德国利用天气雷达估算设计降水量:统计方法比较

IF 4.7 2区 地球科学 Q1 WATER RESOURCES
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引用次数: 0

摘要

研究地区:德国。研究重点:以各种时间尺度和高空间分辨率估算降水回归水平对于风险管理和水文应用至关重要。气象雷达可提供这方面的信息,但这些仪器的设计降水量估计值存在估计偏差,而且可用记录的长度有限。本文研究了从德国 20 年雷达数据中得出设计降水量的两种统计方法的性能:(a) 基于峰值过阈值和指数分布的方法,该方法已用于计算德国的设计降水量达数十年之久(DWA);(b) 非渐近方法,该方法最近被证明可减少与短记录有关的估计不确定性(SMEV)。由于尺度不匹配(点尺度与雷达的 1 平方公里)以及雷达对极端降水的估算存在偏差,雷达数据的设计降水量往往低于观测站数据。SMEV 往往比 DWA 低估得更多,尤其是短时。据报告,DWA 方法的不确定性较大,而 SMEV 估计值往往更稳定,受统计异常值的影响较小。在雷达数据上应用 KOSTRA-DWD-2020 方法得出的结果更接近 SMEV。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Estimation of design precipitation using weather radar in Germany: A comparison of statistical methods

Estimation of design precipitation using weather radar in Germany: A comparison of statistical methods

Study region:

Germany.

Study focus:

Estimation of precipitation return levels on various temporal scales and with high spatial resolution is crucial for risk management and hydrological applications. Weather radars may provide this information, but design precipitation estimates from these instruments suffer from estimation biases and from the limited length of the available records. Here, the performance of two statistical methods for deriving design precipitation from 20 years of radar data for Germany is investigated: (a) a method based on peaks-over-threshold and an exponential distribution, operationally used for decades to compute design precipitation in Germany (DWA); and (b) a non-asymptotic approach that was recently shown to reduce estimation uncertainties related to short records (SMEV). The most recent official design precipitation for Germany derived from station data (KOSTRA-DWD-2020) are used as a benchmark.

New Hydrological Insights for the Region

Design precipitation from radar data tends to be lower than those derived from stations, due to the scale mismatch (point scale versus 1km2 of radar) and to biases in radar estimates of extremes. SMEV tends to underestimate more than DWA, especially for short durations. Larger uncertainties are reported for the DWA method, while SMEV estimates tend to be more stable and less influenced by statistical outliers. Application of the KOSTRA-DWD-2020 method on radar data leads to results closer to SMEV.

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来源期刊
Journal of Hydrology-Regional Studies
Journal of Hydrology-Regional Studies Earth and Planetary Sciences-Earth and Planetary Sciences (miscellaneous)
CiteScore
6.70
自引率
8.50%
发文量
284
审稿时长
60 days
期刊介绍: Journal of Hydrology: Regional Studies publishes original research papers enhancing the science of hydrology and aiming at region-specific problems, past and future conditions, analysis, review and solutions. The journal particularly welcomes research papers that deliver new insights into region-specific hydrological processes and responses to changing conditions, as well as contributions that incorporate interdisciplinarity and translational science.
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