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Precipitation retrieval by the L1‐norm regularization: Typhoon Hagibis case
Quarterly Journal of the Royal Meteorological Society ( IF 3.0 ) Pub Date : 2020-11-05 , DOI: 10.1002/qj.3945
Gen Wang 1, 2 , Wei Han 3 , Shuai Lu 4
Affiliation  

This study examines precipitation retrieval by the L1‐norm regularization using the infrared brightness temperatures from the Himawari‐8/Advanced Himawari Imager (AHI) of the typhoon Hagibis and analyses the advantages of high temporal resolution data. The AHI data covering the entire life cycle of typhoon Hagibis are implemented and we label “precipitation” and “non‐precipitation” fields‐of‐view (FOVs) based on the variation of brightness temperature gradient in AHI channels 8–16. In the precipitation FOVs, we implement the L1‐norm regularization where contribution rate of the “input variable” in the objective functional is obtained by approximating the degree of freedom for signal. The proposed approach is an effective method to obtain sparse solutions, which can express most or all atomic information by a small amount of atomic information. Several experiments reveal that the retrieved precipitation of typhoon Hagibis exhibits good structural similarity in consistency with the global precipitation measurement (GPM) precipitation values. In particular, the proposed approach provides better structural similarity (SSIM) and peak signal‐to‐noise ratio (PSNR) value compared with the random forest method, which is beneficial in extreme precipitation.

中文翻译:

通过L1-范数正则化获取降水:台风哈吉比斯案

这项研究使用来自台风哈格比斯(Haibis)的Himawari-8 / Advanced Himawari Imager(AHI)的红外亮度温度,通过L1范数正则化检查降水,并分析了高时间分辨率数据的优势。AHI数据涵盖了台风哈吉比斯的整个生命周期实施后,我们根据AHI通道8-16中亮度温度梯度的变化来标记“降水”和“非降水”视场(FOV)。在降水视场中,我们实施L1-范数正则化,其中目标函数中“输入变量”的贡献率通过近似信号的自由度来获得。所提出的方法是一种获得稀疏解的有效方法,该稀疏解可以通过少量原子信息来表示大部分或全部原子信息。几个实验表明,台风哈吉比斯的降水恢复了与全球降水量测量(GPM)降水量值一致,显示出良好的结构相似性。特别是,与随机森林方法相比,该方法可提供更好的结构相似性(SSIM)和峰值信噪比(PSNR)值,这对于极端降水是有利的。
更新日期:2020-11-05
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