京都大学防災研究所年報 第 66 号 B
DPRI Annuals, No. 66 B, 2023

Enhancing the Precision of Daily Precipitation Data Using an ANN
Downscaling Model: Case Study in Japan
Sunmin KIM(1), Ilham KHATEEB(1) and Yasuto TACHIKAWA(1)(1) Dept. of Civil & Earth Resources Eng., Graduate School of Eng., Kyoto University, Japan
Synopsis
This study presents an artificial neural network (ANN)-based downscaling model
aimed at enhancing the precision of daily precipitation data. The study employed 112
years of observed daily data, downscaling from a pseudo-coarse spatial resolution of 20
km to a finer spatial resolution of 5 km. The area covered was a square grid of 3x3 at 20km resolution and the target of downscaling was the center grid of this square. The ANN
model was trained using 85 years of data, tested over 12 years, and validated over 15
years in five regions across four seasons in Japan. The results of the study demonstrated
a high level of agreement between the downscaled and original daily data over all regions
and seasons, with the highest and lowest root mean square error (RMSE) being 3.4mm/day and 0.12 mm/day, respectively. In addition, the ANN model successfully
preserved the statistical properties of the original daily data, maintaining a mean andstandard deviation bias within ±8%.
Keywords: Artificial Neural Networks; statistical downscaling; precipitation; spatial
correlation
1.

Introduction

The impact of climate change should beconsidered for better management of energy andwater resources. The foremost hydro-climatologicalfactors, temperature and precipitation, play a crucialrole in hydrological research and the design ofhydraulic infrastructures. Predicting potential shifts
in these parameters, particularly for the future, iscrucial for mitigating the dangers posed by droughts
and floods (Nourani et al., 2019). General circulation
models (GCMs) were used to obtain such data.
However, the outputs of the GCMs are at coarse
resolution and their direct use for regional/local
studies is difficult (Vu et al., 2016). Accordingly, theGCMs outputs were downscaled to a finer resolution
to fill this gap.
As for Japan, Meteorological Agency (JMA) andthe Meteorological Research Institute (MRI) havedeveloped Atmospheric General Circulation Models(AGCMs) with different spatial resolutions: globally
60 km, locally 20 km, and ongoing 5 km for Japan
area. The river basins in Japan span over several
hundred square kilometers in area. Accordingly,
getting precipitation data at 5km resolution proves tobe effective and useful. However, the limited
diversity of ensemble outputs from 5 km compared
to 20 km impedes its applicability in impact
assessment research.
Basically, there are two main methods fordownscaling of precipitation: dynamical andstatistical (Wilby et al., 1998). The former isphysically-based and it is computationally expensive.
Consequently, statistical downscaling is widely used.
Many studies have discussed the hypothesis ofstatistical downscaling and its type, advantages, andlimitations (Wilby et al., 1997; 1998; 2004; Storch
et al., 2013; Trzaska and Schnarr, 2014).

― 247 ―

Among the many statistical downscaling
methods currently used, the artificial neural network(ANN) is one of the most popular tools because of
its efficiency and prompt operation. Vu et al. (2016)
employed an ANN to downscale the monthly
precipitation from four GCMs over Bangkok during
the rainy season. The ANN model was trained using
the selected predictors from the European reanalysis interim dataset (ERAI), and the trained
ANN was applied to the dominant predictors fromselected GCMs to downscale the precipitation for
current and future predictions. This study
demonstrates the applicability of ANN for
precipitation downscaling, as a good agreement was
found between downscaled and station observed data
for the present time, with a correlation coefficient of
0.8. Sachindra et al. (2018) used four machinelearning techniques to downscale monthly
precipitation over 48 stations in Australia: genetic
programming (GP), support vector machine (SVM),
relevance vector machine (RVM), and ANN. The
study suggests that RVM and ANN are suitable forfloods, whereas RVM can be recommended overother methods for drought problems. Nourani et al.
(2019) applied an ANN to downscale the monthly
precipitation and temperature for three GCMs.
Previous studies that used ANN algorithms mainly
focused on utilizing a large number of large-scalevariables (predictors) from GCMs over many grids
surrounding the area of interest to downscalemonthly
precipitation(predictand).
Other
applications use finer resolution data such as thenormalized difference vegetation index (NDVI),
topography or cloud information for downscaling
satellite precipitation data (Immerzeel et al., 2008).
In this study, we introduce a simple, but efficient
ANN-based downscaling method that utilizes daily
observed precipitation data of a pseudo-coarseresolution (20-km) and enhances its spatial
resolution to a finer original scale (5 km). During thedownscaling process, it is important that the original
characteristics of the 5 km precipitation data arepreserved and successfully generated. This includes
maintaining the extreme values of the grid, the mean,
the standard deviation, and other relevant parameters.
For this purpose, long-term 20-km pseudo-coarse
resolution data which has been derived fromspatially averaged 5-km observed data is employed.

