A Unique Satellite-Based Sea Surface Wind Speed Algorithm and Its Application in Tropical Cyclone Intensity Analysis

Hong, Sungwook; Seo, Hwa-Jeong; Kwon, Young-Joo

doi:10.1175/jtech-d-15-0128.1

Cited by 8 publications

(11 citation statements)

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“…The datasets of the other nine models in Table IX mainly focus on microwave datasets [4], [5], [40] or combinations of various datasets (e.g., infrared, water vapor, and microwave data) [6], [15], [34], [36], [39], [48]. For example, the Hong WS algorithm used a combination of satellite-observed microwave data to estimate TC intensity [4]. The PMW-IE combined model employed both Tropical Rainfall Measuring Mission Microwave Imager (TMI) 85-GHz brightness temperatures and near-surface rain-rate retrievals to estimate TC intensity [5].…”

Section: Comparison To Other Satellite Estimation Methodsmentioning

confidence: 99%

“…Strictly speaking, it is not fair to directly compare our model with these nine models [4]- [6], [15], [34], [36], [39], [40], [48], due to the use of different datasets. However, in order to illustrate the performance and advantages of our model, we still listed the performance indices of these nine models in Table IX (these indices were taken directly from the corresponding literature).…”

Section: Comparison To Other Satellite Estimation Methodsmentioning

confidence: 99%

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Tropical Cyclone Intensity Classification and Estimation Using Infrared Satellite Images With Deep Learning

Zhang

Wang

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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A novel tropical cyclone (TC) intensity classification and estimation model (TCICENet) is proposed using infrared geostationary satellite images from the northwest Pacific Ocean basin in combination with a cascading deep convolutional neural network (CNN). The proposed model consists of two CNN network modules: a TC intensity classification (TCIC) module and a TC intensity estimation (TCIE) module. First, the TCIC module is utilized to divide TC intensity into three categories using infrared satellite images. Next, three TCIE models based on the CNN regression network that combine different intensity types of infrared satellite images with the TC best track data are presented. The three TCIE models consider classification error with the TCIC module in order to improve TCIE accuracy. A total of 1001 TCs from 1981-2019 were used to verify the proposed TCICENet model, with 844 TCs from 1981-2013 employed as training samples, 76 TCs from 2014-2016 used as validation samples, and 81 TCs from 2017-2019 used as testing samples. In order to reduce the computation burden of training the TCICENet model, various input image sizes were explored. An image size of 170 × 170 pixels achieved the best performance, with an overall root mean square error of 8.60 kt and a mean absolute error of 6.67 kt compared to the best track. Index Terms-Deep convolutional neural network (CNN), intensity estimation, intensity grade classification, tropical cyclone (TC). I. INTRODUCTIONT HE northwest Pacific Ocean basin is one of the most active tropical cyclone (TC) areas in the world, generating approximately 27 TCs per year. According to the TC standards (http://agora.ex.nii.ac.jp/digital-typhoon/help/unit.html. ja#id2) released by the Japan Meteorological Agency (JMA), TCs can be divided into six grades: tropical depression (TD; ∼33 kt), tropical storm (TS; 34-47 kt), severe tropical storm

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Section: Comparison To Other Satellite Estimation Methodsmentioning

confidence: 99%

Section: Comparison To Other Satellite Estimation Methodsmentioning

confidence: 99%

Tropical Cyclone Intensity Classification and Estimation Using Infrared Satellite Images With Deep Learning

Zhang

Wang

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…The usefulness of Equation (6) was shown in a variety of applications, including in the detection of Asian dust (Hwangsa) [62], the validation of an IR sea-surface emissivity model [63], surface roughness retrieval [64,65], the estimation of global soil moisture [66], sea ice studies [67,68], and wind speed retrieval [69][70][71].…”

Section: Refractive Index Retrievalmentioning

confidence: 99%

Flood Monitoring Using Satellite-Based RGB Composite Imagery and Refractive Index Retrieval in Visible and Near-Infrared Bands

