Data discovery with earthaccess

Summary

In this example we will use the earthaccess library to search for data collections from NASA Earthdata. earthaccess is a Python library that simplifies data discovery and access to NASA Earth science data by providing an abstraction layer for NASA’s Common Metadata Repository (CMR) API Search API. The library makes searching for data more approachable by using a simpler notation instead of low level HTTP queries. earthaccess takes the trouble out of Earthdata Login authentication, makes search easier, and provides a stream-line way to download or stream search results into an xarray object.

For more on earthaccess visit the earthaccess GitHub page and/or the earthaccess documentation site. Be aware that earthaccess is under active development.

Prerequisites

An Earthdata Login account is required to access data from NASA Earthdata. Please visit https://urs.earthdata.nasa.gov to register and manage your Earthdata Login account. This account is free to create and only takes a moment to set up.

Learning Objectives

1. How to authenticate with earthaccess
2. How to use earthaccess to search for data using spatial and temporal filters
3. How to explore and work with search results

Get Started

Import Required Packages

import earthaccess 
from pprint import pprint
import xarray as xr
import geopandas as gpd

Authentication for NASA Earthdata

We will start by authenticating using our Earthdata Login credentials. Authentication is not necessarily needed to search for publicaly available data collections in Earthdata, but is always need to download or access data from the NASA Earthdata archives. We can use login method from the earthaccess library here. This will create a authenticated session using our Earthdata Login credential. Our credentials can be passed along via environmental variables or by a .netrc file save in the home/user profile directory. If your credentials are not available in either location, we will be prompt to input our credentials and a .netrc will be created and saved for us.

auth = earthaccess.login()
# are we authenticated?
if not auth.authenticated:
    # ask for credentials and persist them in a .netrc file
    auth.login(strategy="interactive", persist=True)

EARTHDATA_USERNAME and EARTHDATA_PASSWORD are not set in the current environment, try setting them or use a different strategy (netrc, interactive)
You're now authenticated with NASA Earthdata Login
Using token with expiration date: 02/02/2024
Using .netrc file for EDL

Search for data

There are multiple keywords we can use to discovery data from collections. The table below contains the short_name, concept_id, and doi for some collections we are interested in for other exercises. Each of these can be used to search for data or information related to the collection we are interested in.

Shortname	Collection Concept ID	DOI
GPM_3IMERGDF	C2723754864-GES_DISC	10.5067/GPM/IMERGDF/DAY/07
MOD10C1	C1646609808-NSIDC_ECS	10.5067/MODIS/MOD10C1.061
SPL4SMGP	C2531308461-NSIDC_ECS	10.5067/EVKPQZ4AFC4D
SPL4SMAU	C2537927247-NSIDC_ECS	10.5067/LWJ6TF5SZRG3

But wait…You may be asking “how can we find the shortname, concept_id, and doi for collections not in the table above?”. Let’s take a quick detour.

https://search.earthdata.nasa.gov/search?q=GPM_3IMERGDF

Search by collection

collection_id = 'C2723754864-GES_DISC'

results = earthaccess.search_data(
    concept_id = collection_id,
    cloud_hosted = True,
    count = 10    # Restricting to 10 records returned
)

Granules found: 8400

In this example we used the concept_id parameter to search from our desired collection. However, there are multiple ways to specify the collection(s) we are interested in. Alternative parameters include:

• doi - request collection by digital object identifier (e.g., doi = ‘10.5067/GPM/IMERGDF/DAY/07’)
  
• short_name - request collection by CMR shortname (e.g., short_name = ‘GPM_3IMERGDF’)

NOTE: Each Earthdata collect has a unique concept_id and doi. This is not the case with short_name. A shortname can be associated with multiple versions of a collection. If multiple versions of a collection are publicaly available, using the short_name parameter with return all versions available. It is advised to use the version parameter in conjunction with the short_name parameter with searching.

We can refine our search by passing more parameters that describe the spatiotemporal domain of our use case. Here, we use the temporal parameter to request a date range and the bounding_box parameter to request granules that intersect with a bounding box.

