The Climate X Project software development:
Automated rainfall forecasting and
estimation
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From Theory to Practice: A Forum on Geohazards
March 30, 2011 Phivolcs Auditorium, PHIVOLCS, CP Garcia Avenue,
UP Campus, Diliman, Quezon City
CP David
Environmental Monitoring Laboratory ,NIGS UP Diliman
Climate X Team
2
DOST-funded project entitled, “Quantified flood
forecasting through rain rate estimation using satellite
imagery and generalized watershed runoff calculations”
UP Diliman
National Institute of Geological Sciences
National Institute of Physics
Department of Computer Science
PAGASA-DOST
ASTI-DOST
Tropical Depression
AURINGJanuary 2009
Tropical Depression
CRISINGApril 2009
Typhoon
DANTEMay 2009
Typhoon
EMONGMay 2009
Tropical Storm
FERIAJune 2009
Tropical Depression
GORIOJuly 2009
Tropical Storm
HUANINGJuly 2009
Tropical Storm
ISANGJuly 2009
Tropical Storm
JOLINAJuly 2009
Typhoon
KIKOAugust 2009
Tropical Storm
LABUYOSeptember 2009
Tropical Depression
MARINGSeptember 2009
Tropical Storm
NANDOSeptember 2009
Tropical Storm
ONDOYSeptember 2009
Typhoon
PEPENGOctober 2009
Typhoon
QUEDANOctober 2009Typhoon
RAMILOctober 2009
Typhoon
SANTIOctober 2009
Tropical Depression
TINONovember 2009
Tropical Storm
VINTADecember 2009
Tropical Depression
BISINGFebruary 2009
Tropical Depression
URDUJANovember 2009
Project approach
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Prediction of the quantity and timing of rainfall based on
satellite imagery
Step 1: Acquisition of satellite data
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The EDM software automatically downloads free satellite imagery from the web
Step 1: Acquisition of satellite data
6
Mirroring of PAGASA’s MTSAT data feed
30-minute interval
Near real-time
High resolution images (667x1112 pixels, 0-30o LAT, 100-150o LONG)
Step 2: Image validation
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Archived imagery vs. Actual rainfall
Step 2: Image validation
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Archived imagery vs. Actual rainfall
Step 2: Image validation
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Archived imagery vs. Actual rainfall
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PAGASA weather stations
WS coordinates superimposed
on IR1 image
Step 2: Image validation
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Archived imagery vs. Actual rainfall
Step 2: Image validation
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Accuracy of predicting if “it rained” or “did not rain”
Step 2: Image validation
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Prediction capability increases with rainfall intensity
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Phil_Mc_lg-IR1-IR1-2011-03-12-003200.bmp
Hourly analysis
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Hour:32:00
Val
ue
WS 1-7 (Batanes) – no clouds from
midnight to 2pm.
WS 55-60 (Masbate to Tacloban) – cloudy
from midnight dispersing by 2pm.
Step 3: Cluster analysis
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Image processing to “tag” individual low pressure systems
Location and centroid
Size
Intensity
Shape
Direction
Changes over time
10
25
30
10
5
5
5
P Princesa, 40% chance of rainLeyte, 80% chance of rain
Manila, 10% chance of rain
Bacolod, 60% chance of rain
Tuguegarao, 80% chance of rain
0
10
20
30
40
50
60
70
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
year
# m
ajo
r ra
in d
ays
rain from
typhoons
rain from other
disturbances
45%
55%
40 major rain
days per year
More than half of
the rain come
from other
weather
disturbances
Timetable
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Completion of archived data correlation by April 2011
Statistical analysis of dataset by May 2011
Beta version of rainfall forecasting for testing by June 2011
Inclusion of “experience” characteristics by September
2011
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The Climate X Project
UP Diliman – PAGASA – ASTI
