Accurate information of rainfall estimate from the remotely sensed instrument is highly needed for many applications, including the disaster early warning system that requires heavy rainfall prediction for mitigation purposes. In this study we implemented machine learning methods to estimate rainfall from high temporal resolution data of Himawari-8 over a large area of Indonesia, where floods and landslides caused by heavy rainfall events have been the most frequent disaster for the last 10 years. All brightness temperature of Himawari-8 infrareds (IR) channels were involved to estimate rainfall at the current (near-real) time (t), 1 hour later (t+1), and 2 hours later (t+2) after the data received by using 9 different machine learning models. The rainfall dataset product from Global Satellite Mapping of Precipitation (GSMaP) were used for data training. Two machine learning processes were taken, first is for separating rain and no-rain area, and second is for determining rainfall rate category. The results showed that Multi Linier Perceptron (MLP) model had the highest accuracy in deriving rain area. While, for retrieving rain rate, the Linear Discriminant Analysis (LDA) model was the most accurate compared to the other models. A good accuracy from the LDA has been obtained for current time estimation (accuracy = 81%) as well as for prediction at one and two hours later (accuracy = 79%, and 78% respectively). A comparison test has also been performed to determine which variables have the most significant contribution to the rainfall retrieved. It showed that 3 IR channels at 6.2, 10.4, and 13.3 µm were the minimum predictors that should be used to obtain a minimum 79% accuracy. Addition of other predictors such as other channels of IR or a combination of brightness temperature difference (BTD) could increase its accuracy, i.e. 1%-2% which is not too significant.

1.
G. J.
Huffman
,
D. T.
Bolvin
,
E. J.
Nelkin
, and
D. B.
Wolff
,
J. Hydrometeorol.
, vol.
8
, no.
1
, pp.
38
55
(
2007
).
2.
A. Y.
Hou
 et al,
Bull. Amer. Meteorol. Soc.
, vol.
95
, pp.
701
722
(
2014
).
3.
G. A.
Vicente
,
R. A.
Scofield
, and
W. P.
Menzel
,
Bull. Amer. Meteorol. Soc.
, vol.
79
, no.
9
, pp.
1883
1898
(
1998
).
4.
M.
Kühnlein
,
T.
Appelhans
,
B.
Thies
, and
T.
Nauß
,
J. Appl. Meteorol. Climatol.
, vol.
53
, no.
11
, pp.
2457
2480
(
2014
).
5.
D. I. F.
Grimes
,
E.
Coppola
,
M.
Verdecchia
, and
G.
Visconti
,
J. Hydrometeorol.
, vol.
4
, no.
6
, pp.
1119
1133
(
2003
).
6.
F.
Pedregosa
 et al,
J. Mach. Learn. Res.
, vol.
12
, pp.
2825
2830
(
2011
).
7.
Badan Nasional Penanggulangan Bencana (BNPB)
,
Data Informasi Bencana Indonesia (DIBI)
: http://dibi.bnpb.go.id/ [accessed on 24 July 2019].
8.
K.
Bessho
 et al,
J. Meteorol. Soc. Jpn.
94
,
151
83
. (
2016
).
9.
I.
Gustari
,
T Wahyu
Hadi
,
S
Hadi
,
F
Renggono
,
Jurnal Meteorologi dan Geofisika
, vol.
13
, no.
2
,
119
130
(
2012
).
10.
M.
Lekula
,
Lubczynski
M W
,
Shemang
E M
and
Verhoef
W
,
Physics and Chemistry of the Earth
105
,
84
97
(
2018
).
11.
Min
 et al,
IEEE Transactions on Geoscience and Remote Sensing.
57
.
2557
2570
. (
2019
).
12.
C. P.
da Silva Neto
,
H. A.
Barbosa
,
C.A. A.
Beneti
,
Atmósfera
, vol
29
, issue
4
,
343
358
(
2016
).
13.
T.
Kurino
, “
Rainfall Estimation with the GMS-5 Infrared Split-Window and Water Vapor Measurement
”,
Meteorological Satellite Center Technical, JMA
, Note No.
33
,
91
101
(
1997
).
14.
T. J.
Schmit
 et al,
J. Appl. Meteorol. Climatol.
, vol.
47
, no.
10
, pp.
2696
2711
(
2008
).
This content is only available via PDF.
You do not currently have access to this content.