Daymet

Data from Daily Surface Weather and Climatological Summaries (Daymet)¶

In [1]:

from springtime.datasets.meteo.daymet import DaymetSinglePoint, DaymetMultiplePoints, DaymetBoundingBox

from springtime.datasets.meteo.daymet import DaymetSinglePoint, DaymetMultiplePoints, DaymetBoundingBox

Data at a single point¶

In [2]:

# The latitude of Washington, the USA is 47.751076, and the longitude is -120.740135
target_lat = 47.751076
target_lon = -120.740135

# The latitude of Washington, the USA is 47.751076, and the longitude is -120.740135 target_lat = 47.751076 target_lon = -120.740135

In [3]:

# Create a dataset instance
dataset = DaymetSinglePoint(point=(target_lon,target_lat), years=[1990, 2020])
dataset

# Create a dataset instance dataset = DaymetSinglePoint(point=(target_lon,target_lat), years=[1990, 2020]) dataset

Out[3]:

DaymetSinglePoint(dataset='daymet_single_point', years=YearRange(start=1990, end=2020), variables=(), point=(-120.740135, 47.751076))

In [4]:

# Download dataset
dataset.download()

# Download dataset dataset.download()

In [5]:

# Load downloded data as a dataframe
df = dataset.load()
df.head()

# Load downloded data as a dataframe df = dataset.load() df.head()

Out[5]:

	datetime	geometry	dayl	prcp	srad	swe	tmax	tmin	vp
0	1990-01-01	POINT (-120.74013 47.75108)	29818.61	12.68	86.56	66.10	2.14	-2.68	501.57
1	1990-01-02	POINT (-120.74013 47.75108)	29875.77	10.44	114.20	66.00	1.47	-5.76	397.66
2	1990-01-03	POINT (-120.74013 47.75108)	29937.57	7.58	98.15	65.27	1.33	-4.27	445.06
3	1990-01-04	POINT (-120.74013 47.75108)	30003.96	15.35	111.24	63.49	5.19	-1.76	537.01
4	1990-01-05	POINT (-120.74013 47.75108)	30074.88	11.16	109.80	61.99	4.23	-2.43	510.71

Data at a single point and single variable¶

In [ ]:

# The latitude of Washington, the USA is 47.751076, and the longitude is -120.740135
target_lat = 47.751076
target_lon = -120.740135

# The latitude of Washington, the USA is 47.751076, and the longitude is -120.740135 target_lat = 47.751076 target_lon = -120.740135

In [6]:

# Create a dataset instance
dataset = DaymetSinglePoint(point=(target_lon,target_lat), years=[1990, 2020], variables=("tmin",))
dataset

# Create a dataset instance dataset = DaymetSinglePoint(point=(target_lon,target_lat), years=[1990, 2020], variables=("tmin",)) dataset

Out[6]:

DaymetSinglePoint(dataset='daymet_single_point', years=YearRange(start=1990, end=2020), variables=('tmin',), point=(-120.740135, 47.751076))

In [7]:

# Download dataset
dataset.download()

# Download dataset dataset.download()

In [8]:

# Load downloded data as a dataframe
df_wa_point = dataset.load()
# df_wa_point.head()
df_wa_point

# Load downloded data as a dataframe df_wa_point = dataset.load() # df_wa_point.head() df_wa_point

Out[8]:

	datetime	geometry	tmin
0	1990-01-01	POINT (-120.74013 47.75108)	-2.68
1	1990-01-02	POINT (-120.74013 47.75108)	-5.76
2	1990-01-03	POINT (-120.74013 47.75108)	-4.27
3	1990-01-04	POINT (-120.74013 47.75108)	-1.76
4	1990-01-05	POINT (-120.74013 47.75108)	-2.43
...	...	...	...
11310	2020-12-26	POINT (-120.74013 47.75108)	-5.66
11311	2020-12-27	POINT (-120.74013 47.75108)	-3.84
11312	2020-12-28	POINT (-120.74013 47.75108)	-4.36
11313	2020-12-29	POINT (-120.74013 47.75108)	-4.93
11314	2020-12-30	POINT (-120.74013 47.75108)	-3.71

11315 rows × 3 columns

Data from a bounding box¶

In [ ]:

# Create a bounding box
# top left / bottom right pair (lat,lon,lat,lon)
area = {'name': 'indianapolis', 'bbox':  [-86.5,39.5, -86, 40.1]}

