# Study of Landfalling Typhoon Potential Maximum Gale Forecasting in South China

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## Abstract

**:**

## 1. Introduction

## 2. Data and Methods

#### 2.1. Data

#### 2.2. Methods

#### 2.2.1. DSAEF_LTG Model

#### 2.2.2. Two Improvements of the DSAEF_LTG Model

#### 2.2.3. Other Methods

- (1)
- TC track similarity area index (TSAI)

- (2)
- Evaluation methods

## 3. Experimental Design

#### 3.1. Target TC

#### 3.2. Improvement Experiments

## 4. Results

## 5. Conclusions

- (1)
- The training sample experiments showed that the introduction of TC translation speed similarity or improvement of the ensemble scheme separately showed marked improvement effects, and that the TSsum reached 0.8891 and 0.9284, respectively, which exceeded that of the unimproved model (0.8451). However, when the two improvements were introduced simultaneously, the introduction of the TC translation speed similarity did not produce an improvement effect. Further analysis of the model parameter values revealed that introduction of TC translation speed similarity has no impact on the results when simultaneously adding a new ensemble scheme in the DSAEF_LTG model.
- (2)
- The results of the independent sample forecasting experiments showed that the DSAEF_LTG model can be improved by adding TC translation speed similarity or by adding new ensemble schemes; the TS was 0.26 for a gale greater than Beaufort Scale 7, and the TS was 0.34 when adding a new ensemble scheme, i.e., 31% higher than that of the original ensemble scheme. Moreover, the FAR and MR scores also decreased in comparison with those realized before the model improvement. The improved TS of the ensemble scheme reached 0.25, i.e., 127% higher than the TS of the original scheme, reflecting the advantage of the model for extreme typhoon gales greater than Beaufort Scale 10.
- (3)
- The results of the independent sample forecasting experiments also showed that when TC translation speed similarity and new ensemble schemes are introduced simultaneously, the forecasting effect is the same as that when the TC translation speed similarity is introduced alone, with a TS of 0.35 (0.25) for a gale at the threshold of Beaufort Scale 7 (Beaufort Scale 10). The ensemble scheme improvement can adjust the gale fields of multiple similar TCs, which makes the forecast results more reasonable. This potentially explains why the model performs better after the ensemble scheme improvement than after the introduction of TC translation speed similarity.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Distribution of the 140 observation stations (red dots) in South China (Guangxi, Guangdong, and Hainan provinces) considered in this study.

**Figure 2.**Tracks of (

**a**) the 16 TCs from 2011–2015 selected as the training sample and (

**b**) the eight TCs from 2016–2018 used as the sample for the independent forecast test.

**Figure 3.**Threat scores of (

**a**) DLTG_1 (unimproved model), (

**b**) DLTG_2 (adding translation speed similarity), (

**c**) DLTG_3 (adding five new ensembles schemes), and (

**d**) DLTG_4 (adding both translation speed similarity and five new ensembles schemes) in the potential maximum gale simulation experiment for 16 training samples. Each black dot represents a single scheme, and the red dot indicates the best scheme with the maximum TSsum (TSsum = TS6 + TS8).

**Figure 4.**Comparison of the values of the TS, FAR, and MR of the DSAEF_LTG model for the four experiments.

**Figure 5.**Comparison of the TS in the four DSAEF_LTG model experiments for each TC in the independent forecast experiments: (

**a**) Beaufort Scale 7 and (

**b**) Beaufort Scale 10.

**Figure 6.**Distribution of potential maximum gale (m/s) during Typhoon Haima according to (

**a**) observations; (

**b**) DLTG_1; (

**c**) DLTG_2; and (

**d**) DLTG_3 (and 4). Black solid line is the observed track, black dashed line is the forecasted track, and colored solid line is the best similar TC track. (The first two digits of in the figure stand for the last two numbers of the year, and the last two digits stand for the number of the typhoon in that year. For example, “8926” stands for typhoon No. 26 in 1986).

**Figure 7.**Distribution of potential maximum gale (m/s) during Typhoon Hato according to (

**a**) observations; (

**b**) DLTG_1; (

**c**) DLTG_2; and (

**d**) DLTG_3 (and 4). Black solid line is the observed track, black dashed line is the forecasted track, and colored solid line is the best similar TC track.

