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| Titel |
New algorithm for integration between wireless microwave sensor network and radar for improved rainfall measurement and mapping |
| VerfasserIn |
Y. Liberman, R. Samuels, P. Alpert, H. Messer |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1867-1381
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| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Measurement Techniques ; 7, no. 10 ; Nr. 7, no. 10 (2014-10-17), S.3549-3563 |
| Datensatznummer |
250115935
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| Publikation (Nr.) |
 copernicus.org/amt-7-3549-2014.pdf |
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| Zusammenfassung |
| One of the main challenges for meteorological and hydrological modelling is
accurate rainfall measurement and mapping across time and space. To date, the
most effective methods for large-scale rainfall estimates are radar,
satellites, and, more recently, received signal level (RSL) measurements
derived from commercial microwave networks (CMNs). While these methods provide
improved spatial resolution over traditional rain gauges, they have their
limitations as well. For example, wireless CMNs, which are comprised of
microwave links (ML), are dependant upon existing infrastructure and the ML'
arbitrary distribution in space. Radar, on the other hand, is known in its
limitation for accurately estimating rainfall in urban regions, clutter areas
and distant locations. In this paper the pros and cons of the radar and ML
methods are considered in order to develop a new algorithm for improving rainfall measurement and mapping, which is based on data fusion of the different
sources. The integration is based on an optimal weighted average of the two
data sets, taking into account location, number of links, rainfall intensity
and time step. Our results indicate that, by using the proposed new method,
we not only generate more accurate 2-D rainfall reconstructions, compared with
actual rain intensities in space, but also the reconstructed maps are
extended to the maximum coverage area. By inspecting three significant rain
events, we show that our method outperforms CMNs or the radar alone in
rain rate estimation, almost uniformly, both for instantaneous spatial
measurements, as well as in calculating total accumulated rainfall. These new
improved 2-D rainfall maps, as well as the accurate rainfall measurements over large
areas at sub-hourly timescales, will allow for improved understanding,
initialization, and calibration of hydrological and meteorological models mainly
necessary for water resource management and planning. |
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