# Create an instance of dataset
dataset = DaymetBoundingBox(area=area, years=[2019, 2020])
dataset

# Create a bounding box # top left / bottom right pair (lat,lon,lat,lon) area = {'name': 'indianapolis', 'bbox': [-86.5,39.5, -86, 40.1]} # Create an instance of dataset dataset = DaymetBoundingBox(area=area, years=[2019, 2020]) dataset

Out[ ]:

DaymetBoundingBox(dataset='daymet_bounding_box', years=YearRange(start=2019, end=2020), area=NamedArea(name='indianapolis', bbox=BoundingBox(xmin=-86.5, ymin=39.5, xmax=-86.0, ymax=40.1)), mosaic='na', variables=('dayl', 'prcp', 'srad', 'swe', 'tmax', 'tmin', 'vp'), frequency='daily')

In [ ]:

# Download data
dataset.download()

# Download data dataset.download()

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In [ ]:

# Load data as a xarray.Dataset
xa_wa = dataset.load()
xa_wa

# Load data as a xarray.Dataset xa_wa = dataset.load() xa_wa

Out[ ]:

<xarray.Dataset>
Dimensions:                  (y: 70, x: 52, time: 1095)
Coordinates:
  * y                        (y) float32 -159.0 -160.0 -161.0 ... -227.0 -228.0
  * x                        (x) float32 1.095e+03 1.096e+03 ... 1.146e+03
  * time                     (time) datetime64[ns] 2018-01-01T12:00:00 ... 20...
Data variables:
    lat                      (time, y, x) float32 dask.array<chunksize=(365, 70, 52), meta=np.ndarray>
    lambert_conformal_conic  (time) float64 -3.277e+04 -3.277e+04 ... -3.277e+04
    lon                      (time, y, x) float32 dask.array<chunksize=(365, 70, 52), meta=np.ndarray>
    dayl                     (time, y, x) float32 dask.array<chunksize=(730, 70, 52), meta=np.ndarray>
    prcp                     (time, y, x) float32 dask.array<chunksize=(730, 70, 52), meta=np.ndarray>
    srad                     (time, y, x) float32 dask.array<chunksize=(730, 70, 52), meta=np.ndarray>
    swe                      (time, y, x) float32 dask.array<chunksize=(730, 70, 52), meta=np.ndarray>
    tmax                     (time, y, x) float32 dask.array<chunksize=(730, 70, 52), meta=np.ndarray>
    tmin                     (time, y, x) float32 dask.array<chunksize=(365, 70, 52), meta=np.ndarray>
    vp                       (time, y, x) float32 dask.array<chunksize=(730, 70, 52), meta=np.ndarray>
Attributes:
    start_year:          2019
    source:              Daymet Software Version 4.0
    Version_software:    Daymet Software Version 4.0
    Version_data:        Daymet Data Version 4.0
    Conventions:         CF-1.6
    citation:            Please see http://daymet.ornl.gov/ for current Dayme...
    references:          Please see http://daymet.ornl.gov/ for current infor...
    History:             Translated to CF-1.0 Conventions by Netcdf-Java CDM ...
    geospatial_lat_min:  39.43457395273136
    geospatial_lat_max:  40.166069168093486
    geospatial_lon_min:  -86.62665724948563
    geospatial_lon_max:  -85.85920225420271

xarray.Dataset

• Dimensions:
  • y: 70
  • x: 52
  • time: 1095
• Coordinates: (3)
  • y
    (y)
    float32
    -159.0 -160.0 ... -227.0 -228.0

    units :

    km

    long_name :

    y coordinate of projection

    standard_name :

    projection_y_coordinate

    array([-159., -160., -161., -162., -163., -164., -165., -166., -167., -168.,
           -169., -170., -171., -172., -173., -174., -175., -176., -177., -178.,
           -179., -180., -181., -182., -183., -184., -185., -186., -187., -188.,
           -189., -190., -191., -192., -193., -194., -195., -196., -197., -198.,
           -199., -200., -201., -202., -203., -204., -205., -206., -207., -208.,
           -209., -210., -211., -212., -213., -214., -215., -216., -217., -218.,
           -219., -220., -221., -222., -223., -224., -225., -226., -227., -228.],
          dtype=float32)