et al. 2017

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Abstract:Satellite remote sensing provides significant information for the monitoring of natural disasters. Recently, on a global scale, floods have been increasing both in frequency and in magnitude. In order to map the inundation area, flooding events are investigated using unique RGB composite imagery based on the MODIS surface reflectance (MOD09GA) data obtained from the Terra satellite, which is used to visualize and analyze these events. This study proposes using an RGB combination of MODIS band 6 (1.64 µm), band 5 (1.24 µm), and band 2 (0.86 µm) data from the visible and the near-infrared spectral ranges to map flood events. The flooding events that were investigated in this study occurred on 25 October 2015 along the Pampanga River in the Philippines, and on 28 July 2016 along the Poyang and Dongting Lakes in China. In the case of the Pampanga River, the inundated areas were estimated with surface reflectance (R) thresholds of 0.0 ≤ R 6 ≤ 0.102, 0.0 ≤ R 5 ≤ 0.138, and 0.03 ≤ R 2 ≤ 0.148 for MODIS bands 6, 5, and 2, respectively, which were determined using Otsu's method. The total inundated area was estimated to be 487.75 km 2 . This estimate was indirectly compared with the results obtained from SENTINEL-1A Synthetic Aperture Radar (SAR) data. The total inundated area on 26 October 2015 for the case of the Pampanga River was estimated to be 486.37 km 2 using histogram analysis based on Otsu's method. For the flooding case in China, the total estimated inundated area using MODIS RGB imagery on 28 July 2016 and SAR on 3 August 2016 was 1148.25 km 2 and 1110.096 km 2 , respectively. In addition, RGB imagery results using MODIS 6-5-2 bands were supported by the refractive index retrieval along the inundation area. A threshold of 1.6 for the real part of the complex refractive index allows for the discrimination between the flooded and non-flooded areas using the Hong and ASH approximations. This study shows that the RGB composite techniques using advanced sensors with more bands and higher spatio-temporal resolutions, and supported by the refractive index retrieval method, are useful for estimating flood events.

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“…Recently, Hong et al . () developed a WS retrieval algorithm that was suitable for rainy and non‐rainy conditions using the AMSR‐2’s 6.925 and 10.65GHz channels. Such algorithms are cable of producing ocean surface WS measurements to a very high accuracy of ±1ms −1 during non‐rainy conditions using the data from passive microwave radiometers (Meissner and Wentz, ; Bettenhausen et al ., ).…”

Section: Introductionmentioning

confidence: 99%

“…In the approach described in this article, the estimation of rough and flat estimated WS, sea surface temperature, atmospheric water vapour content, cloud liquid water content and total atmospheric absorption using a model simulation and neural network approach with AMSR-2 measurements at 10.65GHz. Recently, Hong et al (2016) developed a WS retrieval algorithm that was suitable for rainy and nonrainy conditions using the AMSR-2's 6.925 and 10.65GHz channels. Such algorithms are cable of producing ocean surface WS measurements to a very high accuracy of ±1ms −1 during non-rainy conditions using the data from passive microwave radiometers (Meissner and Wentz, 2006;Bettenhausen et al, 2006).…”

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confidence: 99%

A new approach for estimating ocean‐surface wind speed using an SMAP L‐band radiometer

Sharma

2018

Weather

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The measurement of ocean surface wind speed (WS) during cyclones plays a key role in improving cyclone track and intensity predictions; however, limited in situ WS observations during cyclone events are not sufficient for the continuous monitoring of intensity and track. Although satellite measurements provide an alternative means of obtaining such data, the performance of high‐frequency microwave (≥ 5GHz) sensors – which are used for ocean surface WS estimation – is adversely affected by rainy and high WS conditions. As a result, these frequencies cannot be used to measure WS under cyclonic conditions. However, since the L‐band (1.4GHz) microwave frequency is negligibly affected by atmospheric parameters such as water vapour, cloud and rain, it can be used to estimate ocean surface WSs even during cyclonic conditions. An L‐band microwave radiometer is currently in operation onboard the Soil Moisture Active Passive (SMAP) satellite, offering an opportunity to measure high WSs during cyclones. Although WS data from SMAP are available from Remote Sensing Systems (at http://www.remss.com/missions/smap/), a new retrieval technique using SMAP top‐of‐atmosphere horizontal and vertical polarised brightness temperature measurements (TOA_Tb) is presented here. Excess brightness due to the high WS of cyclones is clearly evident in the SMAP TOA_Tbs data. The technique described in this article produced estimates which successfully captured high wind speeds of up to ~50ms−1 during cyclones, and the WS values reported here are close to those from the Automated Tropical Cyclone Forecast.

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A Unique Satellite-Based Sea Surface Wind Speed Algorithm and Its Application in Tropical Cyclone Intensity Analysis

Cited by 8 publications

References 50 publications

Tropical Cyclone Intensity Classification and Estimation Using Infrared Satellite Images With Deep Learning

Tropical Cyclone Intensity Classification and Estimation Using Infrared Satellite Images With Deep Learning

Flood Monitoring Using Satellite-Based RGB Composite Imagery and Refractive Index Retrieval in Visible and Near-Infrared Bands

A new approach for estimating ocean‐surface wind speed using an SMAP L‐band radiometer

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