For our bounding box, we are going to read in a GeoJSON file containing a single feature and extract the coordinate pairs for the southeast corner and the northwest corner (or lowerleft and upperright corners) of the bounding box around the feature.

inGeojson = gpd.read_file('../../2023-Cloud-Workshop-AGU/data/sf_to_sierranvmt.geojson')

xmin, ymin, xmax, ymax = inGeojson.total_bounds

We will assign our start date and end date to a variable named date_range and we’ll assign the southeast and the northwest corner coordinates to a variable named bbox to be passed to our earthaccess search request.

date_range = ("2022-11-19", "2023-04-06")
#bbox = (-127.0761, 31.6444, -113.9039, 42.6310)
bbox = (xmin, ymin, xmax, ymax)

results = earthaccess.search_data(
    concept_id = collection_id,
    cloud_hosted = True,
    temporal = date_range,
    bounding_box = bbox,
)

Granules found: 139

• The short_name and concept_id search parameters can be used to request one or multiple collections per request, but the doi parameter can only request a single collection.
  > concept_ids = [‘C2723754864-GES_DISC’, ‘C1646609808-NSIDC_ECS’]
  
• Use the cloud_hosted search parameter only to search for data assets available from NASA’s Earthdata Cloud.
• There are even more search parameters that can be passed to help refine our search, however those parameters do have to be populated in the CMR record to be leveraged. A non exhaustive list of examples are below:
  • day_night_flag = 'day'
    
  • cloud_cover = (0, 10)

# col_ids = ['C2723754864-GES_DISC', 'C1646609808-NSIDC_ECS', 'C2531308461-NSIDC_ECS', 'C2537927247-NSIDC_ECS']    # Specify a list of collections to pass to the search

# results = earthaccess.search_data(
#     concept_id = col_ids,
#     #cloud_hosted = True,
#     temporal = date_range,
#     bounding_box = bbox,
# )

Working with earthaccess returns

earthaccess provides several convenience methods to help streamline processes that historically have be painful when done using traditional methods. Following the search for data, you’ll likely take one of two pathways with those results. You may choose to download the assets that have been returned to you or you may choose to continue working with the search results within the Python environment.

Download earthaccess results

In some cases you may want to download your assets. earthaccess makes downloading the data from the search results very easy using the earthaccess.download() function.

downloaded_files = earthaccess.download(
    results[0:9],
    local_path='../../2023-Cloud-Workshop-AGU/data',
)

 Getting 9 granules, approx download size: 0.25 GB

earthaccess did a lot of heavy lifting for us. It identified the downloadable links, passed our Earthdata Login credentials, and save off the file with the proper name. Amazing right!?

We’re going to remove those files to keep our space clean.

!rm ../../2023-Cloud-Workshop-AGU/data/*.nc4

Explore earthaccess search response

print(f'The results variable is a {type(results)} of {type(results[0])}')

The results variable is a <class 'list'> of <class 'earthaccess.results.DataGranule'>

len(results)

139

We can explore the first item (earthaccess.results.DataGranule) in our list.

item = results[0]
type(item)

earthaccess.results.DataGranule

Each item contains three keys that can be used to explore the item

item.keys()

dict_keys(['meta', 'umm', 'size'])

item['umm']

{'RelatedUrls': [{'URL': 'https://data.gesdisc.earthdata.nasa.gov/data/GPM_L3/GPM_3IMERGDF.07/2022/11/3B-DAY.MS.MRG.3IMERG.20221119-S000000-E235959.V07.nc4',
   'Type': 'GET DATA',
   'Description': 'Download 3B-DAY.MS.MRG.3IMERG.20221119-S000000-E235959.V07.nc4'},
  {'URL': 's3://gesdisc-cumulus-prod-protected/GPM_L3/GPM_3IMERGDF.07/2022/11/3B-DAY.MS.MRG.3IMERG.20221119-S000000-E235959.V07.nc4',
   'Type': 'GET DATA VIA DIRECT ACCESS',
   'Description': 'This link provides direct download access via S3 to the granule'},
  {'URL': 'https://gpm1.gesdisc.eosdis.nasa.gov/opendap/GPM_L3/GPM_3IMERGDF.07/2022/11/3B-DAY.MS.MRG.3IMERG.20221119-S000000-E235959.V07.nc4',
   'Type': 'USE SERVICE API',
   'Subtype': 'OPENDAP DATA',
   'Description': 'The OPENDAP location for the granule.',
   'MimeType': 'application/x-netcdf-4'},
  {'URL': 'https://data.gesdisc.earthdata.nasa.gov/s3credentials',
   'Type': 'VIEW RELATED INFORMATION',
   'Description': 'api endpoint to retrieve temporary credentials valid for same-region direct s3 access'}],
 'SpatialExtent': {'HorizontalSpatialDomain': {'Geometry': {'BoundingRectangles': [{'WestBoundingCoordinate': -180.0,
      'EastBoundingCoordinate': 180.0,
      'NorthBoundingCoordinate': 90.0,
      'SouthBoundingCoordinate': -90.0}]}}},
 'ProviderDates': [{'Date': '2023-08-25T14:06:33.000Z', 'Type': 'Insert'},
  {'Date': '2023-08-25T14:06:33.000Z', 'Type': 'Update'}],
 'CollectionReference': {'ShortName': 'GPM_3IMERGDF', 'Version': '07'},
 'DataGranule': {'DayNightFlag': 'Unspecified',
  'Identifiers': [{'Identifier': '3B-DAY.MS.MRG.3IMERG.20221119-S000000-E235959.V07.nc4',
    'IdentifierType': 'ProducerGranuleId'}],
  'ProductionDateTime': '2023-08-25T14:06:33.000Z',
  'ArchiveAndDistributionInformation': [{'Name': 'Not provided',
    'Size': 28.37006378173828,
    'SizeUnit': 'MB'}]},
 'TemporalExtent': {'RangeDateTime': {'BeginningDateTime': '2022-11-19T00:00:00.000Z',
   'EndingDateTime': '2022-11-19T23:59:59.999Z'}},
 'GranuleUR': 'GPM_3IMERGDF.07:3B-DAY.MS.MRG.3IMERG.20221119-S000000-E235959.V07.nc4',
 'MetadataSpecification': {'URL': 'https://cdn.earthdata.nasa.gov/umm/granule/v1.6.5',
  'Name': 'UMM-G',
  'Version': '1.6.5'}}