  • x
    (x)
    float32
    1.095e+03 1.096e+03 ... 1.146e+03

    units :

    km

    long_name :

    x coordinate of projection

    standard_name :

    projection_x_coordinate

    array([1094.75, 1095.75, 1096.75, 1097.75, 1098.75, 1099.75, 1100.75, 1101.75,
           1102.75, 1103.75, 1104.75, 1105.75, 1106.75, 1107.75, 1108.75, 1109.75,
           1110.75, 1111.75, 1112.75, 1113.75, 1114.75, 1115.75, 1116.75, 1117.75,
           1118.75, 1119.75, 1120.75, 1121.75, 1122.75, 1123.75, 1124.75, 1125.75,
           1126.75, 1127.75, 1128.75, 1129.75, 1130.75, 1131.75, 1132.75, 1133.75,
           1134.75, 1135.75, 1136.75, 1137.75, 1138.75, 1139.75, 1140.75, 1141.75,
           1142.75, 1143.75, 1144.75, 1145.75], dtype=float32)

  • time
    (time)
    datetime64[ns]
    2018-01-01T12:00:00 ... 2020-12-...

    standard_name :

    time

    bounds :

    time_bnds

    long_name :

    24-hour day based on local time

    _ChunkSizes :

    1

    _CoordinateAxisType :

    Time

    array(['2018-01-01T12:00:00.000000000', '2018-01-02T12:00:00.000000000',
           '2018-01-03T12:00:00.000000000', ..., '2020-12-28T12:00:00.000000000',
           '2020-12-29T12:00:00.000000000', '2020-12-30T12:00:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (10)
  • lat
    (time, y, x)
    float32
    dask.array<chunksize=(365, 70, 52), meta=np.ndarray>

    units :

    degrees_north

    long_name :

    latitude coordinate

    standard_name :

    latitude

    _ChunkSizes :

    [1010 977]

    _CoordinateAxisType :

    Lat

    grid_mapping :

    lambert_conformal_conic

    Array Chunk Bytes 15.20 MiB 5.07 MiB Shape (1095, 70, 52) (365, 70, 52) Dask graph 3 chunks in 87 graph layers Data type float32 numpy.ndarray

  • lambert_conformal_conic
    (time)
    float64
    -3.277e+04 ... -3.277e+04

    grid_mapping_name :

    lambert_conformal_conic

    longitude_of_central_meridian :

    -100.0

    latitude_of_projection_origin :

    42.5

    false_easting :

    0.0

    false_northing :

    0.0

    standard_parallel :

    [25. 60.]

    semi_major_axis :

    6378137.0

    inverse_flattening :

    298.257223563

    _CoordinateTransformType :

    Projection

    _CoordinateAxisTypes :

    GeoX GeoY

    array([-32767., -32767., -32767., ..., -32767., -32767., -32767.])

  • lon
    (time, y, x)
    float32
    dask.array<chunksize=(365, 70, 52), meta=np.ndarray>

    units :

    degrees_east

    long_name :

    longitude coordinate

    standard_name :

    longitude

    _ChunkSizes :

    [1010 977]

    _CoordinateAxisType :

    Lon

    grid_mapping :

    lambert_conformal_conic

    Array Chunk Bytes 15.20 MiB 5.07 MiB Shape (1095, 70, 52) (365, 70, 52) Dask graph 3 chunks in 87 graph layers Data type float32 numpy.ndarray

  • dayl
    (time, y, x)
    float32
    dask.array<chunksize=(730, 70, 52), meta=np.ndarray>

    long_name :

    daylength

    units :

    s

    grid_mapping :

    lambert_conformal_conic

    cell_methods :

    area: mean

    _ChunkSizes :

    [ 1 1000 1000]

    Array Chunk Bytes 15.20 MiB 10.14 MiB Shape (1095, 70, 52) (730, 70, 52) Dask graph 2 chunks in 16 graph layers Data type float32 numpy.ndarray

  • prcp
    (time, y, x)
    float32
    dask.array<chunksize=(730, 70, 52), meta=np.ndarray>

    long_name :

    daily total precipitation

    units :

    mm/day

    grid_mapping :

    lambert_conformal_conic

    cell_methods :

    area: mean time: sum

    _ChunkSizes :

    [ 1 1000 1000]

    Array Chunk Bytes 15.20 MiB 10.14 MiB Shape (1095, 70, 52) (730, 70, 52) Dask graph 2 chunks in 16 graph layers Data type float32 numpy.ndarray

  • srad
    (time, y, x)
    float32
    dask.array<chunksize=(730, 70, 52), meta=np.ndarray>

    long_name :

    daylight average incident shortwave radiation

    units :

    W/m2

    grid_mapping :

    lambert_conformal_conic

    cell_methods :

    area: mean time: mean

    _ChunkSizes :

    [ 1 1000 1000]

