: 5)
Coordinates:
  * lat      (lat) float32 39.8 39.9 40.0 40.1 40.2
  * lon      (lon) float32 122.3 122.4 122.5 122.6 122.7 122.8
  * time     (time) datetime64[ns] 2021-06-01 ... 2021-06-30T23:00:00
Data variables:
    tp       (time, lon, lat) float32 dask.array<chunksize=(24, 6, 5), meta=np.ndarray>
Attributes:
    Conventions:  CF-1.6
    history:      2022-07-21 12:13:40 GMT by grib_to_netcdf-2.25.1: /opt/ecmw...

xarray.Dataset

• Dimensions:
  • time: 720
  • lon: 6
  • lat: 5
• Coordinates: (3)
  • lat
    (lat)
    float32
    39.8 39.9 40.0 40.1 40.2

    long_name :

    latitude

    units :

    degrees_north

    array([39.8, 39.9, 40. , 40.1, 40.2], dtype=float32)

  • lon
    (lon)
    float32
    122.3 122.4 122.5 122.6 122.7 122.8

    long_name :

    longitude

    units :

    degrees_east

    array([122.3, 122.4, 122.5, 122.6, 122.7, 122.8], dtype=float32)

  • time
    (time)
    datetime64[ns]
    2021-06-01 ... 2021-06-30T23:00:00

    long_name :

    time

    array(['2021-06-01T00:00:00.000000000', '2021-06-01T01:00:00.000000000',
           '2021-06-01T02:00:00.000000000', ..., '2021-06-30T21:00:00.000000000',
           '2021-06-30T22:00:00.000000000', '2021-06-30T23:00:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (1)
  • tp
    (time, lon, lat)
    float32
    dask.array<chunksize=(24, 6, 5), meta=np.ndarray>

    long_name :

    Total precipitation

    units :

    m

    Array Chunk Bytes 84.38 kiB 2.81 kiB Shape (720, 6, 5) (24, 6, 5) Dask graph 30 chunks in 6 graph layers Data type float32 numpy.ndarray

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Index([ 39.79999923706055, 39.900001525878906,               40.0,
           40.099998474121094,  40.20000076293945],
          dtype='float32', name='lat'))

  • lon
    PandasIndex

    PandasIndex(Index([122.30000305175781,  122.4000015258789,              122.5,
            122.5999984741211, 122.69999694824219, 122.80000305175781],
          dtype='float32', name='lon'))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2021-06-01 00:00:00', '2021-06-01 01:00:00',
                   '2021-06-01 02:00:00', '2021-06-01 03:00:00',
                   '2021-06-01 04:00:00', '2021-06-01 05:00:00',
                   '2021-06-01 06:00:00', '2021-06-01 07:00:00',
                   '2021-06-01 08:00:00', '2021-06-01 09:00:00',
                   ...
                   '2021-06-30 14:00:00', '2021-06-30 15:00:00',
                   '2021-06-30 16:00:00', '2021-06-30 17:00:00',
                   '2021-06-30 18:00:00', '2021-06-30 19:00:00',
                   '2021-06-30 20:00:00', '2021-06-30 21:00:00',
                   '2021-06-30 22:00:00', '2021-06-30 23:00:00'],
                  dtype='datetime64[ns]', name='time', length=720, freq=None))

• Attributes: (2)

  Conventions :

  CF-1.6

  history :

  2022-07-21 12:13:40 GMT by grib_to_netcdf-2.25.1: /opt/ecmwf/mars-client/bin/grib_to_netcdf.bin -S param -o /cache/data3/adaptor.mars.internal-1658405615.0302866-15351-15-83c8a3b2-abf2-46ba-a14c-ec308c207ff8.nc /cache/tmp/83c8a3b2-abf2-46ba-a14c-ec308c207ff8-adaptor.mars.internal-1658405415.5680676-15351-27-tmp.grib

gpm数据¶

In [10]:

from hydro_opendata.data.minio import GPM

from hydro_opendata.data.minio import GPM

In [11]:

gpm = GPM()

gpm = GPM()

主要通过catalog和reader两个属性对象进行数据操作¶

In [12]:

# dir(gpm.catalog), dir(gpm.reader)

print(gpm.catalog.temporal_resolution)

# dir(gpm.catalog), dir(gpm.reader) print(gpm.catalog.temporal_resolution)

half-hourly; 1 day

利用catalog获取gpm数据的数据集¶

In [13]:

gpm.catalog.datasets

gpm.catalog.datasets

Out[13]:

{'wis': [{'time_resolution': '30 minutes',
   'start_time': numpy.datetime64('2016-01-01T00:00:00.000000000'),
   'end_time': numpy.datetime64('2023-10-11T08:30:00.000000000'),
   'bbox': [73, 3, 136, 54]},
  {'time_resolution': '1 day',
   'start_time': numpy.datetime64('2000-06-01T00:00:00.000000000'),
   'end_time': numpy.datetime64('2023-09-26T23:59:59.000000000'),
   'bbox': [73, 3, 136, 54]}],
 'camels': [{'time_resolution': '30 minutes',
   'start_time': numpy.datetime64('2022-01-01T00:00:00.000000000'),
   'end_time': numpy.datetime64('2023-08-31T23:30:00.000000000'),
   'bbox': [-125, 25, -66, 50]},
  {'time_resolution': '1 day',
   'start_time': numpy.datetime64('2000-06-01T00:00:00.000000000'),
   'end_time': numpy.datetime64('2014-12-31T23:59:59.000000000'),
   'bbox': [-125, 25, -66, 50]}]}

• 利用catalog.search()方法搜索gpm数据的数据集，可指定时间范围和空间范围

In [14]:

g = gpm.catalog.search(aoi=aoi)
g

g = gpm.catalog.search(aoi=aoi) g

Out[14]:

	id	dataset	time_resolution	start_time	end_time	geometry
0	gpm-imerg-early	wis	30 minutes	2016-01-01T00:00:00.000000000	2023-10-11T08:30:00.000000000	POLYGON ((122.39342 39.81027, 122.31744 39.833...
0	gpm-imerg-early	wis	1 day	2000-06-01T00:00:00.000000000	2023-09-26T23:59:59.000000000	POLYGON ((122.39342 39.81027, 122.31744 39.833...