Get data URLs / S3 URIs

Get links to data. The data_links() method is used to return the URL(s)/data link(s) for the item. By default the method returns the HTTPS URL to download or access the item.

item.data_links()

['https://data.gesdisc.earthdata.nasa.gov/data/GPM_L3/GPM_3IMERGDF.07/2022/11/3B-DAY.MS.MRG.3IMERG.20221119-S000000-E235959.V07.nc4']

The data_links() method can also be used to get the s3 URI when we want to perform direct s3 access of the data in the cloud. To get the s3 URI, pass access = 'direct' to the method.

item.data_links(access='direct')

['s3://gesdisc-cumulus-prod-protected/GPM_L3/GPM_3IMERGDF.07/2022/11/3B-DAY.MS.MRG.3IMERG.20221119-S000000-E235959.V07.nc4']

If we want to extract all of the data links from our search results and add or save them to a list, we can.

data_link_list = []

for granule in results:
    for asset in granule.data_links(access='direct'):
        data_link_list.append(asset)
        

data_link_list[0:9]

['s3://gesdisc-cumulus-prod-protected/GPM_L3/GPM_3IMERGDF.07/2022/11/3B-DAY.MS.MRG.3IMERG.20221119-S000000-E235959.V07.nc4',
 's3://gesdisc-cumulus-prod-protected/GPM_L3/GPM_3IMERGDF.07/2022/11/3B-DAY.MS.MRG.3IMERG.20221120-S000000-E235959.V07.nc4',
 's3://gesdisc-cumulus-prod-protected/GPM_L3/GPM_3IMERGDF.07/2022/11/3B-DAY.MS.MRG.3IMERG.20221121-S000000-E235959.V07.nc4',
 's3://gesdisc-cumulus-prod-protected/GPM_L3/GPM_3IMERGDF.07/2022/11/3B-DAY.MS.MRG.3IMERG.20221122-S000000-E235959.V07.nc4',
 's3://gesdisc-cumulus-prod-protected/GPM_L3/GPM_3IMERGDF.07/2022/11/3B-DAY.MS.MRG.3IMERG.20221123-S000000-E235959.V07.nc4',
 's3://gesdisc-cumulus-prod-protected/GPM_L3/GPM_3IMERGDF.07/2022/11/3B-DAY.MS.MRG.3IMERG.20221124-S000000-E235959.V07.nc4',
 's3://gesdisc-cumulus-prod-protected/GPM_L3/GPM_3IMERGDF.07/2022/11/3B-DAY.MS.MRG.3IMERG.20221125-S000000-E235959.V07.nc4',
 's3://gesdisc-cumulus-prod-protected/GPM_L3/GPM_3IMERGDF.07/2022/11/3B-DAY.MS.MRG.3IMERG.20221126-S000000-E235959.V07.nc4',
 's3://gesdisc-cumulus-prod-protected/GPM_L3/GPM_3IMERGDF.07/2022/11/3B-DAY.MS.MRG.3IMERG.20221127-S000000-E235959.V07.nc4']

We can pass or read these lists of data links into libraries like xarray, rioxarray, or gdal, but earthaccess has a built-in module for easily reading these data links in.