    Array Chunk Bytes 15.20 MiB 10.14 MiB Shape (1095, 70, 52) (730, 70, 52) Dask graph 2 chunks in 16 graph layers Data type float32 numpy.ndarray

  • swe
    (time, y, x)
    float32
    dask.array<chunksize=(730, 70, 52), meta=np.ndarray>

    long_name :

    snow water equivalent

    units :

    kg/m2

    grid_mapping :

    lambert_conformal_conic

    cell_methods :

    area: mean time: mean

    _ChunkSizes :

    [ 1 1000 1000]

    Array Chunk Bytes 15.20 MiB 10.14 MiB Shape (1095, 70, 52) (730, 70, 52) Dask graph 2 chunks in 16 graph layers Data type float32 numpy.ndarray

  • tmax
    (time, y, x)
    float32
    dask.array<chunksize=(730, 70, 52), meta=np.ndarray>

    long_name :

    daily maximum temperature

    units :

    degrees C

    grid_mapping :

    lambert_conformal_conic

    cell_methods :

    area: mean time: maximum

    _ChunkSizes :

    [ 1 1000 1000]

    Array Chunk Bytes 15.20 MiB 10.14 MiB Shape (1095, 70, 52) (730, 70, 52) Dask graph 2 chunks in 16 graph layers Data type float32 numpy.ndarray

  • tmin
    (time, y, x)
    float32
    dask.array<chunksize=(365, 70, 52), meta=np.ndarray>

    long_name :

    daily minimum temperature

    units :

    degrees C

    grid_mapping :

    lambert_conformal_conic

    cell_methods :

    area: mean time: minimum

    _ChunkSizes :

    [ 1 1000 1000]

    Array Chunk Bytes 15.20 MiB 5.07 MiB Shape (1095, 70, 52) (365, 70, 52) Dask graph 3 chunks in 7 graph layers Data type float32 numpy.ndarray

  • vp
    (time, y, x)
    float32
    dask.array<chunksize=(730, 70, 52), meta=np.ndarray>

    long_name :

    daily average vapor pressure

    units :

    Pa

    grid_mapping :

    lambert_conformal_conic

    cell_methods :

    area: mean time: mean

    _ChunkSizes :

    [ 1 1000 1000]

    Array Chunk Bytes 15.20 MiB 10.14 MiB Shape (1095, 70, 52) (730, 70, 52) Dask graph 2 chunks in 16 graph layers Data type float32 numpy.ndarray

• Indexes: (3)
  • y
    PandasIndex

    PandasIndex(Float64Index([-159.0, -160.0, -161.0, -162.0, -163.0, -164.0, -165.0, -166.0,
                  -167.0, -168.0, -169.0, -170.0, -171.0, -172.0, -173.0, -174.0,
                  -175.0, -176.0, -177.0, -178.0, -179.0, -180.0, -181.0, -182.0,
                  -183.0, -184.0, -185.0, -186.0, -187.0, -188.0, -189.0, -190.0,
                  -191.0, -192.0, -193.0, -194.0, -195.0, -196.0, -197.0, -198.0,
                  -199.0, -200.0, -201.0, -202.0, -203.0, -204.0, -205.0, -206.0,
                  -207.0, -208.0, -209.0, -210.0, -211.0, -212.0, -213.0, -214.0,
                  -215.0, -216.0, -217.0, -218.0, -219.0, -220.0, -221.0, -222.0,
                  -223.0, -224.0, -225.0, -226.0, -227.0, -228.0],
                 dtype='float64', name='y'))

  • x
    PandasIndex

    PandasIndex(Float64Index([1094.75, 1095.75, 1096.75, 1097.75, 1098.75, 1099.75, 1100.75,
                  1101.75, 1102.75, 1103.75, 1104.75, 1105.75, 1106.75, 1107.75,
                  1108.75, 1109.75, 1110.75, 1111.75, 1112.75, 1113.75, 1114.75,
                  1115.75, 1116.75, 1117.75, 1118.75, 1119.75, 1120.75, 1121.75,
                  1122.75, 1123.75, 1124.75, 1125.75, 1126.75, 1127.75, 1128.75,
                  1129.75, 1130.75, 1131.75, 1132.75, 1133.75, 1134.75, 1135.75,
                  1136.75, 1137.75, 1138.75, 1139.75, 1140.75, 1141.75, 1142.75,
                  1143.75, 1144.75, 1145.75],
                 dtype='float64', name='x'))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2018-01-01 12:00:00', '2018-01-02 12:00:00',
                   '2018-01-03 12:00:00', '2018-01-04 12:00:00',
                   '2018-01-05 12:00:00', '2018-01-06 12:00:00',
                   '2018-01-07 12:00:00', '2018-01-08 12:00:00',
                   '2018-01-09 12:00:00', '2018-01-10 12:00:00',
                   ...
                   '2020-12-21 12:00:00', '2020-12-22 12:00:00',
                   '2020-12-23 12:00:00', '2020-12-24 12:00:00',
                   '2020-12-25 12:00:00', '2020-12-26 12:00:00',
                   '2020-12-27 12:00:00', '2020-12-28 12:00:00',
                   '2020-12-29 12:00:00', '2020-12-30 12:00:00'],
                  dtype='datetime64[ns]', name='time', length=1095, freq='D'))