利用reader读取gpm数据¶

• 通过指定四至范围读取

In [15]:

bbox=(121,39,123,40)
start_time=np.datetime64("2023-06-01T00:00:00.000000000")
end_time=np.datetime64("2023-06-30T23:30:00.000000000")

g1 = gpm.reader.open_dataset(start_time=start_time, end_time=end_time, dataset='wis', bbox=bbox, time_resolution='30m')
g1

bbox=(121,39,123,40) start_time=np.datetime64("2023-06-01T00:00:00.000000000") end_time=np.datetime64("2023-06-30T23:30:00.000000000") g1 = gpm.reader.open_dataset(start_time=start_time, end_time=end_time, dataset='wis', bbox=bbox, time_resolution='30m') g1

Out[15]:

<xarray.DataArray 'precipitationCal' (time: 1440, lon: 20, lat: 10)>
dask.array<getitem, shape=(1440, 20, 10), dtype=float32, chunksize=(48, 20, 10), chunktype=numpy.ndarray>
Coordinates:
  * lat      (lat) float32 39.05 39.15 39.25 39.35 ... 39.65 39.75 39.85 39.95
  * lon      (lon) float32 121.1 121.2 121.2 121.3 ... 122.7 122.8 122.8 122.9
  * time     (time) datetime64[ns] 2023-06-01 ... 2023-06-30T23:30:00
Attributes:
    CodeMissingValue:  -9999.9
    DimensionNames:    time,lon,lat
    Units:             mm/hr
    units:             mm/hr

xarray.DataArray

'precipitationCal'

• time: 1440
• lon: 20
• lat: 10

• dask.array<chunksize=(48, 20, 10), meta=np.ndarray>

  Array Chunk Bytes 1.10 MiB 37.50 kiB Shape (1440, 20, 10) (48, 20, 10) Dask graph 30 chunks in 3 graph layers Data type float32 numpy.ndarray

• Coordinates: (3)
  • lat
    (lat)
    float32
    39.05 39.15 39.25 ... 39.85 39.95

    axis :

    Y

    long_name :

    latitude

    standard_name :

    latitude

    units :

    degrees_north

    array([39.05, 39.15, 39.25, 39.35, 39.45, 39.55, 39.65, 39.75, 39.85, 39.95],
          dtype=float32)

  • lon
    (lon)
    float32
    121.1 121.2 121.2 ... 122.8 122.9

    axis :

    X

    long_name :

    longitude

    standard_name :

    longitude

    units :

    degrees_east

    array([121.05, 121.15, 121.25, 121.35, 121.45, 121.55, 121.65, 121.75, 121.85,
           121.95, 122.05, 122.15, 122.25, 122.35, 122.45, 122.55, 122.65, 122.75,
           122.85, 122.95], dtype=float32)

  • time
    (time)
    datetime64[ns]
    2023-06-01 ... 2023-06-30T23:30:00

    axis :

    T

    standard_name :

    time

    array(['2023-06-01T00:00:00.000000000', '2023-06-01T00:30:00.000000000',
           '2023-06-01T01:00:00.000000000', ..., '2023-06-30T22:30:00.000000000',
           '2023-06-30T23:00:00.000000000', '2023-06-30T23:30:00.000000000'],
          dtype='datetime64[ns]')

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Index([ 39.04999923706055, 39.150001525878906,              39.25,
           39.349998474121094,  39.45000076293945,  39.54999923706055,
           39.650001525878906,              39.75, 39.849998474121094,
            39.95000076293945],
          dtype='float32', name='lat'))

  • lon
    PandasIndex

    PandasIndex(Index([121.05000305175781,  121.1500015258789,             121.25,
            121.3499984741211, 121.44999694824219, 121.55000305175781,
            121.6500015258789,             121.75,  121.8499984741211,
           121.94999694824219, 122.05000305175781,  122.1500015258789,
                       122.25,  122.3499984741211, 122.44999694824219,
           122.55000305175781,  122.6500015258789,             122.75,
            122.8499984741211, 122.94999694824219],
          dtype='float32', name='lon'))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2023-06-01 00:00:00', '2023-06-01 00:30:00',
                   '2023-06-01 01:00:00', '2023-06-01 01:30:00',
                   '2023-06-01 02:00:00', '2023-06-01 02:30:00',
                   '2023-06-01 03:00:00', '2023-06-01 03:30:00',
                   '2023-06-01 04:00:00', '2023-06-01 04:30:00',
                   ...
                   '2023-06-30 19:00:00', '2023-06-30 19:30:00',
                   '2023-06-30 20:00:00', '2023-06-30 20:30:00',
                   '2023-06-30 21:00:00', '2023-06-30 21:30:00',
                   '2023-06-30 22:00:00', '2023-06-30 22:30:00',
                   '2023-06-30 23:00:00', '2023-06-30 23:30:00'],
                  dtype='datetime64[ns]', name='time', length=1440, freq=None))

• Attributes: (4)

  CodeMissingValue :

  -9999.9

  DimensionNames :

  time,lon,lat

  Units :

  mm/hr

  units :

  mm/hr

• 通过矢量数据文件读取

In [16]:

shp = 'basin.geojson'
g2 = gpm.reader.from_shp(start_time=start_time, end_time=end_time, dataset='wis', shp=shp, time_resolution='30m')
g2

shp = 'basin.geojson' g2 = gpm.reader.from_shp(start_time=start_time, end_time=end_time, dataset='wis', shp=shp, time_resolution='30m') g2

Out[16]:

<xarray.DataArray 'precipitationCal' (time: 1440, lon: 5, lat: 4)>
dask.array<getitem, shape=(1440, 5, 4), dtype=float32, chunksize=(48, 5, 4), chunktype=numpy.ndarray>
Coordinates:
  * lat      (lat) float32 39.85 39.95 40.05 40.15
  * lon      (lon) float32 122.3 122.4 122.6 122.7 122.8
  * time     (time) datetime64[ns] 2023-06-01 ... 2023-06-30T23:30:00
Attributes:
    CodeMissingValue:  -9999.9
    DimensionNames:    time,lon,lat
    Units:             mm/hr
    units:             mm/hr

xarray.DataArray

'precipitationCal'

• time: 1440
• lon: 5
• lat: 4

• dask.array<chunksize=(48, 5, 4), meta=np.ndarray>

  Array Chunk Bytes 112.50 kiB 3.75 kiB Shape (1440, 5, 4) (48, 5, 4) Dask graph 30 chunks in 3 graph layers Data type float32 numpy.ndarray

• Coordinates: (3)
  • lat
    (lat)
    float32
    39.85 39.95 40.05 40.15

    axis :

    Y

    long_name :

    latitude

    standard_name :

    latitude

    units :

    degrees_north

    array([39.85, 39.95, 40.05, 40.15], dtype=float32)

  • lon
    (lon)
    float32
    122.3 122.4 122.6 122.7 122.8

    axis :

    X

    long_name :

    longitude

    standard_name :

    longitude

    units :

    degrees_east

    array([122.35, 122.45, 122.55, 122.65, 122.75], dtype=float32)

  • time
    (time)
    datetime64[ns]
    2023-06-01 ... 2023-06-30T23:30:00

    axis :

