Ensemble Representation of Satellite Precipitation Uncertainty using an
Uncalibrated, Nonstationary, Anisotropic Autocorrelation Model
Abstract
The usefulness of satellite multi-sensor precipitation (SMP) and other
satellite-informed precipitation products in water resources modeling
can be hindered by substantial errors which vary considerably with
spatiotemporal scale. One approach to cope with these errors is by
combining SMPs with ensemble generation methods, such that each ensemble
member reflects one plausible realization of the true—but
unknown—precipitation. This requires replicating the spatiotemporal
autocorrelation structure of SMP errors. The climatology of this
structure is unknown for most locations due to a lack of ground
reference observations, while the unique anisotropy and nonstationarity
within any particular precipitation system limit the relevance of this
climataology to the depiction of error in individual storm systems.
Characterizing and simulating this autocorrelation across spatiotemporal
scales has thus been called a grand challenge within the precipitation
community. We introduce the Space-Time Rainfall Error and
Autocorrelation Model (STREAM), which combines anisotropic and
nonstationary SMP spatiotemporal correlation structures with a
pixel-scale precipitation error model to stochastically generate
ensemble precipitation fields that resemble “ground truth”
precipitation. We generate STREAM precipitation ensembles at high
resolution (1-hour, 0.1˚) with minimal reliance on ground-reference
data, and evaluate these ensembles at multiple scales. STREAM ensembles
consistently “bracket” ground-truth observations and replicate the
autocorrelation structure of ground-truth precipitation fields. STREAM
is compatible with pixel-scale error/uncertainty formulations beyond
those presented here, and could be applied globally to other
precipitation sources such as numerical weather predictions or
“blended” products. In combination with recent work in SMP uncertainty
characterization, STREAM could be run without any ground data.