Nevertheless, it remains uncertain whether theoriginal 5 km data can be effectively reconstructedfrom the synthetically upscaled 20 km data using the
ANN algorithm. The object of this study is toinvestigate the applicability and effectiveness of the
ANN-based downscaling method for maintaining theoriginal characteristics of precipitation data under
this simple scenario. The observed data set is used inthis study instead of GCMs data to gain afundamental understanding of the ANN's
performance. The proposed approach eliminates thedifficulties associated with predictor selection, as it
only relies on the precipitation data at different
spatial scales as the input for the ANN downscaling
process. In addition, it has a daily downscaling time
step instead of a month.
2.

Overview of ANN

ANN is considered as a universal mathematical
approximator. It is efficient and robust to solve
large-scale nonlinear problems owing to its ability tolearn and recognize the general relationship between
input and output vectors. A typical ANN structure
comprises an input layer, output layer, and several
hidden layers. The input layer receives the input
variables, which could be temperature, rainfall,water levels, or other variables in hydrologicalmodeling. The output layer involves the values
predicted by networks, which could be any variablein the hydrological response, such as streamflow or
rainfall. The hidden layers contain several nodes fordata processing. The number of hidden layers andnodes of each hidden layer is generally obtained via
trial-and-error process (ASCE, 2000).
During the ANN modeling process, input andoutput data are typically divided into three sections:training, testing, and validation. Training aims totrain the network to generate an output vector that isclose to the target vector, and the weight and bias are
optimized while minimization the error between
input and output. The backpropagation algorithmwith gradient descent technique is employed as oneof the most common algorithms to train the networkand obtain optimal weights and bias. The testing
section selects the model with the best performance
among various combinations of training options andprevents overfitting of the network. Such behavior― 248 ―

3.

20km*3R2R4R75km * 4R1

20km*3can be noticed when the network tries to mimic thenoisy component in individual data during thetraining process, rather than learning the underlying
general relationship. Validation evaluates theperformance of the final model using different
sections of data and can be quantified by the error
percentage between the desired and predicted
outputs (ASCE, 2000).

5km * 4
r1 r2 r3 r4
r5 r6 r7 r8
r9 r10 r11 r12
r13 r14 r15 r16
R8

R3

R6

R9

Data and Methodology

It is worth mentioning that Kim et al. (2014)
developed a downscaling method called Formatted
Regression Frame (FRF) based on observed
precipitation data, which takes advantage of theunique spatial correlation to downscale observed
precipitation data at 20-km from a synthetically
upscaled resolution of 60 km. Also, Kim et al, (2017)
successfully applied the FRF method for AGCM60
data to reproduce AGCM20 precipitation data.
However, the previous study used a simple linear
multivariate regression, and it was for the downscalingof 60-km into 20-km resolution.
For the current study, observed gridded
precipitation data (APHR_JP; Kamiguchi et. al, 2020)
for 112 years from 1900 to 2011 were also utilized.
The original data comprised daily precipitation andhad a spatial resolution of 5 km × 5 km. First, the 5km
data were upscaled to 20 km, and then the spatial
correlation between the two datasets was checked foreach respective region. The ANN-based regression
model for downscaling was established between the
upscaled 20 km and 5 km spatial resolution data.
Finally, the accuracy of the downscaled 5 km
precipitation data was assessed by comparing it withthe original 5 km precipitation data. A square of 3 × 3
with a resolution of 20 km was considered as adownscaling frame, and the downscaling target was
the center grid of this square as demonstrated in Figure.
1 The output of this model was the daily precipitation
data for 16 grids at a square of 4 × 4 with a resolutionof 5 km. This model was tested for five regions acrossJapan as shown in Figure. 1 over four different months.
January represents winter, June is the rainy season due
to frontal rainbands, August is the season with themost unstable atmospheric conditions, and October
represents the mild weather of spring and autumn.
These five regions were also chosen to observe the

Figure 1. Locations of tested regions in Japan for
ANN model. Each region is a square of 20 km x3 gridmodel's behavior in diverse environments. Regions
2~4 are the main concern as they are known for heavy
rainfall, region 1 and 5 were chosen to assess themodel's performance in environments with lessrainfall and low temperatures, respectively.
4.
4.1