    T

    standard_name :

    time

    array(['2023-06-01T00:00:00.000000000', '2023-06-01T00:30:00.000000000',
           '2023-06-01T01:00:00.000000000', ..., '2023-06-30T22:30:00.000000000',
           '2023-06-30T23:00:00.000000000', '2023-06-30T23:30:00.000000000'],
          dtype='datetime64[ns]')

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Index([39.849998474121094, 39.95000076293945, 40.04999923706055,
           40.150001525878906],
          dtype='float32', name='lat'))

  • lon
    PandasIndex

    PandasIndex(Index([ 122.3499984741211, 122.44999694824219, 122.55000305175781,
            122.6500015258789,             122.75],
          dtype='float32', name='lon'))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2023-06-01 00:00:00', '2023-06-01 00:30:00',
                   '2023-06-01 01:00:00', '2023-06-01 01:30:00',
                   '2023-06-01 02:00:00', '2023-06-01 02:30:00',
                   '2023-06-01 03:00:00', '2023-06-01 03:30:00',
                   '2023-06-01 04:00:00', '2023-06-01 04:30:00',
                   ...
                   '2023-06-30 19:00:00', '2023-06-30 19:30:00',
                   '2023-06-30 20:00:00', '2023-06-30 20:30:00',
                   '2023-06-30 21:00:00', '2023-06-30 21:30:00',
                   '2023-06-30 22:00:00', '2023-06-30 22:30:00',
                   '2023-06-30 23:00:00', '2023-06-30 23:30:00'],
                  dtype='datetime64[ns]', name='time', length=1440, freq=None))

• Attributes: (4)

  CodeMissingValue :

  -9999.9

  DimensionNames :

  time,lon,lat

  Units :

  mm/hr

  units :

  mm/hr

• 通过已有aoi对象读取

In [17]:

aoi = gpd.read_file(shp)
g3 = gpm.reader.from_aoi(start_time=start_time, end_time=end_time, dataset='wis', aoi=aoi, time_resolution='30m')
g3

aoi = gpd.read_file(shp) g3 = gpm.reader.from_aoi(start_time=start_time, end_time=end_time, dataset='wis', aoi=aoi, time_resolution='30m') g3

Out[17]:

<xarray.DataArray 'precipitationCal' (time: 1440, lon: 5, lat: 4)>
dask.array<getitem, shape=(1440, 5, 4), dtype=float32, chunksize=(48, 5, 4), chunktype=numpy.ndarray>
Coordinates:
  * lat      (lat) float32 39.85 39.95 40.05 40.15
  * lon      (lon) float32 122.3 122.4 122.6 122.7 122.8
  * time     (time) datetime64[ns] 2023-06-01 ... 2023-06-30T23:30:00
Attributes:
    CodeMissingValue:  -9999.9
    DimensionNames:    time,lon,lat
    Units:             mm/hr
    units:             mm/hr

xarray.DataArray

'precipitationCal'

• time: 1440
• lon: 5
• lat: 4

• dask.array<chunksize=(48, 5, 4), meta=np.ndarray>

  Array Chunk Bytes 112.50 kiB 3.75 kiB Shape (1440, 5, 4) (48, 5, 4) Dask graph 30 chunks in 3 graph layers Data type float32 numpy.ndarray

• Coordinates: (3)
  • lat
    (lat)
    float32
    39.85 39.95 40.05 40.15

    axis :

    Y

    long_name :

    latitude

    standard_name :

    latitude

    units :

    degrees_north

    array([39.85, 39.95, 40.05, 40.15], dtype=float32)

  • lon
    (lon)
    float32
    122.3 122.4 122.6 122.7 122.8

    axis :

    X

    long_name :

    longitude

    standard_name :

    longitude

    units :

    degrees_east

    array([122.35, 122.45, 122.55, 122.65, 122.75], dtype=float32)

  • time
    (time)
    datetime64[ns]
    2023-06-01 ... 2023-06-30T23:30:00

    axis :

    T

    standard_name :

    time

    array(['2023-06-01T00:00:00.000000000', '2023-06-01T00:30:00.000000000',
           '2023-06-01T01:00:00.000000000', ..., '2023-06-30T22:30:00.000000000',
           '2023-06-30T23:00:00.000000000', '2023-06-30T23:30:00.000000000'],
          dtype='datetime64[ns]')

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Index([39.849998474121094, 39.95000076293945, 40.04999923706055,
           40.150001525878906],
          dtype='float32', name='lat'))

  • lon
    PandasIndex

    PandasIndex(Index([ 122.3499984741211, 122.44999694824219, 122.55000305175781,
            122.6500015258789,             122.75],
          dtype='float32', name='lon'))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2023-06-01 00:00:00', '2023-06-01 00:30:00',
                   '2023-06-01 01:00:00', '2023-06-01 01:30:00',
                   '2023-06-01 02:00:00', '2023-06-01 02:30:00',
                   '2023-06-01 03:00:00', '2023-06-01 03:30:00',
                   '2023-06-01 04:00:00', '2023-06-01 04:30:00',
                   ...
                   '2023-06-30 19:00:00', '2023-06-30 19:30:00',
                   '2023-06-30 20:00:00', '2023-06-30 20:30:00',
                   '2023-06-30 21:00:00', '2023-06-30 21:30:00',
                   '2023-06-30 22:00:00', '2023-06-30 22:30:00',
                   '2023-06-30 23:00:00', '2023-06-30 23:30:00'],
                  dtype='datetime64[ns]', name='time', length=1440, freq=None))

• Attributes: (4)

  CodeMissingValue :

  -9999.9

  DimensionNames :

  time,lon,lat

  Units :

  mm/hr

  units :

  mm/hr

gfs数据¶

In [18]:

from hydro_opendata.data.minio import GFS

from hydro_opendata.data.minio import GFS

In [19]:

gfs = GFS('tp')    # 需要指定变量名，tp表示降雨

gfs = GFS('tp') # 需要指定变量名，tp表示降雨

主要通过catalog和reader两个属性对象进行数据操作¶

In [20]:

# dir(gfs.catalog), dir(gfs.reader)

print(gfs.catalog.data_sources)

# dir(gfs.catalog), dir(gfs.reader) print(gfs.catalog.data_sources)

NOAA

利用catalog获取gfs数据的数据集¶

In [21]:

gfs.catalog.datasets

gfs.catalog.datasets

Out[21]:

{'wis': [{'start': '2016-07-10T00',
   'end': '2022-08-31T18',
   'bbox': [115, 38, 136, 54]},
  {'start': '2022-09-01T00',
   'end': '2023-10-11T06',
   'bbox': [73, 3, 136, 54]}]}

• 利用catalog.search()方法搜索gfs数据的数据集，可指定时间范围和空间范围

In [22]:

f = gfs.catalog.search(aoi=aoi)
f

f = gfs.catalog.search(aoi=aoi) f

Out[22]:

	id	dataset	start_time	end_time	geometry
0	gfs_atmos.tp	wis	2016-07-10T00	2022-08-31T18	POLYGON ((122.39342 39.81027, 122.31744 39.833...
0	gfs_atmos.tp	wis	2022-09-01T00	2023-10-11T06	POLYGON ((122.39342 39.81027, 122.31744 39.833...

利用reader读取gfs数据¶

• 通过指定四至范围读取

In [23]:

bbox=(121,39,123,40)
creation_date=np.datetime64("2023-06-01")

f1 = gfs.reader.open_dataset(creation_date=creation_date, creation_time='00', dataset='wis', bbox=bbox)
f1

bbox=(121,39,123,40) creation_date=np.datetime64("2023-06-01") f1 = gfs.reader.open_dataset(creation_date=creation_date, creation_time='00', dataset='wis', bbox=bbox) f1

Out[23]:

<xarray.Dataset>
Dimensions:     (lat: 5, lon: 9, step: 120, time: 1, valid_time: 120)
Coordinates:
  * lat         (lat) float64 39.0 39.25 39.5 39.75 40.0
  * lon         (lon) float64 121.0 121.2 121.5 121.8 ... 122.5 122.8 123.0
  * step        (step) int64 1 2 3 4 5 6 7 8 ... 113 114 115 116 117 118 119 120
  * time        (time) datetime64[ns] 2023-06-01
  * valid_time  (valid_time) datetime64[ns] 2023-06-01T01:00:00 ... 2023-06-06
Data variables:
    tp          (valid_time, step, lat, lon) float64 dask.array<chunksize=(24, 1, 5, 9), meta=np.ndarray>
Attributes:
    centre:             kwbc
    centreDescription:  US National Weather Service - NCEP
    edition:            2
    subCentre:          0

xarray.Dataset

• Dimensions:
  • lat: 5
  • lon: 9
  • step: 120
  • time: 1
  • valid_time: 120
• Coordinates: (5)
  • lat
    (lat)
    float64
    39.0 39.25 39.5 39.75 40.0

    long_name :

    latitude

    standard_name :

    latitude

    units :

    degrees_north

    array([39.  , 39.25, 39.5 , 39.75, 40.  ])

  • lon
    (lon)
    float64
    121.0 121.2 121.5 ... 122.8 123.0

    long_name :

    longitude

    standard_name :

    longitude

    units :

    degrees_east

    array([121.  , 121.25, 121.5 , 121.75, 122.  , 122.25, 122.5 , 122.75, 123.  ])

  • step
    (step)
    int64
    1 2 3 4 5 6 ... 116 117 118 119 120

    array([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,  14,
            15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,  28,
            29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,  42,
            43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,  56,
            57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,  70,
            71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,  84,
            85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,  98,
            99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112,
           113, 114, 115, 116, 117, 118, 119, 120])

  • time
    (time)
    datetime64[ns]
    2023-06-01

    long_name :

    initial time of forecast

    standard_name :

    forecast_reference_time

    array(['2023-06-01T00:00:00.000000000'], dtype='datetime64[ns]')

  • valid_time
    (valid_time)
    datetime64[ns]
    2023-06-01T01:00:00 ... 2023-06-06

    _FillValue :

    1970-01-01T00:00:00.000000000

    array(['2023-06-01T01:00:00.000000000', '2023-06-01T02:00:00.000000000',
           '2023-06-01T03:00:00.000000000', '2023-06-01T04:00:00.000000000',
           '2023-06-01T05:00:00.000000000', '2023-06-01T06:00:00.000000000',
           '2023-06-01T07:00:00.000000000', '2023-06-01T08:00:00.000000000',
           '2023-06-01T09:00:00.000000000', '2023-06-01T10:00:00.000000000',
           '2023-06-01T11:00:00.000000000', '2023-06-01T12:00:00.000000000',
           '2023-06-01T13:00:00.000000000', '2023-06-01T14:00:00.000000000',
           '2023-06-01T15:00:00.000000000', '2023-06-01T16:00:00.000000000',
           '2023-06-01T17:00:00.000000000', '2023-06-01T18:00:00.000000000',
           '2023-06-01T19:00:00.000000000', '2023-06-01T20:00:00.000000000',
           '2023-06-01T21:00:00.000000000', '2023-06-01T22:00:00.000000000',
           '2023-06-01T23:00:00.000000000', '2023-06-02T00:00:00.000000000',
           '2023-06-02T01:00:00.000000000', '2023-06-02T02:00:00.000000000',
           '2023-06-02T03:00:00.000000000', '2023-06-02T04:00:00.000000000',
           '2023-06-02T05:00:00.000000000', '2023-06-02T06:00:00.000000000',
           '2023-06-02T07:00:00.000000000', '2023-06-02T08:00:00.000000000',
           '2023-06-02T09:00:00.000000000', '2023-06-02T10:00:00.000000000',
           '2023-06-02T11:00:00.000000000', '2023-06-02T12:00:00.000000000',
           '2023-06-02T13:00:00.000000000', '2023-06-02T14:00:00.000000000',
           '2023-06-02T15:00:00.000000000', '2023-06-02T16:00:00.000000000',
           '2023-06-02T17:00:00.000000000', '2023-06-02T18:00:00.000000000',
           '2023-06-02T19:00:00.000000000', '2023-06-02T20:00:00.000000000',
           '2023-06-02T21:00:00.000000000', '2023-06-02T22:00:00.000000000',
           '2023-06-02T23:00:00.000000000', '2023-06-03T00:00:00.000000000',
           '2023-06-03T01:00:00.000000000', '2023-06-03T02:00:00.000000000',
           '2023-06-03T03:00:00.000000000', '2023-06-03T04:00:00.000000000',
           '2023-06-03T05:00:00.000000000', '2023-06-03T06:00:00.000000000',
           '2023-06-03T07:00:00.000000000', '2023-06-03T08:00:00.000000000',
           '2023-06-03T09:00:00.000000000', '2023-06-03T10:00:00.000000000',
           '2023-06-03T11:00:00.000000000', '2023-06-03T12:00:00.000000000',
           '2023-06-03T13:00:00.000000000', '2023-06-03T14:00:00.000000000',
           '2023-06-03T15:00:00.000000000', '2023-06-03T16:00:00.000000000',
           '2023-06-03T17:00:00.000000000', '2023-06-03T18:00:00.000000000',
           '2023-06-03T19:00:00.000000000', '2023-06-03T20:00:00.000000000',
           '2023-06-03T21:00:00.000000000', '2023-06-03T22:00:00.000000000',
           '2023-06-03T23:00:00.000000000', '2023-06-04T00:00:00.000000000',
           '2023-06-04T01:00:00.000000000', '2023-06-04T02:00:00.000000000',
           '2023-06-04T03:00:00.000000000', '2023-06-04T04:00:00.000000000',
           '2023-06-04T05:00:00.000000000', '2023-06-04T06:00:00.000000000',
           '2023-06-04T07:00:00.000000000', '2023-06-04T08:00:00.000000000',
           '2023-06-04T09:00:00.000000000', '2023-06-04T10:00:00.000000000',
           '2023-06-04T11:00:00.000000000', '2023-06-04T12:00:00.000000000',
           '2023-06-04T13:00:00.000000000', '2023-06-04T14:00:00.000000000',
           '2023-06-04T15:00:00.000000000', '2023-06-04T16:00:00.000000000',
           '2023-06-04T17:00:00.000000000', '2023-06-04T18:00:00.000000000',
           '2023-06-04T19:00:00.000000000', '2023-06-04T20:00:00.000000000',
           '2023-06-04T21:00:00.000000000', '2023-06-04T22:00:00.000000000',
           '2023-06-04T23:00:00.000000000', '2023-06-05T00:00:00.000000000',
           '2023-06-05T01:00:00.000000000', '2023-06-05T02:00:00.000000000',
           '2023-06-05T03:00:00.000000000', '2023-06-05T04:00:00.000000000',
           '2023-06-05T05:00:00.000000000', '2023-06-05T06:00:00.000000000',
           '2023-06-05T07:00:00.000000000', '2023-06-05T08:00:00.000000000',
           '2023-06-05T09:00:00.000000000', '2023-06-05T10:00:00.000000000',
           '2023-06-05T11:00:00.000000000', '2023-06-05T12:00:00.000000000',
           '2023-06-05T13:00:00.000000000', '2023-06-05T14:00:00.000000000',
           '2023-06-05T15:00:00.000000000', '2023-06-05T16:00:00.000000000',
           '2023-06-05T17:00:00.000000000', '2023-06-05T18:00:00.000000000',
           '2023-06-05T19:00:00.000000000', '2023-06-05T20:00:00.000000000',
           '2023-06-05T21:00:00.000000000', '2023-06-05T22:00:00.000000000',
           '2023-06-05T23:00:00.000000000', '2023-06-06T00:00:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (1)
  • tp
    (valid_time, step, lat, lon)
    float64
    dask.array<chunksize=(24, 1, 5, 9), meta=np.ndarray>

