MeteoCAST: a nowcasting model to predict extreme meteorological events
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The document describes MeteoCAST, a neural ensemble nowcasting model that uses geostationary multispectral imagery to predict rainfall and severe weather events up to 60 minutes in advance. MeteoCAST was developed to help manage environmental risks from heavy precipitation. It uses infrared channels from MSG satellites as input and applies principal component analysis and Bayesian modeling to generate nowcasts. Case studies show MeteoCAST outperforms persistence and steady-state displacement benchmarks in predicting rainfall and tornado events. Current applications include a website and mobile services providing near real-time forecasts for Italy, Switzerland and Austria. Future work involves integrating cell tracking and improving performance on high-impact weather.
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MeteoCAST: a nowcasting model to predict extreme meteorological events
- 1. MeteoCAST: A Neural Ensemble Nowcasting Model based on Geostationary Multispectral Imagery for Hydro-Meteorological Applications Dr. Michele de Rosa1,2, Prof. Frank S. Marzano1 1. ¡°Sapienza¡± University of Rome, via Eudossiana, 18 - 00184 Rome ¨C Italy 2. GEO-K srl, via del Politecnico, 1 ¨C 00133 Rome - Italy 2013/09/16 Eumetsat Conference 2013 ¨C Vienna
- 2. Outline ? ? ? ? ? ? ? ? Introduction The goal The starting point The model The case studies The rainfall estimation The present The future 2013/09/16 Eumetsat Conference 2013 ¨C Vienna
- 3. Introduction ? ? ? A relevant part of environmental risk can be ascribed to meteorological severe events with high precipitation rate. Heavy precipitation associated to severe weather may cause serious damages in terms of economic losses and, in extreme cases, of human life losses. Managing the environmental risk due to precipitation is strictly linked to monitoring and understanding the storms that produces hazards such as flash floods. 2013/09/16 Eumetsat Conference 2013 ¨C Vienna
- 4. The goal ? ? ? Develop a model based on the MSG frames to nowcast (from 30 Mins to 60 Mins) the rain field. The model should predict the MSG IR channels in order to predict the rain field. The model should be flexible, accurate and quick. 2013/09/16 Eumetsat Conference 2013 ¨C Vienna
- 5. The starting point ? The NeuCAST (Marzano et al.) ? ? ? ? Meteosat 7's images application IR channel (10.8 ¦Ìm) nowcast (30 mins) Rain estimation from MW and IR sources, using the nowcasting of the IR channels Model for IR-RR mapping (Neural net) 2013/09/16 Eumetsat Conference 2013 ¨C Vienna
- 6. The model: the multi-channels approach ? MeteoCAST: Meteorological Combined Algorithm for Storm Tracking ? ? ? ? ? Application on MSG images IR channels (4,5,6,7,8,9,10,11) nowcasting from 30 mins to 60 mins Bayesian approach to train the model GLM nowcasting model Model for IR-to-Rain Rate mapping 2013/09/16 Eumetsat Conference 2013 ¨C Vienna
- 7. The model: the multi-channels model tools ? ? ? ? Cao¡¯s method to find the optimal temporal window PCA (Principal Component Analysis) to reduce the number of information sources: the 8 IR channels are replaced by a linear combination of them. Bayesian model to make nowcasting about the next MSG image The Dynamically Averaging Network (DAN) Ensemble 2013/09/16 Eumetsat Conference 2013 ¨C Vienna
- 8. The model: the multi channels approach layout 2013/09/16 Eumetsat Conference 2013 ¨C Vienna
- 9. The case-studies ? ? The area of interest ranges from longitude 7¡ã E to 18¡ã E and from latitude 36.5¡ã N to 48¡ã N Training ? ? Validation ? ? 2007-03-20 Test ? ? 2006-07-24, 2006-08-13, 2006-09-14 2013-05-03 Each frame consists of 275x344 pixels 2013/09/16 Eumetsat Conference 2013 ¨C Vienna
- 10. The case studies: Ensemble setup ? ? ? ? 3 GLMs for each case-study: one GLM for the lower correlation frame, one for the higher correlation frame and one for the median correlation frame (like the worst, best and mean case in computer science). 3 PCA channels 9 components and 27 GLMs Each bayesian GLM consists of 726 inputs Pixel to project ahead (i,j) (nc=5, embed=6), 1 output. 2013/09/16 Eumetsat Conference 2013 ¨C Vienna
- 11. The case studies: the benchmarks ? The Persistence Ft + ?t = Ft ? The Steady State Displacement (SSD) Ft + ?t 2013/09/16 ? = Ft + v Eumetsat Conference 2013 ¨C Vienna
- 12. The case studies: the performance indexes ? BIAS m¦Å ( t k ) = ? 1 N points RMSE ? 1 s¦Å ( t k ) = ? ?N ? points ? ¡Æ [T ( P ,t ) ? T ( P ,t ) ] est b i k b i k 1 2 ?2 est ¡Æ Tb ( Pi ,tk ) ? Tb ( Pi ,tk ) ? ? ? [ ] Correlation index r¦Å (t k ) = [ ¡ÆT ( P ,t ) ? T (t ) ][T ( P ,t ) ? T (t ) ] est b [ ? ?¡ÆT ? 2013/09/16 est b i est b k ( Pi ,t k ) ? T (t k ) est b k b i k ] ¡ÆT ( P ,t [ 2 Eumetsat Conference 2013 ¨C Vienna b i b k ) ? Tb (t k ) ] 2 k 1 ?2 ? ?
