Using Deep Learning to Identify Circulation Patterns of Intense Rainfall in the Beijing–Tianjing–Hebei Region
Abstract
:1. Introduction
2. Data and Methods
2.1. Information Notes
2.2. Selection of Intense Rainfall Events in the Beijing–Tianjin–Hebei Region and Classification of Corresponding Circulation Patterns
2.3. Objective Classification Methods
2.3.1. Method Screening
2.3.2. Principle and Network Structure of FConvNeXt
2.3.3. Introduction to the ConvNeXt Module
2.3.4. Introduction to the Cross-Fusion Feature Extraction Module
2.4. Experimental Design and Dataset Partitioning
2.5. Introduction to Evaluation Indicators
3. Results
3.1. Comparison of Multiple Objective Classification Methods
3.2. Circulation Pattern Classification by FConvNeXt
3.3. Performance of FConvNeXt in 2021 Test Set
4. Discussion
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
Abbreviations
WSF | Weak Synoptic Forcing Type |
LVT | Low Vortex Type |
SPT | Subtropical-high Periphery Type |
UTP | Upper-level Trough Type |
TPT | Typhoon Type |
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Test Method | WSF | LVT | SPT | UTP |
---|---|---|---|---|
ResNet-50 | 37.5% | 40% | 28.5% | 13.6% |
ConvNeXt | 62.5% | 25.7% | 64.3% | 14% |
FConvNeXt | 62.5% | 40% | 61% | 14% |
Weather Type | TP | TN | FP | FN | ACC |
---|---|---|---|---|---|
WSF | 25 | 56 | 29 | 15 | 64.8% |
LVT | 14 | 78 | 12 | 21 | 73.6% |
SPT | 17 | 82 | 15 | 11 | 79.2% |
UTP | 3 | 94 | 9 | 19 | 77.6% |
Weather Type | TP | TN | FP | FN | ACC |
---|---|---|---|---|---|
WSF | 25 | 47 | 38 | 15 | 57.6% |
LVT | 9 | 86 | 4 | 26 | 76% |
SPT | 18 | 82 | 15 | 10 | 80% |
UTP | 3 | 92 | 12 | 19 | 76% |
Weather Type | TP | TN | FP | FN | ACC |
---|---|---|---|---|---|
WSF | 15 | 58 | 27 | 25 | 58.4% |
LVT | 14 | 69 | 21 | 21 | 66.4% |
SPT | 8 | 81 | 16 | 20 | 70.4% |
UTP | 3 | 90 | 13 | 19 | 74.4% |
Weather Type | WSF | LVT | SPT | UTP |
---|---|---|---|---|
ResNet | 0.08 km | 0.19 km | 0.21 km | 0.16 km |
ConvNeXt | 0.13 km | 0.17 km | 0.18 km | 0.12 km |
FConvNeXt | 0.15 km | 0.15 km | 0.14 km | 0.19 km |
Random Sample Grouping | WSF | LVT | SPT | UTP |
---|---|---|---|---|
Group 1 | 62.5% | 40% | 61% | 14% |
Group 2 | 52.5% | 49% | 64% | 5% |
Group 3 | 60% | 37.5% | 61% | 14% |
Weather Type | TP | TN | FP | FN | ACC |
---|---|---|---|---|---|
WSF | 2 | 24 | 18 | 1 | 57.8% |
LVT | 9 | 15 | 4 | 17 | 53.3% |
SPT | 2 | 32 | 6 | 5 | 75.6% |
UTP | 1 | 35 | 3 | 6 | 80% |
Weather Type | WSF | LVT | SPT | UTP |
---|---|---|---|---|
FConvNeXt | 0.343 km | 0.102 km | 0.307 km | 0.248 km |
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Jing, L.; Zhong, Q.; Li, X.; Wang, X.; Shen, L.; Cao, Y. Using Deep Learning to Identify Circulation Patterns of Intense Rainfall in the Beijing–Tianjing–Hebei Region. Atmosphere 2023, 14, 930. https://doi.org/10.3390/atmos14060930
Jing L, Zhong Q, Li X, Wang X, Shen L, Cao Y. Using Deep Learning to Identify Circulation Patterns of Intense Rainfall in the Beijing–Tianjing–Hebei Region. Atmosphere. 2023; 14(6):930. https://doi.org/10.3390/atmos14060930
Chicago/Turabian StyleJing, Linguo, Qi Zhong, Xiaojie Li, Xiuming Wang, Lili Shen, and Yong Cao. 2023. "Using Deep Learning to Identify Circulation Patterns of Intense Rainfall in the Beijing–Tianjing–Hebei Region" Atmosphere 14, no. 6: 930. https://doi.org/10.3390/atmos14060930