Statistical Regression Scheme for Intensity Prediction of Tropical Cyclones in the Northwestern Pacific

Li, Qinglan; Li, Zenglu; Peng, Yunfeng; Wang, Xiaoxue; Li, Lei; Lan, Hongping; Shi, Feng; Sun, Liwei; Li, Guangxin; Wei, Xiaolin

doi:10.1175/waf-d-18-0001.1

Cited by 6 publications

(19 citation statements)

References 35 publications

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“…TCs intensity is influenced by two main physical processes (Wang and Wu, 2004;Elsberry et al, 2013), which are synoptic variables such as vertical wind shear, humidity, sea surface temperature, water vapor, divergency (DeMaria, 1996;Ge et al, 2013;Gao et al, 2016;Mercer and Grimes, 2017), and climatological and persistent variables such as latitude, longitude, Julian day, and sea-land ratio (SL ratio) (DeMaria and Kaplan, 1994a;Gao et al, 2016;Li et al, 2018). Statistical-dynamical models were used to predict TCs intensity and rapid intensification probability and outperformed the forecasts of individual physics-based dynamical models (Knaff et al, 2005;Kaplan et al, 2010;Gao and Chiu, 2012).…”

Section: Introductionmentioning

confidence: 99%

“…The storm decay over land was further considered by SHIPS (DeMaria et al, 2005). Besides the conventional synoptic and climatological variables, Li et al (2018) paid close attention to the land effect on TCs intensity change by proposing a new factor involving the ratio of seawater area to land area (SL ratio) in the statistical regression model. TCs intensity changes over the entire TCs life span, including over the ocean basin, near the coast, and after landfall, were considered in the model (Li et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

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Tropical Cyclones Intensity Prediction in the Western North Pacific Using Gradient Boosted Regression Tree Model

Zhu

Zhao

et al. 2022

Front. Earth Sci.

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As an artificial intelligence method, machine learning (ML) has been widely used in prediction models of high-dimensional datasets. This study proposes an ML method, the Gradient Boosted Regression Tree (GBRT), to predict the intensity changes of tropical cyclones (TCs) in the Western North Pacific at 12-, 24-, 36-, 48-, 60-, and 72-h (hr) forecasting lead time and the model is optimized by the Bayesian Optimization algorithm. The model predictands are the TCs intensity changes at different forecasting lead times, obtained from the best track data of the Shanghai Typhoon Institute (STI) and the Joint Typhoon Warning Center (JTWC) from 2000 to 2019. The model predictors are the synoptic variables, climatological and persistent variables derived from the reanalysis data obtained from the National Centers for Environmental Prediction (NCEP), and the sea surface temperature (SST) data obtained from the National Oceanic and Atmospheric Administration (NOAA). The results show that the GBRT model can capture the TCs intensity changes well for the succeeding 12-h, 24-h, 36-h, and 72-h. Compared with the traditional multiple linear regression (MLR) model, the GBRT model has better performance in predicting TCs intensity changes. Compared with the MLR model, R2 of the GBRT model for TCs intensity forecast increases by an average of 8.47% and 4.45% for STI data and JTWC data. MAE (RMSE) drops by 26.24% (25.14%) and 10.51% (4.68%) for the two datasets, respectively. The potential future intensity change (POT), the intensity changes during the previous 12 h (Dvmax), Initial storm maximum wind speed (Vmax), SST, and the Sea-Land ratio are the most significant predictors for the GBRT model in predicting TCs intensity change over the Western North Pacific.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Tropical Cyclones Intensity Prediction in the Western North Pacific Using Gradient Boosted Regression Tree Model

Zhu

Zhao

et al. 2022

Front. Earth Sci.