• Attributes: (12)

  start_year :

  2019

  source :

  Daymet Software Version 4.0

  Version_software :

  Daymet Software Version 4.0

  Version_data :

  Daymet Data Version 4.0

  Conventions :

  CF-1.6

  citation :

  Please see http://daymet.ornl.gov/ for current Daymet data citation information

  references :

  Please see http://daymet.ornl.gov/ for current information on Daymet references

  History :

  Translated to CF-1.0 Conventions by Netcdf-Java CDM (CFGridWriter2) Original Dataset = /daacftp/daymet/Daymet_Daily_V4R1/data/daymet_v4_daily_na_dayl_2019.nc; Translation Date = 2023-04-14T14:22:45.287Z

  geospatial_lat_min :

  39.43457395273136

  geospatial_lat_max :

  40.166069168093486

  geospatial_lon_min :

  -86.62665724948563

  geospatial_lon_max :

  -85.85920225420271

Data at multiple points¶

In [ ]:

# Create an instance of dataset
points = [(-120.740135, 47.751076), (-120.742, 47.76)]
dataset = DaymetMultiplePoints(points=points, years=[2019, 2020])
dataset

# Create an instance of dataset points = [(-120.740135, 47.751076), (-120.742, 47.76)] dataset = DaymetMultiplePoints(points=points, years=[2019, 2020]) dataset

Out[ ]:

DaymetMultiplePoints(dataset='daymet_multiple_points', points=[(-120.740135, 47.751076), (-120.742, 47.76)], years=(2019, 2020))

In [ ]:

# Download data
dataset.download()

# Download data dataset.download()

R version 4.2.2 (2022-10-31) -- "Innocent and Trusting"
Copyright (C) 2022 The R Foundation for Statistical Computing
Platform: x86_64-conda-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

  Natural language support but running in an English locale

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

>         library(daymetr)
>         daymetr::download_daymet(
+             site = "daymet_single_point_-120.740135_47.751076",
+             lat = 47.751076,
+             lon = -120.740135,
+             start = 2019,
+             end =  2020,
+             path="/tmp/data",
+             internal = FALSE)

Downloading DAYMET data for: daymet_single_point_-120.740135_47.751076 at 47.751076/-120.740135 latitude/longitude !

Done !

>         
> 

R version 4.2.2 (2022-10-31) -- "Innocent and Trusting"
Copyright (C) 2022 The R Foundation for Statistical Computing
Platform: x86_64-conda-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

  Natural language support but running in an English locale

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

>         library(daymetr)
>         daymetr::download_daymet(
+             site = "daymet_single_point_-120.742_47.76",
+             lat = 47.76,
+             lon = -120.742,
+             start = 2019,
+             end =  2020,
+             path="/tmp/data",
+             internal = FALSE)

Downloading DAYMET data for: daymet_single_point_-120.742_47.76 at 47.76/-120.742 latitude/longitude !

>         
> 

Done !

In [ ]:

# Load data as a dataframe
xa_wa = dataset.load()
xa_wa

# Load data as a dataframe xa_wa = dataset.load() xa_wa

Out[ ]:

