Abstract
Weather radar networks are an excellent tool for quantitative precipitation estimation (QPE), due to their high resolution in space and time, particularly in remote mountain areas such as the Tropical Andes. Nevertheless, reduction of the temporal and spatial resolution might severely reduce the quality of QPE. Thus, the main objective of this study was to analyze the impact of spatial and temporal resolutions of radar data on the cumulative QPE. For this, data from the world’s highest X-band weather radar (4450 m a.s.l.), located in the Andes of Ecuador (Paute River basin), and from a rain gauge network were used. Different time resolutions (1, 5, 10, 15, 20, 30, and 60 min) and spatial resolutions (0.5, 0.25, and 0.1 km) were evaluated. An optical flow method was validated for 11 rainfall events (with different features) and applied to enhance the temporal resolution of radar data to 1-min intervals. The results show that 1-min temporal resolution images are able to capture rain event features in detail. The radar–rain gauge correlation decreases considerably when the time resolution increases (r from 0.69 to 0.31, time resolution from 1 to 60 min). No significant difference was found in the rain total volume (3%) calculated with the three spatial resolution data. A spatial resolution of 0.5 km on radar imagery is suitable to quantify rainfall in the Andes Mountains. This study improves knowledge on rainfall spatial distribution in the Ecuadorian Andes, and it will be the basis for future hydrometeorological studies.
Highlights
An adequate representation of spatio-temporal rainfall variability is of utmost importance for many meteorological, hydrological, and ecological studies [1,2]
This holds true for the tropical Andes Mountains, which are affected by a high spatio-temporal variability of precipitation but are at the same time characterized by a lack of hydrometeorological monitoring [3,4]
This technology has allowed us to study rainfall with high spatial and temporal resolutions [6], and it can be used in several applications, such as input data in rainfall forecasting studies [7,8,9,10,11], different hydrological models [12,13], early warning systems for floods [14], erosion studies [1,15], research related to atmospheric chemistry [16], and many others
Summary
An adequate representation of spatio-temporal rainfall variability is of utmost importance for many meteorological, hydrological, and ecological studies [1,2] This holds true for the tropical Andes Mountains, which are affected by a high spatio-temporal variability of precipitation but are at the same time characterized by a lack of hydrometeorological monitoring [3,4]. In this regard, rainfall data acquired from different remote sensors, such as weather radar systems, have become more accessible over the last few years [5]. Cost-effective systems derived from ship radar technology are available to establish radar networks with limited funds at hand [17].
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