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“…Therefore, estimating MSWS from other tropical cyclone parameters is a significant problem. Much work has been done towards this problem; see [6,7] and references therein for a complete history of the work relating to cyclone intensity prediction.…”

Section: Introductionmentioning

confidence: 99%

Tropical cyclone intensity estimations over the Indian ocean using Machine Learning

Biswas¹,

Kumar²,

Pandey³

2021

Preprint

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Tropical cyclones are one of the most powerful and destructive natural phenomena on earth. Tropical storms and heavy rains can cause floods, which lead to human lives and economic loss. Devastating winds accompanying cyclones heavily affect not only the coastal regions, even distant areas. Our study focuses on the intensity estimation, particularly cyclone grade and maximum sustained surface wind speed (MSWS) of a tropical cyclone over the North Indian Ocean. We use various machine learning algorithms to estimate cyclone grade and MSWS. We have used the basin of origin, date, time, latitude, longitude, estimated central pressure, and pressure drop as attributes of our models. We use multi-class classification models for the categorical outcome variable, cyclone grade, and regression models for MSWS as it is a continuous variable. Using the best track data of 28 years over the North Indian Ocean, we estimate grade with an accuracy of 88% and MSWS with a root mean square error (RMSE) of 2.3. For higher grade categories (5-7), accuracy improves to an average of 98.84%. We tested our model with two recent tropical cyclones in the North Indian Ocean, Vayu and Fani. For grade, we obtained an accuracy of 93.22% and 95.23% respectively, while for MSWS, we obtained RMSE of 2.2 and 3.4 and R 2 of 0.99 and 0.99, respectively.

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“…For example, Li et al (2015aLi et al ( , 2016 used historical data of typhoon paths and the observation data from densely distributed surface automatic weather stations to estimate the wind and rain caused by TCs in Shenzhen, a city in southern China; these estimations have already been used in operational applications. In addition, based on the best track data, Li et al (2018) employed a statistical regression scheme to improve the performance in predicting changes in TC intensity. Although the estimation method proposed by Li et al still has a large uncertainty, which is reflected in the existence of a large estimation interval in the precipitation prediction range, this approach still adds knowledge to the field.…”

Section: Introductionmentioning

confidence: 99%

Estimation of precipitation induced by tropical cyclones based on machine‐learning‐enhanced analogue identification of numerical prediction

Liu

et al. 2021

Meteorological Applications

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A tropical cyclone (TC) is an extremely hazardous weather event. These events include heavy rain as an important hazard factor, which poses a serious threat to the public safety of coastal cities. Presently, numerical weather prediction (NWP) is an important and commonly used method to support the forecasting of the impact of TCs. However, relatively high uncertainty still remains in quantitative precipitation predictions provided by NWP, which makes it difficult to meet the demands of public safety management in regions affected by TCs. To remediate this deficiency, the study combines machine learning (ML) techniques with NWP and proposes a new analogue identification method for TC precipitation estimation. The method consists of three parts. First, the output data of historical NWPs, including wind, temperature, humidity of 850 hPa and sea-level pressure field, are deposited into a massive sample library. Second, the dimensionality of the sample library is reduced by using the locally linear embedding (LLE) method, and a characteristic subspace containing the features of the original sample library is formed. Finally, when a new NWP prediction appears, it can be projected into the characteristic subspace, and the historical NWP sample having the greatest similarity with the current NWP prediction can be identified. The observational precipitation corresponding to the most similar historical NWP sample can then be used to estimate the potential impact of current TCs. This method is verified using observation results from the coastal city of Shenzhen, China. The results show that the prediction of the method proposed in the current study exhibited significant improvement compared with prediction results provided by both the NWP's direct output and a traditional method used in the city.

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Statistical Regression Scheme for Intensity Prediction of Tropical Cyclones in the Northwestern Pacific

Cited by 6 publications

References 35 publications

Tropical Cyclones Intensity Prediction in the Western North Pacific Using Gradient Boosted Regression Tree Model

Tropical Cyclones Intensity Prediction in the Western North Pacific Using Gradient Boosted Regression Tree Model

Tropical cyclone intensity estimations over the Indian ocean using Machine Learning

Estimation of precipitation induced by tropical cyclones based on machine‐learning‐enhanced analogue identification of numerical prediction

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