Download MRMS data

Initial configuration

from pandas import Timestamp, Timedelta
import emaremes as mrms

The first step is to configure where the data will be stored locally. By default, the data will be stored in the emaremes folder in your home directory.

Setting up these paths is done with the path_config object.

path_config = mrms.fetch.path_config
path_config

defaultpath: /home/edwin/emaremes
prefered: /home/edwin/emaremes

To set another directory to store GRIB files, you can use the following command:

new_dir = "./data"
path_config.set_prefered(new_dir)

Prefered path to store *new* Gribfiles is  data

We can add other folders where GRIB files are stored. emaremes only downloads the files that are not already present in the folder specified in path_config. This is useful to connect other local folders with MRMS data like external drives.

another_dir = "./other_data"
path_config.add_path(another_dir)

print(f"The preferred data directory is {path_config.prefered_path}")

The preferred data directory is data

To list all the paths available, we can print the all_paths attribute of path_config:

print(path_config.all_paths)

{PosixPath('other_data'), PosixPath('data'), PosixPath('/home/edwin/emaremes')}

Download precipitation rate data

MRMS data is available every 2 minutes. We can download the data using the fetch module in emaremes.

preciprate_files = mrms.fetch.timerange(
    "2024-09-27T12:00:00",
    "2024-09-28",
    frequency=Timedelta(minutes=60),
    verbose=True,
)

-> 13 files requested...
-> 13 *new* files will be downloaded...
Saved data/20240928/PrecipRate_00.00_20240928-000000.grib2.gz :)
Saved data/20240927/PrecipRate_00.00_20240927-210000.grib2.gz :)
Saved data/20240927/PrecipRate_00.00_20240927-140000.grib2.gz :)
Saved data/20240927/PrecipRate_00.00_20240927-230000.grib2.gz :)
Saved data/20240927/PrecipRate_00.00_20240927-220000.grib2.gz :)
Saved data/20240927/PrecipRate_00.00_20240927-180000.grib2.gz :)
Saved data/20240927/PrecipRate_00.00_20240927-160000.grib2.gz :)
Saved data/20240927/PrecipRate_00.00_20240927-190000.grib2.gz :)
Saved data/20240927/PrecipRate_00.00_20240927-150000.grib2.gz :)
Saved data/20240927/PrecipRate_00.00_20240927-170000.grib2.gz :)
Saved data/20240927/PrecipRate_00.00_20240927-200000.grib2.gz :)
Saved data/20240927/PrecipRate_00.00_20240927-130000.grib2.gz :)
Saved data/20240927/PrecipRate_00.00_20240927-120000.grib2.gz :)

fetch.timerange start and end times are both inclusive:

print("First file: ", preciprate_files[0])
print("Last file: ", preciprate_files[-1])

First file:  /home/edwin/emaremes/20240927/PrecipRate_00.00_20240927-120000.grib2.gz
Last file:  data/20240928/PrecipRate_00.00_20240928-000000.grib2.gz

These files are stored locally and new network requests will be skipped if these files are fetched again.

preciprate_files = mrms.fetch.timerange(
    "2024-09-27T12:00:00",
    "2024-09-28",
    frequency=Timedelta(minutes=60),
    verbose=True,
)

-> 13 files requested...
Nothing new to download :D

Download other data_type

The fetch.timerange function downloads precipiration rate data by default. Besides precipiration rate data, other data_type are available, like daily accumulated precipitation and precipitation flags.

Precipitation flags

Precipitation flags refer to the following classification, derived from the mrms-support repo:

Flag	Description
-3	no coverage
0	no precipitation
1	warm stratiform rain
3	snow
6	convective rain
7	rain mixed with hail
10	cold stratiform rain
91	tropical/stratiform rain mix
96	tropical/convective rain mix

To download precipitation flags, use the data_type="precip_flag" keyword argument:

precipflags_files = mrms.fetch.timerange(
    "2024-09-27T12:00:00",
    Timestamp("2024-09-28T00:00:00"),
    frequency=Timedelta(minutes=60),
    data_type="precip_flag",
    verbose=True,
)

-> 13 files requested...
-> 13 *new* files will be downloaded...
Saved data/20240927/PrecipFlag_00.00_20240927-160000.grib2.gz :)
Saved data/20240928/PrecipFlag_00.00_20240928-000000.grib2.gz :)
Saved data/20240927/PrecipFlag_00.00_20240927-180000.grib2.gz :)
Saved data/20240927/PrecipFlag_00.00_20240927-130000.grib2.gz :)
Saved data/20240927/PrecipFlag_00.00_20240927-200000.grib2.gz :)Saved data/20240927/PrecipFlag_00.00_20240927-120000.grib2.gz :)

Saved data/20240927/PrecipFlag_00.00_20240927-150000.grib2.gz :)
Saved data/20240927/PrecipFlag_00.00_20240927-230000.grib2.gz :)
Saved data/20240927/PrecipFlag_00.00_20240927-210000.grib2.gz :)
Saved data/20240927/PrecipFlag_00.00_20240927-190000.grib2.gz :)
Saved data/20240927/PrecipFlag_00.00_20240927-220000.grib2.gz :)
Saved data/20240927/PrecipFlag_00.00_20240927-170000.grib2.gz :)
Saved data/20240927/PrecipFlag_00.00_20240927-140000.grib2.gz :)

Download accumulated precipitation

Accumulates are available for 1, 24 and 72 hours periods. To fetch this data, set data_type to "precip_accum_1h", "precip_accum_24h" or "precip_accum_72h".

daily_files = mrms.fetch.timerange(
    "2024-09-26T12:00:00",
    "2024-09-29",  # <-- If only a date is provided, it defaults to midnight
    frequency=Timedelta(days=1),
    data_type="precip_accum_24h",
)

In the last example, we did not specify verbose=True, so no messages were shown about the files that were downloaded.