ANN Setup

ANN architecture
In this study, a simple feed-forward ANN withback-propagation algorithm and stochastic gradientdescent technique was used for training, as it allowed
the ANN to converge and reach the global minimum
more quickly (Hitokoto & Sakuraba, 2020). It
comprised an input layer, single hidden layer, and an
output layer, and is a widely used ANN structure inhydrological studies (Ahmed et al., 2015). The
activation function is a sigmoid function that provides
a nonlinear response and is one of the most commonlyused activation functions in the field of hydrology(Mekanik et al., 2013).
The selection of the ANN architecture and training
options is an important step, as the training results canvary based on the given ANN structures and hyperparameters, whereas the numbers of input and output
nodes depend on the number of given predictors andpredictands, as illustrated in Figure. S1. There is
flexibility in determining the number of hidden layers
and nodes in each hidden layer. The optimalarchitecture is usually obtained by trial and error― 249 ―

(ASCE, 2000).
Seasonal data for each region were used, anddivided as follows: 85 years for training (1900–1984),
12 years for testing (1985–1996), and 15 years forvalidation (1997–2011). Table 1 presents the daily
maximum values for each stage in each region.
The standardization of data is a prerequisite for
applying machine learning (ASCE, 2000). In addition,
the output of the sigmoid function applied in the model
ranged from 0 – 1. However, owing to the sensitivityof this function to very low and high values near 0 and
1, respectively, the input data were scaled between 0.2
and 0.8.
Table 1 Maximum daily precipitation in each stage foreach season and region
RegionRegion_1

Region_2

Region_3

Region_4

Region_5

4.2

Data

Train

Test

January

97.48

54.83

80.03

June

99.44

78.61

115.86

August172.65195.97120.32October

118.50114.15

87.60

January

64.80

56.45

61.08

June

204.83283.08213.22

August301.43167.16189.82October

194.03175.1858.70

January

65.27

39.97

85.77

Division

Validation

June

193.54225.86185.55

August211.25124.94182.09October

203.44145.50105.05January

74.49

50.10

102.92June

194.84201.02217.67

August284.62185.73223.10

October

254.75148.74

96.32

January

35.03

22.45

47.27

June

49.44

41.07

44.71

August155.24

86.73

90.85

October

97.96

46.95

38.78

Tuning of the hyperparameters
In this study, different combinations ofhyperparameters were tested during the training
process: neuron size (9, 18, 36), learning rate (0.01, 0.1,0.8), epoch (100, 1000, 2000), and batch size (10, 20,30). The learning rate is defined as the size of thechange in each bias and weight when updated, and it

also controls the size of the step taken downhill within
the gradient descent. An epoch represents a complete
iteration through the entire training dataset, whereas
the mini-batch size is the number of samples processed
before the weights and biases are updated within themodel (Kim and Tachikawa, 2018). Other types oftraining parameters, such as the momentum anddropout ratio, were not used in this study.
In total, we used 81 cases to determine the best
performance model for each season in five regions.
During this process and for each case, training was
conducted over a period (1900–1984), and the trained
ANN was applied to the test data over a period (1985–
1996) for prediction. The normalized test error(RMSE) was calculated and checked at every step sothat the case of the minimum test error among the 81
cases was selected as the best combination to achieve
the best result and deliver the best model for the season.
This process is also important because we aimed todetermine the optimal architecture for the ANN-based
downscaling models for each region and calendar
season by adjusting the hyperparameters, and also
overcome the ANN overfitting problem as the learning
rate, which controls the size of the updated weight andbias, is checked with many cases.
4.3 Model validation
Verification was performed after the training andtesting using the best combination among 81 cases todetermine the best performance model. The model
performance was evaluated based on the RMSE
criteria. Smaller values of the RMSE value indicatedbetter accuracy and fitting of the data. It was calculated
to include 0 mm/d precipitation. The RMSE indicatesthe general performance of the ANN model. Therefore,
the bias in the average and standard deviation of thedownscaled precipitation were calculated to measure
the ability of the model to simulate the individual
statistical properties for each season. Bias (as apercentage) for the statistic of interest is equal to thedifference between the simulated and observed values
divided by the observed statistic. A negative bias
indicates an underestimation of precipitation, while apositive bias implies overestimation (Sachindra et al.,2018).

― 250 ―

5.

