Automated Recognition of Macro Downburst Using Doppler Weather Radar
Abstract
:1. Introduction
2. Doppler Weather Radar Data Preprocessing
2.1. Remove Isolated Points
2.2. Fourier Interpolation Algorithm Improves the Resolution of Reflectivity Products
2.3. Filter
- Median filter
- 2.
- Moving average filter
2.4. Two-Dimensional Multi-Channel Algorithm Reduces Velocity Ambiguity in Radial Velocity Products
2.5. Radial Velocity Interpolation
3. The Principle of Automatic Identification Algorithm for Downburst Area
3.1. Radial Velocity Binarization
3.2. Eight-Neighborhood Method for Matching Positive and Negative Velocity Connected Regions
- The distance between the positive and negative velocity and the center of mass of the connected area is less than 5 km;
- The number of pixels in the connected area of positive and negative velocity is greater than 10.
3.3. Zero Doppler Velocity Line Extraction Method
- Traverse the radial velocity in the detection range. When the absolute value of the radial velocity ≤ 0.5 m/s, mark this point as a zero Doppler velocity suspected point.
- Within the detection range, use a 3 × 3 window to traverse every suspected point of zero Doppler velocity and record the number of data that satisfy the absolute value of the radial velocity ≤ 0.5 m/s.
- When the total value of the 3 × 3 window of the zero Doppler velocity suspected point meets the condition is ≥2, the zero Doppler velocity suspected point is marked as the zero Doppler velocity point.
3.4. Recognition Method of Downburst Area Center
- As shown in Figure 6, the maximum and minimum values of a pair of positive and negative velocities versus regional azimuth and distance libraries are used as the search range of velocity extremes.
- 2.
- In the search range, find the maximum value of positive velocity and the minimum value of negative velocity, and record the value and coordinates.
- 3.
- Take the absolute value of the maximum value of the positive velocity and the minimum value of the negative velocity. If one of the absolute values is ≥ 10 m/s [25], proceed to step (4); otherwise, the positive and negative velocity have no downburst center.
- 4.
- Calculate the sum of the squares of the distances between all points on the zero Doppler velocity line and the maximum and minimum positive and negative velocity values. As shown in Figure 7, the suspected point at the center of the downburst area is the one where the sum of the squares of the distance is the smallest.
- 5.
- When the reflectivity of the suspected point at the center of the downburst area is greater than 35 dBZ, mark this point as the center point of the downburst area.
- 6.
- If there are multiple pairs of positive and negative velocities, repeat step (1) to step (5).
4. Case Analysis
4.1. Recognition of Downburst on 25 July 2006
4.1.1. Weather Process Description
4.1.2. Recognition of Downburst at 16:02 on 25 July 2006
4.1.3. Identification of Downburst during This Process
4.2. Recognition of Downbursts on 27 June 2009
4.2.1. Weather Process Description
4.2.2. Identification of Downburst during This Process
4.3. Algorithm Comparison
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
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Wang, X.; Wang, H.; He, J.; Shi, Z.; Xie, C. Automated Recognition of Macro Downburst Using Doppler Weather Radar. Atmosphere 2022, 13, 672. https://doi.org/10.3390/atmos13050672
Wang X, Wang H, He J, Shi Z, Xie C. Automated Recognition of Macro Downburst Using Doppler Weather Radar. Atmosphere. 2022; 13(5):672. https://doi.org/10.3390/atmos13050672
Chicago/Turabian StyleWang, Xu, Hailong Wang, Jianxin He, Zhao Shi, and Chenghua Xie. 2022. "Automated Recognition of Macro Downburst Using Doppler Weather Radar" Atmosphere 13, no. 5: 672. https://doi.org/10.3390/atmos13050672