Meteocast: a real time nowcasting system

Geostationary Multispectral
Imagery Using Neural Models For
Meteorological Applications

Dr. Michele de Rosa1,2,Prof. Frank S. Marzano1,
Dr. Antonio Eleuteri4, Dr. Giancarlo Rivolta3
1 Sapienza University of Rome, via Eudossiana, 18 - 00184 Rome – Italy
2 T.R.S. S.p.A., via della Bufalotta, 378 – 00139 Rome – Italy
3 Logica UK at the European Space Agency (ESA) - ESRIN (EOP-GTR), Po-Box 64 - 00044
Frascati (RM) - Italy
4 Royal Liverpool & Broadgreen University Hospitals NHS Trust (RLBUHT) and the University of
Liverpool, Prescot Street, L7 8XP, Liverpool, UK

RMets Conference 2011 - Exeter, 2011/06/28

Summary
Introduction
The problem
The starting point
The model
The case studies
The rainfall estimation
The future
The near future

RMets Conference 2011, Exeter UK

Introduction

Precipitation is a key factor in regulating equilibrium
and life on Earth. It is a crucial geophysical
parameter and one of the main actors in the global
water cycle.
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.


Introduction: the global hydrological cycle


Introduction: The rainfall remote sensing

The remote sensing provides an indirect measurements of
rainfall.
It is done through measurements of the radiative properties of
the hydrometeors (i.e. inferring cloud/rain structures by
measuring their radiative properties), both in a passive way (i.e.
measuring the radiation spontaneously emitted by the
hydrometeors and sensed by a radiometer) as in an active way
(i.e. inferring the rain/cloud structure by measuring the reflected
portion of the radiation emitted by a radar towards the
precipitating cloud).
Visible to IR estimates of rainfall are only indirect because they
try to infer the underlying cloud structure from the top-of-the-
cloud appearance


Introduction: The MSG
(Meteosat Second Generation)

Geostationary satellite
First mission 1977 (Meteosat 1)
12 Channels (3 Vis,8 IR,1 HRis)
Vis and IR resolution 3712x3712
HRis resolution 11136x5568
15 minutes observation period
About 3 x 3 Km of resolution


Introduction: The MSG channels
Channel Spectral Band(µm) Spectral Band(µm) M application
ain
λ cen λ min λ max
1 VIS0.6 0,635 0,56 0,71 Surface, clouds, windfields
2 VIS0.8 0,81 0,74 0,88 Surface, clouds, windfields
3 N 1.6
IR 1,64 1,5 1,78 Surface, cloudphase
4 IR3.9 3,9 3,48 4,36 Surface, clouds, windfields
5 W 6.2
V 6,25 5,35 7,15 W vapor, highlevel clouds, atm
ater ospheric instability
6 W 7.3
V 7,35 6,85 7,85 W vapor, atm
ater ospheric instability
7 IR8.7 8,7 8,3 9,1 Surface, clouds, atmospheric instability
8 IR9.7 9,66 9,38 9,94 Ozone
9 IR10.8 10,8 9,8 11,8 Surface, clouds, windfields, atmospheric instability
10 IR12.0 12 11 13 Surface, clouds, atmospheric instability
11 IR13.4 13,4 12,4 14,4 Cirrus cloudheight, atm ospheric instability
12 HRV Broadband(about 0.4 – 1.1 Surface, clouds


The problem

Develop a model based on the MSG frames
to make nowcasts (from 30 MINs to 60 MINs)
about the rainfield.
The model would predict the MSG IR
channels in order to predict the rainfield.
The model would be flexible, accurate and
quick.


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 IR channel nowcast
Model for IR-MW mapping (Neural net)


The model: the multichannel approach
MSG's images application
IR channels (4,5,6,7,8,9,10,11) nowcast (30 min-1
Hr)
Rain estimation from MW and IR sources, using
the IR channels nowcast
Bayesian approach to train the model
GLM nowcast model
Model for IR-to-Rain Rate mapping


The model: The multichannel 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 nowcast the next frame
The Dynamically Averaging Network (DAN)
Ensemble


The model: the Bayesian approach

The bayesian framework was developed by
David J. C. Mackay in the context of the
neural networks.
The framework implements the Occam Razor
in order to penalize complex models vs.
simple models.
The framework applies the evidence
approach to penalize the complex models.
The framework is general.