Open results in xarray

We use earthaccess’s open() method to make a connection to and open the files from our search result.

fileset = earthaccess.open(results)

 Opening 139 granules, approx size: 3.75 GB

Then we pass the fileset object to xarray.

ds = xr.open_mfdataset(fileset, chunks = {})

Some really cool things just happened here! Not only were we able to seamlessly stream our earthaccess search results into a xarray dataset using the open_mfdataset() (multi-file) method, but earthaccess determined that we were working from within AWS us-west-2 and accessed the data via direct S3 access! We didn’t have to create a session or a filesystem to authenticate and connect to the data. earthaccess did this for us using the auth object we created at the beginning of this tutorial. If we were not working in AWS us-west-2, earthaccess would “automagically” switch to accessing the data via the HTTPS endpoints and would again handle the authentication for us.

Let’s take a quick lock at our xarray dataset

ds

<xarray.Dataset>
Dimensions:                         (time: 139, lon: 3600, lat: 1800, nv: 2)
Coordinates:
  * lon                             (lon) float32 -179.9 -179.9 ... 179.9 179.9
  * lat                             (lat) float64 -89.95 -89.85 ... 89.85 89.95
  * time                            (time) datetime64[ns] 2022-11-19 ... 2023...
Dimensions without coordinates: nv
Data variables:
    precipitation                   (time, lon, lat) float32 dask.array<chunksize=(1, 3600, 1800), meta=np.ndarray>
    precipitation_cnt               (time, lon, lat) int8 dask.array<chunksize=(1, 3600, 1800), meta=np.ndarray>
    precipitation_cnt_cond          (time, lon, lat) int8 dask.array<chunksize=(1, 3600, 1800), meta=np.ndarray>
    MWprecipitation                 (time, lon, lat) float32 dask.array<chunksize=(1, 3600, 1800), meta=np.ndarray>
    MWprecipitation_cnt             (time, lon, lat) int8 dask.array<chunksize=(1, 3600, 1800), meta=np.ndarray>
    MWprecipitation_cnt_cond        (time, lon, lat) int8 dask.array<chunksize=(1, 3600, 1800), meta=np.ndarray>
    randomError                     (time, lon, lat) float32 dask.array<chunksize=(1, 3600, 1800), meta=np.ndarray>
    randomError_cnt                 (time, lon, lat) int8 dask.array<chunksize=(1, 3600, 1800), meta=np.ndarray>
    probabilityLiquidPrecipitation  (time, lon, lat) int8 dask.array<chunksize=(1, 3600, 1800), meta=np.ndarray>
    time_bnds                       (time, nv) datetime64[ns] dask.array<chunksize=(1, 2), meta=np.ndarray>
Attributes:
    BeginDate:       2022-11-19
    BeginTime:       00:00:00.000Z
    EndDate:         2022-11-19
    EndTime:         23:59:59.999Z
    FileHeader:      StartGranuleDateTime=2022-11-19T00:00:00.000Z;\nStopGran...
    InputPointer:    3B-HHR.MS.MRG.3IMERG.20221119-S000000-E002959.0000.V07A....
    title:           GPM IMERG Final Precipitation L3 1 day 0.1 degree x 0.1 ...
    DOI:             10.5067/GPM/IMERGDF/DAY/07
    ProductionTime:  2023-08-25T14:03:25.792Z

xarray.Dataset

• Dimensions:
  • time: 139
  • lon: 3600
  • lat: 1800
  • nv: 2
• Coordinates: (3)
  • lon
    (lon)
    float32
    -179.9 -179.9 ... 179.9 179.9

    units :

    degrees_east

    long_name :

    Longitude

    array([-179.95, -179.85, -179.75, ...,  179.75,  179.85,  179.95],
          dtype=float32)

  • lat
    (lat)
    float64
    -89.95 -89.85 ... 89.85 89.95

    units :

    degrees_north

    long_name :

    Latitude

    array([-89.95, -89.85, -89.75, ...,  89.75,  89.85,  89.95])

  • time
    (time)
    datetime64[ns]
    2022-11-19 ... 2023-04-06

    standard_name :

    time

    long_name :

    time

    bounds :

