Machine-Learning-Based Algorithm for Significant Wave Height Retrieval From Microwave Radiometer During Tropical Cyclones

This study develops a machine-learning-based approach to retrieve significant wave height (SWH) from soil moisture active passive (SMAP) radiometer data under tropical cyclone (TC) conditions, accounting for limited fetch effects. Using over 1000 SMAP measurements within 400 km of the eye from 400 T...

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Vydáno v:IEEE geoscience and remote sensing letters Ročník 22; s. 1 - 5
Hlavní autoři: Hu, Yuyi, Shao, Weizeng, Xu, Ying, Jiang, Xingwei
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Altimeters
Artificial neural networks
Cyclones
Extraterrestrial measurements
Hurricanes
Learning algorithms
Machine learning
Maximum winds
Microwave imaging
Microwave radiometers
Microwave radiometry
Microwave theory and techniques
Moisture content
Neural networks
Radio frequency
Radiometers
Remote sensing
Root-mean-square errors
Sea measurements
Sea surface
Significant wave height
significant wave height (SWH)
Soil moisture
tropical cyclone (TC)
Tropical cyclones
Wave height
Wind speed
ISSN:1545-598X, 1558-0571
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Shrnutí:This study develops a machine-learning-based approach to retrieve significant wave height (SWH) from soil moisture active passive (SMAP) radiometer data under tropical cyclone (TC) conditions, accounting for limited fetch effects. Using over 1000 SMAP measurements within 400 km of the eye from 400 TCs in 2018-2024, we used the symmetric hurricane estimates for wind (SHEW) model to determine TC eye locations and maximum wind radii radius of maximum wind (RMW). The three machine learning methods [i.e., eXtreme Gradient Boosting (XGBoost), random forest (RF), and convolutional neural networks (CNNs)] were trained to establish the relationship between SMAP wind speeds and WAVEWATCH-III (WW3)-hindcasted SWH, incorporating key parameters including wind speed, distance to TC eye, RMW, and TC translation speed/direction. Validation against both WW3 hindcasts and Haiyang-2 (HY-2) altimeter measurements in 2023-2024 demonstrated the superior performance of the XGBoost model compared with empirical wave-growth, RF, and CNNs models, i.e., lower root mean square error (RMSE) of 0.50 m (WW3) and 0.71 m (HY-2), along with higher correlation (Cor) of 0.96 (WW3) and 0.92 (HY-2).
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ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2025.3631086