How much it rained (GPM, NOAA-20)
What it measures. Estimates of precipitation and related rainfall details along the satellite's track, at about 17-kilometer detail, produced roughly every hour and a half.
How it's made. Retrieved from the ATMS microwave sensor on the NOAA-20 satellite using the GPROF method, which compares the sensor's readings against a large library of known rain patterns to estimate rainfall.
How & where you'd use it. Contributes to global rainfall monitoring as part of the GPM mission, useful for weather, flood, and water-resource applications.
What's measured
Coverage & cadence
- Time span2022-04-30 → ongoing
- Measured byNOAA-20 (ATMS)
- Processing levelLevel 2
- Spatial extent-180, -90, 180, 90
- StatusCOMPLETE
What you can do with it
- Map air pollutants — NO₂, aerosols, ozone
- Track greenhouse gases and Earth's energy budget
- Feed weather and air-quality analysis
Official description
Version 07 is the current version of the data set. The 2AGPROF (also known as, GPM GPROF (Level 2)) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors: GMI, SSMI (DMSP F15), SSMIS (DMSP F16, F17, F18) AMSR2 (GCOM-W1), TMI MHS (NOAA 18&19, METOP A&B), ATMS (SNPP and NOAA-20). This provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are near-realtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided. The GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an a-priori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.
Get the data
import earthaccess
earthaccess.login(strategy="netrc") # free Earthdata Login
results = earthaccess.search_data(
short_name="GPM_2AGPROFNOAA20ATMS",
version="07",
bounding_box=(-122.5, 37.2, -121.8, 37.9), # your area (W,S,E,N)
temporal=("2024-01-01", "2024-12-31"), # your dates
)
files = earthaccess.open(results) # stream straight from GES_DISC Browsing CMR needs no login. Downloading or streaming bytes needs a free Earthdata Login + the earthaccess package. Official links
- Access the data via HTTPS GET DATA
- Access the data via the OPeNDAP protocol USE SERVICE API
- Use the Earthdata Search to find and retrieve data sets across multiple data centers. GET DATA
- README Document VIEW RELATED INFORMATION
- ALGORITHM THEORETICAL BASIS DOCUMENT (ATBD) VIEW RELATED INFORMATION
- FILE SPECIFICATION DOCUMENT VIEW RELATED INFORMATION
- GPM and partner sensors anomalous events VIEW RELATED INFORMATION
- Instrument Description from NOAA VIEW RELATED INFORMATION