- 13. The case studies: training set 60 mins ahead mean performance BIAS 2 1.5 1 K 0.5 0 2013/09/16 MeteoCAST SSD Persistence Eumetsat Conference 2013 ¨C Vienna Ch. Ch. Ch. Ch. Ch. Ch. Ch. Ch. 10 11 4 5 6 7 8 9
- 14. The case studies: training set 60 mins ahead mean performance RMSE 20 15 10 K 5 0 2013/09/16 MeteoCAST SSD Persistence Eumetsat Conference 2013 ¨C Vienna Ch. Ch. Ch. Ch. Ch. Ch. Ch. Ch. 10 11 4 5 6 7 8 9
- 15. The case studies: training set 60 mins ahead mean performance Correlation 100 80 60 % 40 20 0 2013/09/16 MeteoCAST SSD Persistence Eumetsat Conference 2013 ¨C Vienna Ch. Ch. Ch. Ch. Ch. Ch. Ch. Ch. 10 11 4 5 6 7 8 9
- 16. The case studies: 2007/03/20 13:30 UTC 60 mins ahead BIAS 2 1.5 1 K 0.5 0 -0.5 2013/09/16 MeteoCAST SSD Persistence Eumetsat Conference 2013 ¨C Vienna Ch. Ch. Ch. Ch. Ch. Ch. Ch. Ch. 10 11 4 5 6 7 8 9
- 17. The case studies: 2007/03/20 13:30 UTC 60 mins ahead RMSE 16 14 12 10 K 8 6 4 2 0 2013/09/16 MeteoCAST SSD Persistence Eumetsat Conference 2013 ¨C Vienna Ch. Ch. Ch. Ch. Ch. Ch. Ch. Ch. 10 11 4 5 6 7 8 9
- 18. The case studies: 2007/03/20 13:30 UTC 60 mins ahead Correlation 100 80 60 % 40 20 0 2013/09/16 MeteoCAST SSD Persistence Eumetsat Conference 2013 ¨C Vienna Ch. Ch. Ch. Ch. Ch. Ch. Ch. Ch. 10 11 4 5 6 7 8 9
- 19. The case studies: tornado over Modena 2013/09/16 Eumetsat Conference 2013 ¨C Vienna
- 20. The case studies: tornado over Modena from MSG 2013/09/16 Eumetsat Conference 2013 ¨C Vienna
- 21. The case studies: 2013/05/03 14:00 UTC 60 mins ahead BIAS 2.5 2 1.5 K 1 0.5 0 2013/09/16 MeteoCAST SSD Persistence Eumetsat Conference 2013 ¨C Vienna Ch. Ch. Ch. Ch. Ch. Ch. Ch. Ch. 10 11 4 5 6 7 8 9
- 22. The case studies: 2013/05/03 14:00 UTC 60 mins ahead RMSE 16 14 12 10 8 K 6 4 2 0 2013/09/16 MeteoCAST SSD Persistence Eumetsat Conference 2013 ¨C Vienna Ch. Ch. Ch. Ch. Ch. Ch. Ch. Ch. 10 11 4 5 6 7 8 9
- 23. The case studies: 2013/05/03 14:00 UTC 60 mins ahead Correlation 80 70 60 50 40 % 30 20 10 0 2013/09/16 MeteoCAST SSD Persistence Eumetsat Conference 2013 ¨C Vienna Ch. Ch. Ch. Ch. Ch. Ch. Ch. Ch. 10 11 4 5 6 7 8 9
- 24. The rainfall estimation ? ? Use the produced synthetic images in a waterfall manner Some intermediate products are generated: ? ? ? ? CM LST RR Integration with the PGE01 and PGE05 products of the NWCSAF (for calibration purposes) 2013/09/16 Eumetsat Conference 2013 ¨C Vienna
- 25. The rainfall estimation: the model layout MSG BTs First level Second level Third level 2013/09/16 DEM LST Estimator GLM Cloud Mask RR Classifier RR Estimator Eumetsat Conference 2013 ¨C Vienna
- 26. The rainfall estimation : tornado over Modena Performance Indexes 60 Min BIAS mm/h RMSE 10.49 mm/h Correlation 2013/09/16 2.17 53.00 % Eumetsat Conference 2013 ¨C Vienna
- 27. The rainfall estimation: a static case 2010/01/26 10:15 UTC - 60 Mins ahead. Performance Indexes 60 Min BIAS 1.33 mm/h RMSE 9.05 mm/h Correlation 2013/09/16 Eumetsat Conference 2013 ¨C Vienna 68.47 %
- 28. The present ? ? ? ? ? The www.mondometeo.org website publishes the near real time outputs of the MeteoCAST model The KMZ service The Augmented Reality service The Twitter service Covered countries: Italy, Swiss, Austria (almost all covered) and Brazil (Sao Paulo region). 2013/09/16 Eumetsat Conference 2013 ¨C Vienna
- 29. The future ? ? ? ? ? ? CellTrack integration (attend the talk of Davide Melfi tomorrow morning) RSS integration in order to improve the performance on heavy dynamic events Integration with the NWCSAF v.2013 Synthetic images (extended to VIS) as input to the NWCSAF Coverage of other countries: Africa and South America Extension to other satellites: GOES and MTSAT 2013/09/16 Eumetsat Conference 2013 ¨C Vienna
- 30. Acknowledgements Thanks to the Italian Air Force Meteorological Office for the support 2013/09/16 Eumetsat Conference 2013 ¨C Vienna
- 31. Thank you for your attention michele.derosa@geok.it mic_der@yahoo.it 2013/09/16 Eumetsat Conference 2013 ¨C Vienna