    time_bnds

    array(['2022-11-19T00:00:00.000000000', '2022-11-20T00:00:00.000000000',
           '2022-11-21T00:00:00.000000000', '2022-11-22T00:00:00.000000000',
           '2022-11-23T00:00:00.000000000', '2022-11-24T00:00:00.000000000',
           '2022-11-25T00:00:00.000000000', '2022-11-26T00:00:00.000000000',
           '2022-11-27T00:00:00.000000000', '2022-11-28T00:00:00.000000000',
           '2022-11-29T00:00:00.000000000', '2022-11-30T00:00:00.000000000',
           '2022-12-01T00:00:00.000000000', '2022-12-02T00:00:00.000000000',
           '2022-12-03T00:00:00.000000000', '2022-12-04T00:00:00.000000000',
           '2022-12-05T00:00:00.000000000', '2022-12-06T00:00:00.000000000',
           '2022-12-07T00:00:00.000000000', '2022-12-08T00:00:00.000000000',
           '2022-12-09T00:00:00.000000000', '2022-12-10T00:00:00.000000000',
           '2022-12-11T00:00:00.000000000', '2022-12-12T00:00:00.000000000',
           '2022-12-13T00:00:00.000000000', '2022-12-14T00:00:00.000000000',
           '2022-12-15T00:00:00.000000000', '2022-12-16T00:00:00.000000000',
           '2022-12-17T00:00:00.000000000', '2022-12-18T00:00:00.000000000',
           '2022-12-19T00:00:00.000000000', '2022-12-20T00:00:00.000000000',
           '2022-12-21T00:00:00.000000000', '2022-12-22T00:00:00.000000000',
           '2022-12-23T00:00:00.000000000', '2022-12-24T00:00:00.000000000',
           '2022-12-25T00:00:00.000000000', '2022-12-26T00:00:00.000000000',
           '2022-12-27T00:00:00.000000000', '2022-12-28T00:00:00.000000000',
           '2022-12-29T00:00:00.000000000', '2022-12-30T00:00:00.000000000',
           '2022-12-31T00:00:00.000000000', '2023-01-01T00:00:00.000000000',
           '2023-01-02T00:00:00.000000000', '2023-01-03T00:00:00.000000000',
           '2023-01-04T00:00:00.000000000', '2023-01-05T00:00:00.000000000',
           '2023-01-06T00:00:00.000000000', '2023-01-07T00:00:00.000000000',
           '2023-01-08T00:00:00.000000000', '2023-01-09T00:00:00.000000000',
           '2023-01-10T00:00:00.000000000', '2023-01-11T00:00:00.000000000',
           '2023-01-12T00:00:00.000000000', '2023-01-13T00:00:00.000000000',
           '2023-01-14T00:00:00.000000000', '2023-01-15T00:00:00.000000000',
           '2023-01-16T00:00:00.000000000', '2023-01-17T00:00:00.000000000',
           '2023-01-18T00:00:00.000000000', '2023-01-19T00:00:00.000000000',
           '2023-01-20T00:00:00.000000000', '2023-01-21T00:00:00.000000000',
           '2023-01-22T00:00:00.000000000', '2023-01-23T00:00:00.000000000',
           '2023-01-24T00:00:00.000000000', '2023-01-25T00:00:00.000000000',
           '2023-01-26T00:00:00.000000000', '2023-01-27T00:00:00.000000000',
           '2023-01-28T00:00:00.000000000', '2023-01-29T00:00:00.000000000',
           '2023-01-30T00:00:00.000000000', '2023-01-31T00:00:00.000000000',
           '2023-02-01T00:00:00.000000000', '2023-02-02T00:00:00.000000000',
           '2023-02-03T00:00:00.000000000', '2023-02-04T00:00:00.000000000',
           '2023-02-05T00:00:00.000000000', '2023-02-06T00:00:00.000000000',
           '2023-02-07T00:00:00.000000000', '2023-02-08T00:00:00.000000000',
           '2023-02-09T00:00:00.000000000', '2023-02-10T00:00:00.000000000',
           '2023-02-11T00:00:00.000000000', '2023-02-12T00:00:00.000000000',
           '2023-02-13T00:00:00.000000000', '2023-02-14T00:00:00.000000000',
           '2023-02-15T00:00:00.000000000', '2023-02-16T00:00:00.000000000',
           '2023-02-17T00:00:00.000000000', '2023-02-18T00:00:00.000000000',
           '2023-02-19T00:00:00.000000000', '2023-02-20T00:00:00.000000000',
           '2023-02-21T00:00:00.000000000', '2023-02-22T00:00:00.000000000',
           '2023-02-23T00:00:00.000000000', '2023-02-24T00:00:00.000000000',
           '2023-02-25T00:00:00.000000000', '2023-02-26T00:00:00.000000000',
           '2023-02-27T00:00:00.000000000', '2023-02-28T00:00:00.000000000',
           '2023-03-01T00:00:00.000000000', '2023-03-02T00:00:00.000000000',
           '2023-03-03T00:00:00.000000000', '2023-03-04T00:00:00.000000000',
           '2023-03-05T00:00:00.000000000', '2023-03-06T00:00:00.000000000',
           '2023-03-07T00:00:00.000000000', '2023-03-08T00:00:00.000000000',
           '2023-03-09T00:00:00.000000000', '2023-03-10T00:00:00.000000000',
           '2023-03-11T00:00:00.000000000', '2023-03-12T00:00:00.000000000',
           '2023-03-13T00:00:00.000000000', '2023-03-14T00:00:00.000000000',
           '2023-03-15T00:00:00.000000000', '2023-03-16T00:00:00.000000000',
           '2023-03-17T00:00:00.000000000', '2023-03-18T00:00:00.000000000',
           '2023-03-19T00:00:00.000000000', '2023-03-20T00:00:00.000000000',
           '2023-03-21T00:00:00.000000000', '2023-03-22T00:00:00.000000000',
           '2023-03-23T00:00:00.000000000', '2023-03-24T00:00:00.000000000',
           '2023-03-25T00:00:00.000000000', '2023-03-26T00:00:00.000000000',
           '2023-03-27T00:00:00.000000000', '2023-03-28T00:00:00.000000000',
           '2023-03-29T00:00:00.000000000', '2023-03-30T00:00:00.000000000',
           '2023-03-31T00:00:00.000000000', '2023-04-01T00:00:00.000000000',
           '2023-04-02T00:00:00.000000000', '2023-04-03T00:00:00.000000000',
           '2023-04-04T00:00:00.000000000', '2023-04-05T00:00:00.000000000',
           '2023-04-06T00:00:00.000000000'], dtype='datetime64[ns]')

