The destruction created by cyclones depends upon their intensity. Ocean wind is the crucial parameter to understand and forecast the intensity of cyclones. During cyclones, due to dynamic ocean conditions and the limited data availability, a neural network with the regularization approach is used to retrieve cyclonic wind (CW) speed. Several optimization algorithms are compared to get a robust neural network. The Bayesian regularization with the Levenberg–Marquardt optimized network (BNN) is found suitable to develop the geophysical model to retrieve CW speed using Cyclone Global Navigation Satellite System (CYGNSS) measurements. To select the suitable observables for BNN, sensitivity analysis of CYGNSS observables is carried out. The root-mean-square difference between retrieved CW speed and the airborne radiometer data is found to be 4.35 ms − 1, which is smaller than the value quoted in the literature for CYGNSS CW speed. Further, a rigorous analysis is also done to find out the effect of the rain on the retrieved wind speed. Independent validation of our approach is carried out using Soil Moisture Active Passive (SMAP) radiometer high-wind data with a special case of category-5 cyclone Lorenzo. The results support that the proposed algorithm agrees well with the SMAP CW data.

中文翻译：

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基于CYGNSS数据的贝叶斯正则化神经网络的旋风风速反演

气旋造成的破坏取决于它们的强度。海洋风是了解和预测气旋强度的关键参数。在气旋期间，由于动态海洋条件和有限的数据可用性，采用正则化方法的神经网络用于检索气旋风 (CW) 速度。比较了几种优化算法以获得鲁棒的神经网络。发现使用 Levenberg-Marquardt 优化网络 (BNN) 的贝叶斯正则化适合开发地球物理模型，以使用 Cyclone 全球导航卫星系统 (CYGNSS) 测量来检索 CW 速度。为了为 BNN 选择合适的观测值，对 CYGNSS 观测值进行了敏感性分析。发现检索到的 CW 速度与机载辐射计数据之间的均方根差为 4.35 ms − 1，这小于文献中引用的 CYGNSS CW 速度值。此外，还进行了严格的分析，以找出雨水对检索到的风速的影响。我们的方法的独立验证是使用土壤湿度主动被动 (SMAP) 辐射计大风数据和 5 类气旋洛伦佐的特例进行的。结果支持所提出的算法与 SMAP CW 数据非常吻合。