<xarray.Dataset>
Dimensions:                  (y: 70, x: 52, time: 1095)
Coordinates:
  * y                        (y) float32 -159.0 -160.0 -161.0 ... -227.0 -228.0
  * x                        (x) float32 1.095e+03 1.096e+03 ... 1.146e+03
  * time                     (time) datetime64[ns] 2018-01-01T12:00:00 ... 20...
Data variables:
    lat                      (time, y, x) float32 dask.array<chunksize=(365, 70, 52), meta=np.ndarray>
    lambert_conformal_conic  (time) float64 -3.277e+04 -3.277e+04 ... -3.277e+04
    lon                      (time, y, x) float32 dask.array<chunksize=(365, 70, 52), meta=np.ndarray>
    dayl                     (time, y, x) float32 dask.array<chunksize=(730, 70, 52), meta=np.ndarray>
    prcp                     (time, y, x) float32 dask.array<chunksize=(730, 70, 52), meta=np.ndarray>
    srad                     (time, y, x) float32 dask.array<chunksize=(730, 70, 52), meta=np.ndarray>
    swe                      (time, y, x) float32 dask.array<chunksize=(730, 70, 52), meta=np.ndarray>
    tmax                     (time, y, x) float32 dask.array<chunksize=(730, 70, 52), meta=np.ndarray>
    tmin                     (time, y, x) float32 dask.array<chunksize=(365, 70, 52), meta=np.ndarray>
    vp                       (time, y, x) float32 dask.array<chunksize=(730, 70, 52), meta=np.ndarray>
Attributes:
    start_year:          2019
    source:              Daymet Software Version 4.0
    Version_software:    Daymet Software Version 4.0
    Version_data:        Daymet Data Version 4.0
    Conventions:         CF-1.6
    citation:            Please see http://daymet.ornl.gov/ for current Dayme...
    references:          Please see http://daymet.ornl.gov/ for current infor...
    History:             Translated to CF-1.0 Conventions by Netcdf-Java CDM ...
    geospatial_lat_min:  39.43457395273136
    geospatial_lat_max:  40.166069168093486
    geospatial_lon_min:  -86.62665724948563
    geospatial_lon_max:  -85.85920225420271

xarray.Dataset

• Dimensions:
  • y: 70
  • x: 52
  • time: 1095
• Coordinates: (3)
  • y
    (y)
    float32
    -159.0 -160.0 ... -227.0 -228.0

    units :

    km

    long_name :

    y coordinate of projection

    standard_name :

    projection_y_coordinate

    array([-159., -160., -161., -162., -163., -164., -165., -166., -167., -168.,
           -169., -170., -171., -172., -173., -174., -175., -176., -177., -178.,
           -179., -180., -181., -182., -183., -184., -185., -186., -187., -188.,
           -189., -190., -191., -192., -193., -194., -195., -196., -197., -198.,
           -199., -200., -201., -202., -203., -204., -205., -206., -207., -208.,
           -209., -210., -211., -212., -213., -214., -215., -216., -217., -218.,
           -219., -220., -221., -222., -223., -224., -225., -226., -227., -228.],
          dtype=float32)

  • x
    (x)
    float32
    1.095e+03 1.096e+03 ... 1.146e+03

    units :

    km

    long_name :

    x coordinate of projection

    standard_name :

    projection_x_coordinate

    array([1094.75, 1095.75, 1096.75, 1097.75, 1098.75, 1099.75, 1100.75, 1101.75,
           1102.75, 1103.75, 1104.75, 1105.75, 1106.75, 1107.75, 1108.75, 1109.75,
           1110.75, 1111.75, 1112.75, 1113.75, 1114.75, 1115.75, 1116.75, 1117.75,
           1118.75, 1119.75, 1120.75, 1121.75, 1122.75, 1123.75, 1124.75, 1125.75,
           1126.75, 1127.75, 1128.75, 1129.75, 1130.75, 1131.75, 1132.75, 1133.75,
           1134.75, 1135.75, 1136.75, 1137.75, 1138.75, 1139.75, 1140.75, 1141.75,
           1142.75, 1143.75, 1144.75, 1145.75], dtype=float32)

  • time
    (time)
    datetime64[ns]
    2018-01-01T12:00:00 ... 2020-12-...

    standard_name :

    time

    bounds :

    time_bnds

    long_name :

    24-hour day based on local time

    _ChunkSizes :

    1

    _CoordinateAxisType :

    Time

    array(['2018-01-01T12:00:00.000000000', '2018-01-02T12:00:00.000000000',
           '2018-01-03T12:00:00.000000000', ..., '2020-12-28T12:00:00.000000000',
           '2020-12-29T12:00:00.000000000', '2020-12-30T12:00:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (10)
  • lat
    (time, y, x)
    float32
    dask.array<chunksize=(365, 70, 52), meta=np.ndarray>

    units :

    degrees_north

    long_name :

    latitude coordinate

    standard_name :

    latitude

    _ChunkSizes :

    [1010 977]

    _CoordinateAxisType :

    Lat

    grid_mapping :

    lambert_conformal_conic

    Array Chunk Bytes 15.20 MiB 5.07 MiB Shape (1095, 70, 52) (365, 70, 52) Dask graph 3 chunks in 87 graph layers Data type float32 numpy.ndarray

