Using eXtreme Gradient BOOSTing to Predict Changes in Tropical Cyclone Intensity over the Western North Pacific

Jin, Qingwen; Fan, Xiangtao; Liu, Jian; Xue, Zhuxin; Jian, Hu

doi:10.3390/atmos10060341

Cited by 26 publications

(12 citation statements)

References 45 publications

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“…Jin et al (2019) built a training model with XGBoost to predict the intensities of TCs. They chose persistence, climatology and environmental factors, a series of brainstorm features and the intensity category as predictors.…”

Section: Machine Learning Model For Tc Intensity Forecastmentioning

confidence: 99%

Machine learning in calibrating tropical cyclone intensity forecast of ECMWF EPS

Chan

Wong

Au‐Yeung

2021

Meteorological Applications

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Intensity prediction of tropical cyclones (TC) has been one of the major challenges for the operational forecast and warning service, as well as consequential assessment of impacts including high winds, storm surge and heavy rainfall caused by TC. With the advances in global numerical weather prediction (NWP) modelling systems, TC track and intensity forecasts for medium range are available every 6 or 12 h, and ensemble prediction system (EPS) outputs provide various scenarios for producing probabilistic forecasts. The TC intensity forecast from the EPS of the European Centre for Medium‐Range Weather Forecasts (ECMWF) has shown systematic negative biases, although the performance is better than other global models in general. A machine learning model based on XGBoost, a decision‐tree‐based machine learning algorithm, is introduced in this paper to post‐process ECMWF EPS outputs and generate an improved forecast of TC intensity. The predictors such as selected percentiles of ensemble members in maximum wind and minimum pressure of previous TC cases were applied in the XGBoost model to generate a calibrated forecast for the maximum wind of TCs. Verification of the XGBoost model was made using TCs over the western North Pacific during 2016–2019. It is found that the negative biases of the intensity forecast from ECMWF EPS and HRES can be reduced with improvement in the overall accuracy.

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Section: Machine Learning Model For Tc Intensity Forecastmentioning

confidence: 99%

Machine learning in calibrating tropical cyclone intensity forecast of ECMWF EPS

Chan

Wong

Au‐Yeung

2021

Meteorological Applications

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“…To fully show the effects of environmental factors, we established some square and cubic terms based on the existing factors [8,9]. In addition to the predictors in Table 3, other potential predictors are described in Jin [22].…”

Section: Climatology and Persistencementioning

confidence: 99%

“…The theory and application of the decision tree method have developed significantly since Chen's proposal of the eXtreme Gradient Boosting (XGBoost) model [18]; this model has been applied in several studies, on topics including image classification [19], speech recognition [20], and biomedical studies [21]. The XGBoost model has also proved to be useful in predicting TC intensity [22]. TC intensity is affected by several factors that are often ambiguous and uncertain.…”

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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“…XGB scalability is determined by the optimization of vital algorithms, including a new tree learning algorithm for processing sparse data and a weighted quantile sketch process. XGB can simplify learning through models and prevent overfitting; therefore, its calculative abilities are superior to those of traditional gradient-boosted decision trees [ 56 ]. Therefore, XGB has been used by various authors such as Chakraborty and Alajali [ 57 ] and Yuan et al [ 58 ].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Rainfall Forecasts Based on Radar Reflectivity during Typhoons: Case Study in Southeastern Taiwan

Wei

Hsu

2021

Sensors

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This study developed a real-time rainfall forecasting system that can predict rainfall in a particular area a few hours before a typhoon’s arrival. The reflectivity of nine elevation angles obtained from the volume coverage pattern 21 Doppler radar scanning strategy and ground-weather data of a specific area were used for accurate rainfall prediction. During rainfall prediction and analysis, rainfall retrievals were first performed to select the optimal radar scanning elevation angle for rainfall prediction at the current time. Subsequently, forecasting models were established using a single reflectivity and all elevation angles (10 prediction submodels in total) to jointly predict real-time rainfall and determine the optimal predicted values. This study was conducted in southeastern Taiwan and included three onshore weather stations (Chenggong, Taitung, and Dawu) and one offshore weather station (Lanyu). Radar reflectivities were collected from Hualien weather surveillance radar. The data for a total of 14 typhoons that affected the study area in 2008–2017 were collected. The gated recurrent unit (GRU) neural network was used to establish the forecasting model, and extreme gradient boosting and multiple linear regression were used as the benchmarks. Typhoons Nepartak, Meranti, and Megi were selected for simulation. The results revealed that the input data set merged with weather-station data, and radar reflectivity at the optimal elevation angle yielded optimal results for short-term rainfall forecasting. Moreover, the GRU neural network can obtain accurate predictions 1, 3, and 6 h before typhoon occurrence.

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Using eXtreme Gradient BOOSTing to Predict Changes in Tropical Cyclone Intensity over the Western North Pacific

Cited by 26 publications

References 45 publications

Machine learning in calibrating tropical cyclone intensity forecast of ECMWF EPS

Machine learning in calibrating tropical cyclone intensity forecast of ECMWF EPS

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

Real-Time Rainfall Forecasts Based on Radar Reflectivity during Typhoons: Case Study in Southeastern Taiwan

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