Toward A Globally-Applicable Uncertainty Quantification Framework for
Satellite Multisensor Precipitation Products based on GPM DPR
Abstract
The usefulness of satellite multisensor precipitation products such as
NASA’s 30-minute, 0.1° Integrated Multi-satellitE Retrievals for the
Global Precipitation Mission (IMERG) is hindered by their associated
errors. Reliable estimates of uncertainty would mitigate this
limitation, especially in near-real time. Creating such estimates is
challenging, however, due both to the complex discrete-continuous nature
of satellite precipitation errors and to the lack of “ground truth”
data precisely in the places—including complex terrain and developing
countries—that could benefit most from satellite precipitation
estimates. In this work, we use swath-based precipitation products from
the Global Precipitation Mission (GPM) Dual-frequency Precipitation
Radar (DPR) as an alternative to ground-based observations to facilitate
IMERG uncertainty estimation. We compare the suitability of two DPR
derived products, 2ADPR and 2BCMB, against higher-fidelity Ground
Validation Multi-Radar Multi-Sensor (GV-MRMS) ground reference data over
the contiguous United States. 2BCMB is selected to train mixed
discrete-continuous error models based on Censored Shifted Gamma
Distributions. Uncertainty estimates from these error models are
compared against alternative models trained on GV-MRMS. Using
information from NASA’s Modern-Era Retrospective analysis for Research
and Applications, Version 2 (MERRA-2) reanalysis, we also demonstrate
how IMERG uncertainty estimates can be further constrained using
additional precipitation-related predictors. Though several critical
issues remain unresolved, the proposed method shows promise for yielding
robust uncertainty estimates in near-real time for IMERG and other
similar precipitation products at their native resolution across the
entire globe.