• Data variables: (10)
  • precipitation
    (time, lon, lat)
    float32
    dask.array<chunksize=(1, 3600, 1800), meta=np.ndarray>

    units :

    mm/day

    long_name :

    Daily mean precipitation rate (combined microwave-IR) estimate. Formerly precipitationCal.

    Array Chunk Bytes 3.36 GiB 24.72 MiB Shape (139, 3600, 1800) (1, 3600, 1800) Dask graph 139 chunks in 279 graph layers Data type float32 numpy.ndarray

  • precipitation_cnt
    (time, lon, lat)
    int8
    dask.array<chunksize=(1, 3600, 1800), meta=np.ndarray>

    units :

    count

    long_name :

    Count of all valid half-hourly precipitation retrievals for the day

    Array Chunk Bytes 858.99 MiB 6.18 MiB Shape (139, 3600, 1800) (1, 3600, 1800) Dask graph 139 chunks in 279 graph layers Data type int8 numpy.ndarray

  • precipitation_cnt_cond
    (time, lon, lat)
    int8
    dask.array<chunksize=(1, 3600, 1800), meta=np.ndarray>

    units :

    count

    long_name :

    Count of half-hourly precipitation retrievals for the day where precipitation is at least 0.01 mm/hr

    Array Chunk Bytes 858.99 MiB 6.18 MiB Shape (139, 3600, 1800) (1, 3600, 1800) Dask graph 139 chunks in 279 graph layers Data type int8 numpy.ndarray

  • MWprecipitation
    (time, lon, lat)
    float32
    dask.array<chunksize=(1, 3600, 1800), meta=np.ndarray>

    units :

    mm/day

    long_name :

    Daily mean High Quality precipitation rate from all available microwave sources. Formerly HQprecipitation.

    Array Chunk Bytes 3.36 GiB 24.72 MiB Shape (139, 3600, 1800) (1, 3600, 1800) Dask graph 139 chunks in 279 graph layers Data type float32 numpy.ndarray

  • MWprecipitation_cnt
    (time, lon, lat)
    int8
    dask.array<chunksize=(1, 3600, 1800), meta=np.ndarray>

    units :

    count

    long_name :

    Count of all valid half-hourly MWprecipitation retrievals for the day

    Array Chunk Bytes 858.99 MiB 6.18 MiB Shape (139, 3600, 1800) (1, 3600, 1800) Dask graph 139 chunks in 279 graph layers Data type int8 numpy.ndarray

  • MWprecipitation_cnt_cond
    (time, lon, lat)
    int8
    dask.array<chunksize=(1, 3600, 1800), meta=np.ndarray>

    units :

    count

    long_name :

    Count of half-hourly MWprecipitation retrievals for the day where precipitation is at least 0.01 mm/hr

    Array Chunk Bytes 858.99 MiB 6.18 MiB Shape (139, 3600, 1800) (1, 3600, 1800) Dask graph 139 chunks in 279 graph layers Data type int8 numpy.ndarray

  • randomError
    (time, lon, lat)
    float32
    dask.array<chunksize=(1, 3600, 1800), meta=np.ndarray>

    units :

    mm/day

    long_name :

    Root-mean-square error estimate for combined microwave-IR daily precipitation rate