  • lambert_conformal_conic
    (time)
    float64
    -3.277e+04 ... -3.277e+04

    grid_mapping_name :

    lambert_conformal_conic

    longitude_of_central_meridian :

    -100.0

    latitude_of_projection_origin :

    42.5

    false_easting :

    0.0

    false_northing :

    0.0

    standard_parallel :

    [25. 60.]

    semi_major_axis :

    6378137.0

    inverse_flattening :

    298.257223563

    _CoordinateTransformType :

    Projection

    _CoordinateAxisTypes :

    GeoX GeoY

    array([-32767., -32767., -32767., ..., -32767., -32767., -32767.])

  • lon
    (time, y, x)
    float32
    dask.array<chunksize=(365, 70, 52), meta=np.ndarray>

    units :

    degrees_east

    long_name :

    longitude coordinate

    standard_name :

    longitude

    _ChunkSizes :

    [1010 977]

    _CoordinateAxisType :

    Lon

    grid_mapping :

    lambert_conformal_conic

    Array Chunk Bytes 15.20 MiB 5.07 MiB Shape (1095, 70, 52) (365, 70, 52) Dask graph 3 chunks in 87 graph layers Data type float32 numpy.ndarray

  • dayl
    (time, y, x)
    float32
    dask.array<chunksize=(730, 70, 52), meta=np.ndarray>

    long_name :

    daylength

    units :

    s

    grid_mapping :

    lambert_conformal_conic

    cell_methods :

    area: mean

    _ChunkSizes :

    [ 1 1000 1000]

    Array Chunk Bytes 15.20 MiB 10.14 MiB Shape (1095, 70, 52) (730, 70, 52) Dask graph 2 chunks in 16 graph layers Data type float32 numpy.ndarray

  • prcp
    (time, y, x)
    float32
    dask.array<chunksize=(730, 70, 52), meta=np.ndarray>

    long_name :

    daily total precipitation

    units :

    mm/day

    grid_mapping :

    lambert_conformal_conic

    cell_methods :

    area: mean time: sum

    _ChunkSizes :

    [ 1 1000 1000]

    Array Chunk Bytes 15.20 MiB 10.14 MiB Shape (1095, 70, 52) (730, 70, 52) Dask graph 2 chunks in 16 graph layers Data type float32 numpy.ndarray

  • srad
    (time, y, x)
    float32
    dask.array<chunksize=(730, 70, 52), meta=np.ndarray>

    long_name :

    daylight average incident shortwave radiation

    units :

    W/m2

    grid_mapping :

    lambert_conformal_conic

    cell_methods :

    area: mean time: mean

    _ChunkSizes :

    [ 1 1000 1000]

    Array Chunk Bytes 15.20 MiB 10.14 MiB Shape (1095, 70, 52) (730, 70, 52) Dask graph 2 chunks in 16 graph layers Data type float32 numpy.ndarray

  • swe
    (time, y, x)
    float32
    dask.array<chunksize=(730, 70, 52), meta=np.ndarray>

    long_name :

    snow water equivalent

    units :

    kg/m2

    grid_mapping :

    lambert_conformal_conic

    cell_methods :

    area: mean time: mean

    _ChunkSizes :

    [ 1 1000 1000]

    Array Chunk Bytes 15.20 MiB 10.14 MiB Shape (1095, 70, 52) (730, 70, 52) Dask graph 2 chunks in 16 graph layers Data type float32 numpy.ndarray

  • tmax
    (time, y, x)
    float32
    dask.array<chunksize=(730, 70, 52), meta=np.ndarray>

    long_name :

    daily maximum temperature

    units :

    degrees C

    grid_mapping :

    lambert_conformal_conic

    cell_methods :

    area: mean time: maximum

    _ChunkSizes :

    [ 1 1000 1000]

    Array Chunk Bytes 15.20 MiB 10.14 MiB Shape (1095, 70, 52) (730, 70, 52) Dask graph 2 chunks in 16 graph layers Data type float32 numpy.ndarray

  • tmin
    (time, y, x)
    float32
    dask.array<chunksize=(365, 70, 52), meta=np.ndarray>

    long_name :

    daily minimum temperature

    units :

    degrees C

    grid_mapping :

    lambert_conformal_conic

    cell_methods :

    area: mean time: minimum

    _ChunkSizes :

    [ 1 1000 1000]

