Detecting Hailstorms in China from FY-4A Satellite with an Ensemble Machine Learning Model
Abstract
:1. Introduction
2. Data and Methods
2.1. Data
2.1.1. Satellite Data
- (a)
- FY-4A
- (b)
- GPM PF database
- (c)
- GPM Hail Climatology Data Products
2.1.2. Reanalysis Data
2.1.3. Hail Data
- (a)
- Hail Event Records
- (b)
- 1-h Hail Event Records
- (c)
- Direct Economic Losses
2.2. Methods
- (a)
- Convective Cloud Identification
- (b)
- Sample Preparation
- (c)
- Model Training and Integration
- (d)
- Model Evaluation
3. Results
3.1. Results of Convective Cloud Identification
3.2. Statistical Results of Selected and Unselected Features
3.3. Confusion Matrix Evaluation Results
3.4. Statistical Results of Hail Occurrence Probability Spatial Distribution
3.4.1. Dtree vs. BPNN vs. BPNN+Dtree vs. OBS
3.4.2. BPNN+Dtree vs. OT vs. OTfilter vs. OBS
3.4.3. BPNN+Dtree vs. Ni17 vs. Mroz17 vs. CB12 vs. BC19 vs. OBS
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Hand, W.H.; Cappelluti, G. A global hail climatology using the UK Met Office convection diagnosis procedure (CDP) and model analyses. Meteorol. Appl. 2011, 18, 446–458. [Google Scholar] [CrossRef]
- Zhang, X.; Sun, J.; Zheng, Y.; Zhang, Y.; Ma, R.; Yang, X.; Zhou, K.; Han, X. Progress in Severe Convective Weather Forecasting in China since the 1950s. J. Meteorol. Res. 2020, 34, 699–719. [Google Scholar] [CrossRef]
- Chinese Meteorological Administration. Yearbook of Meteorological Disasters in China; China Meteorological Press: Beijing, China, 2013. (In Chinese) [Google Scholar]
- Chinese Meteorological Administration. Yearbook of Meteorological Disasters in China; China Meteorological Press: Beijing, China, 2014. (In Chinese) [Google Scholar]
- Chinese Meteorological Administration. Yearbook of Meteorological Disasters in China; China Meteorological Press: Beijing, China, 2015. (In Chinese) [Google Scholar]
- Chinese Meteorological Administration. Yearbook of Meteorological Disasters in China; China Meteorological Press: Beijing, China, 2016. (In Chinese) [Google Scholar]
- Chinese Meteorological Administration. Yearbook of Meteorological Disasters in China; China Meteorological Press: Beijing, China, 2017. (In Chinese) [Google Scholar]
- Chinese Meteorological Administration. Yearbook of Meteorological Disasters in China; China Meteorological Press: Beijing, China, 2018. (In Chinese) [Google Scholar]
- Chinese Meteorological Administration. Yearbook of Meteorological Disasters in China; China Meteorological Press: Beijing, China, 2019. (In Chinese) [Google Scholar]
- Chinese Meteorological Administration. Yearbook of Meteorological Disasters in China; China Meteorological Press: Beijing, China, 2020. (In Chinese) [Google Scholar]
- Chinese Meteorological Administration. Yearbook of Meteorological Disasters in China; China Meteorological Press: Beijing, China, 2021. (In Chinese) [Google Scholar]
- Chinese Meteorological Administration. Yearbook of Meteorological Disasters in China; China Meteorological Press: Beijing, China, 2022. (In Chinese) [Google Scholar]
- Punge, H.J.; Kunz, M. Hail observations and hailstorm characteristics in Europe: A review. Atmos. Res. 2016, 176, 159–184. [Google Scholar] [CrossRef]
- Witt, A.; Eilts, M.D.; Stumpf, G.J.; Johnson, J.T.; Mitchell, E.D.; Thomas, K.W. An enhanced hail detection algorithm for the WSR-88D. Weather Forecast. 1998, 13, 286–303. [Google Scholar] [CrossRef]
- Wang, P.; Shi, J.; Hou, J.; Hu, Y. The Identification of Hail Storms in the Early Stage Using Time Series Analysis. J. Geophys. Res.-Atmos. 2018, 123, 929–947. [Google Scholar] [CrossRef]
