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Full catalog/PACE_HARP2_L3M_CLOUD_GPC_NRT
PACE_HARP2_L3M_CLOUD_GPC_NRT·v3.0·dataset

Cloud properties from polarized light, near real-time (PACE HARP2)

PACE HARP2 Level-3 Global Mapped Cloud Optical Properties, GISS Polarimetric Cloud (GPC) Algorithm - Near Real-time (NRT) Data, version 3.0
atmosphere NASA OB_CLOUD Level 3 active netCDF-4
In plain English

What it measures. Properties of clouds derived from polarized light, such as the size of cloud droplets, mapped globally. Several variables are experimental and intended for diagnostics and future refinement.

How it's made. Produced from the HARP2 polarimeter aboard the PACE satellite using a polarimetric cloud algorithm, as a near-real-time product made with best-available (less-than-optimal) ancillary data.

How & where you'd use it. Helps study cloud microphysics; because it is a novel, near-real-time, partly experimental product, it is best suited for early assessment rather than definitive analysis.

What's measured

Atmosphere › Clouds

Coverage & cadence

  • Time span2024-02-05 → ongoing
  • Measured byPACE (HARP2)
  • Processing levelLevel 3
  • Spatial extent-180, -90, 180, 90
  • FormatsnetCDF-4
  • StatusACTIVE

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

The Ocean Biology DAAC produces near real-time (NRT) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Near real-time products provide a snapshot of the data during a short time period within a single orbit. The CLOUD_GPC suite provides cloud properties derived from the GISS Polarimetric Cloud (GPC) algorithm, along with additional variables obtained through combination with the OCI CLOUD suite. Given the novelty of implementing a polarimetric algorithm on a global scale, note that several experimental variables—particularly those intended for diagnostic purposes and future refinement—are included and may change in subsequent, more mature versions. Core geophysical variables in this suite include: - cloud_bow_droplet_effective_radius - Cloud droplet effective radius from Cloud Bow polarimetric retrievals using parametric fitting technique (µm) - cloud_bow_droplet_effective_variance - Cloud droplet effective variance from Cloud Bow polarimetric retrievals using parametric fitting technique (unitless) - cloud_bow_fit_correlation - Parametric fitting correlation for Cloud Bow retrievals of droplet radius and variance (unitless) - cloud_bow_rms - Root-mean-square error for Cloud Bow retrievals of droplet radius and variance (unitless) - cloud_liquid_index - Index measuring the strength of the primary rainbow in polarized reflectances from liquid cloud droplets (unitless) - cloud_rft_mode_fraction_0 - Fractional contribution of droplet mode 1 from Rainbow Fourier Transform retrievals (unitless; range: 0–1) - cloud_rft_mode_fraction_1 - Fractional contribution of droplet mode 1 from Rainbow Fourier Transform retrievals (unitless; 0–1)

Get the data

pace_harp2_l3m_cloud_gpc_nrt_access.py
import earthaccess
earthaccess.login(strategy="netrc")          # free Earthdata Login

results = earthaccess.search_data(
    short_name="PACE_HARP2_L3M_CLOUD_GPC_NRT",
    version="3.0",
    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 OB_CLOUD
Browsing CMR needs no login. Downloading or streaming bytes needs a free Earthdata Login + the earthaccess package.