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Precipitation Measurement and Hydrology

Jürg Joss, A. Waldvogel, C. G. Collier

Open publisher page 286 citations

Abstract

The most important advantage of using radar for precipitation measurements is the coverage of a large area with high spatial and temporal resolution from a single point and in real time. Furthermore, the three-dimensional picture of the weather situation can be extended over a very large area by compositing data from several radars. However, we have not been able until recently to make measurements over a large area with an accuracy acceptable for hydrological applications. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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What this paper is about

The most important advantage of using radar for precipitation measurements is the coverage of a large area with high spatial and temporal resolution from a single point and in real time. Furthermore, the three-dimensional picture of the weather situation can be extended over a very large area by compositing data from several radars. However, we have not been able until recently to make measurements over a large area with an accuracy acceptable for hydrological applications. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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OpenAlex reports 286 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

The most important advantage of using radar for precipitation measurements is the coverage of a large area with high spatial and temporal resolution from a single point and in real time. Furthermore, the three-dimensional picture of the weather situation can be extended over a very large area by compositing data from several radars. However, we have not been able until recently to make measurements over a large area with an accuracy acceptable for hydrological applications. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Key concepts: Compositing, Precipitation, Radar, Environmental science, Remote sensing, Meteorology, Weather radar, Hydrology (agriculture)

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