- Bang, S.D.; Cecil, D.J. Testing Passive Microwave-Based Hail Retrievals Using GPM DPR Ku-Band Radar. J. Appl. Meteorol. Climatol. 2021, 60, 255–271. [Google Scholar] [CrossRef]
- Cecil, D.J. Passive Microwave Brightness Temperatures as Proxies for Hailstorms. J. Appl. Meteorol. Climatol. 2009, 48, 1281–1286. [Google Scholar] [CrossRef]
- Ni, X.; Liu, C.; Cecil, D.J.; Zhang, Q. On the Detection of Hail Using Satellite Passive Microwave Radiometers and Precipitation Radar. J. Appl. Meteorol. Climatol. 2017, 56, 2693–2709. [Google Scholar] [CrossRef]
- Mroz, K.; Battaglia, A.; Lang, T.J.; Cecil, D.J.; Tanelli, S.; Tridon, F. Hail-Detection Algorithm for the GPM Core Observatory Satellite Sensors. J. Appl. Meteorol. Climatol. 2017, 56, 1939–1957. [Google Scholar] [CrossRef]
- Cecil, D.J.; Blankenship, C.B. Toward a Global Climatology of Severe Hailstorms as Estimated by Satellite Passive Microwave Imagers. J. Clim. 2012, 25, 687–703. [Google Scholar] [CrossRef]
- Bang, S.D.; Cecil, D.J. Constructing a Multifrequency Passive Microwave Hail Retrieval and Climatology in the GPM Domain. J. Appl. Meteorol. Climatol. 2019, 58, 1889–1904. [Google Scholar] [CrossRef]
- Punge, H.J.; Bedka, K.M.; Kunz, M.; Werner, A. A new physically based stochastic event catalog for hail in Europe. Nat. Hazards 2014, 73, 1625–1645. [Google Scholar] [CrossRef]
- Bedka, K.; Brunner, J.; Dworak, R.; Feltz, W.; Otkin, J.; Greenwald, T. Objective Satellite-Based Detection of Overshooting Tops Using Infrared Window Channel Brightness Temperature Gradients. J. Appl. Meteorol. Climatol. 2010, 49, 181–202. [Google Scholar] [CrossRef]
- Bedka, K.M. Overshooting cloud top detections using MSG SEVIRI Infrared brightness temperatures and their relationship to severe weather over Europe. Atmos. Res. 2011, 99, 175–189. [Google Scholar] [CrossRef]
- Adler, R.F.; Markus, M.J.; Fenn, D.D. Detection of severe midwest thunderstorms using geosynchronous satellite data. Mon. Weather Rev. 1985, 113, 769–781. [Google Scholar] [CrossRef]
- Dworak, R.; Bedka, K.; Brunner, J.; Feltz, W. Comparison between GOES-12 Overshooting-Top Detections, WSR-88D Radar Reflectivity, and Severe Storm Reports. Weather Forecast. 2012, 27, 684–699. [Google Scholar] [CrossRef]
- Marion, G.; Trapp, R.J.; Nesbitt, S.W. Using overshooting top area to discriminate potential for large, intense tornadoes. Geophys. Res. Lett. 2019, 46, 12520–12526. [Google Scholar] [CrossRef]
- Punge, H.J.; Bedka, K.M.; Kunz, M.; Reinbold, A. Hail frequency estimation across Europe based on a combination of overshooting top detections and the ERA-INTERIM reanalysis. Atmos. Res. 2017, 198, 34–43. [Google Scholar] [CrossRef]
- Merino, A.; Lopez, L.; Sanchez, J.L.; Garcia-Ortega, E.; Cattani, E.; Levizzani, V. Daytime identification of summer hailstorm cells from MSG data. Nat. Hazards Earth Syst. Sci. 2014, 14, 1017–1033. [Google Scholar] [CrossRef]
- Melcón, P.; Merino, A.; Luis Sanchez, J.; Lopez, L.; Hermida, L. Satellite remote sensing of hailstorms in France. Atmos. Res. 2016, 182, 221–231. [Google Scholar] [CrossRef]
- Bergen, K.J.; Johnson, P.A.; de Hoop, M.V.; Beroza, G.C. Machine learning for data-driven discovery in solid Earth geoscience. Science 2019, 363, eaau0323. [Google Scholar] [CrossRef] [PubMed]
- Bonavita, M.; Arcucci, R.; Carrassi, A.; Dueben, P.; Geer, A.J.; Le Saux, B.; Longepe, N.; Mathieu, P.-P.; Raynaud, L. Machine Learning for Earth System Observation and Prediction. Bull. Am. Meteorol. Soc. 2021, 102, E710–E716. [Google Scholar] [CrossRef]
- Dramsch, J.S. 70 years of machine learning in geoscience in review. Adv. Geophys. 2020, 61, 1–55. [Google Scholar] [CrossRef]