The model: the Ensemble of models

An ensemble is a composition of different
models.
In general, the ensemble is used to average
between different models.
The output of an ensemble minimizes the
average error with respect to the ensemble’s
components (see Bishop C. M.).


The model: the different kinds of
Ensemble
The GEM (General Ensemble Model)
n
fGEM = ∑αi fi (x)
i =1

The BEM (Basic Ensemble Model)
1 n
f BEM = ∑ fi (x)
n i=1


The model: the DAN Ensemble

Let y be the output of a neural net and let p y be the
probability associated to y.
Let the DAN (Dynamically Averaging Networks)
ensemble be defined as:
n
f DAN = ∑ wi yi
Certainty
i =1
where:
c ( p yi )  py
 p y ≥ 0.5
wi = and c( p y ) = 
1 − p y otherwise
n

∑ c( p yj ) 
j =1


The model: the probability computation

If Errn (y) is the estimated error bar related to
i, j

the prediction y of the pixel (i,j) of the
ensemble’s component n then:
Erri nj ( y)
p in, j ( y) =
,
n

∑ ,
Erri nj ( y)
i =1

so that:
n
0 ≤ pin, j ( y ) ≤ 1 and ∑p n
i, j ( y) = 1
i =1


The model: the multichannel approach
layout


The case-studies

The area analyzed is East longitude ranging
from 7°to 18 °and North Latitude ranging
from 36.5°to 48 °
2006-07-24
2006-08-13
2006-09-14
2007-03-20 (for generalization test)
Each frame consists of 275x344 pixels


The case studies


The case studies: the performance
indexes

m ε (t k ) = ∑ [T (Pi , t k ) − Tb (Pi , t k )]
1 est
BIAS (K) b
N points
1
 1 2 2
RMSE (K) s ε (t k ) = 
N ∑ [T b
est
(Pi , t k ) − T b (Pi , t k ) 
]
 points 

Correlation rε ( t k ) =
∑ [T b
est
( Pi , t k ) − T best (t k ) ][T b (Pi , t k ) − T b (t k )]
1
index (%) 
 ∑ Tb

[ est
( Pi , t k ) − Tb est
(t k ) ] ∑ [T b (Pi , t k ) −
2
T b (t k )] 2 2




The case studies: the benchmarks

The Persistence Ft + ∆t = Ft
The Steady State Displacement
(SSD) Ft + ∆t = Ft + v
r


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
Each bayesian GLM consists of 726 inputs
(nc=5, embed=6), 1 output.
9 components and 27 GLMs


The case studies:
30 mins ahead mean values
B S
IA

1.4
1.2 C .1
h 0
1 C .1
h 1
K 0 .8 C .4
h
0.6 C .5
h
0.4 C .6
h
0.2 C .7
h
0 C .8
h
-0.2 C .9
h
D N
A SD
S P rs te c
e is n e

The case studies :
R S
ME

12
C .1
h 0
10
C .1
h 1
8 C .4
h
K
6 C .5
h
4 C .6
h
C .7
h
2
C .8
h
0 C .9
h
D N
A SD
S P rs te c
e is n e

The case studies:
C rre tio
o la n

94
C .1
h 0
92
C .1
h 1
90
C .4
h
% 88
C .5
h
86
C .6
h
84
C .7
h
82 C .8
h
80 C .9
h
D N
A SD
S P rs te c
e is n e


The case studies:
B S
IA

2
C .1
h 0
1.5 C .1
h 1
C .4
h
K
1 C .5
h
C .6
h
0.5 C .7
h
C .8
h
0 C .9
h
D N
A SD
S P rs te c
e is n e


The case studies :
R S
ME

20
C .1
h 0
15 C .1
h 1
K C .4
h
10 C .5
h
C .6
h
5 C .7
h
C .8
h
0 C .9
h
D N
A SD
S P rs te c
e is n e

The case studies:
C rre tio
o la n

10
0
C .1
h 0
80 C .1
h 1
% 60 C .4
h
C .5
h
40 C .6
h
20 C .7
h
C .8
h
0 C .9
h
D N
A SD
S P rs te c
e is n e


