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Rainfall Type Estimation from the Information on Life Stage of Deep Convection (Feasibility of assigning the life stage of deep convection) Toshiro Inoue, Daniel Vila* and Tomoo Ushio** Meteorological Research Institute, Tsukuba, Ibaraki, Japan * INA, Buenos Aires, Argentina - PowerPoint PPT Presentation
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Rainfall Type Estimation from the Information on Life Stage of Deep Convection
(Feasibility of assigning the life stage of deep convection)
Toshiro Inoue, Daniel Vila* and Tomoo Ushio**Meteorological Research Institute, Tsukuba, Ibaraki, Japan
*INA, Buenos Aires, Argentina
** Osaka Prefecture Univ., Osaka, Japan
Motivation
IR rainfall estimation is less physical than PMW and PR rainfall estimation. Because IR observes just cloud instead of rain.
Advantage of the IR is high temporal observation from geostationary orbit.
If we can define the life stage by tracking the deep convection using high temporal observations from geostationary satellite.
Then, we might use the information on the life cycle of deep convection for rainfall estimation.
Developing Mature Decaying
convective rain stratiform rain
TYPE:1 Con. ; TYPE:2 Strat.
Rainfall Rate at 2Km observed by PR/TRMM
~1 mm/h ~8 mm/h
TBB-BTD Characteristics for Ice(Function of effective radius and optical thickness)
TBB
BT
D
EffectiveRadius
Ci-Dense
Cu/Sc
Ci-ThinCi-Thick
Cb
MSG-IR MSG-VIS
MSG-BTDSplit WindowLarger BTD-whiteSmaller BTD-black
Data
GOES-W,E 0.1*0.1 Lat/Lon Grid Hourly Split Window Over Eastern Pacific, South America
MSG(Meteosat-8) 0.05*0.05 Lat/Lon Grid 15 minutes Split Window Over Africa
253K
CiBTD>1Cb
BTD<1
Cloud type (Cb, Ci) classified by the BTD within 253K cloud area.
Definition of Deep Convection
T1
T2
T3
T4
T5
T6
T7
T8
T9
T10
T11
T12
Cloud Type Map Classified by the Split Window
15 minutes interval taken by Meteosat-8
The deep convection starts from 17:45 UTC on 24 May, 2003 to 20:35 UTC on May 24, 2003.
Cloud Type Map Classified by the Split Window
GOES-W 2 hourly from 12UTC May 09, 2001
1°
0
20
40
60
80
100
120
1 2 3 4 5 6 7 8 9 10 11 12HOUR
# O
F P
IXELS
CB CI
Time Evolution of Deep Convection
Cloud number of Cb (blue) and Ci (green) within 253K cloud area.
% of Cb and Ci within Convection
0%
20%
40%
60%
80%
100%
1 2 3 4 5 6 7Time (every 15 minutes)
CbCi (Anvil)
% of Cb and Ci within Convection
0%20%40%60%80%
100%
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Time (hour)
CbCi (Anvil)
Evolution of Percentage of Cb and Ci within DC
The life time is short in left case, while the life timeis longer in right case.
0
20
40
60
80
100
120
140
1 2 3 4 5 6 7 8
Time (hour)
# o
f 0.1
grid 3 hour
4 hour5 hour6 hour7 hour8 hour
0
5
10
15
20
25
30
35
40
45
1 2 3 4 5 6 7 8
Time (hour)
# o
f C
i w
ithin
253K C
loud
3 hour4 hour5 hour6 hour7 hour8 hour
Time evolution of size (top) and % of Ci (bottom)
Cloud Type Map
Percentage of convectiveRain by PR
2°
Evolution of Cb and CiConvective-rain>50%
Convective-rain<50%
1°
Summary
The percentage of Ci classified by the split window within 253K cloud is a good indicator to tell the life stage for simple deep convection case from single snap shot image.
We could assign different rainfall rate for the same TBB depending on the life stage.
Mean Size of Deep Convection
0
20
40
60
80
100
120
140
1 2 3 4 5 6 7
Time (hour)
Size
(# o
f 0.1
lat/
lon
grid
)
3 hour4 hour5 hour6 hour7 hour
% of Cirrus Cloud within 253K Cloud
0
10
20
30
40
50
60
70
1 2 3 4 5 6 7
Time (hour)
% of
Cirr
us C
loud 3 hour
4 hour5 hour6 hour7 hour
VIS and BTD
VISBTD87-11
BTD11-12IR
0
10
20
30
40
50
60
1 2 3 4 5 6 7 8 9 10 11 12 13
系列1 系列2 系列3 系列4 系列5 系列6
系列7 系列8 系列9 系列10 系列11 系列12
0
50
100
150
200
250
1 2 3 4 5 6 7 8 9 10 11 12 13
系列1 系列2 系列3 系列4 系列5 系列6
系列7 系列8 系列9 系列10 系列11 系列12
Time evolution of size (top) and # of Ci (bottom)
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