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Elevation Influence on Rainfall and a Parameterization Algorithm in the Beijing Area

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Abstract

Using a high-density automatic weather stations (AWS) dataset of hourly rainfall observations, the present study investigates the relationship between rainfall and elevation in the Beijing area, and further proposes a rainfall amount dependent parameterized algorithm considering the elevation effect on rainfall on hourly timescale. The parameterization equation is defined as a segmented nonlinear model, which calculates the mountain rainfall as a function of valley rainfall amount. Results show that there exists an evident enhancement of rainfall amount by elevation effect in the Beijing area. In particular, larger rainfall amount is generally found in higher mountains, especially for slight rain and moderate rain. Furthermore, six representative station pairs located in valleys and on mountains respectively are selected to estimate the values of optimal parameters in the parameterization equation. The parameterization algorithm of elevation dependence can produce a reduction in the root-mean-square error and obtain a much closer mountain rainfall total to the observations compared with those using no elevation dependence. Furthermore, the spatial distribution of rainfall is more realistic and accurate in mountainous terrain when elevation dependence is considered. This study helps to understand the variability of rainfall with complex terrain in the Beijing area, and gives a possible way to parameterize rainfall-elevation relationship on hourly timescale.

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Correspondence to Linye Song.

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Supported by the National Natural Science Foundation of China (41605031), National Key Research and Development Program of China (2018YFF0300102 and 2018YFC1507504), and Beijing Municipal Science and Technology Plan (Z151100002115012).

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Song, L., Chen, M., Gao, F. et al. Elevation Influence on Rainfall and a Parameterization Algorithm in the Beijing Area. J Meteorol Res 33, 1143–1156 (2019). https://doi.org/10.1007/s13351-019-9072-3

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  • DOI: https://doi.org/10.1007/s13351-019-9072-3

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