Rain detection for rain-contaminated ground-based microwave radiometer data using physics-informed machine learning method

IF 5.9 1区 地球科学 Q1 ENGINEERING, CIVIL Journal of Hydrology Pub Date : 2024-11-22 DOI:10.1016/j.jhydrol.2024.132365
Wenyue Wang , Wenzhi Fan , Klemens Hocke
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Abstract

Because the radiation signal is strongly influenced by emission and scattering from rain, microwave radiometer data suffer from rain contamination. The traditional method of using rain gauges to detect rain for microwave radiometers has limitations. For example, it can only detect rain that reaches the ground and is ineffective for raindrops suspended in the atmosphere that can still contaminate remote sensing data. This article presents a rain detection method for microwave radiometer measurements, based on Gradient Boosted Decision Trees (GBDT). First, the characteristic that the increase in microwave radiometer brightness temperature when raindrops are present in the atmosphere, along with the seasonal dependency of rainfall patterns, is combined with meteorological variables to form feature vectors. Then, the GBDT is employed to classify data into rain-free and rain-contaminated categories. Microwave radiometer (MWR) measurements and simultaneous Micro Rain Radar (MRR) target classification collected from the Swiss Plateau in 2008 are utilized to train the model, which is subsequently tested using two testing schemes: ten-fold cross-validation technique and time series test sets. Compared with the detection accuracy of the integrated liquid water (ILW) threshold method (73.6% and 68.3%) in both testing schemes, our GBDT-based method achieved superior accuracy, recording approximately 100% and 98.4%, respectively. The proposed method exhibits strong generalization capabilities, allowing it to directly detect rain contamination in time series data and effectively overcome the time dependence of rainfall occurrence. In addition, compared with the ILW threshold method, the GBDT-based method considers various rainfall patterns contained in various seasons. Features selected for this method enable its direct application to other tropospheric microwave radiometer systems.
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利用物理信息机器学习法对雨水污染的地基微波辐射计数据进行雨水检测
由于辐射信号受雨水的发射和散射影响很大,微波辐射计的数据会受到雨水的污染。微波辐射计使用雨量计探测雨量的传统方法有其局限性。例如,它只能探测到到达地面的雨水,对悬浮在大气中的雨滴不起作用,而雨滴仍然会污染遥感数据。本文提出了一种基于梯度提升决策树(GBDT)的微波辐射计测量雨水探测方法。首先,将大气中存在雨滴时微波辐射计亮度温度升高的特征以及降雨模式的季节依赖性与气象变量相结合,形成特征向量。然后,利用 GBDT 将数据分为无雨和有雨两类。利用 2008 年在瑞士高原采集的微波辐射计(MWR)测量数据和同步微雨雷达(MRR)目标分类数据来训练模型,随后使用两种测试方案对模型进行测试:十倍交叉验证技术和时间序列测试集。与综合液态水(ILW)阈值方法在两种测试方案中的检测准确率(73.6% 和 68.3%)相比,我们基于 GBDT 的方法取得了更高的准确率,分别达到约 100%和 98.4%。所提出的方法具有很强的泛化能力,可直接检测时间序列数据中的雨水污染,并有效克服降雨发生的时间依赖性。此外,与 ILW 临界值方法相比,基于 GBDT 的方法考虑了不同季节的各种降雨模式。为该方法选择的特征使其能够直接应用于其他对流层微波辐射计系统。
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来源期刊
Journal of Hydrology
Journal of Hydrology 地学-地球科学综合
CiteScore
11.00
自引率
12.50%
发文量
1309
审稿时长
7.5 months
期刊介绍: The Journal of Hydrology publishes original research papers and comprehensive reviews in all the subfields of the hydrological sciences including water based management and policy issues that impact on economics and society. These comprise, but are not limited to the physical, chemical, biogeochemical, stochastic and systems aspects of surface and groundwater hydrology, hydrometeorology and hydrogeology. Relevant topics incorporating the insights and methodologies of disciplines such as climatology, water resource systems, hydraulics, agrohydrology, geomorphology, soil science, instrumentation and remote sensing, civil and environmental engineering are included. Social science perspectives on hydrological problems such as resource and ecological economics, environmental sociology, psychology and behavioural science, management and policy analysis are also invited. Multi-and interdisciplinary analyses of hydrological problems are within scope. The science published in the Journal of Hydrology is relevant to catchment scales rather than exclusively to a local scale or site.
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