trmm_3b42rt
Near real time rainfall estimates from NASA's Tropical Rainfall Measuring Mission
Load in Python
from intake import open_catalog
cat = open_catalog("https://raw.githubusercontent.com/pangeo-data/pangeo-datastore/master/intake-catalogs/atmosphere.yaml")
ds = cat["trmm_3b42rt"].to_dask()
Working with requester pays data
Several of the datasets within the cloud data catalog are contained in requester pays storage buckets. This means that a user requesting data must provide their own billing project (created and authenticated through Google Cloud Platform) to be billed for the charges associated with accessing a dataset. To set up an GCP billing project and use it for authentication in applications:- Create a project on GCP; if this is the first time using GCP, a prompt will appear to choose a Google account to link to all GCP-related activities.
- Create a Cloud Billing account associated with the project and enable billing for the project through this account.
- Using Google Cloud IAM, add the Service Usage Consumer role to your account, which enables it to make billed requests on the behalf of the project.
- Through command line, install the Google Cloud SDK; this can be done using conda:
conda install -c conda-forge google-cloud-sdk
- Initialize the
gcloud
command line interface, logging into the account used to create the aforementioned project and selecting it as the default project; this will allow the project to be used for requester pays access through the command line:gcloud auth login gcloud init
- Finally, use
gcloud
to establish application default credentials; this will allow the project to be used for requester pays access through applications:gcloud auth application-default login
Metadata
url | https://trmm.gsfc.nasa.gov/data_dir/data.html |
tags | ['precipitation', 'satellite'] |
Dataset Contents
xarray.Dataset
- lat: 480
- lon: 1440
- time: 41320
- lat(lat)float6459.88 59.62 59.38 ... -59.62 -59.88
array([ 59.875, 59.625, 59.375, ..., -59.375, -59.625, -59.875])
- lon(lon)float640.125 0.375 0.625 ... 359.6 359.9
array([1.25000e-01, 3.75000e-01, 6.25000e-01, ..., 3.59375e+02, 3.59625e+02, 3.59875e+02])
- time(time)datetime64[ns]2000-03-01T12:00:00 ... 2014-04-22T09:00:00
array(['2000-03-01T12:00:00.000000000', '2000-03-01T15:00:00.000000000', '2000-03-01T18:00:00.000000000', ..., '2014-04-22T03:00:00.000000000', '2014-04-22T06:00:00.000000000', '2014-04-22T09:00:00.000000000'], dtype='datetime64[ns]')
- precipitation(time, lat, lon)float32dask.array<chunksize=(40, 480, 1440), meta=np.ndarray>
Array Chunk Bytes 114.24 GB 110.59 MB Shape (41320, 480, 1440) (40, 480, 1440) Count 1034 Tasks 1033 Chunks Type float32 numpy.ndarray