Spatial uncertainty to determine reliable daily precipitation maps
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Spatial uncertainty to determine reliable daily precipitation maps
Spatial uncertainty to determine reliable daily precipitation maps

Author: Adrian Chappell, Luigi Renzullo, and Malcolm Haylock | Size: 6.5 MB | Format: PDF | Quality: Unspecified | Publisher: Wiley | Year: 2012 | pages: 14

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Daily precipitation observations are commonly used with related variables to make
estimates at unsampled locations to provide maps and gridded data for hydrological and
climate model applications. Uncertainty in the way gridded data (maps) are prepared, given
the available information, is rarely considered. Over a study period of one year, we used
conditional simulations to produce multiple equally likely realizations of Australian
precipitation per day. Together those realizations represented an ensemble measure of
spatial uncertainty for rainfall for a given day. An independent gauge data set had values
within the 5th–95th percentile uncertainty range 94% of the time. Combined with other
measures they confirmed the reliability of the ensemble spatial uncertainty ranges. We
compared several established mapping techniques to an independent gauge data set using
local error statistics and to the spatial uncertainty maps. Those statistics showed little
difference between the mapping techniques and overall assessment of performance was
largely dependent on skill scores. However, the mapping techniques were different when
compared to the spatial uncertainty ranges. These findings support the assertion that
assessment of mapping techniques using local error statistics is insensitive to the
uncertainty in producing the maps as a whole. We conclude that uncertainty information in
precipitation estimates should not be overlooked when comparing precipitation estimation
techniques. The focus of performance assessment is traditionally on local error estimates,
and this tradition diverts attention away from the issues of uncertainty and reliability.
Reliable uncertainty characterization is necessary for the rigorous detection of spatial
patterns and longer time series trends in precipitation

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