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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >A neural network approach to estimating rainfall from spaceborne microwave data
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A neural network approach to estimating rainfall from spaceborne microwave data

机译：用神经网络方法估计星载微波数据的降雨

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摘要

Various techniques use microwave (MW) brightness temperature (BT) data, obtained from remote sensing orbiting platforms, to calculate rain rates. The most commonly used techniques are based on regressions or other statistical methods. An emerging tool in rainfall estimation using satellite data is artificial neural networks (NNs), NNs are mathematical models that are capable of learning complex relationships. They consist of highly interconnected, interactive data processing units. NNs are implemented in this study to estimate rainfall, and backpropagation is used as a learning scheme. The inputs for the training phase are BTs and the outputs are rainfall rates, all generated by three-dimensional (3D) simulations based on a 3D stochastic, space-time rainfall model, and a 3D radiative transfer model. Once training is complete the NNs are presented with multi-frequency and polarized (horizontal and vertical) BT data, obtained from the Special Sensor Microwave/Imager (SSM/I) instrument onboard the F10 and F11 polar-orbiting meteorological satellites. Hence, rainrates corresponding to real BT measurements are generated. The rainfall rates are also estimated using a log-linear regression model. Comparison of the two approaches, using simulated data, shows that the NN can represent more accurately the underlying relationship between BT and rainrate than the regression model, Comparison of the rates, estimated by both methods, with radar-estimated rainrates shows that NNs outperform the regression model. This study demonstrates the great potential of NNs in estimating rainfall from remotely sensed data.
机译：各种技术都使用从遥感轨道平台获得的微波（MW）亮度温度（BT）数据来计算降雨率。最常用的技术基于回归或其他统计方法。使用卫星数据进行降雨估算的一种新兴工具是人工神经网络（NN），NN是能够学习复杂关系的数学模型。它们由高度互连的交互式数据处理单元组成。在这项研究中使用神经网络来估计降雨量，并将反向传播用作学习方案。培训阶段的输入是BT，输出是降雨率，所有这些都是基于3D随机，时空降雨模型和3D辐射传递模型的三维（3D）模拟生成的。训练完成后，将使用从F10和F11极轨气象卫星上的特殊传感器微波/成像仪（SSM / I）仪器获得的多频和极化（水平和垂直）BT数据显示NN。因此，产生对应于实际BT测量的降雨率。还使用对数线性回归模型估算降雨率。两种方法的比较，使用模拟数据显示，与回归模型相比，NN可以更准确地表示BT和降雨率之间的潜在关系。两种方法估算的速率与雷达估算的降雨率的比较表明，NN的性能优于回归模型。这项研究证明了神经网络在从遥感数据估算降雨中的巨大潜力。

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