Parameters | Description | Number of Values |
---|---|---|

Initial time (P1) | 1: 1200 UTC on Day1, 2: 0000 UTC on Day 1, 3: 1200 UTC on Day 2, 4: 0000 UTC on Day 2. (Day 1: the day of TC gale occurring on land; Day 2: the day before Day 1) | 2 × 2 = 4 |

Similarity region (P2) | Parameters of TSAI with rectangular shape. Its southeastern vertex (C) can be the TC position at 00, 12, 24, 36, or 48 h prior to the initial time, and the northwestern vertex (A) can be the TC position at 00, 06, or 12 h prior to the maximum lead time. The 1st–15th values are combinations of C and A. The 16th–20th values are based on the first value, i.e., C represents the TC position at the initial time and A represents the TC position at the maximum lead time. Further details regarding the 16th–20th values can be found in Jia et al. [25] | 20 |

Threshold of the segmentation ratio of a latitude extreme point (P3) | A parameter of the TSAI: 1: 0.1; 2: 0.2; 3: 0.3 | 3 |

The overlapping percentage threshold of two TC tracks (P4) | A parameter of the TSAI: 1: 0.9; 2: 0.8; 3: 0.7; 4: 0.6; 5: 0.5; 6: 0.4 | 6 |

Seasonal similarity (P5) | A parameter indicating the TC landfall date: 1: entire year; 2: May–November; 3: July–September 4: same landfall month as the target TC 5: within 15 d of the target TC landfall time | 5 |

Intensity similarity (P6) | Four categories 1: average intensity on the first windy day 2: maximum intensity on the first windy day 3: average intensity on all windy days 4: maximum intensity on all windy days Five levels 1: all grades; 2: the target TC intensity is the same grade or above that of the historical TC; 3: the same grade or below; 4: only the same grade 5: the same grade or one grade different | 4 × 5 |

Translation speed similarity (P7) | Three categories: 1. Average TC translation speed on the first windy day * 2. Minimum average TC translation speed on the first windy day * 3. Average TC translation speed on all windy days * Two Grading criteria: 1. mean *; 2. K-means clustering* Five levels: 1: all grades *; 2: the target TC intensity is the same grade or above that of the historical TC *; 3: the same grade or below *; 4: only the same grade *; 5: the same grade or one grade different * | 30 |

Number (N) of analog TCs screened for the ensemble forecast (P8) | 1–10 for 1, 2, …, 10, respectively | 10 |

Ensemble forecast scheme (P9) | 1. mean; 2. maximum; 3. 90th percentile *; 4. fuse *; 5. probability matching mean (PM) *; 6. equal difference-weighted mean (ED-WM) *; 7. TSAI-weighted mean (TSAI-WM) *. | 7 |

Total number of schemes | 4 × 20 × 3 × 6 × 5 × 20 × 30 × 10 × 7 | 302,400,000 |

**Table 2.**Correlation coefficients of six TC translation speed indicators with a single-station TC potential maximum gale.

TC Translation Speed Indicator | Single-Station TC Potential Maximum Gale |
---|---|

Average TC translation speed on the first windy day | 0.1254 * |

Maximum TC translation speed on the first windy day | 0.0531 |

Minimum TC translation speed on the first windy day | 0.1809 * |

Average TC translation speed on all windy days | 0.1201 * |

Maximum TC translation speed on all windy days | 0.1077 |

Minimum TC translation speed on all windy days | 0.0390 |

TC Translation Speed Indicator | Method | TC Translation Speed of Cut-Off Points (km/h) | ||||||
---|---|---|---|---|---|---|---|---|

First day’s average translation speed | Average segmentation | 12.79 | 14.82 | 17.16 | 19.63 | 22.38 | 26.37 | 12.79 |

K-means clustering | 6.83 | 11.42 | 15.51 | 19.39 | 23.7 | 28.34 | 6.83 | |

First day’s minimum translation speed | Average segmentation | 8.19 | 11.38 | 12.97 | 15.19 | 18.02 | 20.48 | 8.19 |

