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DOI: https://doi.org/10.7522/j.issn.1000-0534.2021.00022

基于地理地形因子动态调整的复杂地形区雷达定量估测降水技术

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  • 兰州中心气象台，甘肃 兰州　730020

网络出版日期: 2021-03-15

基金资助

国家自然科学基金项目（41505036）；甘肃省气象局创新团队项目（GXQXCXTD-2020-01）

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Radar Quantitative Precipitation Estimation in Complex Terrain Based on Dynamic Adjustment of Geographical Terrain Factors

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  • Lanzhou Central Meteorological Observatory, Lanzhou 730020, Gansu, China

Online published: 2021-03-15

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摘要

雷达定量降水估测可以提供时间连续、水平分辨率高和覆盖范围广阔的降水产品，目前已成为气象监测的重要手段之一。尽管当前针对不同地区雷达估测降水的方法有了长足的发展，但是复杂地形区的雷达降水估测效果仍需改进。本文针对青藏高原东北边坡复杂地形区雷达降水估测，在固定经验Z-I关系、云分类经验Z-I关系、回波分级统计Z-I关系3种已有算法的基础上，建立了基于准稳定移动窗口的地理地形因子实时动态调整降水估测订正算法，并利用2017—2018年汛期临夏地区降水天气个例开展估测效果评估。结果表明：未经订正算法估测降水值往往较实况观测值偏小，而经过订正后的估测降水平均值与观测实况较为一致；不同算法对于小量级降水估测的累计概率分布（CDF）与观测实况偏差较大，而随着降水量级逐渐增加，估测降水的CDF与实况观测逐渐接近，基于回波分级统计Z-I关系的订正算法估测性能更为稳定；不同算法估测降水量的误差差异较大，未订正算法对所有降水以低估为主，基于回波分级统计Z-I关系订正算法和基于固定经验Z-I关系订正算法的整体估测偏差较小；未经订正算法对对流性降水天气主要降水落区估测较好，但对降水强度估测较差，而订正后估测效果明显提升，基于云分类经验Z-I关系的订正算法对于对流性降水天气估测能力更佳，特别是对短时强降水的估测能力效果较其他算法明显更优；未订正算法对于稳定性降水量级估测较弱，而经过订正后估测结果与观测实况更为接近。

关键词： ; font-family:Times New Roman; ">雷达估测降水; 动态调整; 实时订正; 地理地形因子

本文引用格式

刘维成, 沙宏娥, 肖玮, 苟尚, 王基鑫, 张伟 .

基于地理地形因子动态调整的复杂地形区雷达定量估测降水技术
[J]. 高原气象, 0 : 1 . DOI: 10.7522/j.issn.1000-0534.2021.00022

Abstract

Radar Quantitative Precipitation Estimation（QPE） can provide continuous, high-resolution and wide-coverage precipitation products which have become critical means of meteorological monitoring. Although the current methods in different regions have made considerable progress, the effect of QPE in complex terrain areas still needs to be strengthened. Aiming at the QPE in the complex terrain area of the northeast slope of the Qinghai-Tibet Plateau, this paper establishes a real-time-dynamic-correction algorithm used geographic terrain factors based on a quasi-stable moving-window based on existing estimation algorithms: fixed empirical Z-I relationship, cloud classification empirical Z-I relationship, and echo classification statistics Z-I relationship. The estimation performance evaluation was conducted using the precipitation events in Linxia during the flood season from 2017 to 2018. The results demonstrate that the estimated precipitation value without the correction algorithm is often smaller than the actual observation value, and the corrected average precipitation value is more consistent with the actual observation value. For small rainfall events, the cumulative distribution function (CDF) obtained by different estimation algorithms deviates greatly from the actual observation. As the precipitation level gradually increases, the CDF of estimated precipitation gradually approaches the actual observation. It is more stable in the estimation performance of the correction algorithm based on the statistics Z-I relationship of the echo classification. The error of precipitation estimation by different algorithms is quite different and the uncorrected algorithm mainly underestimates to all precipitation events. The overall estimation deviation of the correction algorithm both based on Z-I relationship of echo classification statistics and Z-I relationship of fixed experience is relatively small. The uncorrected algorithm has the advantage to estimate the main precipitation area of convective-precipitation-weather events, but it has a poor ability to estimate the precipitation intensity, and the estimated effect is a boost after the correction. In particular, the correction algorithm based on cloud classification experience Z-I relationship has better estimation ability for convective precipitation weather events, especially the estimation ability of short-duration heavy rainfall is significantly better than other algorithms. The uncorrected algorithm has weaker precipitation estimates for stable precipitation weather events, and the corrected estimates get closer to observations.

Key words：

Radar Quantitative Precipitation Estimation; dynamic adjustment; real-time correction; geographic and topographic factors

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