1998 Seasonal variation 37 GHz V and H
Land Emissivity Map obtained from
TRMM PR, TMI and JRA-25 data
Fum ie Akimot o Furuzawa
・ Hirohiko M asunaga ・ Kenji Nakam ura
(HyARC, Nagoya Universit y, Japan)
20 09 06 11 12 ： 0 5-12 ： 20 （ 15 min. ）
Session 5 : M onit oring Land Surf ace (ex. Heat Fluxes, Rain)
2 nd Wor kshop on Remot e Sensing and M odeling
of Surf ace Pr opert ies, Toulouse, June 2 009
Project ：　one of GPM / JAXA
project s
「 Examinat ion and t rial product of
DPR/ GM I rain ret rieval combined
algorit hm 」
Object ives of t he project
Rain est imat e f rom combined ut ilizat ion of
GPM core sat ellit e sensors; DPR and GMI
Cont ribut ion f or im provem ent of rain est im at e
f rom const ellat ion sat ellit es
Import ance of land rain ret rieval
algorit hm f or GPM microwave
radiomet ers
Observat ion region ext ends t o high lat it ude and wide
land area is covered & observat ion sampling is
f requent ly done by microwave radiom et ers launched
wit h const ellat ion sat ellit es→ cont ribut ion f or hydrology
or prevent ing of nat ural hazard, et c will be expect ed.
Objective
For rain est imat ion f rom passive microwave
radiomet er
 ocean: emission of low f req. 10,1 9 GHz is used.
 land : scat t ering of high f req. 37 ,85 GHz is used.
∵ land emission >> rain emission ＆
large variat ion of land emissivit y
 Assuming t hat land emissivit y is const ant , t he
case which fi t s wit h scat t ing signal f rom t he look
up t able made f rom cloud resolving m odel
← object ive-1 ： assumed emissivit y
check
 Object ive-2 ： rain est imat ion f rom bright ness
t emperat ure of all f requencies by using
charact erist ics of variat ion of land emissivit y.
 Theref ore, development of ε est imat ion is
necessary. I invest igat ed spat ial/ t ime variat ion
of ε and will t ry t o est im at e rain rat e by using

Data for ε estimation
TRMM Satellite data
PR (2A2 3 V6 ) rain ident ifi cat ion
TM I (1 B1 1V6 ) br ight ness t emperat ure TB
(1 0 GV/ H, 1 9 GH/ V ,21 GV, 37 GV/ H, 8 5 GV/ H)
 TM I (2 A1 2 V6 ) land defi nit ion


JRA-25 data (JMA reanalysis data)
6 hourly dat a (0 0 ,0 6,1 2,1 8 Z)
geopot ent ial height , air t emperat ure,
specifi c humidit y, cloud wat er cont ent at
each alt it ude : 2 3 levels (1 000 -0 .4 h Pa) (1.25
deg-r esolut ion) are select ed f rom ±3 hrs.
 Ground t emperat ure (1 .1 25 deg-resolut ion) (if no,
surf ace 2 m air t emperat ure) is est imat ed by fi t t ing
during ±9 hrs.
 Lf egt opo (1 .1 25 deg-resolut ion)


Estimation of ε
a.
a.
When all dat a wit hin one grid show PR(2A2 3)
rain fl ag of “no-rain” / ”no-rain+possible”, and TM I
（ 2 A1 2 ) indicat es “over land”, t he map of
inst ant aneous TM I(1B11 ) 0.2 deg x 0.2 deg
bright ness t emper at ure T B is made f or each
local t ime and f or each f requency(5f req.9 ch).
(← advant age: use
of PR rain ident ifi cat ion)
Absorpt ion coeffi cient （ cloud wat er, wat er vapor, O2 ）
and opt ical dept h τ f or above PR-no-rain
land pixel are calculat ed f rom JRA-25 dat a
wit hin ±3 hrs, assuming t hat no cloud ice
exist s.
a.
Ground t em perat ure is calculat ed f rom t hree
JRA-2 5 dat a wit hin ±9 hrs by fi t t ing wit h
quadrat ic f unct ion.
Estimation of ε
Used equat ion (Prigent et al. 2 0 0 6,
et c):
(From int egrat ed
t ransf er equat ion f or a
nonscat t ering planepar allel at mosphere over
Used values:
a fl at surf ace)
zenit h angle:5 2 .8 (bef ore boost ), 5 3.4 (af t er boost )
sat ellit e alt it ude:3 50 km(bef ore boost ), 4 0 2.5 km (af t er boost )
Estimation of ε
a.
Emissivit y is calculat ed f rom result s of ① (TMI
T B ) and ② (τ ,α ).
(1 ) St ep f unct ion of alt it ude assumed f or calculat ion of
Tat m ↑ and Tat m ↓ is a linear f unct ion .
（２） Lf egt opo value (geopot ent ial) divided by 9 .80 665
is used f or surf ace alt it ude and calculat ion is done
over t he surf ace alt it ude.
1 1 yrs, 5f req./ 9 ch, 0 .2 deg x 0 .2 deg
inst ant aneous
emissivit y map f or each local t ime f or each
TRM M pass
M ont hly averaged emissivit y map f or each yrs
and
Results
example
V
37GHz ： 1998-June monthly ε
199806
H
P
0.2 deg x 0.2 deg
←similar with TB-polarization map .
Results
example
V
37GHz ： 12yrs Jan. monthly ε
199801~
200901
H
P
0.2 deg x 0.2 deg
Check
Simulation of TB
RTM ： Aonashi & Liu (2000) code
Assuming that JRA25 air profile and cloud water
content follows a 10-linear interpolated function and
there is no ice cloud, TB is simulated from our land
emissivity dataset.
 X axis is TRMM TMI 2A12 TB.