K-means clustering | 1.73 | 5.82 | 10.22 | 15.47 | 21.63 | 28.02 | 1.73 | |

Average translation speed | Average segmentation | 12.95 | 15.22 | 17.96 | 20.1 | 22.7 | 26.08 | 12.95 |

K-means clustering | 11.79 | 16.18 | 20.1 | 23.62 | 27.61 | 31.9 | 11.79 |

Name | Computational Procedure |
---|---|

90th percentile | For each station, $\mathrm{G}\mathrm{a}\mathrm{l}\mathrm{e}$ (i), i = 1, 2, …, m, where m is sorted from minimum to maximum. $\mathrm{G}\mathrm{a}\mathrm{l}\mathrm{e}$ (r) is the potential maximum gale ranked r. d = 1 + (m − 1)$\times $0.9 The integer part of d is r and the decimal part is f $\mathrm{G}\mathrm{a}\mathrm{l}\mathrm{e}\mathrm{s}=\mathrm{G}\mathrm{a}\mathrm{l}\mathrm{e}\left(\mathrm{r}\right)+\left[\mathrm{G}\mathrm{a}\mathrm{l}\mathrm{e}\right(\mathrm{r}+1)-\mathrm{G}\mathrm{a}\mathrm{l}\mathrm{e}(\mathrm{r}\left)\right]\times \mathrm{f}$ |

Fuse | Calculation rules of forecast potential maximum gale at each station: If $\mathrm{M}\mathrm{a}\mathrm{x}\left(\mathrm{G}\mathrm{a}\mathrm{l}\mathrm{e}\left(\mathrm{i}\right)\right)\ge 24.5\mathrm{m}/\mathrm{s}$, $\mathrm{G}\mathrm{a}\mathrm{l}\mathrm{e}\mathrm{s}=\mathrm{M}\mathrm{a}\mathrm{x}\left(\mathrm{G}\mathrm{a}\mathrm{l}\mathrm{e}\left(\mathrm{i}\right)\right)$; If the 90% percentile values of $\mathrm{G}\mathrm{a}\mathrm{l}\mathrm{e}$(i) ≥ 17.2 $\mathrm{m}/\mathrm{s}$, $\mathrm{G}\mathrm{a}\mathrm{l}\mathrm{e}\mathrm{s}$ = the 90% percentile value of $\mathrm{G}\mathrm{a}\mathrm{l}\mathrm{e}$(i); If the 75% percentile values of $\mathrm{G}\mathrm{a}\mathrm{l}\mathrm{e}$(i) ≥ 17.2 $\mathrm{m}/\mathrm{s}$, $\mathrm{G}\mathrm{a}\mathrm{l}\mathrm{e}\mathrm{s}$ = the 75% percentile value of $\mathrm{G}\mathrm{a}\mathrm{l}\mathrm{e}$(i); If the median value of $\mathrm{G}\mathrm{a}\mathrm{l}\mathrm{e}$(i) ≥ 10.8$\mathrm{m}/\mathrm{s}$, $\mathrm{G}\mathrm{a}\mathrm{l}\mathrm{e}\mathrm{s}$ = the median value of $\mathrm{G}\mathrm{a}\mathrm{l}\mathrm{e}$(i); If none of the above happen, $\mathrm{G}\mathrm{a}\mathrm{l}\mathrm{e}\mathrm{s}$ = the 10% percentile value. |

Probability matching mean (PM) | All gale data for m members of 140 stations were arranged in ascending order (containing gale data for 140 × n stations). The data were divided into 140 equal parts from 140 × n maximum to minimum, and the median of each part was retained as glm(k), k = 1, 2, …, 140. Averaging Gale(i) over each station, ranking the average values from largest to smallest, and recording the position of each value in the series. Corresponding to the glm(k) of each station based on the k of each station, and glm(k) is the predicted gale for this station, gale = glm(k). |

Equal difference-weighted mean (ED-WM) | The weight of the potential maximum gale for the selected similar TC, the similarity rank I of which is: $\mathrm{W}\left(\mathrm{i}\right)=\frac{(2\times \mathrm{m}-\mathrm{i})\times 2}{(3\times \mathrm{m}-1)\times \mathrm{m}}$ (i = 1, 2, …, m), $\mathrm{G}\mathrm{a}\mathrm{l}\mathrm{e}\mathrm{s}$ = ${\sum}_{\mathrm{i}=1}^{\mathrm{m}}\mathrm{W}\left(\mathrm{i}\right)\times \mathrm{G}\mathrm{a}\mathrm{l}\mathrm{e}\left(\mathrm{i}\right)$. |

