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smooth interpolation techniques which effect is similar to that of low-pass filtering. The issue of nonorthogonal errors across products is particularly critical when using techniques such as total least squares or triple collocation for QPEs evaluation (e.g., Alemohammad et al. 2015 ; Li et al. 2018 ; Lu et al. 2021 ), and more generally when ranking QPEs based on their consistency with supposedly “independent” data. For example, a QPE that would correctly reproduce the fine-scale variability of
smooth interpolation techniques which effect is similar to that of low-pass filtering. The issue of nonorthogonal errors across products is particularly critical when using techniques such as total least squares or triple collocation for QPEs evaluation (e.g., Alemohammad et al. 2015 ; Li et al. 2018 ; Lu et al. 2021 ), and more generally when ranking QPEs based on their consistency with supposedly “independent” data. For example, a QPE that would correctly reproduce the fine-scale variability of
resolutions are critical for near-real-time applications such as rapid monitoring and forecasting of high-impact societal events like flash floods, debris flows, and shallow landslides. Such resolution can be obtained primarily from satellite sensors on board geostationary Earth orbit (GEO) platforms. NOAA’s Advanced Baseline Imager (ABI) sensor on board the latest generation of Geostationary Operational Environmental Satellites (GOES-R Series) provides 3 times more spectral channels, 4 times the
resolutions are critical for near-real-time applications such as rapid monitoring and forecasting of high-impact societal events like flash floods, debris flows, and shallow landslides. Such resolution can be obtained primarily from satellite sensors on board geostationary Earth orbit (GEO) platforms. NOAA’s Advanced Baseline Imager (ABI) sensor on board the latest generation of Geostationary Operational Environmental Satellites (GOES-R Series) provides 3 times more spectral channels, 4 times the
indication of the limitation associated with the forward-only propagated PMW estimates. Looking at the IMERG components, PMW ( Fig. 1e ) shows overestimation over the Gulf Coast and the Northwest coastline. As expected, morph ( Fig. 1g ) shows a similar spatial distribution compared to PMW but with significantly lower conditional mean rainfall. This could be due to smoothing effects of forward- and backward-propagation techniques applied on PMW estimates to calculate morph dataset. Hence, even though the
indication of the limitation associated with the forward-only propagated PMW estimates. Looking at the IMERG components, PMW ( Fig. 1e ) shows overestimation over the Gulf Coast and the Northwest coastline. As expected, morph ( Fig. 1g ) shows a similar spatial distribution compared to PMW but with significantly lower conditional mean rainfall. This could be due to smoothing effects of forward- and backward-propagation techniques applied on PMW estimates to calculate morph dataset. Hence, even though the