Results and Discussions

5.1 Spatial correlation
The main concept of the downscaling method
proposed in this study is that the daily precipitation
at a 5 km grid horizontal resolution is spatiallycorrelated with the precipitation pattern at a 20 km
resolution. As shown in Figure. S2, the correlation
with 20 km grids was checked and was evidently
high for all regions during all seasons.
The correlation in the winter season (October) is
stronger than in the summer (June). This has asignificant impact on the output of the ANN model.
5.2 Tuning of the hyperparameters
Figure. S3 shows the normalized test errors for
all regions. The test error is presented in anormalized form (using scaled data within 0.2–0.8)
to eliminate the impact of varying precipitation
magnitudes across regions and seasons. In all theregions, the median of the boxplots is close to zero,
indicating good training results. These regions arelocated in southwest Japan and experience the same
weather and atmospheric conditions.
Table 2 lists the optimal hyperparameter
combinations chosen by the ANN for each region.
Regions 1 and 2 share the same combination for allseasons, except August, with 36 neurons instead of
9. In region 3, January has a different combinationof (36, 0.01, 1000) for the neuron size, learning rate,and epochs, respectively. Region 4 has the samecombination for all seasons except January andTable 2 Optimal hypermeters for each region
Neurons’
Best

size

in

hyper-

theparameters

hiddenlearning

Epoch

batch

rate

size

size

layer
Region 1

9, 36

0.8

2000

10

Region 2

9, 36

0.8

2000

10

Region 3

9, 36

Region 4
Region 5

1000,

10

0.8,0.01

2000

9, 36

0.8

2000

10

9

0.8

2000

10

October, with 36 neurons. Region 5 shows greater
consistency for all seasons. They represent the
optimal architecture for ANN-based downscaling
models for each region and calendar season.
5.2 Analysis of the validation results
The model performance was further verified using
15 years of separate validation data for each season
and region. Figure. 2 illustrates the representative
results for ANN based downscaling model fromregion 2, for some grids (r5, r6, and r15) for the two
seasons of January and June during the validation
stage. The red points represent the downscaled
versus the original 5km daily precipitation data,
while the green points represent grid R5 of 20 km
versus the original 5km daily precipitation data. The
red points on the graph are positioned on the 45degree line, indicating a close match and highagreement between the downscaled and original 5km
daily precipitation data for almost all seasons in all
regions. However, the green points are not alignedwith the 45-degree line, indicating a difference
between grid R5 of 20km and the original 5km daily
precipitation data as shown in Figure. 2. Thisevidently illustrates the clear improvement owing todownscaling used in this study.
Figure. 3 depicts the RMSE results for all seasonsin the regions during the validation period. As
illustrated in this figure, the June season had thehighest RMSE, followed by the August season for all
grids over most of the regions. This indicates thatwinter and wetter climates are more skillfully
downscaled compared to summer and drier climate
conditions, owing to their stronger connection withlarge-scale atmospheric circulation (Fowler et al.,2007). In region 5, the January season has the highest
error, followed by August, owing to different
weather conditions, i.e., snowing. Regions 2, 3, and4 have higher RMSE than regions 1 and 5. This canbe explained by the severe rainfall events caused by
the frequent typhoons that occur in these regions.
Across all regions and seasons, the RMSE varies
between 0.12 – 3.4 mm/day, with the lowest
observed in June in region 5, and the highest
observed in region 2. The maximum RMSE forregions 1 and 5 does not exceed 1.3 mm/day and 0.5mm/day, respectively. Table 3 displays the values of
RMSE as an average of 16 grids for each region. The― 251 ―

RMSE varies between 0.17 – 1.92 mm/day. These
reflect the temporal and spatial changes inprecipitation owing to seasonal variations within thesame region, as well as regional variations. Thesechanges occur because of varying weather andclimatic conditions throughout Japan. It isnoteworthy that some grids have higher RMSE
values than others, possibly owing to differences in
topography, land cover, and land-use characteristics,
as each grid has a horizontal resolution of 5 km.
Finally, the bias in the mean and the standard
deviation over the validation period (15 years) for all
regions considering all seasons was within ±8%, asshown in Figure. S4 and Figure. S5. Some regions,such as region 5, show less bias in the mean with ±2. The highest bias in the mean was found in regions
2 and 3. The mean is mainly underestimated in
October, region 2 with (-8%). The standard deviation
is evidently underestimated for the October andJanuary seasons in region 3 and January in region 5
by -6%, -8%, and -8%, respectively. Nonetheless,the ANN-based downscaling model successfully
preserves the statistical properties of the original
daily data. According to Sachindra et al. (2018), themean is better estimated regardless of the climate
regime and machine learning technique employed intheir study, whereas the standard deviation,
maximum values, and trend of high percentiles(above the 90th percentile) of monthly precipitation
are underestimated. Our study showed that the ANN
model effectively estimates both the mean andvariance of the original daily data.
5.3 Comparing the training, test, and validation
RMSE.
For all regions and seasons, it was observed thatwhen the maximum precipitation value (Pmax) from112 years of data was included in the test stage, theRMSE for all the grids in this stage was higher thanthe combined RMSE of the validation and training
stages. Similarly, if Pmax was in the validation stage,
the RMSE exceeded the RMSE of the training andtest stages for all grids. This affects model
performance and downscaling results.
Figure. S6 displays RMSE for three stages forthree months: August, June, and January in three
regions 1, 2 and 4, respectively, when Pmax is