    Array Chunk Bytes 15.20 MiB 5.07 MiB Shape (1095, 70, 52) (365, 70, 52) Dask graph 3 chunks in 7 graph layers Data type float32 numpy.ndarray

  • vp
    (time, y, x)
    float32
    dask.array<chunksize=(730, 70, 52), meta=np.ndarray>

    long_name :

    daily average vapor pressure

    units :

    Pa

    grid_mapping :

    lambert_conformal_conic

    cell_methods :

    area: mean time: mean

    _ChunkSizes :

    [ 1 1000 1000]

    Array Chunk Bytes 15.20 MiB 10.14 MiB Shape (1095, 70, 52) (730, 70, 52) Dask graph 2 chunks in 16 graph layers Data type float32 numpy.ndarray

• Indexes: (3)
  • y
    PandasIndex

    PandasIndex(Float64Index([-159.0, -160.0, -161.0, -162.0, -163.0, -164.0, -165.0, -166.0,
                  -167.0, -168.0, -169.0, -170.0, -171.0, -172.0, -173.0, -174.0,
                  -175.0, -176.0, -177.0, -178.0, -179.0, -180.0, -181.0, -182.0,
                  -183.0, -184.0, -185.0, -186.0, -187.0, -188.0, -189.0, -190.0,
                  -191.0, -192.0, -193.0, -194.0, -195.0, -196.0, -197.0, -198.0,
                  -199.0, -200.0, -201.0, -202.0, -203.0, -204.0, -205.0, -206.0,
                  -207.0, -208.0, -209.0, -210.0, -211.0, -212.0, -213.0, -214.0,
                  -215.0, -216.0, -217.0, -218.0, -219.0, -220.0, -221.0, -222.0,
                  -223.0, -224.0, -225.0, -226.0, -227.0, -228.0],
                 dtype='float64', name='y'))

  • x
    PandasIndex

    PandasIndex(Float64Index([1094.75, 1095.75, 1096.75, 1097.75, 1098.75, 1099.75, 1100.75,
                  1101.75, 1102.75, 1103.75, 1104.75, 1105.75, 1106.75, 1107.75,
                  1108.75, 1109.75, 1110.75, 1111.75, 1112.75, 1113.75, 1114.75,
                  1115.75, 1116.75, 1117.75, 1118.75, 1119.75, 1120.75, 1121.75,
                  1122.75, 1123.75, 1124.75, 1125.75, 1126.75, 1127.75, 1128.75,
                  1129.75, 1130.75, 1131.75, 1132.75, 1133.75, 1134.75, 1135.75,
                  1136.75, 1137.75, 1138.75, 1139.75, 1140.75, 1141.75, 1142.75,
                  1143.75, 1144.75, 1145.75],
                 dtype='float64', name='x'))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2018-01-01 12:00:00', '2018-01-02 12:00:00',
                   '2018-01-03 12:00:00', '2018-01-04 12:00:00',
                   '2018-01-05 12:00:00', '2018-01-06 12:00:00',
                   '2018-01-07 12:00:00', '2018-01-08 12:00:00',
                   '2018-01-09 12:00:00', '2018-01-10 12:00:00',
                   ...
                   '2020-12-21 12:00:00', '2020-12-22 12:00:00',
                   '2020-12-23 12:00:00', '2020-12-24 12:00:00',
                   '2020-12-25 12:00:00', '2020-12-26 12:00:00',
                   '2020-12-27 12:00:00', '2020-12-28 12:00:00',
                   '2020-12-29 12:00:00', '2020-12-30 12:00:00'],
                  dtype='datetime64[ns]', name='time', length=1095, freq='D'))

• Attributes: (12)

  start_year :

  2019

  source :

  Daymet Software Version 4.0

  Version_software :

  Daymet Software Version 4.0

  Version_data :

  Daymet Data Version 4.0

  Conventions :

  CF-1.6

  citation :

  Please see http://daymet.ornl.gov/ for current Daymet data citation information

  references :

  Please see http://daymet.ornl.gov/ for current information on Daymet references

  History :

  Translated to CF-1.0 Conventions by Netcdf-Java CDM (CFGridWriter2) Original Dataset = /daacftp/daymet/Daymet_Daily_V4R1/data/daymet_v4_daily_na_dayl_2019.nc; Translation Date = 2023-04-14T14:22:45.287Z

  geospatial_lat_min :

  39.43457395273136

  geospatial_lat_max :

  40.166069168093486

  geospatial_lon_min :

  -86.62665724948563

  geospatial_lon_max :

  -85.85920225420271

In [ ]: