A neural network PMW / IR combined procedure for short

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University of
Birmingham,
UK
Nal. Council
of Research,
Italy
University of
L’Aquila,
Italy
1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP
Madrid, 23 – 27 September 2002
A Neural Network
PMW/IR Combined Procedure for
Short Term/Small Area
Rainfall Estimates
Francisco J. Tapiador & Chris Kidd
University of Birmingham, UK
Vincenzo Levizzani
National Council of Research, Italy
Frank S. Marzano
University of L’Aquila, Italy
University of
Birmingham,
UK
Outline
•
Highlights
University of
L’Aquila,
Italy
Neural Nets
Case Study
1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP
Madrid, 23 – 27 September 2002
Products
Objectives of today’s presentation
1.
2.
3.
•
Nal. Council
of Research,
Italy
Present a methodology of data fusion of IR and PMW data at global scale:
• Short term, large coverage and high resolution rainfall estimates
• Methodology to be applied to MSG (soon) and GPM products
Assess the quality of these estimates:
• Intercomparison / Validation: HM method
• Down-top approach
Present further research and operative products schedule
Scheme:
–
–
–
Some comments on Neural Nets
Histogram matching
Validation / Intercomparison case study:
•
–
Global research products
•
•
–
Andalusia, Spain: 3 months of 30 minutes rain gauge data for validation
Global IR – derived estimates
METEOSAT - derived estimates
Further work in this line
Future work
Nal. Council
of Research,
Italy
University of
Birmingham,
UK
Outline
Highlights
Neural Nets
University of
L’Aquila,
Italy
Case Study
1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP
Madrid, 23 – 27 September 2002
Products
Future work
• Highlights
– Why fuse PMW and IR?
• Direct response vs indirect relationship
• “Bad” spatial and temporal resolutions vs geostationary capabilities
• Re-inforce the strengths and avoid the weaknesses
– Inputs processing
• IR data from the Global IR database (Janowiak et al 2001) and EUMETSAT archive
• PMW Rainfall retrieval based upon Kidd&Barrett SSM/I algorithm:
–
–
V19-V85 or H19-H85 combination over ocean and over land
Polarization Corrected Temperatures (PCT) over coast
• Gauge processing: point to area estimates using maximum entropy interpolation
• Histogram matching and GPI calculation for inter-comparison
– Neural nets
 Inputs selection
 Model selection
 Inversion procedures
University of
Birmingham,
UK
Outline
Nal. Council
of Research,
Italy
Highlights
Neural Nets
University of
L’Aquila,
Italy
Case Study
1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP
Madrid, 23 – 27 September 2002
Products
Neural Networks
Future work
University of
Birmingham,
UK
Outline
Nal. Council
of Research,
Italy
Highlights
Neural Nets
University of
L’Aquila,
Italy
Case Study
1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP
Madrid, 23 – 27 September 2002
Products
Future work
• Neural Networks
• NN works fairy well in rainfall estimation
– Operative system: PERSIANN (Sooroshian et al 2000)
– Bellerby et al. 2000, etc.
• Neural Nets are not black-boxes
– It is possible to make an objective NN selection (Murata et al 1994)
– There are inversion procedures to investigate inside
– They allow both deterministic and probabilistic approach
• Some advantages over other methods
– Any function (Dirichlet’s, not pathological function) can be
approximate with an arbitrary degree of accuracy with a NN:
Universal Aproximator.
– An easy method to simulate complex physical models in a quick
(operative) way.
University of
Birmingham,
UK
Outline
Nal. Council
of Research,
Italy
Highlights
• Input selection
Neural Nets
University of
L’Aquila,
Italy
Case Study
1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP
Madrid, 23 – 27 September 2002
Products
Future work
University of
Birmingham,
UK
Outline
Nal. Council
of Research,
Italy
Highlights
Neural Nets
University of
L’Aquila,
Italy
Case Study
Correlations for some simple models
1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP
Madrid, 23 – 27 September 2002
Products
Future work
Nal. Council
of Research,
Italy
University of
Birmingham,
UK
Outline
Highlights
Neural Nets
University of
L’Aquila,
Italy
Case Study
1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP
Madrid, 23 – 27 September 2002
Products
Future work
• Several NN architectures
 Hopfield nets
 SOM (cloud characterization)+(GOES data)
 Multilayer Perceptron (MLP)
 Adaptative Resonance Theory Nets (Grossberg 1969, Carpenter et al
1997)
 ART1 and ART2
 ARTMAP
 Distributed ARTMAP
 Fuzzy ARTMAP (including a voting procedure (ref))
University of
Birmingham,
UK
Outline
Nal. Council
of Research,
Italy
Highlights
Neural Nets
• Model selection: Results
University of
L’Aquila,
Italy
Case Study
1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP
Madrid, 23 – 27 September 2002
Products
Future work
University of
Birmingham,
UK
Outline
Nal. Council
of Research,
Italy
Highlights
University of
L’Aquila,
Italy
Neural Nets
1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP
Madrid, 23 – 27 September 2002
Case Study
Products
Future work
• Model selection into MLP
• Calculate (not guess) the number of neurons in the hidden layer
• Network information criterion (NIC) (Murata et al. 1994)

