High Resolution Satellite Precipitation

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Electrical & Computer Engineering

High Resolution Satellite Precipitation
Estimate Using Cluster Ensemble
Cloud Classification

Majid

Mahrooghy
, Nicolas H.
Younan
, Valentine
G.
Anantharaj
, and James
Aanstoos

Mississippi State University

Mississippi State, MS 39762


IGARSS, July 2011


Vancouver, Canada




Electrical & Computer Engineering

Outline


Background


Existing Methods


Methodology


Results


Summary

Electrical & Computer Engineering

Background



Rainfall estimation (RE) at high spatial and temporal
resolutions is beneficial for research and applications


Ground
-
based (RE)



Facilitate routine monitoring of rainfall


Coverage is not available over all regions


Coverage is not spatially and temporally uniform


RE over the oceans is also important for climate studies, which
cannot be provided


Satellite
-
based


Monitor the earth’s environment regularly with wide coverage


Offers a viable solution for monitoring global precipitation patterns
at sufficient spatial and temporal resolutions









Electrical & Computer Engineering

Existing Methods


Satellite Precipitation Estimation (SPE) algorithms
(IR, VIS, active and passive radars
-

based)


CMORPH ( CPC morphing technique algorithm) (Joyce, 2004)


PERSIANN and PERSIANN
-
CCS (Precipitation Estimation from
Remotely Sensed Imagery using an Artificial Neural Network)
(Hsu, 1997;
Sorooshian
, 2000 and Hong, 2004)


TMPA(Tropical Rainfall Monitoring Mission (TRMM)
Multisatellite

Precipitation Analysis (Huffman, 2007)


NRL (Naval Research Laboratory (NRL) blended technique )(
Turk, 2005)



Electrical & Computer Engineering

Methodology


In our study, we adopt the PERSIANN
-
CSS
methodology enhanced with


Wavelet features


Cluster Ensemble Cloud Classification


Polynomial curve fitting

Electrical & Computer Engineering

Methodology


Block Diagram

Electrical & Computer Engineering

Segmentation


Region Growing Method


The minimum brightness temperature (
Tbmin
)
is used as seeds


Incrementing
Tbmin

by 1 K and iteratively
increasing it to a maximum of 255 K (growing
the seeds or new seeds)


Remove/merge tiny regions using a
morphological operation


Electrical & Computer Engineering

Feature Extraction


Statistical (Coldness) features (minimum
mean and standard deviation of a patch)


Texture features (local mean and standard
deviation, Wavelet, and Grey
-
Level Co
-
occurrence Matrix (GLCM) for thresholds
of
220
,
235
, and
255
K)


Geometry features (Area and Shape Index


for thresholds of
220
,
235
, and
255
K )


Electrical & Computer Engineering

Classification
-
Cluster

Ensemble


Cluster Ensemble techniques combine multiple data
partitions from different clustering.


There are different cluster ensemble methods such as:


Voting
-
based (H. G.
Ayad
, 2008, 2010)


Evidence accumulation (A.L.N. Fred, 2005)


Link
-
based (LCE) (N.
Iam
-
on, 2010).


The LCE method which is recently developed is employed
to a cloud
-
patch
-
based HSPE to cluster cloud patches

Electrical & Computer Engineering

Link
-
based Cluster Ensemble

( N.
Iam
-
on, 2010 )

RM



Cluster

Association

Matrix

SPEC



Spectral

Clustering

Electrical & Computer Engineering

Link
-
based Cluster Ensemble

1) Creating M
-
base clustering


Single clustering, for instance the
Kmeans

with different
initialization ( used in this work)


Multiple clustering algorithms such as SOM,
Kmeans
, and Fuzzy
Cmean
.

2) Generating refined cluster
-
association matrix (RM)



This matrix represents an association degree between each sample
and each cluster of the base clustering.

3)
Applying a consensus function to obtain final clustering


In this work, the consensus function is a graph
-
based clustering so
the cluster
-
association matrix is transformed to the weighted
bipartite graph, and then spectral graph partitioning (SPEC) is
performed.



Electrical & Computer Engineering

,

𝑅



,
𝑐

=


1














𝑐
=


 

𝑐
,










 



















 





,



=

𝑊 𝑇

𝑊 𝑇
𝑎

×





𝑊 𝑇

=


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=
1

,




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=
 



,



,



and


=
















.



denotes the

samples belonging to cluster


, and q
represent
s

all triples between the



and


. DC is also a constant delay
factor
.

Link
-
based Cluster Ensemble

Electrical & Computer Engineering

Data


Area of Study : United States extending between
30
N to
38
N
and
-
95
E to
-

85
E


Testing Time :
W
inter
2008
(Jan, Feb)


Training Time : one month before the respective testing
month


Training data : IR (GOES
-
12
), The National Weather
Service Next Generation Weather Radar (NEXRAD) Stage
IV precipitation products


Testing data : IR (GOES
-
12
)


Validation data : NEXRAD Stage
IV


Electrical & Computer Engineering

Results
-

Segmentation

Electrical & Computer Engineering

Results:
Temp
e
rature
-
RainRate

Electrical & Computer Engineering

Comparison Results (
Hourly Estimate)

Estimated hourly rainy area ending at 1500 UTC on February 6, 2008:

(a) LCE
-
based; (b) SOM
-
based; (c) PERSIANN
-
CCS; and (d) NEXRAD
-
Stage IV

Electrical & Computer Engineering

Results: Validation

Verification result for January through March
2008
:


(a) False Alarm ratio; (b) Probability of Detection; and (c)
Heidke

Skill Score

Electrical & Computer Engineering

Summary


A link
-
based cluster ensemble method is
incorporated into a high resolution precipitation
estimation (PERSIANN_CCS) to enhance rainfall
estimation


In comparison with the SOM
-
based and the
PERSIANN
-
CCS algorithms, the cluster
ensemble method improves the POD and HSS at
all rainfall thresholds


This improvement is
about
12
% for POD and
5
%
to
7
% for HSS at medium and high level rainfall
thresholds for
winter
2008





Electrical & Computer Engineering

References


Y. Hong, K. L. Hsu, S.
Sorooshian
, and X. G.
Gao
, “Precipitation Estimation from Remotely Sensed Imagery using
an Artificial Neural Network Cloud Classification System,” Journal of Applied Meteorology, vol. 43, pp. 1834
-
1852, 2004


R. J. Joyce, J. E.
Janowiak
, P. A.
Arkin
, and P.
Xie
, “ CMORPH: A method that produces global precipitation
estimates from passive microwave and infrared data at high spatial and temporal resolution,” Journal of
Hydrometeorology, vol. 5, pp. 487
-
503, 2004.


G. J. Huffman, R. F. Adler, D. T.
Bolvin
, G. J.
Gu
, E. J.
Nelkin
, K. P. Bowman, Y. Hong, E.
F.Stocker
, and D. B.
Wolff, “ The TRMM
multisatellite

precipitation analysis (TMPA): Quasi
-
global, multiyear, combined
-
sensor
precipitation estimates at fine scales,” Journal of Hydrometeorology, vol. 8, pp. 38
-
55, 2007.


F. J. Turk, and S. D. Miller, “Toward improved characterization of remotely sensed precipitation regimes with
MODIS/AMSR
-
E blended data techniques,” IEEE Trans.
Geosci
. Remote Sens., vol. 43, pp. 1059

1069, 2005.



Sorooshian
, S., K. L. Hsu , X.
Gao

, H. V. Gupta ,
B.Imam

and D. Braithwaite, “Evaluation of PERSIANN system
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Meteorol
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Topchy
, A. K. Jain, W. Punch, "Clustering ensembles: models of consensus and weak partitions," IEEE
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-
1881, Dec. 2005.


H. G.
Ayad

and M. S.
Kamel
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-
based consensus of cluster ensemble,” Patter recognition J.,
vol

43, pp.
1943
-
1953, 2010.


H. G.
Ayad
, M. S.
Kamel
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Clusters," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.30, no.1, pp.160
-
173, Jan. 2008.


A.L.N. Fred, A. K., Jain, "Combining multiple clustering using evidence accumulation," IEEE Transactions on
Pattern Analysis and Machine Intelligence, vol.27, no.6, pp. 835
-

850, Jun 2005.


N.
Iam
-
on, T.
Boongoen
, and S. Garrett “LCE: a link
-
based cluster ensemble method for improved gene expression
data analysis,” Bioinformatics, vol.26, pp. 1513
-
1519, 2010.


T.
Kohonen
, “Self
-
organized formation of topologically correct features maps,” Biol. Cybernetics, vol. 43, pp. 59

69, 1982.


E.E. Ebert, J.E.
Janowiak
, and C. Kidd. “Comparison of near
-
real
-
time precipitation estimates from satellite
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-
64, 2007.