Reconstruction of the Radar Reflectivity of Convective Storms Based on Deep Learning and Himawari-8 Observations
Abstract
:1. Introduction
2. Data and Method
2.1. Data
2.1.1. Satellite Data
2.1.2. Combined Reflectivity Factor
2.2. Method
3. Model and Settings
3.1. Choosing the CNN Architecture
3.2. Setting the Loss Function
3.3. Model Evaluation
4. Results
4.1. Model Performance
4.1.1. Model Performance of U-Net_Full
4.1.2. The Performance of Different Inputs in the CREF Reconstruction
4.2. Case Study
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Proud, S.R. Analysis of aircraft flights near convective weather over Europe. Weather 2015, 70, 292–296. [Google Scholar] [CrossRef]
- Roberts, R.D.; Rutledge, S. Nowcasting Storm Initiation and Growth Using GOES-8 and WSR-88D Data. Weather Forecast. 2003, 18, 562–584. [Google Scholar] [CrossRef] [Green Version]
- Hong, Y.; Tang, G.; Ma, Y.; Huang, Q.; Han, Z.; Zeng, Z.; Yang, Y.; Wang, C.; Guo, X. Remote Sensing Precipitation: Sensors, Retrievals, Validations, and Applications. In Observation and Measurement of Ecohydrological Processes; Li, X., Ed.; Springer: Cham, Swizerland, 2018; pp. 107–128. [Google Scholar]
- Mecikalski, J.R.; Bedka, K.M. Forecasting Convective Initiation by Monitoring the Evolution of Moving Cumulus in Daytime GOES Imagery. Mon. Weather Rev. 2006, 134, 49–78. [Google Scholar] [CrossRef] [Green Version]
- Mecikalski, J.R.; Feltz, W.F.; Murray, J.J.; Johnson, D.B.; Bedka, K.; Bedka, S.; Wimmers, A.J.; Pavolonis, M.J.; Berendes, T.A.; Haggerty, J.; et al. Aviation Applications for Satellite-Based Observations of Cloud Properties, Convection Initiation, In-Flight Icing, Turbulence, and Volcanic Ash. Bull. Am. Meteorol. Soc. 2007, 88, 1589–1607. [Google Scholar] [CrossRef] [Green Version]
- Mecikalski, J.R.; Bedka, K.M.; Paech, S.J.; Litten, L.A. A Statistical Evaluation of GOES Cloud-Top Properties for Nowcasting Convective Initiation. Mon. Weather Rev. 2008, 136, 4899–4914. [Google Scholar] [CrossRef] [Green Version]
- Walker, J.R.; MacKenzie, W.M.; Mecikalski, J.R.; Jewett, C.P. An Enhanced Geostationary Satellite-Based Convective Initiation Algorithm for 0–2-h Nowcasting with Object Tracking. J. Appl. Meteor. Climatol. 2012, 51, 1931–1949. [Google Scholar] [CrossRef]
- Liu, Y.; Fu, Q.; Song, P. Satellite retrieval of precipitation: An overview. Adv. Atmos. Sci. 2011, 26, 1162–1172. [Google Scholar]
- Arkin, P.A.; Meisner, B.N. The relationship between large-scale convective rainfall and cold cloud over the Western Hemisphere during 1982–1984. Mon. Weather Rev. 1987, 115, 51–74. [Google Scholar] [CrossRef] [Green Version]
- Sun, S.; Li, W.; Huang, Y. Retrieval of Precipitation by Using Himawari-8 Infrared Images. Acta Sci. Nat. Univ. Pekinensis. 2019, 55, 0479–8023. (In Chinese) [Google Scholar]
- Xia, J.; Li, H.; Kang, Y.; Li, C.; Ji, L.; Wu, L.; Lou, X.; Zhu, G.; Wang, Z.; Yan, Z.; et al. Machine learning−based weather support for the 2022 Winter Olympics. Adv. Atmos. Sci. 2020, 37, 927–932. [Google Scholar] [CrossRef]