    NV :

    0

    cfName :

    unknown

    cfVarName :

    unknown

    dataDate :

    20230601

    dataTime :

    0

    dataType :

    fc

    endStep :

    1

    gridDefinitionDescription :

    Latitude/longitude. Also called equidistant cylindrical, or Plate Carree

    gridType :

    regular_ll

    missingValue :

    9999

    name :

    Total Precipitation

    numberOfPoints :

    51865

    paramId :

    228228

    shortName :

    tp

    stepType :

    accum

    stepUnits :

    1

    typeOfLevel :

    surface

    units :

    kg m**-2

    Array Chunk Bytes 4.94 MiB 8.44 kiB Shape (120, 120, 5, 9) (24, 1, 5, 9) Dask graph 600 chunks in 3 graph layers Data type float64 numpy.ndarray

• Indexes: (5)
  • lat
    PandasIndex

    PandasIndex(Index([39.0, 39.25, 39.5, 39.75, 40.0], dtype='float64', name='lat'))

  • lon
    PandasIndex

    PandasIndex(Index([121.0, 121.25, 121.5, 121.75, 122.0, 122.25, 122.5, 122.75, 123.0], dtype='float64', name='lon'))

  • step
    PandasIndex

    PandasIndex(Index([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,
           ...
           111, 112, 113, 114, 115, 116, 117, 118, 119, 120],
          dtype='int64', name='step', length=120))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2023-06-01'], dtype='datetime64[ns]', name='time', freq=None))

  • valid_time
    PandasIndex

    PandasIndex(DatetimeIndex(['2023-06-01 01:00:00', '2023-06-01 02:00:00',
                   '2023-06-01 03:00:00', '2023-06-01 04:00:00',
                   '2023-06-01 05:00:00', '2023-06-01 06:00:00',
                   '2023-06-01 07:00:00', '2023-06-01 08:00:00',
                   '2023-06-01 09:00:00', '2023-06-01 10:00:00',
                   ...
                   '2023-06-05 15:00:00', '2023-06-05 16:00:00',
                   '2023-06-05 17:00:00', '2023-06-05 18:00:00',
                   '2023-06-05 19:00:00', '2023-06-05 20:00:00',
                   '2023-06-05 21:00:00', '2023-06-05 22:00:00',
                   '2023-06-05 23:00:00', '2023-06-06 00:00:00'],
                  dtype='datetime64[ns]', name='valid_time', length=120, freq=None))

• Attributes: (4)

  centre :

  kwbc

  centreDescription :

  US National Weather Service - NCEP

  edition :

  2

  subCentre :

  0

• 通过矢量数据文件读取

In [24]:

shp = 'basin.geojson'
f2 = gfs.reader.from_shp(creation_date=creation_date, creation_time='00', dataset='wis', shp=shp)
f2

shp = 'basin.geojson' f2 = gfs.reader.from_shp(creation_date=creation_date, creation_time='00', dataset='wis', shp=shp) f2

Out[24]:

<xarray.Dataset>
Dimensions:     (lat: 3, lon: 3, step: 120, time: 1, valid_time: 120)
Coordinates:
  * lat         (lat) float64 39.75 40.0 40.25
  * lon         (lon) float64 122.2 122.5 122.8
  * step        (step) int64 1 2 3 4 5 6 7 8 ... 113 114 115 116 117 118 119 120
  * time        (time) datetime64[ns] 2023-06-01
  * valid_time  (valid_time) datetime64[ns] 2023-06-01T01:00:00 ... 2023-06-06
Data variables:
    tp          (valid_time, step, lat, lon) float64 dask.array<chunksize=(24, 1, 3, 3), meta=np.ndarray>
Attributes:
    centre:             kwbc
    centreDescription:  US National Weather Service - NCEP
    edition:            2
    subCentre:          0

xarray.Dataset

• Dimensions:
  • lat: 3
  • lon: 3
  • step: 120
  • time: 1
  • valid_time: 120
• Coordinates: (5)
  • lat
    (lat)
    float64
    39.75 40.0 40.25

    long_name :

    latitude

    standard_name :

    latitude

    units :

    degrees_north

    array([39.75, 40.  , 40.25])

