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The correlation between CMORPH and gauge data is the highest.
On the contrary, the correlation between 3B42 RT and gauge data is more scattered; statistically, this results in lower bias.
A new technique for merging radar precipitation estimates and rain gauge data is developed and evaluated to improve multisensor quantitative precipitation estimation (QPE), in particular, of heavy-to-extreme precipitation.
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Overall, our assessment is that blending the 3B42RT satellite and rain gauge data was not worthwhile.
Nevertheless, standard deviations of height time series with respect to radar gauge data are typically [4 5] cm.
The VLM estimates from altimetry and tide gauge data are in good agreement both with GPS derived vertical velocity estimates and those inferred from geological and archaeological investigations.
Precipitation trends in the datasets differ in magnitude and sign at smaller scales, and are sensitive to how temporal inhomogeneities in the underlying precipitation gauge data are handled.
EM density gauge data were collected from these slabs using two common types of gauges, i.e., PQI 301 and PaveTracker 2701B, and compared with their bulk densities measured by the standard saturated surface dry (SSD) method.
Six-minute rain gauge data were available and the GR4 rainfall runoff model was run with precipitation inputs at eight different time steps ranging from 6 min to 1 day.
This study demonstrate the possibility of reconstructing past extreme floods where gauge data are not available and has relevance to the prediction of future extreme floods which will have possible disastrous socio-economic impacts on the Taihu catchment.
The proposed methodology entails two main steps: (1) Temporal interpolation of radar images from the originally-available temporal resolutions (i.e. 5 10 min) to finer resolutions at which local rain gauge data are commonly available (i.e. 1 2 min).
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