1
tr BA
NIC   log Lwˆ  
n
n
1

n
ŵ

2
log L
Number of observations
Set of parameters
Gradient
Hessian
Estimated maximum log likelihood

A  E  2 log Li


B  E  log Li  log L' i
• This allow a conscious design of the net based on
Information Theory results

University of
Birmingham,
UK
Outline
Nal. Council
of Research,
Italy
Highlights
University of
L’Aquila,
Italy
Neural Nets
Case Study
1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP
Madrid, 23 – 27 September 2002
Products
Future work
• Research after training: model inversion
 I ih 
y  f x, W    w g   w j  xi
i 1
 j 0

H
h
i

min ex  f x,W   o*
x ki 1  x ki  
e
 x ki
e
j
 x ki


 j  o j o j  o*j 
 j   o j    j w j ,m
jH ,O
• What kind of inputs generate an output?: insight
into precipitation processes at IR-focus
University of
Birmingham,
UK
Outline
Nal. Council
of Research,
Italy
Highlights
Neural Nets
University of
L’Aquila,
Italy
Case Study
1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP
Madrid, 23 – 27 September 2002
Products
Histogram Matching
Future work
University of
Birmingham,
UK
Outline
Nal. Council
of Research,
Italy
Highlights
Neural Nets
University of
L’Aquila,
Italy
Case Study
1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP
Madrid, 23 – 27 September 2002
Products
Future work
University of
Birmingham,
UK
Outline
Nal. Council
of Research,
Italy
Highlights
Neural Nets
University of
L’Aquila,
Italy
Case Study
Validation
(case study)
1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP
Madrid, 23 – 27 September 2002
Products
Future work
Nal. Council
of Research,
Italy
University of
Birmingham,
UK
Outline
Highlights
Neural Nets
• Case study data:
– Global IR (Meteosat 5)
– DMSP SSM/I
– 30 min gauge validation data
• Resolutions:
– Spatial:
– Temporal:
4 Km
30 min
• Coverage:
– Andalusia (Spain)
– Oct-Dec 2001
University of
L’Aquila,
Italy
Case Study
1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP
Madrid, 23 – 27 September 2002
Products
Future work
University of
Birmingham,
UK
Outline
Nal. Council
of Research,
Italy
Highlights
• Methodology
Neural Nets
University of
L’Aquila,
Italy
Case Study
1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP
Madrid, 23 – 27 September 2002
Products
Future work
University of
Birmingham,
UK
Outline
•
Nal. Council
of Research,
Italy
Highlights
Neural Nets
University of
L’Aquila,
Italy
Case Study
1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP
Madrid, 23 – 27 September 2002
Products
Future work
What means “field truth” in satellite estimates validation?
–
–
Point estimates: more close to the truth AGL
Areal interpolations: encompassing errors and odd effects
Nal. Council
of Research,
Italy
University of
Birmingham,
UK
Outline
Highlights
University of
L’Aquila,
Italy
Neural Nets
Case Study
1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP
Madrid, 23 – 27 September 2002
Products
Future work
Maximum Entropy Interpolation
The (theoretically) less-biased interpolation method available: an appropriate base to compare
1) Maximize the entropy function (using variational methods)
 
m
m

S Z    f  z1 ,  , z n  log f  z1 ,  , z n  dz1  dz n
n
Constraints
i 1
m
i 1
m
  f z
,  , z n  dz1  dz n  1
 f z
,  , z n g k  z1 ,  , z n  dz1  dz n 
i 1
m
n
i 1
n
1
i 1
m
i 1
1
2) Solving…
f z 0 , z1 ,, z n  
1
2 
n 1