  • lon
    (lon)
    float64
    122.2 122.5 122.8

    long_name :

    longitude

    standard_name :

    longitude

    units :

    degrees_east

    array([122.25, 122.5 , 122.75])

  • step
    (step)
    int64
    1 2 3 4 5 6 ... 116 117 118 119 120

    array([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,  14,
            15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,  28,
            29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,  42,
            43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,  56,
            57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,  70,
            71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,  84,
            85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,  98,
            99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112,
           113, 114, 115, 116, 117, 118, 119, 120])

  • time
    (time)
    datetime64[ns]
    2023-06-01

    long_name :

    initial time of forecast

    standard_name :

    forecast_reference_time

    array(['2023-06-01T00:00:00.000000000'], dtype='datetime64[ns]')

  • valid_time
    (valid_time)
    datetime64[ns]
    2023-06-01T01:00:00 ... 2023-06-06

    _FillValue :

    1970-01-01T00:00:00.000000000

    array(['2023-06-01T01:00:00.000000000', '2023-06-01T02:00:00.000000000',
           '2023-06-01T03:00:00.000000000', '2023-06-01T04:00:00.000000000',
           '2023-06-01T05:00:00.000000000', '2023-06-01T06:00:00.000000000',
           '2023-06-01T07:00:00.000000000', '2023-06-01T08:00:00.000000000',
           '2023-06-01T09:00:00.000000000', '2023-06-01T10:00:00.000000000',
           '2023-06-01T11:00:00.000000000', '2023-06-01T12:00:00.000000000',
           '2023-06-01T13:00:00.000000000', '2023-06-01T14:00:00.000000000',
           '2023-06-01T15:00:00.000000000', '2023-06-01T16:00:00.000000000',
           '2023-06-01T17:00:00.000000000', '2023-06-01T18:00:00.000000000',
           '2023-06-01T19:00:00.000000000', '2023-06-01T20:00:00.000000000',
           '2023-06-01T21:00:00.000000000', '2023-06-01T22:00:00.000000000',
           '2023-06-01T23:00:00.000000000', '2023-06-02T00:00:00.000000000',
           '2023-06-02T01:00:00.000000000', '2023-06-02T02:00:00.000000000',
           '2023-06-02T03:00:00.000000000', '2023-06-02T04:00:00.000000000',
           '2023-06-02T05:00:00.000000000', '2023-06-02T06:00:00.000000000',
           '2023-06-02T07:00:00.000000000', '2023-06-02T08:00:00.000000000',
           '2023-06-02T09:00:00.000000000', '2023-06-02T10:00:00.000000000',
           '2023-06-02T11:00:00.000000000', '2023-06-02T12:00:00.000000000',
           '2023-06-02T13:00:00.000000000', '2023-06-02T14:00:00.000000000',
           '2023-06-02T15:00:00.000000000', '2023-06-02T16:00:00.000000000',
           '2023-06-02T17:00:00.000000000', '2023-06-02T18:00:00.000000000',
           '2023-06-02T19:00:00.000000000', '2023-06-02T20:00:00.000000000',
           '2023-06-02T21:00:00.000000000', '2023-06-02T22:00:00.000000000',
           '2023-06-02T23:00:00.000000000', '2023-06-03T00:00:00.000000000',
           '2023-06-03T01:00:00.000000000', '2023-06-03T02:00:00.000000000',
           '2023-06-03T03:00:00.000000000', '2023-06-03T04:00:00.000000000',
           '2023-06-03T05:00:00.000000000', '2023-06-03T06:00:00.000000000',
           '2023-06-03T07:00:00.000000000', '2023-06-03T08:00:00.000000000',
           '2023-06-03T09:00:00.000000000', '2023-06-03T10:00:00.000000000',
           '2023-06-03T11:00:00.000000000', '2023-06-03T12:00:00.000000000',
           '2023-06-03T13:00:00.000000000', '2023-06-03T14:00:00.000000000',
           '2023-06-03T15:00:00.000000000', '2023-06-03T16:00:00.000000000',
           '2023-06-03T17:00:00.000000000', '2023-06-03T18:00:00.000000000',
           '2023-06-03T19:00:00.000000000', '2023-06-03T20:00:00.000000000',
           '2023-06-03T21:00:00.000000000', '2023-06-03T22:00:00.000000000',
           '2023-06-03T23:00:00.000000000', '2023-06-04T00:00:00.000000000',
           '2023-06-04T01:00:00.000000000', '2023-06-04T02:00:00.000000000',
           '2023-06-04T03:00:00.000000000', '2023-06-04T04:00:00.000000000',
           '2023-06-04T05:00:00.000000000', '2023-06-04T06:00:00.000000000',
           '2023-06-04T07:00:00.000000000', '2023-06-04T08:00:00.000000000',
           '2023-06-04T09:00:00.000000000', '2023-06-04T10:00:00.000000000',
           '2023-06-04T11:00:00.000000000', '2023-06-04T12:00:00.000000000',
           '2023-06-04T13:00:00.000000000', '2023-06-04T14:00:00.000000000',
           '2023-06-04T15:00:00.000000000', '2023-06-04T16:00:00.000000000',
           '2023-06-04T17:00:00.000000000', '2023-06-04T18:00:00.000000000',
           '2023-06-04T19:00:00.000000000', '2023-06-04T20:00:00.000000000',
           '2023-06-04T21:00:00.000000000', '2023-06-04T22:00:00.000000000',
           '2023-06-04T23:00:00.000000000', '2023-06-05T00:00:00.000000000',
           '2023-06-05T01:00:00.000000000', '2023-06-05T02:00:00.000000000',
           '2023-06-05T03:00:00.000000000', '2023-06-05T04:00:00.000000000',
           '2023-06-05T05:00:00.000000000', '2023-06-05T06:00:00.000000000',
           '2023-06-05T07:00:00.000000000', '2023-06-05T08:00:00.000000000',
           '2023-06-05T09:00:00.000000000', '2023-06-05T10:00:00.000000000',
           '2023-06-05T11:00:00.000000000', '2023-06-05T12:00:00.000000000',
           '2023-06-05T13:00:00.000000000', '2023-06-05T14:00:00.000000000',
           '2023-06-05T15:00:00.000000000', '2023-06-05T16:00:00.000000000',
           '2023-06-05T17:00:00.000000000', '2023-06-05T18:00:00.000000000',
           '2023-06-05T19:00:00.000000000', '2023-06-05T20:00:00.000000000',
           '2023-06-05T21:00:00.000000000', '2023-06-05T22:00:00.000000000',
           '2023-06-05T23:00:00.000000000', '2023-06-06T00:00:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (1)
  • tp
    (valid_time, step, lat, lon)
    float64
    dask.array<chunksize=(24, 1, 3, 3), meta=np.ndarray>