    Array Chunk Bytes 3.36 GiB 24.72 MiB Shape (139, 3600, 1800) (1, 3600, 1800) Dask graph 139 chunks in 279 graph layers Data type float32 numpy.ndarray

  • randomError_cnt
    (time, lon, lat)
    int8
    dask.array<chunksize=(1, 3600, 1800), meta=np.ndarray>

    units :

    count

    long_name :

    Count of valid half-hourly randomError retrievals for the day

    Array Chunk Bytes 858.99 MiB 6.18 MiB Shape (139, 3600, 1800) (1, 3600, 1800) Dask graph 139 chunks in 279 graph layers Data type int8 numpy.ndarray

  • probabilityLiquidPrecipitation
    (time, lon, lat)
    int8
    dask.array<chunksize=(1, 3600, 1800), meta=np.ndarray>

    units :

    percent

    long_name :

    Probability of liquid precipitation

    description :

    Probability of liquid precipitation estimated with a diagnostic parameterization using ancillary data. 0=missing values; 1=likely solid; 100=likely liquid or no precipitation. Screen by positive precipitation or precipitation_cnt_cond to locate meaningful probabilities.

    Array Chunk Bytes 858.99 MiB 6.18 MiB Shape (139, 3600, 1800) (1, 3600, 1800) Dask graph 139 chunks in 279 graph layers Data type int8 numpy.ndarray

  • time_bnds
    (time, nv)
    datetime64[ns]
    dask.array<chunksize=(1, 2), meta=np.ndarray>

    coordinates :

    time nv

    Array Chunk Bytes 2.17 kiB 16 B Shape (139, 2) (1, 2) Dask graph 139 chunks in 279 graph layers Data type datetime64[ns] numpy.ndarray

Indexes: (3)

• lon
  PandasIndex

  PandasIndex(Float64Index([ -179.9499969482422, -179.85000610351562,             -179.75,
                -179.64999389648438,  -179.5500030517578,  -179.4499969482422,
                -179.35000610351562,             -179.25, -179.14999389648438,
                 -179.0500030517578,
                ...
                  179.0500030517578,  179.14999389648438,              179.25,
                 179.35000610351562,   179.4499969482422,   179.5500030517578,
                 179.64999389648438,              179.75,  179.85000610351562,
                  179.9499969482422],
               dtype='float64', name='lon', length=3600))

• lat
  PandasIndex

  PandasIndex(Float64Index([            -89.95, -89.85000000000001,             -89.75,
                            -89.65,             -89.55,             -89.45,
                -89.35000000000001,             -89.25,             -89.15,
                            -89.05,
                ...
                             89.05,  89.15000000000002,  89.25000000000001,
                 89.35000000000001,              89.45,              89.55,
                 89.65000000000002,  89.75000000000001,  89.85000000000001,
                             89.95],
               dtype='float64', name='lat', length=1800))

• time
  PandasIndex

  PandasIndex(DatetimeIndex(['2022-11-19', '2022-11-20', '2022-11-21', '2022-11-22',
                 '2022-11-23', '2022-11-24', '2022-11-25', '2022-11-26',
                 '2022-11-27', '2022-11-28',
                 ...
                 '2023-03-28', '2023-03-29', '2023-03-30', '2023-03-31',
                 '2023-04-01', '2023-04-02', '2023-04-03', '2023-04-04',
                 '2023-04-05', '2023-04-06'],
                dtype='datetime64[ns]', name='time', length=139, freq=None))

Attributes: (9)

BeginDate :

2022-11-19

BeginTime :

00:00:00.000Z

EndDate :

2022-11-19

EndTime :

23:59:59.999Z

FileHeader :

StartGranuleDateTime=2022-11-19T00:00:00.000Z; StopGranuleDateTime=2022-11-19T23:59:59.999Z

InputPointer :