- Fleming, S.W.; Watson, J.R.; Ellenson, A.; Cannon, A.J.; Vesselinov, V.C. Machine learning in Earth and environmental science requires education and research policy reforms. Nat. Geosci. 2021, 14, 878–880. [Google Scholar] [CrossRef]
- Im, J.; Park, H.; Takeuchi, W. Advances in Remote Sensing-Based Disaster Monitoring and Assessment. Remote Sens. 2019, 11, 2181. [Google Scholar] [CrossRef]
- Karpatne, A.; Ebert-Uphoff, I.; Ravela, S.; Babaie, H.A.; Kumar, V. Machine Learning for the Geosciences: Challenges and Opportunities. Ieee Trans. Knowl. Data Eng. 2019, 31, 1544–1554. [Google Scholar] [CrossRef]
- Reichstein, M.; Camps-Valls, G.; Stevens, B.; Jung, M.; Denzler, J.; Carvalhais, N.; Prabhat, F. Deep learning and process understanding for data-driven Earth system science. Nature 2019, 566, 195–204. [Google Scholar] [CrossRef] [PubMed]
- Sehad, M.; Ameur, S. A multilayer perceptron and multiclass support vector machine based high accuracy technique for daily rainfall estimation from MSG SEVIRI data. Adv. Space Res. 2020, 65, 1250–1262. [Google Scholar] [CrossRef]
- Yang, Z.; Liu, P.; Yang, Y. Convective/Stratiform Precipitation Classification Using Ground-Based Doppler Radar Data Based on the K-Nearest Neighbor Algorithm. Remote Sens. 2019, 11, 2277. [Google Scholar] [CrossRef]
- Su, H.; Wu, L.; Jiang, J.H.; Pai, R.; Liu, A.; Zhai, A.J.; Tavallali, P.; DeMaria, M. Applying Satellite Observations of Tropical Cyclone Internal Structures to Rapid Intensification Forecast with Machine Learning. Geophys. Res. Lett. 2020, 47, e2020GL089102. [Google Scholar] [CrossRef]
- Kim, D.; Park, M.-S.; Park, Y.-J.; Kim, W. Geostationary Ocean Color Imager (GOCI) Marine Fog Detection in Combination with Himawari-8 Based on the Decision Tree. Remote Sens. 2020, 12, 149. [Google Scholar] [CrossRef]
- Yao, H.; Li, X.; Pang, H.; Sheng, L.; Wang, W. Application of random forest algorithm in hail forecasting over the Shandong Peninsula. Atmos. Res. 2020, 244, 105093. [Google Scholar] [CrossRef]
- Dotse, S.-Q.; Larbi, I.; Limantol, A.M.; De Silva, L.C. A review of the application of hybrid machine learning models to improve rainfall prediction. Model. Earth Syst. Environ. 2024, 10, 19–44. [Google Scholar] [CrossRef]
- Allen, J.T.; Giammanco, I.M.; Kumjian, M.R.; Punge, H.J.; Zhang, Q.; Groenemeijer, P.; Kunz, M.; Ortega, K. Understanding Hail in the Earth System. Rev. Geophys. 2020, 58, e2019RG000665. [Google Scholar] [CrossRef]
- Hou, A.Y.; Kakar, R.K.; Neeck, S.; Azarbarzin, A.A.; Kummerow, C.D.; Kojima, M.; Oki, R.; Nakamura, K.; Iguchi, T. The global precipitation measurement mission. Bull. Am. Meteorol. Soc. 2014, 95, 701–722. [Google Scholar] [CrossRef]
- Nesbitt, S.W.; Zipser, E.J.; Cecil, D.J. A census of precipitation features in the tropics using TRMM: Radar, ice scattering, and lightning observations. J. Clim. 2000, 13, 4087–4106. [Google Scholar] [CrossRef]
- Liu, C.; Zipser, E.J. The global distribution of largest, deepest, and most intense precipitation systems. Geophys. Res. Lett. 2015, 42, 3591–3595. [Google Scholar] [CrossRef]
- Liu, C.; Zipser, E.J.; Cecil, D.J.; Nesbitt, S.W.; Sherwood, S. A Cloud and Precipitation Feature Database from Nine Years of TRMM Observations. J. Appl. Meteorol. Climatol. 2008, 47, 2712–2728. [Google Scholar] [CrossRef]
- Liu, N.; Liu, C. Global distribution of deep convection reaching tropopause in 1year GPM observations. J. Geophys. Res.-Atmos. 2016, 121, 3824–3842. [Google Scholar] [CrossRef]
- Cecil, D.J.; Bang, S.D. Passive Microwave Hail Climatology Data Products. 2022. Available online: https://doi.org/10.5067/MODEL/DATA101 (accessed on 25 April 2024).