- Krizhevsky, A.; Sutskever, I.; Hinton, G. ImageNet Classification with Deep Convolutional Neural Networks. Adv. Neural Inf. Process. Syst. 2012, 25, 1097–1105. [Google Scholar] [CrossRef]
- Simonyan, K.; Zisserman, A. Very Deep Convolutional Networks for Large-Scale Image Recognition. In Proceedings of the third International Conference on Learning Representations, San Diego, CA, USA, 7–9 May 2015. [Google Scholar]
- Szegedy, C.; Vanhoucke, V.; Ioffe, S.; Shlens, J.; Wojna, Z. Rethinking the Inception Architecture for Computer Vision. In Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 27–30 June 2016; pp. 2818–2826. [Google Scholar]
- He, K.; Zhang, X.; Ren, S.; Sun, J. Deep Residual Learning for Image Recognition. In Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 27–30 June 2016; pp. 770–778. [Google Scholar]
- Badrinarayanan, V.; Kendall, A.; Cipolla, R. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation. In IEEE Transactions on Pattern Analysis and Machine Intelligence; IEEE: Honolulu, HI, USA, 2017; Volume 39, pp. 2481–2495. [Google Scholar]
- Lee, H.J.; Kim, J.U.; Lee, S.; Kim, H.G.; Ro, Y.M. Structure Boundary Preserving Segmentation for Medical Image with Ambiguous Boundary. In Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, 13–19 June 2020; pp. 4816–4825. [Google Scholar]
- Wang, Y.; Wei, X.; Liu, F.; Chen, J.; Zhou, Y.; Shen, W.; Fishman, E.; Yuille, A. Deep Distance Transform for Tubular Structure Segmentation in CT Scans. In Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, 13–19 June 2020; pp. 3832–3841. [Google Scholar]
- Reichstein, M.; Camps-Valls, G.; Stevens, B.; Jung, M.; Denzler, J.; Carvalhais, N.; Prabhat. Deep learning and process understanding for data-driven Earth system science. Nature 2019, 566, 195–204. [Google Scholar] [CrossRef] [PubMed]
- Beusch, L.; Foresti, L.; Gabella, M.; Hamann, U. Satellite-Based Rainfall Retrieval: From Generalized Linear Models to Artificial Neural Networks. Remote Sens. 2018, 10, 939. [Google Scholar] [CrossRef] [Green Version]
- Veillette, M.S.; Hassey, E.P.; Mattioli, C.J.; Iskenderian, H.; Lamey, P.M. Creating Synthetic Radar Imagery Using Convolutional Neural Networks. J. Atmos. Ocean. Technol. 2018, 35, 2323–2338. [Google Scholar] [CrossRef]
- Wang, C.; Xu, J.; Tang, G.; Yang, Y.; Hong, Y. Infrared Precipitation Estimation Using Convolutional Neural Network. IEEE Trans. Geosci. Remote Sens. 2020, 58, 8612–8625. [Google Scholar] [CrossRef]
- Hilburn, K.A.; Ebert-Uphoff, I.; Miller, S.D. Development and Interpretation of a Neural Network-Based Synthetic Radar Reflectivity Estimator Using GOES-R Satellite Observations. J. Appl. Meteor. Climatol. 2020, 60, 1–61. [Google Scholar] [CrossRef]
- Yasuhiko, S.; Hiroshi, S.; Takahito, I.; Akira, S. Convective Cloud Information derived from Himawari-8 data. In Meteorological Satellite Center Technical Note; Meteorological Satellite Center (MSC): Kiyose, Tokyo, 2017; p. 22. [Google Scholar]
- Ronneberger, O.; Fischer, P.; Brox, T. 2015: U-Net: Convolutional Networks for Biomedical Image Segmentation. In Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention-MICCAI, Munich, Germany, 5–19 November 2015; Springer: Cham, Switzerland, 2015; pp. 234–241. [Google Scholar]