1
2
ck
k  1,  , r

z   i  z j   j 
 1 n n
exp   aij i

i
 j 

 2 i 0 j 0
2) Which means that we can solve the computational problems using a
simple spherical kriging
University of
Birmingham,
UK
Outline
Nal. Council
of Research,
Italy
Highlights
Neural Nets
Point measures (average)
Small intercomparison of
interpolation methods
(Niger 2000 and Andalusia 2001)
•IDW
•Bilinear
•Kriging
•MEM
University of
L’Aquila,
Italy
Case Study
1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP
Madrid, 23 – 27 September 2002
Products
Future work
Maximum Entropy Interpolation
Inverse Distance Weighted
University of
Birmingham,
UK
Instantaneous Intercomparison
Outline
Nal. Council
of Research,
Italy
Highlights
Neural Nets
University of
L’Aquila,
Italy
Case Study
SSM/I
NN
HM
NN*
1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP
Madrid, 23 – 27 September 2002
Products
Future work
University of
Birmingham,
UK
Small area, short-duration events
Outline
Nal. Council
of Research,
Italy
Highlights
Neural Nets
University of
L’Aquila,
Italy
Case Study
1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP
Madrid, 23 – 27 September 2002
Products
Future work
Nal. Council
of Research,
Italy
University of
Birmingham,
UK
Outline
Highlights
Instantaneous
estimates
Neural Nets
University of
L’Aquila,
Italy
Case Study
1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP
Madrid, 23 – 27 September 2002
Products
Future work
University of
Birmingham,
UK
Outline
Nal. Council
of Research,
Italy
Highlights
Neural Nets
• Results: Skill Scores
University of
L’Aquila,
Italy
Case Study
1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP
Madrid, 23 – 27 September 2002
Products
Future work
University of
Birmingham,
UK
Outline
Nal. Council
of Research,
Italy
Highlights
• Coincident data
histogram
comparison
(October 2001)
Neural Nets
University of
L’Aquila,
Italy
Case Study
1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP
Madrid, 23 – 27 September 2002
Products
Future work
University of
Birmingham,
UK
Outline
Nal. Council
of Research,
Italy
Highlights
University of
L’Aquila,
Italy
Neural Nets
1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP
Madrid, 23 – 27 September 2002
Case Study
Products
• 0.1º Accumulated results
50
2
R = 0.57
NN estimate at 0.1º (mm/month)
40
30
20
10
0
0
10
20
30
40
Gauge accumulated at 0.1º (mm/month)
50
Future work
University of
Birmingham,
UK
Outline
Nal. Council
of Research,
Italy
Highlights
University of
L’Aquila,
Italy
Neural Nets
1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP
Madrid, 23 – 27 September 2002
Case Study
Products
Future work
• 0.5º / 3 month accumulated data
800
NN estimate at 0.5º (mm / three months)
R2 = 0.67
700
600
500
400
300
200
100
0
0
100
200
300
400
500
600
Gauge estimate at 0.5º (mm / three months)
700
800
University of
Birmingham,
UK
Outline
Nal. Council
of Research,
Italy
Highlights
University of
L’Aquila,
Italy
Neural Nets
1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP
Madrid, 23 – 27 September 2002
Case Study
Products
Future work
NN estimates at 0.5º (mm/month) restricted to cells with
more than 4 gauge stations in
• 0.5º accumulated results
100
R2 = 0.73
80
60
40
20
0
0
20
40
60
80
100
Gauge accumulated at 0.5º (mm/month) restricted to cells with more
than 4 gauge stations in
Nal. Council
of Research,
Italy
University of
Birmingham,
UK
Outline
Highlights
Neural Nets
University of
L’Aquila,
Italy
Case Study
1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP
Madrid, 23 – 27 September 2002
Products
• Grid size, averaging periods and correlations
(Turk et. al 2002)
Future work
University of
Birmingham,
UK
Outline
Nal. Council
of Research,
Italy
Highlights
Neural Nets
University of
L’Aquila,
Italy
Case Study
1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP
Madrid, 23 – 27 September 2002
Products
Global Coverage
(Reseach Products)
Future work
University of
Birmingham,
UK
Outline
Nal. Council
of Research,
Italy
Highlights
University of
L’Aquila,
Italy
Neural Nets
• Global-IR coverage (HM)
Case Study
1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP
Madrid, 23 – 27 September 2002
Products
Future work
University of
Birmingham,
UK
Outline
Nal. Council
of Research,
Italy
Highlights
• Meteosat coverage (NN)
•
Product to be validated using landGPCC or other dataset
•
Oriented to MSG: we are ready to
apply this methodology
Neural Nets
University of
L’Aquila,
Italy
Case Study
1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP
Madrid, 23 – 27 September 2002
Products
Future work
Nal. Council
of Research,
Italy
University of
Birmingham,
UK
Outline
Highlights
University of
L’Aquila,
Italy
Neural Nets
Case Study
1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP
Madrid, 23 – 27 September 2002
Products
Future work
•IR/PMW Advection Scheme
GOES-E 14:32
SSM/I F14 14:30
Trajectories
IR temperature along trajectory
•Wind (CMW?) trajectories found by 19x19 correlation matching over 19x19 region.
•SSM/I rain then advected along trajectories and adjusted by dIR and tied at end points
GOES-E 15:45
SSM/I F15 15:44
Nal. Council
of Research,
Italy
University of
Birmingham,
UK
Outline
Highlights
• Subscenes:
- Guinea Gulf
- GIS integration
Neural Nets
University of
L’Aquila,
Italy
Case Study
1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP
Madrid, 23 – 27 September 2002
Products
Future work
Nal. Council
of Research,
Italy
University of
Birmingham,
UK
Outline
Highlights
University of
L’Aquila,
Italy
Neural Nets
Case Study
1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP
Madrid, 23 – 27 September 2002
Products
• Future operational applications
• QPE / QPF:
• SSM/I estimates improve the forecasting (Hou et al 2002)
• We can simulate SSM/I
• Agriculture
• Hydrology
• Natural Hazards
But only when the product become operative and better results will be obtained
Future work
University of
Birmingham,
UK
Outline
Nal. Council
of Research,
Italy
Highlights
Neural Nets
University of
L’Aquila,
Italy
Case Study
1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP
Madrid, 23 – 27 September 2002
Products
• Future research work: MSG and GPM
• Radar data for validation/calibration
• Operativity of the global coverage products: intercomparison
• Integration in forecasting models: RAMS
• Use of MSG channels:
• More information means more discrimination capabilities
• Bidirectional reflectance model
• GPM and EGPM addressing
Future work
University of
Birmingham,
UK
Outline
Nal. Council
of Research,
Italy
Highlights
Neural Nets
University of
L’Aquila,
Italy
Case Study
1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP
Madrid, 23 – 27 September 2002
Products
Future work
Nal. Council
of Research,
Italy
University of
Birmingham,
UK
Outline
Highlights
Neural Nets
University of
L’Aquila,
Italy
Case Study
1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP
Madrid, 23 – 27 September 2002
Products
Future work
• Conclusions
• Accumulated areal estimates at 0.1º and 0.5º at monthly scale are
similar to other works, but the down-top approach allow to know about
small scale and short term estimates.
• There is an almost-operative product to analyse and to improve with
further research.
• There are many reseach directions in NN data fusion to follow:
• Inversion
• New methods (probabilistic nets)
• Integration of other models
• Other physical models can be integrated into the NN methodology.
• Any meteorological information can be integrated without major
modifications
• Complex models can be speed up simulating the result using NN
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