Estimating uncertainties associated with quasi-global satellite
infrared-based retrievals over land
Abstract
An accurate characterization of the global hydrologic cycle is essential
not only to study and forecast climate variations, but also for extreme
event mitigation and agricultural planning. Since precipitation is the
major driving force of the hydrological cycle, current and future
satellite missions are critical to estimate precipitation globally.
Error estimates associated with satellite precipitation retrievals are
crucial to allow inferences about the reliability of such products in
their operational applications. However, evaluating satellite
precipitation error characteristics is challenging because of the
inherent temporal and spatial variability of precipitation, measurement
errors, and sampling uncertainties, especially at fine temporal and
spatial resolutions. This study proposes to use a stochastic error model
– PUSH (Probability Uncertainty in Satellite Hydrology) – for
estimating uncertainties associated with fine resolution satellite
precipitation products. The framework is tested on the daily IMERG
(Integrated Multi-satellitE Retrievals for GPM) infrared-only (IR)
precipitation component using a satellite-based radar product (the
Level-3 Dual-frequency Precipitation Radar, 3DPRD) as reference. PUSH
decomposes the error into four components and employs different modeling
approaches for each case: correct no-precipitation detection; missed
precipitation; false alarm; hit bias. PUSH is calibrated globally over
land for different climatological regions. The calibrated parameters are
validated using an independent period to verify whether they can be
applied to estimate uncertainties associated with future IR retrievals
without degrading the model performance. The four error components are
then investigated as a function of climate region to study their spatial
variability.