- Zhou, Z.; Zhang, Q.; Allen, J.T.; Ni, X.; Ng, C.-P. How Many Types of Severe Hailstorm Environments Are There Globally? Geophys. Res. Lett. 2021, 48, e2021GL095485. [Google Scholar] [CrossRef]
- Liu, J.; Ma, C.; Liu, C.; Qin, D.; Gu, X. An extended maxima transform-based region growing algorithm for convective cell detection on satellite images. Remote Sens. Lett. 2014, 5, 971–980. [Google Scholar] [CrossRef]
- Wu, Q.; Shou, Y.-X.; Ma, L.-M.; Lu, Q.; Wang, R. Estimation of Maximum Hail Diameters from FY-4A Satellite Data with a Machine Learning Method. Remote Sens. 2022, 14, 73. [Google Scholar] [CrossRef]
- Kononenko, I.; Simec, E.; RobnikSikonja, M. Overcoming the myopia of inductive learning algorithms with RELIEFF. Appl. Intell. 1997, 7, 39–55. [Google Scholar] [CrossRef]
- Robnik-Sikonja, M.; Kononenko, I. Theoretical and empirical analysis of ReliefF and RReliefF. Mach. Learn. 2003, 53, 23–69. [Google Scholar] [CrossRef]
- Glorot, X.; Bengio, Y. Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, Sardinia, Italy, 13–15 May 2010; pp. 249–256. [Google Scholar]
- Loh, W.Y. Classification and regression trees. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 2011, 1, 14–23. [Google Scholar] [CrossRef]
Year | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 |
---|---|---|---|---|---|---|---|---|---|---|
Direct Economic Losses (in Billion Yuan) | 344.1 | 456.2 | 276.7 | 322.7 | 463.9 | 147.5 | 168.3 | 183.4 | 282.3 | 268.7 |
Parameters | Abbreviations | Units |
---|---|---|
BT of 6.25-μm channel | BT6.25 | °C |
BT of 7.10-μm channel | BT7.10 | °C |
BT of 8.50-μm channel | BT8.50 | °C |
BT of 10.8-μm channel | BT10.8 | °C |
BT of 12.0-μm channel | BT12.0 | °C |
BT of 13.5-μm channel | BT13.5 | °C |
BTD between 6.25- and 10.8-μm channels | BTD6.25–10.8 | °C |
BTD between 7.10- and 10.8-μm channels | BTD7.10–10.8 | °C |
BTD between 6.25- and 7.10-μm channels | BTD6.25–7.10 | °C |
BTD between 8.50- and 10.8-μm channels | BTD8.50–10.8 | °C |
BTD between 12.0- and 10.8-μm channels | BTD12.0–10.8 | °C |
BTD between 13.5- and 10.8-μm channels | BTD13.5–10.8 | °C |
Convective available potential energy | CAPE | J kg−1 |
Convective inhibition | CIN | J kg−1 |
Precipitable water | PW | cm |
K index | KI | °C |
Total totals | TT | °C |
Showalter index | SI | °C |
Lifted index | LI | °C |
Boyden index | BI | unitless |
Height of 0 °C | H0 °C | km |
Height of −20 °C | H−20 °C | km |
Wet-bulb zero height | WBZ | km |
Hail growth zone (−10 to −30 °C) thickness | HGZ | km |
0–6 km vertical wind shear | VWS0–6 | m s−1 |
0–3 km vertical wind shear | VWS0–3 | m s−1 |
Storm-relative helicity | SRH | m2 s−2 |
Bulk Richardson Number | BRN | unitless |
Mean high-level (400 to 200 hpa) potential vorticity | PVH | K m2 kg−1 s−1 |
Model | Hyperparameter | Search Range | Value |
---|---|---|---|
Dtree | MaxNumSplits | 6 | 6 |
MinLeafSize | 1:1:30 | 19 | |
PP | 1 for hailstorms | 1 | |
0.1:0.05:0.5 for non-hailstorms | 0.25 | ||
BPNN | HLayersize | 20:10:40 | 20 |
EW | 1 for hailstorms | 1 | |
0.1:0.1:0.5 for non-hailstorms | 0.4 |
POD | FAR | CSI | |
---|---|---|---|
BPNN | 0.58 | 0.34 | 0.45 |
Dtree | 0.49 | 0.50 | 0.33 |
BPNN+Dtree | 0.69 | 0.48 | 0.42 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Wu, Q.; Shou, Y.-X.; Zheng, Y.-G.; Wu, F.; Wang, C.-Y. Detecting Hailstorms in China from FY-4A Satellite with an Ensemble Machine Learning Model. Remote Sens. 2024, 16, 3354. https://doi.org/10.3390/rs16183354
Wu Q, Shou Y-X, Zheng Y-G, Wu F, Wang C-Y. Detecting Hailstorms in China from FY-4A Satellite with an Ensemble Machine Learning Model. Remote Sensing. 2024; 16(18):3354. https://doi.org/10.3390/rs16183354
Chicago/Turabian StyleWu, Qiong, Yi-Xuan Shou, Yong-Guang Zheng, Fei Wu, and Chun-Yuan Wang. 2024. "Detecting Hailstorms in China from FY-4A Satellite with an Ensemble Machine Learning Model" Remote Sensing 16, no. 18: 3354. https://doi.org/10.3390/rs16183354