TSAI-weighted mean (TSAI-WM). | $\mathrm{A}\left(\mathrm{i}\right)=\frac{1}{\mathrm{T}\mathrm{S}\mathrm{A}\mathrm{I}\left(\mathrm{i}\right)}\left(\mathrm{i}=1,2,...,\mathrm{m}\right)$, the weight of the potential maximum gale for the selected similar TC whose similarity rank i is: $\mathrm{W}\left(\mathrm{i}\right)=\frac{\mathrm{A}\left(\mathrm{i}\right)}{{\sum}_{\mathrm{i}=1}^{\mathrm{m}}\mathrm{A}\left(\mathrm{i}\right)}$, $\mathrm{G}\mathrm{a}\mathrm{l}\mathrm{e}\mathrm{s}$ = ${\sum}_{\mathrm{i}=1}^{\mathrm{m}}\mathrm{W}\left(\mathrm{i}\right)\times \mathrm{G}\mathrm{a}\mathrm{l}\mathrm{e}\left(\mathrm{i}\right)$. |

Parameter | DLTG_1 | DLTG_2 | DLTG_3 | DLTG_4 |
---|---|---|---|---|

Initial time | 3 (1200 UTC on Day 2) | 3 | 3 | 3 |

Similarity region | 20: shifting A1B1C1D1 (the 16th scheme) to the right by the distance of D1D2 and downward by the distance of B1B2. | 16 or 17: (ABCD: the first kind of parameter value of the original similarity region; and A1B1C1D1, a square with side length of 2000 km, is the 16th scheme of the similarity region. The midpoints of B and B1 are taken as B2, and the midpoints of D and D1 are taken as D2, which makes A2B2C2D2 the 17th scheme. | 17 | 17 |

Threshold of the segmentation ratio of a latitudinal extreme point | 2 (0.2) | 3 (0.3) | 2 (0.2) | 2 (0.2) |

Overlapping percentage threshold of two TC tracks | 5 (0.5) | 6 (0.4) | 6 (0.4) | 6 (0.4) |

Seasonal similarity | 1 (entire year) | 1 or 2 (entire year or May–November) | 1 or 2 | 1 or 2 |

Intensity similarity | 2/5 (maximum intensity on the first windy day/the same grade or one grade different) | 1/5 (1: average intensity on the first windy day/the same grade or one grade different) | 1/5 | 1/5 |

Translation speed similarity | / | 2/2/3 (minimum TC translation speed on the first windy day/K-means clustering/the same grade or below) | / | 1–3/1–2/1 (all categories/all grading criteria/all levels) |

Number (N) of analog TCs screened for the ensemble forecast | 2 | 2 | 3 | 3 |

Ensemble forecast scheme | 2 (maximum) | 2 | 7 (probability matching mean (PM)) | 7 |

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## Share and Cite

**MDPI and ACS Style**

Su, Z.; Li, L.; Ren, F.; Zhu, J.; Liu, C.; Wan, Q.; Sun, Q.; Jia, L.
Study of Landfalling Typhoon Potential Maximum Gale Forecasting in South China. *Atmosphere* **2023**, *14*, 888.
https://doi.org/10.3390/atmos14050888

**AMA Style**

Su Z, Li L, Ren F, Zhu J, Liu C, Wan Q, Sun Q, Jia L.
Study of Landfalling Typhoon Potential Maximum Gale Forecasting in South China. *Atmosphere*. 2023; 14(5):888.
https://doi.org/10.3390/atmos14050888

**Chicago/Turabian Style**

Su, Zhizhong, Lifang Li, Fumin Ren, Jing Zhu, Chunxia Liu, Qilin Wan, Qiongbo Sun, and Li Jia.
2023. "Study of Landfalling Typhoon Potential Maximum Gale Forecasting in South China" *Atmosphere* 14, no. 5: 888.
https://doi.org/10.3390/atmos14050888