3B-HHR.MS.MRG.3IMERG.20221119-S000000-E002959.0000.V07A.HDF5;3B-HHR.MS.MRG.3IMERG.20221119-S003000-E005959.0030.V07A.HDF5;3B-HHR.MS.MRG.3IMERG.20221119-S010000-E012959.0060.V07A.HDF5;3B-HHR.MS.MRG.3IMERG.20221119-S013000-E015959.0090.V07A.HDF5;3B-HHR.MS.MRG.3IMERG.20221119-S020000-E022959.0120.V07A.HDF5;3B-HHR.MS.MRG.3IMERG.20221119-S023000-E025959.0150.V07A.HDF5;3B-HHR.MS.MRG.3IMERG.20221119-S030000-E032959.0180.V07A.HDF5;3B-HHR.MS.MRG.3IMERG.20221119-S033000-E035959.0210.V07A.HDF5;3B-HHR.MS.MRG.3IMERG.20221119-S040000-E042959.0240.V07A.HDF5;3B-HHR.MS.MRG.3IMERG.20221119-S043000-E045959.0270.V07A.HDF5;3B-HHR.MS.MRG.3IMERG.20221119-S050000-E052959.0300.V07A.HDF5;3B-HHR.MS.MRG.3IMERG.20221119-S053000-E055959.0330.V07A.HDF5;3B-HHR.MS.MRG.3IMERG.20221119-S060000-E062959.0360.V07A.HDF5;3B-HHR.MS.MRG.3IMERG.20221119-S063000-E065959.0390.V07A.HDF5;3B-HHR.MS.MRG.3IMERG.20221119-S070000-E072959.0420.V07A.HDF5;3B-HHR.MS.MRG.3IMERG.20221119-S073000-E075959.0450.V07A.HDF5;3B-HHR.MS.MRG.3IMERG.20221119-S080000-E082959.0480.V07A.HDF5;3B-HHR.MS.MRG.3IMERG.20221119-S083000-E085959.0510.V07A.HDF5;3B-HHR.MS.MRG.3IMERG.20221119-S090000-E092959.0540.V07A.HDF5;3B-HHR.MS.MRG.3IMERG.20221119-S093000-E095959.0570.V07A.HDF5;3B-HHR.MS.MRG.3IMERG.20221119-S100000-E102959.0600.V07A.HDF5;3B-HHR.MS.MRG.3IMERG.20221119-S103000-E105959.0630.V07A.HDF5;3B-HHR.MS.MRG.3IMERG.20221119-S110000-E112959.0660.V07A.HDF5;3B-HHR.MS.MRG.3IMERG.20221119-S113000-E115959.0690.V07A.HDF5;3B-HHR.MS.MRG.3IMERG.20221119-S120000-E122959.0720.V07A.HDF5;3B-HHR.MS.MRG.3IMERG.20221119-S123000-E125959.0750.V07A.HDF5;3B-HHR.MS.MRG.3IMERG.20221119-S130000-E132959.0780.V07A.HDF5;3B-HHR.MS.MRG.3IMERG.20221119-S133000-E135959.0810.V07A.HDF5;3B-HHR.MS.MRG.3IMERG.20221119-S140000-E142959.0840.V07A.HDF5;3B-HHR.MS.MRG.3IMERG.20221119-S143000-E145959.0870.V07A.HDF5;3B-HHR.MS.MRG.3IMERG.20221119-S150000-E152959.0900.V07A.HDF5;3B-HHR.MS.MRG.3IMERG.20221119-S153000-E155959.0930.V07A.HDF5;3B-HHR.MS.MRG.3IMERG.20221119-S160000-E162959.0960.V07A.HDF5;3B-HHR.MS.MRG.3IMERG.20221119-S163000-E165959.0990.V07A.HDF5;3B-HHR.MS.MRG.3IMERG.20221119-S170000-E172959.1020.V07A.HDF5;3B-HHR.MS.MRG.3IMERG.20221119-S173000-E175959.1050.V07A.HDF5;3B-HHR.MS.MRG.3IMERG.20221119-S180000-E182959.1080.V07A.HDF5;3B-HHR.MS.MRG.3IMERG.20221119-S183000-E185959.1110.V07A.HDF5;3B-HHR.MS.MRG.3IMERG.20221119-S190000-E192959.1140.V07A.HDF5;3B-HHR.MS.MRG.3IMERG.20221119-S193000-E195959.1170.V07A.HDF5;3B-HHR.MS.MRG.3IMERG.20221119-S200000-E202959.1200.V07A.HDF5;3B-HHR.MS.MRG.3IMERG.20221119-S203000-E205959.1230.V07A.HDF5;3B-HHR.MS.MRG.3IMERG.20221119-S210000-E212959.1260.V07A.HDF5;3B-HHR.MS.MRG.3IMERG.20221119-S213000-E215959.1290.V07A.HDF5;3B-HHR.MS.MRG.3IMERG.20221119-S220000-E222959.1320.V07A.HDF5;3B-HHR.MS.MRG.3IMERG.20221119-S223000-E225959.1350.V07A.HDF5;3B-HHR.MS.MRG.3IMERG.20221119-S230000-E232959.1380.V07A.HDF5;3B-HHR.MS.MRG.3IMERG.20221119-S233000-E235959.1410.V07A.HDF5

title :

GPM IMERG Final Precipitation L3 1 day 0.1 degree x 0.1 degree (GPM_3IMERGDF)

DOI :

10.5067/GPM/IMERGDF/DAY/07

ProductionTime :

2023-08-25T14:03:25.792Z