# Novel Method for Determining the Height of the Stable Boundary Layer under Low-Level Jet by Judging the Shape of the Wind Velocity Variance Profile

^{1}

^{2}

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## Abstract

**:**

## 1. Introduction

## 2. Methods

_{u}and δ

_{v}, with a time resolution of 10 s, are calculated separately. We calculate the variance of the horizontal wind speed as δ = δ

_{u}+ δ

_{v}. It can be clearly seen from the variance profile in Figure 1c that when a low-level jet is extant, the variance profile has a relatively large value near the ground, while there is a significant decrease in the upper air. The Richardson number distribution in Figure 1d shows that there is an obvious large Richardson number at the time and location of the low-level jet. This demonstrates that there is a laminar flow layer, that is, a stable boundary layer.

## 3. Results and Discussion

## 4. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Temporal and spatial distribution of the horizontal wind speed (

**a**), temperature (

**b**), wind speed variance (

**c**), and Richardson number (

**d**) on 2 August 2022.

**Figure 2.**Vertical profiles of the horizontal wind speed (

**a**), wind speed variance (

**b**), virtual potential temperature (

**c**), and Richardson number (

**d**) at different times on 2 August 2022.

**Figure 3.**Correlation between the stable boundary layer (SBL) height obtained by the Richardson number method (SBL@Ri) and that retrieved from the variance profile shape (SBL@Wind).

**Figure 5.**Comparison of the wind speed and variance between wind lidar data and meteorological gradient tower observations at the same time and place.

**Figure 6.**Correlation between the boundary layer height detected by wind lidar and obtained by the gradient tower data.

**Figure 7.**Height distribution of the stable boundary layer at night in Shenzhen in 2022: (

**a**) April, (

**b**) May, and (

**c**) June.

**Table 1.**Frequency and proportion of the four types of wind speed variance profiles based on the data from August 2022.

Type | A | B | C | D |
---|---|---|---|---|

Frequency | 16,840 | 16,521 | 1460 | 18,995 |

Proportion | 31.3% | 30.7% | 2.7% | 35.3% |

**Table 2.**Error of the stable boundary layer height obtained by the Richardson number method and that retrieved from variance profile shape.

Correlation Coefficient | Average Error (m) | Relative Error (%) |
---|---|---|

0.8520 | 27.48 | 17.17 |

Metrics | Technical Performance Requirements |
---|---|

Minimum detection altitude | ≤30 m |

Maximum detection altitude | 6 km |

Distance resolution | 15 m |

Temporal resolution of wind profile | 5 s |

Errors of wind speed measurement (standard deviation) | ≤0.3 m s^{−1} |

Errors of wind direction measurement (root mean squared error) | ≤3° |

Range of vertical wind speed measurement | 0–60 m s^{−1} |

Range of wind direction measurement | 0°–360° |

Correlation Coefficient | Average Error (m) | Relative Error (%) |
---|---|---|

0.9209 | 17.62 | 8.21 |

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**MDPI and ACS Style**

Xian, J.; Zhang, N.; Lu, C.; Yang, H.; Qiu, Z.
Novel Method for Determining the Height of the Stable Boundary Layer under Low-Level Jet by Judging the Shape of the Wind Velocity Variance Profile. *Remote Sens.* **2023**, *15*, 3638.
https://doi.org/10.3390/rs15143638

**AMA Style**

Xian J, Zhang N, Lu C, Yang H, Qiu Z.
Novel Method for Determining the Height of the Stable Boundary Layer under Low-Level Jet by Judging the Shape of the Wind Velocity Variance Profile. *Remote Sensing*. 2023; 15(14):3638.
https://doi.org/10.3390/rs15143638

**Chicago/Turabian Style**

Xian, Jinhong, Ning Zhang, Chao Lu, Honglong Yang, and Zongxu Qiu.
2023. "Novel Method for Determining the Height of the Stable Boundary Layer under Low-Level Jet by Judging the Shape of the Wind Velocity Variance Profile" *Remote Sensing* 15, no. 14: 3638.
https://doi.org/10.3390/rs15143638