PHURIE: hurricane intensity estimation from infrared satellite imagery using machine learning

Asif, Amina; Dawood, Muhammad; Jan, Bismillah; Khurshid, Javaid; DeMaria, Mark; Minhas, Fayyaz

doi:10.1007/s00521-018-3874-6

Cited by 24 publications

(17 citation statements)

References 18 publications

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“…In order to verify the performance of our model, it was compared with the existing TC intensity estimation models listed in Table IX. Using our IR satellite image dataset, the performance between our model and nine other models-the ETCI [22], DAVT [18], PHURIE-SVR [24], MLR [20], RVM+DADI [27], RVM+DAGCOM [26], DeepCNN [35], VGG19 [41], and Deep-PHURIE [38]-was compared.…”

Section: Comparison To Other Satellite Estimation Methodsmentioning

confidence: 99%

“…Researchers have utilized traditional machine learning with infrared satellite images for TC intensity estimation. The multivariate linear regression models [14]- [20], the K-nearest neighbors algorithm [21], [22], the multilayer perceptron [23], support vector machine (SVM) [24], and relevance vector machine (RVM) [25]- [27] have been successfully used to estimate TC intensity. These methods mainly focus on the manual extraction of statistical features [1], [2], [14], [23], [25]- [27] or structural features [7]- [10], [12], [13], [15]- [17], [42] of TC.…”

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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%

mentioning

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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“…All of the compared classification methodologies are briefly discussed; similarly, the feature selection phase, in which the importance of the features studied is analyzed, is also described. The methodology follows the same steps than the ones proposed in [ 25 ] for hurricane intensity estimation.…”

Section: Methodsmentioning

confidence: 99%

Composition Classification of Ultra-High Energy Cosmic Rays

Herrera

Peixoto

Baños

et al. 2020

Entropy

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The study of cosmic rays remains as one of the most challenging research fields in Physics. From the many questions still open in this area, knowledge of the type of primary for each event remains as one of the most important issues. All of the cosmic rays observatories have been trying to solve this question for at least six decades, but have not yet succeeded. The main obstacle is the impossibility of directly detecting high energy primary events, being necessary to use Monte Carlo models and simulations to characterize generated particles cascades. This work presents the results attained using a simulated dataset that was provided by the Monte Carlo code CORSIKA, which is a simulator of high energy particles interactions with the atmosphere, resulting in a cascade of secondary particles extending for a few kilometers (in diameter) at ground level. Using this simulated data, a set of machine learning classifiers have been designed and trained, and their computational cost and effectiveness compared, when classifying the type of primary under ideal measuring conditions. Additionally, a feature selection algorithm has allowed for identifying the relevance of the considered features. The results confirm the importance of the electromagnetic-muonic component separation from signal data measured for the problem. The obtained results are quite encouraging and open new work lines for future more restrictive simulations.

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“…The satellite predictors were extracted as area-averaged data, extracting detailed information using the mean results in the details being hidden or diluted. The satellite data are particularly rich around the center of the TC, and this data can describe the convective characteristics affecting TC intensity [14]. Wang [15] suggested that convection near the TC center is a key feature of overall convection in tropical cyclogenesis and that the spatial pattern of the convection intensity might also be important.…”

Section: Introductionmentioning

confidence: 99%

Estimating Tropical Cyclone Intensity in the South China Sea Using the XGBoost Model and FengYun Satellite Images

et al. 2020

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Conventional numerical methods have made significant advances in forecasting tropical cyclone (TC) tracks, using remote sensing data with high spatial and temporal resolutions. However, over the past two decades, no significant improvements have been made with regard to the accuracy of TC intensity prediction, which remains challenging, as the internal convection and formation mechanisms of such storms are not fully understood. This study investigated the relationship between remote sensing data and TC intensity to improve the accuracy of TC intensity prediction. An intensity forecast model for the South China Sea was built using the eXtreme Gradient Boosting (XGBoost) model and FengYun-2 (FY-2) satellite data, environmental data, and best track datasets from 2006 to 2017. First, correlation analysis algorithms were used to extract the TC regions in which the satellite data were best correlated, with TC intensity at lead times of 6, 12, 18, and 24 h. Then, satellite, best track, and environmental data were used as source data to develop three different XGBoost models for predicting TC intensity: model A1 (climatology and persistence predictors + environmental predictors), model A2 (A1 + satellite-based predictors extracted as mean values), and model A3 (A1 + satellite-based predictors extracted by our method). Finally, we analyzed the impact of the FY-2 satellite data on the accuracy of TC intensity prediction using the forecast skill parameter. The results revealed that the equivalent blackbody temperature (TBB) of the FY-2 data has a strong correlation with TC intensity at 6, 12, 18, and 24 h lead times. The mean absolute error (MAE) of model A3 was reduced by 0.47%, 1.79%, 1.91%, and 5.04% in 6, 12, 18, and 24 h forecasts, respectively, relative to those of model A2, respectively, and by 2.73%, 7.58%, 7.64%, and 5.04% in 6, 12, 18, and 24 h forecasts, respectively, relative to those of model A1. Furthermore, the accuracy of TC intensity prediction is improved by FY-2 satellite images, and our extraction method was found to significantly improve upon the traditional extraction method.

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PHURIE: hurricane intensity estimation from infrared satellite imagery using machine learning

Cited by 24 publications

References 18 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

Composition Classification of Ultra-High Energy Cosmic Rays

Estimating Tropical Cyclone Intensity in the South China Sea Using the XGBoost Model and FengYun Satellite Images

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