    NV :

    0

    cfName :

    unknown

    cfVarName :

    unknown

    dataDate :

    20230601

    dataTime :

    0

    dataType :

    fc

    endStep :

    1

    gridDefinitionDescription :

    Latitude/longitude. Also called equidistant cylindrical, or Plate Carree

    gridType :

    regular_ll

    missingValue :

    9999

    name :

    Total Precipitation

    numberOfPoints :

    51865

    paramId :

    228228

    shortName :

    tp

    stepType :

    accum

    stepUnits :

    1

    typeOfLevel :

    surface

    units :

    kg m**-2

    Array Chunk Bytes 0.99 MiB 1.69 kiB Shape (120, 120, 3, 3) (24, 1, 3, 3) Dask graph 600 chunks in 3 graph layers Data type float64 numpy.ndarray

• Indexes: (5)
  • lat
    PandasIndex

    PandasIndex(Index([39.75, 40.0, 40.25], dtype='float64', name='lat'))

  • lon
    PandasIndex

    PandasIndex(Index([122.25, 122.5, 122.75], dtype='float64', name='lon'))

  • step
    PandasIndex

    PandasIndex(Index([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,
           ...
           111, 112, 113, 114, 115, 116, 117, 118, 119, 120],
          dtype='int64', name='step', length=120))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2023-06-01'], dtype='datetime64[ns]', name='time', freq=None))

  • valid_time
    PandasIndex

    PandasIndex(DatetimeIndex(['2023-06-01 01:00:00', '2023-06-01 02:00:00',
                   '2023-06-01 03:00:00', '2023-06-01 04:00:00',
                   '2023-06-01 05:00:00', '2023-06-01 06:00:00',
                   '2023-06-01 07:00:00', '2023-06-01 08:00:00',
                   '2023-06-01 09:00:00', '2023-06-01 10:00:00',
                   ...
                   '2023-06-05 15:00:00', '2023-06-05 16:00:00',
                   '2023-06-05 17:00:00', '2023-06-05 18:00:00',
                   '2023-06-05 19:00:00', '2023-06-05 20:00:00',
                   '2023-06-05 21:00:00', '2023-06-05 22:00:00',
                   '2023-06-05 23:00:00', '2023-06-06 00:00:00'],
                  dtype='datetime64[ns]', name='valid_time', length=120, freq=None))

• Attributes: (4)

  centre :

  kwbc

  centreDescription :

  US National Weather Service - NCEP

  edition :

  2

  subCentre :

  0

• 通过已有aoi对象读取

In [25]:

aoi = gpd.read_file(shp)
f3 = gfs.reader.from_aoi(creation_date=creation_date, creation_time='00', dataset='wis', aoi=aoi)
f3

aoi = gpd.read_file(shp) f3 = gfs.reader.from_aoi(creation_date=creation_date, creation_time='00', dataset='wis', aoi=aoi) f3

Out[25]:

<xarray.Dataset>
Dimensions:     (lat: 3, lon: 3, step: 120, time: 1, valid_time: 120)
Coordinates:
  * lat         (lat) float64 39.75 40.0 40.25
  * lon         (lon) float64 122.2 122.5 122.8
  * step        (step) int64 1 2 3 4 5 6 7 8 ... 113 114 115 116 117 118 119 120
  * time        (time) datetime64[ns] 2023-06-01
  * valid_time  (valid_time) datetime64[ns] 2023-06-01T01:00:00 ... 2023-06-06
Data variables:
    tp          (valid_time, step, lat, lon) float64 dask.array<chunksize=(24, 1, 3, 3), meta=np.ndarray>
Attributes:
    centre:             kwbc
    centreDescription:  US National Weather Service - NCEP
    edition:            2
    subCentre:          0

xarray.Dataset

• Dimensions:
  • lat: 3
  • lon: 3
  • step: 120
  • time: 1
  • valid_time: 120
• Coordinates: (5)
  • lat
    (lat)
    float64
    39.75 40.0 40.25

    long_name :

    latitude

    standard_name :

    latitude

    units :

    degrees_north

    array([39.75, 40.  , 40.25])

  • lon
    (lon)
    float64
    122.2 122.5 122.8

    long_name :

    longitude

    standard_name :

    longitude

    units :

    degrees_east

    array([122.25, 122.5 , 122.75])

  • step
    (step)
    int64
    1 2 3 4 5 6 ... 116 117 118 119 120

    array([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,  14,
            15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,  28,
            29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,  42,
            43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,  56,
            57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,  70,
            71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,  84,
            85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,  98,
            99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112,
           113, 114, 115, 116, 117, 118, 119, 120])

  • time
    (time)
    datetime64[ns]
    2023-06-01

    long_name :

    initial time of forecast

    standard_name :

    forecast_reference_time

    array(['2023-06-01T00:00:00.000000000'], dtype='datetime64[ns]')

  • valid_time
    (valid_time)
    datetime64[ns]
    2023-06-01T01:00:00 ... 2023-06-06

    _FillValue :