The case studies:
case-study 13:30 2007/03/20 UTC
30 mins ahead BIA S

1.2
C .1
h 0
1
C .1
h 1
0.8
C .4
h
K 0.6
C .5
h
0.4
C .6
h
0.2
C .7
h
0 C .8
h
-0.2 C .9
h
D N
A SD
S P rs te c
e is n e


The case studies :
case-study 13:30 2007/03/20 UTC
30 mins ahead RMSE
14
C .1
h 0
12
C .1
h 1
10
C .4
h
K 8
C .5
h
6
C .6
h
4
C .7
h
2 C .8
h
0 C .9
h
D N
A SD
S P rs te c
e is n e


The case studies:
case-study 13:30 2007/03/20 UTC
30 mins ahead Correlation
10
0
C .1
h 0
80 C .1
h 1
% 60 C .4
h
C .5
h
40 C .6
h
20 C .7
h
C .8
h
0 C .9
h
D N
A SD
S P rs te c
e is n e

The case studies:
case-study 13:30 2007/03/20 UTC
60 mins ahead BIA S

2
C .1
h 0
1.5 C .1
h 1
1 C .4
h
K
C .5
h
0.5 C .6
h
0 C .7
h
C .8
h
-0.5 C .9
h
D N
A SD
S P rs te c
e is n e


The case studies :
case-study 13:30 2007/03/20 UTC
60 mins ahead RMSE
16
14 C .1
h 0
12 C .1
h 1
K 10 C .4
h
8 C .5
h
6 C .6
h
4 C .7
h
2 C .8
h
0 C .9
h
D N
A SD
S P rs te c
e is n e

The case studies:
case-study 13:30 2007/03/20 UTC
60 mins ahead Correlation
10
0
C .1
h 0
80 C .1
h 1
% 60 C .4
h
C .5
h
40 C .6
h
20 C .7
h
C .8
h
0 C .9
h
D N
A SD
S P rs te c
e is n e

The case studies: real statistics
01/03/2010 - 31/03/2010
Corr(Model)>Corr(Persistence) = 90,47%
Corr(Model)>Corr(SSD) = 85,53%
Corr(Model)>Corr(Persistence) = 92,23%
(Cloud pixels)
Corr(Model)>Corr(SSD) = 87,65% (Cloud
pixels)
Corr(Model vs meanTB) = 99,37%
Computation time about 800 secs (13 mins).


Conclusions (1)

The model is flexible.
The ensemble nowcast performances are
very good.
The model seems to generalize very well.
A procedure, to find the optimal frame size in
order to reduce the prediction error, has been
found.


The rainfall estimation: the components

The Eumetsat Multi-sensor Precipitation Estimate
(product used to validate the model)
The GLM Cloud Mask product used to filter the “no
rain” pixels. This model uses the 4,9,10 MSG
channels.
A Land Surface Temperature (LST) estimator. The
estimator uses the 9,10,11 MSG channels.
A MLP Neural Net rain classifier. The classifier uses
the 9,10,11 MSG channels.
A MLP Neural Net rain estimator. The estimator
uses the 4,5,9,10 MSG channels.


The rainfall estimation: the model layout


The rainfall estimation: the classes

Class 1. Light rain: 0 < RRMax ≤ 2 mm/h
Class 2. Moderate rain: 2 mm/h < RRMax ≤
10 mm/h
Class 3. Heavy rain: 10 < RRMax ≤ 50 mm/h
Class 4. Violent rain: RRMax > 50 mm/h


The rainfall estimation: a case study
10:15 2010/01/26 UTC: thunderstorm over Central
Italy


A case study: 10:15 2010/01/26 UTC -
the rainfall classification 30 Mins ahead.