- Ioffe, S.; Szegedy, C. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. Appl. Intell. 2018, 48, 142–155. [Google Scholar]
- Kingma, D.; Ba, J. ADAM: A method for stochastic optimization. In Proceedings of the third International Conference on Learning Representations, San Diego, CA, USA, 7–9 May 2015. [Google Scholar]
- Roebber, P. Visualizing Multiple Measures of Forecast Quality. Weather Forecast. 2008, 24, 601–608. [Google Scholar] [CrossRef] [Green Version]
- Doswell, C., III; Davies-Jones, R.; Keller, D. On Summary Measures of Skill in Rare Event Forecasting Based on Contingency Tables. Weather Forecast. 1990, 5, 576–585. [Google Scholar] [CrossRef] [Green Version]
- Zhang, X.D.; Sun, J. Application analysis of Himawari-8 in Monitoring Heavy Rain Convective Clouds. Meteor. Mon. 2018, 44, 1245–1254. (In Chinese) [Google Scholar]
Band | Central Wavelength | Physical Meaning |
---|---|---|
Band 07 | 3.9 µm | Shortwave infrared window, low clouds, fog |
Band 09 | 6.9 µm | Mid-level water vapor |
Band 13 | 10.4 µm | Clean and dry atmospheric window, cloud-top temperature |
Band 16 | 13.3 µm | Cloud-top height |
Band 16–13 | - | Cloud phase state |
CREF (dBZ) | Proportion (%) | The Corresponding Echo Level |
---|---|---|
0 ≤ y < 5 | 80.877% | No echo |
5 ≤ y < 20 | 10.945% | Drizzle |
20 ≤ y < 35 | 6.506% | Small to moderate rain |
35 ≤ y < 50 | 1.543% | Large to heavy rain |
50 ≤ y ≤ 80 | 0.130% | Heavy rain to the rainstorm (the hail) |
Real Category | |||
---|---|---|---|
1 | 0 | ||
Predicted category | 1 | True positive (TP) | False positive (FP) |
0 | False negative (FN) | True negative (TN) |
RMSE | POD35 | FAR35 | CSI35 | HSS35 | ACC35 | |
---|---|---|---|---|---|---|
07:00 | 12.361 | 0.686 | 0.721 | 0.248 | 0.343 | 0.879 |
08:00 | 12.527 | 0.680 | 0.736 | 0.235 | 0.316 | 0.855 |
09:00 | 12.364 | 0.603 | 0.705 | 0.247 | 0.323 | 0.852 |
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Duan, M.; Xia, J.; Yan, Z.; Han, L.; Zhang, L.; Xia, H.; Yu, S. Reconstruction of the Radar Reflectivity of Convective Storms Based on Deep Learning and Himawari-8 Observations. Remote Sens. 2021, 13, 3330. https://doi.org/10.3390/rs13163330
Duan M, Xia J, Yan Z, Han L, Zhang L, Xia H, Yu S. Reconstruction of the Radar Reflectivity of Convective Storms Based on Deep Learning and Himawari-8 Observations. Remote Sensing. 2021; 13(16):3330. https://doi.org/10.3390/rs13163330
Chicago/Turabian StyleDuan, Mingshan, Jiangjiang Xia, Zhongwei Yan, Lei Han, Lejian Zhang, Hanmeng Xia, and Shuang Yu. 2021. "Reconstruction of the Radar Reflectivity of Convective Storms Based on Deep Learning and Himawari-8 Observations" Remote Sensing 13, no. 16: 3330. https://doi.org/10.3390/rs13163330
APA StyleDuan, M., Xia, J., Yan, Z., Han, L., Zhang, L., Xia, H., & Yu, S. (2021). Reconstruction of the Radar Reflectivity of Convective Storms Based on Deep Learning and Himawari-8 Observations. Remote Sensing, 13(16), 3330. https://doi.org/10.3390/rs13163330