    1970-01-01T00:00:00.000000000

    array(['2023-06-01T01:00:00.000000000', '2023-06-01T02:00:00.000000000',
           '2023-06-01T03:00:00.000000000', '2023-06-01T04:00:00.000000000',
           '2023-06-01T05:00:00.000000000', '2023-06-01T06:00:00.000000000',
           '2023-06-01T07:00:00.000000000', '2023-06-01T08:00:00.000000000',
           '2023-06-01T09:00:00.000000000', '2023-06-01T10:00:00.000000000',
           '2023-06-01T11:00:00.000000000', '2023-06-01T12:00:00.000000000',
           '2023-06-01T13:00:00.000000000', '2023-06-01T14:00:00.000000000',
           '2023-06-01T15:00:00.000000000', '2023-06-01T16:00:00.000000000',
           '2023-06-01T17:00:00.000000000', '2023-06-01T18:00:00.000000000',
           '2023-06-01T19:00:00.000000000', '2023-06-01T20:00:00.000000000',
           '2023-06-01T21:00:00.000000000', '2023-06-01T22:00:00.000000000',
           '2023-06-01T23:00:00.000000000', '2023-06-02T00:00:00.000000000',
           '2023-06-02T01:00:00.000000000', '2023-06-02T02:00:00.000000000',
           '2023-06-02T03:00:00.000000000', '2023-06-02T04:00:00.000000000',
           '2023-06-02T05:00:00.000000000', '2023-06-02T06:00:00.000000000',
           '2023-06-02T07:00:00.000000000', '2023-06-02T08:00:00.000000000',
           '2023-06-02T09:00:00.000000000', '2023-06-02T10:00:00.000000000',
           '2023-06-02T11:00:00.000000000', '2023-06-02T12:00:00.000000000',
           '2023-06-02T13:00:00.000000000', '2023-06-02T14:00:00.000000000',
           '2023-06-02T15:00:00.000000000', '2023-06-02T16:00:00.000000000',
           '2023-06-02T17:00:00.000000000', '2023-06-02T18:00:00.000000000',
           '2023-06-02T19:00:00.000000000', '2023-06-02T20:00:00.000000000',
           '2023-06-02T21:00:00.000000000', '2023-06-02T22:00:00.000000000',
           '2023-06-02T23:00:00.000000000', '2023-06-03T00:00:00.000000000',
           '2023-06-03T01:00:00.000000000', '2023-06-03T02:00:00.000000000',
           '2023-06-03T03:00:00.000000000', '2023-06-03T04:00:00.000000000',
           '2023-06-03T05:00:00.000000000', '2023-06-03T06:00:00.000000000',
           '2023-06-03T07:00:00.000000000', '2023-06-03T08:00:00.000000000',
           '2023-06-03T09:00:00.000000000', '2023-06-03T10:00:00.000000000',
           '2023-06-03T11:00:00.000000000', '2023-06-03T12:00:00.000000000',
           '2023-06-03T13:00:00.000000000', '2023-06-03T14:00:00.000000000',
           '2023-06-03T15:00:00.000000000', '2023-06-03T16:00:00.000000000',
           '2023-06-03T17:00:00.000000000', '2023-06-03T18:00:00.000000000',
           '2023-06-03T19:00:00.000000000', '2023-06-03T20:00:00.000000000',
           '2023-06-03T21:00:00.000000000', '2023-06-03T22:00:00.000000000',
           '2023-06-03T23:00:00.000000000', '2023-06-04T00:00:00.000000000',
           '2023-06-04T01:00:00.000000000', '2023-06-04T02:00:00.000000000',
           '2023-06-04T03:00:00.000000000', '2023-06-04T04:00:00.000000000',
           '2023-06-04T05:00:00.000000000', '2023-06-04T06:00:00.000000000',
           '2023-06-04T07:00:00.000000000', '2023-06-04T08:00:00.000000000',
           '2023-06-04T09:00:00.000000000', '2023-06-04T10:00:00.000000000',
           '2023-06-04T11:00:00.000000000', '2023-06-04T12:00:00.000000000',
           '2023-06-04T13:00:00.000000000', '2023-06-04T14:00:00.000000000',
           '2023-06-04T15:00:00.000000000', '2023-06-04T16:00:00.000000000',
           '2023-06-04T17:00:00.000000000', '2023-06-04T18:00:00.000000000',
           '2023-06-04T19:00:00.000000000', '2023-06-04T20:00:00.000000000',
           '2023-06-04T21:00:00.000000000', '2023-06-04T22:00:00.000000000',
           '2023-06-04T23:00:00.000000000', '2023-06-05T00:00:00.000000000',
           '2023-06-05T01:00:00.000000000', '2023-06-05T02:00:00.000000000',
           '2023-06-05T03:00:00.000000000', '2023-06-05T04:00:00.000000000',
           '2023-06-05T05:00:00.000000000', '2023-06-05T06:00:00.000000000',
           '2023-06-05T07:00:00.000000000', '2023-06-05T08:00:00.000000000',
           '2023-06-05T09:00:00.000000000', '2023-06-05T10:00:00.000000000',
           '2023-06-05T11:00:00.000000000', '2023-06-05T12:00:00.000000000',
           '2023-06-05T13:00:00.000000000', '2023-06-05T14:00:00.000000000',
           '2023-06-05T15:00:00.000000000', '2023-06-05T16:00:00.000000000',
           '2023-06-05T17:00:00.000000000', '2023-06-05T18:00:00.000000000',
           '2023-06-05T19:00:00.000000000', '2023-06-05T20:00:00.000000000',
           '2023-06-05T21:00:00.000000000', '2023-06-05T22:00:00.000000000',
           '2023-06-05T23:00:00.000000000', '2023-06-06T00:00:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (1)
  • tp
    (valid_time, step, lat, lon)
    float64
    dask.array<chunksize=(24, 1, 3, 3), meta=np.ndarray>

    NV :

    0

    cfName :

    unknown

    cfVarName :

    unknown

    dataDate :

    20230601

    dataTime :

    0

    dataType :

    fc

    endStep :

    1

    gridDefinitionDescription :

    Latitude/longitude. Also called equidistant cylindrical, or Plate Carree

    gridType :

    regular_ll

    missingValue :

    9999

    name :

    Total Precipitation

    numberOfPoints :

    51865

    paramId :

    228228

    shortName :

    tp

    stepType :

    accum

    stepUnits :

    1

    typeOfLevel :

    surface

    units :

    kg m**-2

    Array Chunk Bytes 0.99 MiB 1.69 kiB Shape (120, 120, 3, 3) (24, 1, 3, 3) Dask graph 600 chunks in 3 graph layers Data type float64 numpy.ndarray

• Indexes: (5)
  • lat
    PandasIndex

    PandasIndex(Index([39.75, 40.0, 40.25], dtype='float64', name='lat'))

  • lon
    PandasIndex

    PandasIndex(Index([122.25, 122.5, 122.75], dtype='float64', name='lon'))

  • step
    PandasIndex

    PandasIndex(Index([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,
           ...
           111, 112, 113, 114, 115, 116, 117, 118, 119, 120],
          dtype='int64', name='step', length=120))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2023-06-01'], dtype='datetime64[ns]', name='time', freq=None))

  • valid_time
    PandasIndex

    PandasIndex(DatetimeIndex(['2023-06-01 01:00:00', '2023-06-01 02:00:00',
                   '2023-06-01 03:00:00', '2023-06-01 04:00:00',
                   '2023-06-01 05:00:00', '2023-06-01 06:00:00',
                   '2023-06-01 07:00:00', '2023-06-01 08:00:00',
                   '2023-06-01 09:00:00', '2023-06-01 10:00:00',
                   ...
                   '2023-06-05 15:00:00', '2023-06-05 16:00:00',
                   '2023-06-05 17:00:00', '2023-06-05 18:00:00',
                   '2023-06-05 19:00:00', '2023-06-05 20:00:00',
                   '2023-06-05 21:00:00', '2023-06-05 22:00:00',
                   '2023-06-05 23:00:00', '2023-06-06 00:00:00'],
                  dtype='datetime64[ns]', name='valid_time', length=120, freq=None))

• Attributes: (4)

  centre :

  kwbc

  centreDescription :

  US National Weather Service - NCEP

  edition :

  2

  subCentre :

  0

---

Last update: 2023-10-11