meteocast: a nowcasting model to predict extreme meteorological events
DESCRIPTION
Presentation about the MeteoCAST very short term forecast model at the Eumetsat Conference 2013.TRANSCRIPT
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2013/09/16Eumetsat Conference 2013 –
Vienna
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 – Italy2. GEO-K srl, via del Politecnico, 1 – 00133 Rome - Italy
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2013/09/16Eumetsat Conference 2013 –
Vienna
Outline
Introduction The goal The starting point The model The case studies The rainfall estimation The present The future
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2013/09/16Eumetsat Conference 2013 –
Vienna
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.
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2013/09/16Eumetsat Conference 2013 –
Vienna
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.
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2013/09/16Eumetsat Conference 2013 –
Vienna
The starting point
The NeuCAST (Marzano et al.) 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)
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2013/09/16Eumetsat Conference 2013 –
Vienna
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
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2013/09/16Eumetsat Conference 2013 –
Vienna
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
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2013/09/16Eumetsat Conference 2013 –
Vienna
The model: the multi channels approach layout
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2013/09/16Eumetsat Conference 2013 –
Vienna
The case-studies
The area of interest ranges from longitude 7° E to 18° E and from latitude 36.5° N to 48° N
Training 2006-07-24, 2006-08-13, 2006-09-14
Validation 2007-03-20
Test 2013-05-03
Each frame consists of 275x344 pixels
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2013/09/16Eumetsat Conference 2013 –
Vienna
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
(nc=5, embed=6), 1 output.Pixel to project ahead
(i,j)
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2013/09/16Eumetsat Conference 2013 –
Vienna
The case studies: the benchmarks The Persistence
The Steady State Displacement (SSD)
ttt FF
vFF ttt
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2013/09/16Eumetsat Conference 2013 –
Vienna
The case studies: the performance indexes
BIAS
RMSE
Correlation index
kibkiestb
pointskε t,PTt,PT
N=tm
1
21
1 2
kibki
estb
pointskε t,PTt,PT
N=ts
21
)(
22
kbkibkestbki
estb
kbkibkestbki
estb
kε
tTt,PTtTt,PT
tTt,PTtTt,PT=tr
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2013/09/16Eumetsat Conference 2013 –
Vienna
The case studies: training set 60 mins ahead mean performance
0
0.5
1
1.5
2
MeteoCAST SSD Persistence
BIAS
Ch. 10
Ch. 11
Ch. 4
Ch. 5
Ch. 6
Ch. 7
Ch. 8
Ch. 9
K
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2013/09/16Eumetsat Conference 2013 –
Vienna
The case studies: training set 60 mins ahead mean performance
0
5
10
15
20
MeteoCAST SSD Persistence
RMSE
Ch. 10
Ch. 11
Ch. 4
Ch. 5
Ch. 6
Ch. 7
Ch. 8
Ch. 9
K
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2013/09/16Eumetsat Conference 2013 –
Vienna
The case studies: training set 60 mins ahead mean performance
0
20
40
60
80
100
MeteoCAST SSD Persistence
Correlation
Ch. 10
Ch. 11
Ch. 4
Ch. 5
Ch. 6
Ch. 7
Ch. 8
Ch. 9
%
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2013/09/16Eumetsat Conference 2013 –
Vienna
The case studies: 2007/03/20 13:30 UTC 60 mins ahead
-0.5
0
0.5
1
1.5
2
MeteoCAST SSD Persistence
BIAS
Ch. 10
Ch. 11
Ch. 4
Ch. 5
Ch. 6
Ch. 7
Ch. 8
Ch. 9
K
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2013/09/16Eumetsat Conference 2013 –
Vienna
The case studies: 2007/03/20 13:30 UTC 60 mins ahead
02468
10121416
MeteoCAST SSD Persistence
RMSE
Ch. 10
Ch. 11
Ch. 4
Ch. 5
Ch. 6
Ch. 7
Ch. 8
Ch. 9
K
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2013/09/16Eumetsat Conference 2013 –
Vienna
The case studies: 2007/03/20 13:30 UTC 60 mins ahead
0
20
40
60
80
100
MeteoCAST SSD Persistence
Correlation
Ch. 10
Ch. 11
Ch. 4
Ch. 5
Ch. 6
Ch. 7
Ch. 8
Ch. 9
%
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2013/09/16Eumetsat Conference 2013 –
Vienna
The case studies: tornado over Modena
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2013/09/16Eumetsat Conference 2013 –
Vienna
The case studies: tornado over Modena from MSG
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2013/09/16Eumetsat Conference 2013 –
Vienna
The case studies: 2013/05/03 14:00 UTC 60 mins ahead
0
0.5
1
1.5
2
2.5
MeteoCAST SSD Persistence
BIAS
Ch. 10
Ch. 11
Ch. 4
Ch. 5
Ch. 6
Ch. 7
Ch. 8
Ch. 9
K
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2013/09/16Eumetsat Conference 2013 –
Vienna
The case studies: 2013/05/03 14:00 UTC 60 mins ahead
02468
10121416
MeteoCAST SSD Persistence
RMSE
Ch. 10
Ch. 11
Ch. 4
Ch. 5
Ch. 6
Ch. 7
Ch. 8
Ch. 9
K
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2013/09/16Eumetsat Conference 2013 –
Vienna
The case studies: 2013/05/03 14:00 UTC 60 mins ahead
01020304050607080
MeteoCAST SSD Persistence
Correlation
Ch. 10
Ch. 11
Ch. 4
Ch. 5
Ch. 6
Ch. 7
Ch. 8
Ch. 9
%
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2013/09/16Eumetsat Conference 2013 –
Vienna
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)
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2013/09/16Eumetsat Conference 2013 –
Vienna
The rainfall estimation: the model layout
DEMMSGBTs
GLMCloud Mask
LSTEstimator
RR Classifier
RREstimator
First level
Second level
Third level
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2013/09/16Eumetsat Conference 2013 –
Vienna
The rainfall estimation : tornado over Modena
Performance Indexes 60 Min
BIAS 2.17 mm/h
RMSE 10.49 mm/h
Correlation 53.00 %
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2013/09/16Eumetsat Conference 2013 –
Vienna
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 68.47 %
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2013/09/16Eumetsat Conference 2013 –
Vienna
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).
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2013/09/16Eumetsat Conference 2013 –
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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
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2013/09/16Eumetsat Conference 2013 –
Vienna
Acknowledgements
Thanks to the Italian Air Force Meteorological Office
for the support
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2013/09/16Eumetsat Conference 2013 –
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Thank you for your attention