Tropical Cyclone Wind Field Reconstruction and Validation Using Measurements from SFMR and SMAP Radiometer

Li, Xiaohui; Yang, Jingsong; Ren, Lin; Zheng, Gang; Chen, Peng; Zhang, Han

doi:10.3390/rs14163929

Cited by 17 publications

(9 citation statements)

References 60 publications

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“…The CWPD 1/3 and SHPD 1/3 are calculated using ERA5 data. Note that previous studies indicated that ERA5 tends to underestimate peak intensity for strong storms (Graham et al 2019, Li et al 2022, and thereby the CWPD 1/3 and SHPD 1/3 are underestimated. The underestimate could influence the trends, while considering that the decreasing trend of CWPD 1/3 is mainly caused by the decline of cold wake size, the influence might not be large.…”

Section: Conclusion and Discussionmentioning

confidence: 86%

Decreasing trend of tropical cyclone-induced ocean warming in recent decades

Shi

Zhang

et al. 2023

Environ. Res. Lett.

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Tropical cyclones (TCs) can influence climate by pumping heat into the ocean, yet little is known about how the TC-induced ocean heat uptake (OHU) has changed in recent decades. Based on ocean reanalysis, we calculated OHU and found a significant decline of TC-induced OHU from 1982 to 2018. If all the ocean heat gain is balanced by poleward heat transport, approximately 15% of peak ocean heat transport would have been reduced during the study period. The decreasing trend of OHU is consistent with the enhanced ocean stratification, the shallowed mixed layer depth and the reduced cold wake size. The reduction of OHU primarily occurs in the Northwest Pacific, where the shortened TC lifespan contributes as well. Furthermore, the decline of OHU might offset about 28% of the upper ocean warming in the subtropical Northwest Pacific.

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Section: Conclusion and Discussionmentioning

confidence: 86%

Decreasing trend of tropical cyclone-induced ocean warming in recent decades

Shi

Zhang

et al. 2023

Environ. Res. Lett.

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“…The accuracy of these products was systematically investigated. Oceanic modeling primarily relies on atmospheric numerical simulations, and thus, the inherent error for the winds is inevitably included in the modeling techniques, e.g., the under-estimation by ECMWF for TCs [55]. With the abundant remote-sensed data, an interesting question is whether remote-sensed products in near real time could be applied to forecasting and hindcasting, as these do not rely on atmospheric numerical simulations.…”

Section: Discussionmentioning

confidence: 99%

Can Sea Surface Waves Be Simulated by Numerical Wave Models Using the Fusion Data from Remote-Sensed Winds?

Shi,

Shao,

Shi

et al. 2023

Remote Sensing

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The purpose of our work is to investigate the performance of fusion wind from multiple remote-sensed data in forcing numeric wave models, and the experiment is described herein. In this study, 0.125° gridded wind fields at 12 h intervals were fused by using swath products from an advanced scatterometer (ASCAT) (a Haiyang-2B (HY-2B) scatterometer) and a spaceborne polarimetric microwave radiometer (WindSAT) during the period November 2019 to October 2020. The daily average wind speeds were compared with observations from National Data Buoy Center (NDBC) buoys from the National Oceanic and Atmospheric Administration (NOAA), yielding a 1.66 m/s root mean squared error (RMSE) with a 0.81 correlation (COR). This suggests that fusion wind was reliable for our work. The fusion winds were used for hindcasting sea surface waves by using two third-generation numeric wave models, denoted as WAVEWATCH-III (WW3) and Simulation Wave Nearshore (SWAN). The WW3-simulated waves in the North Pacific Ocean and the SWAN-simulated waves in the Gulf of Mexico were validated against the measurements from the NDBC buoys and the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA-5) for the period June−September 2020. The analysis of significant wave heights (SWHs) up to 9 m yielded a < 0.5 m RMSE with a > 0.8 COR for the WW3 and SWAN models. Therefore, it was believed that the accuracy of the simulation using the two numeric models was comparable with that forced by a numeric atmospheric model. An error analysis was systematically conducted by comparing the modeled WW3-simulated SWHs with the monthly average products from the HY-2B and a Jason-3 altimeter over global seas. The seasonal analysis showed that the differences in the SWHs (i.e., altimeter minus the WW3) were within ±1.5 m in March and June; however, the difference was quite significant in December. It was concluded that remote-sensed fusion wind can serve as a driving force for hindcasting waves using numeric wave models.

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“…However, as shown in the scatter plot in Figure 10, reconstructed high wind speeds (greater than 20 m/s) are significantly underestimated compared to SMAP. We believe this phenomenon is due to the underestimation of strong TC winds in the ECMWF [58,59]. Although the model can reconstruct TC images through learned features, it cannot avoid the limitations of the training set.…”

Section: Reconstruction On Sentinel1-a/bmentioning

confidence: 99%

“…ECMWF is a very commonly used dataset in the wind speed retrieval of remote sensing satellites. However, some studies have shown that ECMWF underestimates high wind speeds [58,59], which may lead to some bias in the features learned by the proposed model. This point is also confirmed by the comparison with SMAP, which indicates that our model tends to underestimate wind speeds in high wind speed ranges.…”

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confidence: 99%

Dual-Level Contextual Attention Generative Adversarial Network for Reconstructing SAR Wind Speeds in Tropical Cyclones

Han

Yang

et al. 2023

Remote Sensing

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Synthetic Aperture Radar (SAR) imagery plays an important role in observing tropical cyclones (TCs). However, the C-band attenuation caused by rain bands and the problem of signal saturation at high wind speeds make it impossible to retrieve the fine structure of TCs effectively. In this paper, a dual-level contextual attention generative adversarial network (DeCA-GAN) is tailored for reconstructing SAR wind speeds in TCs. The DeCA-GAN follows an encoder–neck–decoder architecture, which works well for high wind speeds and the reconstruction of a large range of low-quality data. A dual-level encoder comprising a convolutional neural network and a self-attention mechanism is designed to extract the local and global features of the TC structure. After feature fusion, the neck explores the contextual features to form a reconstructed outline and up-samples the features in the decoder to obtain the reconstructed results. The proposed deep learning model has been trained and validated using the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric model product and can be directly used to improve the data quality of SAR wind speeds. Wind speeds are reconstructed well in regions of low-quality SAR data. The root mean square error of the model output and ECMWF in these regions is halved in comparison with the existing SAR wind speed product for the test set. The results indicate that deep learning methods are effective for reconstructing SAR wind speeds.

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Tropical Cyclone Wind Field Reconstruction and Validation Using Measurements from SFMR and SMAP Radiometer

Cited by 17 publications

References 60 publications

Decreasing trend of tropical cyclone-induced ocean warming in recent decades

Decreasing trend of tropical cyclone-induced ocean warming in recent decades

Can Sea Surface Waves Be Simulated by Numerical Wave Models Using the Fusion Data from Remote-Sensed Winds?

Dual-Level Contextual Attention Generative Adversarial Network for Reconstructing SAR Wind Speeds in Tropical Cyclones

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