Presentation about the MeteoCAST very short term forecast model at the Eumetsat Conference 2013.

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 – Italy
2. GEO-K srl, via del Politecnico, 1 – 00133 Rome - Italy
2013/09/16
Eumetsat Conference 2013 – Vienna

2. Outline
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Introduction
The goal
The starting point
The model
The case studies
The rainfall estimation
The present
The future
2013/09/16
Eumetsat Conference
2013 – Vienna

3. Introduction
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

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 – Vienna

4. The goal
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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 – Vienna

5. The starting point
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The NeuCAST (Marzano et al.)
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Meteosat 7's images application
IR channel (10.8 μm) 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 – Vienna

6. The model: the multi-channels approach
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MeteoCAST: Meteorological Combined
Algorithm for Storm Tracking
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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 – Vienna

7. The model: the multi-channels model tools
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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 – Vienna

8. The model: the multi channels approach
layout
2013/09/16
Eumetsat Conference
2013 – Vienna

9. The case-studies
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The area of interest ranges from longitude 7° E
to 18° E and from latitude 36.5° N to 48° N
Training
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Validation
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2007-03-20
Test
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2006-07-24, 2006-08-13, 2006-09-14
2013-05-03
Each frame consists of 275x344 pixels
2013/09/16
Eumetsat Conference
2013 – Vienna

10. The case studies: Ensemble setup
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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 – 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 – Vienna

12. The case studies: the performance indexes
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BIAS
mε ( t k ) =

1
N points
RMSE
 1
sε ( t k ) = 
N
 points

∑ [T ( P ,t ) − T ( P ,t ) ]
est
b
i
k
b
i
k
1
2 2
est
∑ Tb ( Pi ,tk ) − Tb ( Pi ,tk ) 


[
]
Correlation index
rε (t k ) =
[
∑T ( P ,t ) − T (t ) ][T ( P ,t ) − T (t ) ]
est
b
[

∑T

2013/09/16
est
b
i
est
b
k
( Pi ,t k ) − T (t k )
est
b
k
b
i
k
] ∑T ( P ,t
[
2
Eumetsat Conference
2013 – 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 – 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 – 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 – 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
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MeteoCAST
SSD
Persistence
Eumetsat Conference
2013 – 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 – 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 – Vienna
Ch.
Ch.
Ch.
Ch.
Ch.
Ch.
Ch.
Ch.
10
11
4
5
6
7
8
9

19. The case studies: tornado over Modena
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Eumetsat Conference
2013 – Vienna

20. The case studies: tornado over Modena from
MSG
2013/09/16
Eumetsat Conference
2013 – 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 – 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 – 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 – Vienna
Ch.
Ch.
Ch.
Ch.
Ch.
Ch.
Ch.
Ch.
10
11
4
5
6
7
8
9

24. The rainfall estimation
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Use the produced synthetic images in a waterfall
manner
Some intermediate products are generated:
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CM
LST
RR
Integration with the PGE01 and PGE05
products of the NWCSAF (for calibration
purposes)
2013/09/16
Eumetsat Conference
2013 – 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 – 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 – 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 – Vienna
68.47
%

28. The present
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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 – Vienna

29. The future
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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 – Vienna

30. Acknowledgements
Thanks to the Italian Air Force
Meteorological Office
for the support
2013/09/16
Eumetsat Conference
2013 – Vienna

31. Thank you for your attention
michele.derosa@geok.it
mic_der@yahoo.it
2013/09/16
Eumetsat Conference
2013 – Vienna