Comparison study on rainrall retrieval
Algorithm: model simulation and armar data
Results
Comparison study of Tb-R relations
The effectiveness of cloud models we
established is validated with the comparison
of the real observation of Tb-R (brightness
temperature versus rainfall rate) and radiative
transfer calculation with model structure and
rainfall rate as input. Fig.3 and Fig.4 show the
comparison results. Tb-R scatterogram is the
observed real data obtained from ARMAR
data. Tb is the observed brightness
temperature from airborne radiometer and the
rainfall rate was derived from corresponding
radar reflectivities with attenuation correction
[Dou et al 1997]. The solid curves are the
simulation results based on the model
structures with radiative transfer model. It can
be seen that the tendency of real observation
and model simulation are rather consistent. It
is indicated that these cloud models can be
used as the basis for study of algorithm
retrieval rain rate with other MW sensors
from R-T model simulation
Fig.3 Tb-R relationship in simulation and real
observed data for stratified cloud. Solid lines
are the simulation result; dots are the
observed data.
Fig.4 same as Fig.3 but for convective cloud
Simulation Results
Retrieval methods of rainfall rate usually
depend on empirical and/or physical methods.
When we have enough representative data of
collocated satellite observation, Tbi, with
surface rainfall measurement, empirical
formula could be established. We have
established this kind of retrieval formula for
SSM/I ( LU et al , 1998). When we have to
extend the existed formulae to new
combination of MW channels, such as AMSR
on ADEOS II, new retrieval formulae relating
Tbi sets of AMSR to surface rainfall rate
should be established. Since there are no
observation existed, we have to depend on the
simulated results of MW brightness
temperature from certain precipitating cloud
model. If the precipitating cloud model is
valid for retrieval with existed observation,
this precipitating cloud model will be used as
the basis for retrieval of rainfall rate with new
MW sensors.
As mentioned above, the established
vertical structure cloud models have been
validated with existed observation by
ARMAR data at 13.8 GHz (see 3.1). For
extending the retrieval formulae obtained
with SSM/I to AMSR, we need to investigate
them by radiative transfer model.
Based on the analysis of Tbi-R relation
of SSM/I data and surface rainfall rate data
provided by NASDA, Japan, LU et al.
(1998) have proposed a probability pairing
method for retrieving rainfall rate with SSM/I
data. First, the relation of Rl (we call Rain
index) and Tbi (combined brightness
temperature) was established. Which is
Rl = 28.66 + 0.2845ln(Tb
b19v-T
b19h) +
0.5455ln(T
b22v -180) –
6.066ln(T
b85v -T
b19v +100) (1)
After probability pairing, i.e. conducted
the SSM/I and collocated radar-rain guage
data, we made empirical fitting for R (rain
rate) and Rl (rain index) relationship from
pairing curve:
R = 0.0 (Rl <1.41)
R = -1.01122 + 0.224002Rl +0.364442Rl
2-0.0192194Rl
3 (1.41<Rl<12)
R = -23.3259 + 5.50855Rl - 0.137673Rl
2 (Rl>12) (2)
For comparison study, Rf , scattering
index proposed by Ferraro(1995) is chosen as
rain index, which is:
Rf = - 174.4 + 0.72Tb
b19v + 2.439 T
b22v –
0.00504T
2b22v - T
b85v (3)
Then using probability pairing as above,
we obtained the relation of R-Rf :
R = 0.0 (Rf<1.94)
R = -0.0470175 + 0.0365984Rf +0.00125964Rf
2 (1.94<Rf<120)
R = 33.7097 - 0.496671Rf -0.000356007Rf
2 (Rf>120) (4)
Fig.5: Relationship of retrieved rain rate and
‘observed’ rain rate by using SSM/I data.
Fig. 6 same as Fig.5 but for AMSR data.
For extending the retrieval method to
AMSR data, we choose the adjacent channels
of AMSR instead of the channels of SSM/I
which were used in formulae (1)-(4), i.e. we
chose 18.7, 23.8, 36.5, 89.0 GHz of AMSR
channels instead of 19.35, 22.235, 37.0 , 85.5
GHz of SSM/I data respectively.
By using these formulae and radiative
transfer model with the established cloud
models we may obtain the R-Tbi relationship.
Fig.5 shows the comparison results of the
‘observed’ and retrieval rain rate with Rl and
Rf as rain index for SSM/I in stratified
precipitating cloud . It can be found that the
agreement between retrieval and ‘observe’
rainfall rate are acceptable; Also, the
simulation retrieval results have good
consistence for chosen Rl and Rf as rain
index in probability pairing method. Fig.6 is
the results of AMSR data. Comparing Fig.5
and Fig.6, it can be seen that Tbi-R relation
for AMSR data is better than SSM/I data. In
this point, it means that the probability paring
method derived from SSM/I data may be
extended to the AMSR data.
Conclusion and Discussion
- Based on the TOGA/COARE airborne
radar-radiometer observation and cloud
dynamic model two vertical structure cloud
models were established for MW remote
sensing of rainfall rate. These models were
validated by comparison the simulation and
real observed data at 13.8 GHz.
- Probability pairing method derived from
SSM/I data and ground truth rain rate data
were extend to AMSR data by a vector
radiative transfer model with changing the
adjacent channels and the established cloud
model. The computation results revealed that
the Tbi – R relationship for SSM/I and
AMSR data are acceptable. It seems that the
probability method derived from SSM/I can
be extend to AMSR and both Rl & Rf can be
chosen as the rain index.
- For next step of MW retrieved of rainfall,
we are searching for the method, which
relating Tbi with vertical structure of cloud,
such as ice content, mixed layer water content,
water content in lowest layer. In fact, rainfall
rate is closely related to the liquid water
content in the lowest layer of the atmosphere,
and the space-borne observed brightness
temperature is closely related to the total
water content of precipitating cloud and ice
particle content at upper layer, depending on
MW frequencies.
Acknowledgement
This project is supported by the National Natural
Science Foundation of China (No.49885001 &
49705058), Part of this work is supported by a contract
of NASDA, Japan, no. AMSR RA-0017 for ADEOS II
AMSR retrieval algorithm development.
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