Predicted/True Light Moderate Heavy Violent POD

Light 40551 112 261 11 99.06%

Moderate 1978 166 759 70 5.58%

Heavy 435 73 895 119 58.80%

Violent 48 9 262 388 54.88%

Classification Rate 91.03%


A case study: 10:15 2010/01/26 UTC -
the rainfall estimation 30 Mins ahead.

Performance Indexes 30 Min
BIAS 2.56 mm/h
RMSE 10.29 mm/h
Correlation 76.27 %


A case study : 10:15 2010/01/26 UTC -
the rainfall classification 60 Mins ahead.

Predicted/True Light Moderate Heavy Violent POD

Light 41082 24 80 0 99.75%

Moderate 2795 34 138 0 1.15%

Heavy 1359 33 132 0 8.66%

Violent 289 25 325 68 9.62%

Classification Rate 89.07%


A case study: 10:15 2010/01/26 UTC -
the rainfall estimation 60 Mins ahead.

Performance Indexes 60 Min
BIAS 1.33 mm/h
RMSE 9.05 mm/h
Correlation 68.47 %


Conclusions (2)

The rainfall classifier is very sensitive to the
prediction error.
The rainfall estimator works better on
“violent” events.
The estimator performs poor on “Light” and
“Moderate” events (due to the model
structure).
It should be possible to generate a lot of
meteorological product using the ensemble
model.

The future

Characterize better the rainfall estimator in order to
perform better.
In order to nowcast the rainfield, it should be possible to
correlate the MSG data with Meteorological Radar.
Apply the multichannel to a real-time system.
Try to apply the frame prediction in order to nowcast
other meteorological entities (for example using the SAF
suite).
Continue the TRS collaboration in order to enrich the
Weather Products functionalities and to develop new
products.


The near future

Work in progress: the development of a software to
convert the forecasts into kml files in order to load
the forecast with Google Earth.
Our forecasts, in kml format, will be published on our
web sites www.mondometeo.org (italian) and
www.kwos.org (english).
Work in progress: the development of a software,
named MeteoCast and running on Android platform,
to have weather information (Rain, Thunderstorm
etc.) about the area where the user is.


The near future : A KML example


Collaborations

All people and/or organizations,
interested in our work, are welcome.


Bibliography
Imran Maqsood et al., An ensemble of neural networks for weather forecasting,
Neural Comput & Applic (2004) 13: 112–122
Yinyin Liu et al., OPTIMIZING NUMBER OF HIDDEN NEURONS IN NEURAL
NETWORKS, Proceedings of the 25th IASTED Internation multiconference February 12-
14, 2007, Innsbruck, Austria
George Dahl et al., PARALLELIZING NEURAL NETWORK TRAINING FOR CLUSTER
SYSTEMS, Proceedings of the 25th IASTED Internation multiconference February 12-14,
2007, Innsbruck, Austria
Frank S. Marzano et al., Rainfall Nowcast from Multi-Satellite Passive Rainfall
Nowcast from Multi-Satellite Passive
Cao, L., Pratical Method for Determining the Minimum Embedding Dimension of a
Scalar Time Series.
Jollife I. T., Principal Component Analysis, New York: Springer-Verlag
Kohonen T., Self-Organized formation of topology correct feature maps, Biological
Cybernetics 43, 59-69.
MacKay, D. J. C., A Practical Bayesian Framework for Backpropagation Networks,
Neural Computation 1992 vol.4 n° pags. 448-472.
.3
MacKay, D. J. C., The Evidence Framework Applied to Classification Networks, Neural
Computation 1992 vol.4 n° pags. 698-714.
.5
P.M. Granitto, P.F. Verdes, H.A. Ceccatto, Neural Networks Ensemble: Evaluation of
Aggregation Algorithms, Elsevier Science 2005.
Bishop C. M., Neural Networks for Pattern recognition, Oxford Press 1995, ISBN 0-19-
853864-2


Thanks for your attention.
mic_der@yahoo.it


Meteocast: a real time nowcasting system

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