Delineation of Rain Areas with TRMM Microwave Observations Based on PNN
Abstract
:1. Introduction
2. Data and Method
2.1. Study Area
2.2. Data
2.2.1. Rain Gauge Observations
2.2.2. Microwave Brightness Temperature Datasets
2.2.3. TRMM Microwave Imager (TMI) Level 2 Hydrometeor Profile Product
2.3. Methods
2.3.1. Scatter Index
2.3.2. Dynamic Cluster K-Means Method
2.3.3. Brief Description of Probabilistic Neural Network
2.3.4. Rain Area Delineation Method Based on PNN
2.3.5. Validation
Rain/No Rain Area Delineation by the Models | |||
---|---|---|---|
Rain | No Rain | ||
Ground observations | Rain | h | m |
No rain | f | z |
3. Results
3.1. The Results of PNN and Other Rain Area Delineation Methods
Rain Event | Time | Number of Rain Pixels | Number of No_Rain Pixels |
---|---|---|---|
1 | June 25 at 2 am | 419 | 2757 |
2 | June 27 at 8 pm | 549 | 2607 |
3 | June 28 at 7 pm | 879 | 2831 |
4 | June 29 at 0 am | 1048 | 2286 |
5 | July 2 at 5 pm | 242 | 3470 |
6 | July 6 at 3 pm | 128 | 3518 |
7 | July 23 at 7 pm | 453 | 2807 |
8 | July 26 at 4 am | 482 | 2215 |
9 | July 26 at 9 am | 926 | 2662 |
10 | July 27 at 8 am | 483 | 2525 |
11 | July 29 at 3 am | 330 | 3268 |
12 | July 31 at 3 am | 483 | 2470 |
13 | August 4 at 1 am | 446 | 2646 |
14 | August 5 at 0 am | 317 | 2935 |
15 | August 5 at 5 am | 295 | 3349 |
16 | August 9 at 9 pm | 621 | 2297 |
3.2. Validation and Comparison of the PNN Method
3.3. Improvement of SI with PNN Method
4. Discussion
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Xu, S.; Wu, C.; Gonsamo, A.; Shen, Y. Delineation of Rain Areas with TRMM Microwave Observations Based on PNN. Remote Sens. 2014, 6, 12118-12137. https://doi.org/10.3390/rs61212118
Xu S, Wu C, Gonsamo A, Shen Y. Delineation of Rain Areas with TRMM Microwave Observations Based on PNN. Remote Sensing. 2014; 6(12):12118-12137. https://doi.org/10.3390/rs61212118
Chicago/Turabian StyleXu, Shiguang, Chaoyang Wu, Alemu Gonsamo, and Yan Shen. 2014. "Delineation of Rain Areas with TRMM Microwave Observations Based on PNN" Remote Sensing 6, no. 12: 12118-12137. https://doi.org/10.3390/rs61212118