Abstract
Meteorological radar networks are suited to remotely provide atmospheric precipitation retrieval over a wide geographic area for severe weather monitoring and near-real-time nowcasting. However, blockage due to buildings, hills, and mountains can hamper the potential of an operational weather radar system. The Abruzzo region in central Italy’s Apennines, whose hydro-geological risks are further enhanced by its complex orography, is monitored by a heterogeneous system of three microwave radars at the C and X bands with different features. This work shows a systematic intercomparison of operational radar mosaicking methods, based on bi-dimensional rainfall products and dealing with both C and X bands as well as single- and dual-polarization systems. The considered mosaicking methods can take into account spatial radar-gauge adjustment as well as different spatial combination approaches. A data set of 16 precipitation events during the years 2018–2020 in the central Apennines is collected (with a total number of 32,750 samples) to show the potentials and limitations of the considered operational mosaicking approaches, using a geospatially-interpolated dense network of regional rain gauges as a benchmark. Results show that the radar-network pattern mosaicking, based on the anisotropic radar-gauge adjustment and spatial averaging of composite data, is better than the conventional maximum-value merging approach. The overall analysis confirms that heterogeneous weather radar mosaicking can overcome the issues of single-frequency fixed radars in mountainous areas, guaranteeing a better spatial coverage and a more uniform rainfall estimation accuracy over the area of interest.
Highlights
In mountainous regions, heavy rainfall represents a problem that can manifest itself in the form of flash floods, especially in relatively small river basins
In the case of polarimetric radars, such as the TO radar at the X band in the Abruzzo network, additional variables are available, so that horizontal specific attenuation Ahh can be derived to correct for two-way path extinction effects
RWRNet and the ones RRG measured from rain gauges and indicating with angle brackets the space-time average, we can compute: Mean error or error bias
Summary
Heavy rainfall represents a problem that can manifest itself in the form of flash floods, especially in relatively small river basins. The detection and warning of severe events are typically approached using both rain gauges (RGs) and remote sensing instruments, from the ground and from space, in order to obtain a quantitative precipitation estimation (QPE) as accurately as possible [2,3,4,5,6,7,8] In this respect, the ground-based microwave weather radar (WR) monitoring of precipitation systems is a well-established technique (e.g., [2,3]). Speaking, to improve the accuracy of radar QPE while preserving their spatial description of rainfall fields, many approaches suggest adjusting radar QPEs as a function of rain gauge measurements [4] In this way it is possible to combine the advantages of in-situ sensors with radar remote sensing ones, partially overcoming their respective drawbacks [5].
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