best case
worst case
10GHz V
Δ ＝ -0.2 ～ 0.5K
19980600
85 GHz H
Δ ＝ -9 ～ 5K
→reconstracted
This ε-estimation method is valid.
Correlation between surface type index and ε
Normalized Histogram of ε ｖ & ε ｐ for 10/85 GHz
εp εv εp 7
10 GHz
(19-37GHz: similar)
shrubs with bare soil
groundcover bare soil
9
εv 11
85 GHz
(for all chs)
εv:small difference.but
JRA25 surface type index
1 evergreen broadleaf trees
1 broadleaf shrubs with groundcover forest:small,soil:large.
2 broadleaf deciduous trees
εH is invert.
9 broadleaf shrubs with bare soil
3 broadleaf and needle leaf trees
∴P is the smallest for
4 evergreen needle leaf trees
forest and largest for soil.
6 broadleaf trees with groundcover
7 groundcover
11 bare soil
1.125 deg-res.
Correlation between Soil-wetness and ε
Histogram of ε ｖ & ε ｐ for 10/85 GHz
all wet
10 GHz
(19-37GHz: similar)
all dry
εp εv εp 85 GHz
εv All: wet,mid,dry (0.4,0.6)
Deep: wet, dry (0.3,0.7)
Surface: wet, dry
(all chs)
dry deep ＋ wet surf.→mid-P
εv:wet:small, dry:large. and
(0.1@10GHz)
εH is invert.
wet deep+dry surf→mid-P
∴P is small for wet, large for dry.
(0.03-0.13@10GHz)
Seasonal variation of ε over China region 2000
Histogram of ε ｖ for 85 GHz V
Open Shrub land
winter
summer
Dry Field
winter
summer
J:black
F:red
M:green
A:blue
M:lightblue
J:magenta
J:red+magenta
A: orange
S:green+cyan
N:blue+cyan
D:blue+magenta
LUCC: Land-Use and
Land-Cover Change
Land type >50 %
37.5N
17N
68E
140E
81E
127E
winter
Paddy Field summer
Interpolation of land emissivity
Land emissivity over rainy region can not be
obtained. Therefore, interpolation of land emissivity
obtained over clear-air-condition is done. Wide
instantaneous ε dataset is obtained.
 Assuming Gaussian distribution and weighting with
the distance, interpolation is done within the radius of
0.4 deg.


10yrs’ 5freq/9ch monthly ave. map is obtained from
the instantaneous interpolated emissivity.
Annual variation
1998 ～ 2009 January animation
19GHz V and P
Check
Simulation of TB
RTM ： Aonashi & Liu (200 0 ) code
Assuming t hat JRA2 5 air profi le and cloud
wat er cont ent f ollows a 1 0-linear int erpolat ed
f unct ion, t here is no ice cloud, and PR rainprofi le, TB is sim ulat ed f rom our land
19980600
int erpolat ed emissivit y dat aset .
 X axis is TRM M TM I 2 A1 2 TB.
200
Simulated TB
250 300


10GHz V 19GHz V 37GHz V 85 GHz V
200
Simulated TB
250
TRMM-TMI TBobs
10GHz H TRMM-TMI TBobs
19GHz H 37GHz H
85 GHz H
The interpolated ε seems to be valid.
εV
Determination of surface temperature
199806
Correlation of ε Example:
Broadleaf trees with groundcover
19GHz 0LT 37GHz 0LT
εV ＝ 0.54(±0.01) ｘ εH
＋ 0.45(±0.01)
19GHz 12LT
εH
εV ＝ 0.60(±0.01) ｘ εH
＋ 0.40(±0.01)
εV ＝ 0.56(±0.01) ｘ εH
＋ 0.425(±0.01)
19GHz Inclination
offset Similar except for 85GHz
(const)
Estimated-Ts
Determination of surface temperature
Example: 199806
10GHz 0LT 10GHz 12LT
correlation
coefficient
0.78
JRA25-Ts
JRA25-Ts
&Estimated-Ts
30oS
correlation
coefficient
0.76
Eq
30oN
Correlation coefficient
10GHz ave. 0.76
19GHz
0.75
37GHz
85GHz
0.71
0.35
C.C. : lowest at morning.
Conclusions
Over no-rain land TRM M PR observat ion area,
1 1 (+α ) yrs inst ant aneous ε dat aset and mont hly
ε m ap f or each yrs and over-1 1yrs climat ology
were m ade f or TM I-5 f req/ 9ch. Assumpt ion ： t he
CLW over 0 -deg level is 0 [kg/ kg]. Validit y of t his
dat aset is confi rmed by simulat ion using LIU
radiat ion code and comparing wit h TM I TB, and
moreover, comparison wit h result s of Dr. Uesawa
＠ EORC and Dr. Prigent .
 Obt ained m ont hly averaged map shows regional
seasonal, annual variat ions, and diff erent
dependencies on surf ace t ype (JRA2 5, landsat,
LUCC) and soil-wet ness f or 5 -f r eq/ 9 -ch.
 Int erpolat ed ε dat a over rainy area was made.
Simulat ion using LIU code and t he int erpolat ed
ε was done and compared wit h TM I TB,
assuming JRA2 5 cloud wat er cont ent +

Future Plan
By including t heε dat aset int o our GPM / GMI
combined algorit hm which Nagoya Univ,. has
been developing, and some ot her algorit hms, I
will check whet her t he rain est imat ion is
improved or not .
 I will change t he limit at ion of “no-rain only” int o
“no-rain only + r ain possible” and compar e wit h
t he int erpolat ed ε dat aset .
 I will improve t he met hod t o est imat e surf ace
t emperat ure.