Figure 2. Scatter plot of downscaled vs original 5km daily precipitation values (red), and its own 20 km gridR5 vs original 5km daily precipitation (green) for some grids (r5, r6, r15) in two seasons June (first row)
and January (second row) in region 2 in the validation stage.

― 252 ―

included and excluded from the training stage. In
August region 1 and June region 2, Pmax is includedin the test stage while in January in region 3 Pmax is
included in the validation stage. As shown in thisFigure. S6 (left side), RMSE test error for all thegrids surpassed the training and validation errors inAugust region 1 and June region 2, while RMSE
validation errors is higher than combined training
and test errors.
The year with the Pmax value has been
artificially excluded from the test or validation andadded to the training stage. Significant improvementin RMSE values is observed when Pmax is includedin the training, as shown in Figure. S6 (right side).
Consequently, the RMSE decreased across all grids
during the test and validation stages. Specifically,
for June in region 2, the RMSE decreased from 3.4
to 2.29 mm/day during the validation stage.
Figure. S7 displays the downscaled and observed
maximum daily precipitation for each year for Junein region 2 before and after including Pmax in the
training stage. The figure demonstrates that the ANN
model is well capturing maximum daily precipitation
for both cases, but less for very extreme cases.
Sharifi et al. (2019) demonstrated that the ANN
model was ineffective in accurately depicting
extreme monthly precipitation cases.

Figure 3. Plots of RMSE for 5 km grids from (r1 tor16) during all seasons in all regions 1,2 ,3,4, and 5
in the validation stage.
Table 3 Average RMSE values of 16 grids
over all regions, RMSE (unit: mm/day)
Season

Jan

June

Aug

Oct

Region 1

0.34

0.74

0.73

0.41

Region 2

0.31

1.91

1.05

0.44

Region 3

0.96

0.97

0.70

0.55

Region 4

0.71

1.38

0.84

0.58

Region 5

0.41

0.17

0.28

0.17

Finally, obtaining the maximum precipitation
dataset at the training stage is crucial and affects― 253 ―

6.

Concluding Remarks

The current study presents a simple but efficient
ANN method for downscaling daily observed
precipitation at two distinct spatial resolutions: 5-km
and synthetically upscaled 20-km. By comparing thedownscaled 5 km precipitation data for each gridwith its own 20 km grid, clear improvement wasachieved by the downscaling method for almost all
grids from r1 to r16 in the tested regions considering
all seasons. The June season had the maximum
RMSE of 3.4 mm/day. Additionally, the ANN model
successfully preserved the statistical properties ofthe original daily data, maintaining bias in mean andstandard deviation within ±8%. This model performs
better in estimating maximum values. It effectively
addresses several limitations and requirements
associated with conventional downscaling methods.
It neither utilizes a considerable number of largescale predictors over many grids for the area ofinterest nor depends on assumptions such as thenormal distribution of variables and the linear
relationship between predictors and predictands.
Given the encouraging results obtained from theobserved data, the proposed ANN method exhibits ahigh potential to be applied for 20-km and 5-km
AGCMs data for our next study. However, there arecertain limitations in this study. Firstly, the proposed
method has been evaluated using only observed
precipitation data. Even though the method shows
successful reconstruction of coarse-resolution
precipitation data into fine-resolution data, it isbased on daily precipitation data. Application tohourly data might ask for more sophisticated model
structures. Secondly, AGCM output from different
models and resolutions would provide diverse
characteristics of its precipitation output. Revisionof the model structure as well as careful input
selection might be necessary for applying the
proposed method to AGCM output, which will be
the focus of our future research.
Acknowledgments
The authors would like to express sincere gratitude
to the Ministry of Education, Culture, Sports,
Science, and Technology (MEXT), Japan, for theirvaluable support in conducting this work. The

research was carried out by Theme 4 of theAdvanced Studies of Climate Change Projection(SENTAN
Program),
Grant
Number
JPMXD0722678534.
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Appendix
In this appendix, we present supplementary figuresthat provide further support and information relevantto the main findings and discussions presented in themain text and contribute to a comprehensive
understanding of our research.

― 255 ―

(Received August 31, 2023)