IEEE Geoscience and Remote Sensing Magazine - June 2014 - 35

data users as a 'HOW TO USE' guideline in the practical
world. We developed a practical approach involving a
simple non-linear regression model to estimate the error
variance for satellite precipitation products and used it
for merging (linearly weighted according to inverse of
error variance). A user can apply this approach to estimate probable error variance and then use it as a proxy
for data quality and decision making. We argue that
topography, climate and seasons are considered readily available geophysical features for the end-user at any
location. The use of topography, climate and season as
major governing factors in the development of regression framework is logical to identify the uncertainty
type associated with satellite rainfall estimates.
In summary, high resolution and multi-sensor satellite-based precipitation estimates, such as those analyzed
in this study and those anticipated from the Global Precipitation Measurement (GPM; Hou et al., 2008) satellites, now hold great promise for hydrologic applications,
especially over parts of the world where surface observation networks are sparse, declining or non-existent.
However, the usefulness of such precipitation products
for hydrological applications will depend on their error
characteristics and how successful we are in intelligently
harnessing the implications of uncertainty for surface
hydrology. The decline of the few existing global ground
based measurement networks for rain and stream flow
and the absence of in-situ measurement in most parts of
the world represent a 'paradoxical' situation for evaluating satellite rainfall estimation uncertainty. By developing simple models for estimation of error variance for
satellite data that a user can use anywhere and anytime
using only readily available geophysical features, our
study represents a first comprehensive step at resolving
the paradox for making satellite precipitation data work
around the world during the GPM era. More importantly,
we have demonstrated that the accuracy of satellite rainfall products can be improved by merging the existing
satellite rainfall products based on their hydrologic predictability. This will contribute an important finding to
the existing NASA investigation on the merging of algorithms and processing sequence for the Integrated MultisatellitE Retrievals for GPM (IMERGE).
The mission statement provided on the GPM website
(http://pmm.nasa.gov) states "The GPM mission will help
advance our understanding of Earth's water and energy
cycles, improve the forecasting of extreme events that
cause natural disasters, and extend current capabilities of
using satellite precipitation information to directly benefit
society." If "to directly benefit society" is indeed a community priority, much more must be done so that the scientific advancements translate to tangible products or utilities for the world that societies can actually benefit from.
GPM has the unique potential as a pathfinder mission to
show how satellites can truly improve lives of millions of
people through more cost-effective water management.
june 2014

ieee Geoscience and remote sensing magazine

6. REFERENCES
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http://pmm.nasa.gov

Table of Contents for the Digital Edition of IEEE Geoscience and Remote Sensing Magazine - June 2014