Use of APHRODITE Rain Gauge–Based Precipitation and TRMM 3B43Products for Improving Asian Monsoon Seasonal Precipitation Forecasts

by the Superensemble Method

AKIYO YATAGAI*

Research Institute for Sustainable Humanosphere, Kyoto University, Kyoto, Japan

T. N. KRISHNAMURTI, VINAY KUMAR, A. K. MISHRA, AND ANU SIMON

The Florida State University, Tallahassee, Florida

(Manuscript received 13 June 2013, in final form 25 September 2013)

ABSTRACT

A multimodel superensemble developed by the Florida State University combines multiple model

forecasts based on their past performance (training phase) to make a consensus forecast. Because observed

precipitation reflects local characteristics such as orography, quantitative high-resolution precipitation

products are useful for downscaling coarse model outputs. The Asian Precipitation–Highly-Resolved

Observational Data Integration Toward Evaluation of Water Resources (APHRODITE) and Tropical

Rainfall Measuring Mission (TRMM) 3B43 products are used for downscaling and as training data in the

superensemble training phase. Seven years (1998–2004) of monthly precipitation (June–August) over the

Asian monsoon region (08–508N, 608–1508E) and results of four coupled climate models were used. TRMM

3B43 was adjusted by APHRODITE (m-TRMM). For seasonal climate forecasts, a synthetic super-

ensemble technique was used. A cross-validation technique was adopted, in which the year to be forecast

was excluded from the calculations for obtaining the regression coefficients. The principal results are as

follows: 1) Seasonal forecasts of Asian monsoon precipitation were considerably improved by use of

APHRODITE rain gauge–based data or them-TRMMproduct. These forecasts are much superior to those

from the best model of the suite and ensemble mean. 2) Use of a statistical downscaling and synthetic

superensemble method for multimodel forecasts of seasonal climate significantly improved precipitation

prediction at higher resolution. This is confirmed by cross-evaluation of superensemble with using other

observation data than the data used in the training phase. 3) Availability of a dense rain gauge network–

based analysis was essential for the success of this work.

1. Introduction

Seasonal monsoon forecasts for Asian countries are

undoubtedly important to society. This is a scientifically

challenging endeavor, however, especially for precipitation.

Operationally, observed precipitation is sometimes used

for postprocessing of numerical forecasts. This is because

precipitation is a localized phenomenon for which it is

difficult to reproduce realistic patterns, even qualitatively.

Quantitative estimates are difficult to attain in both short-

term and long-term forecasts.

There have been many studies using multimodel en-

sembles to reducemodel biases. The superensemble,which

uses observation data to minimize bias of multiple model

simulation results, has shown significant improvement of

precipitation forecast scores (Krishnamurti et al. 1999). An

accurate observed precipitation dataset is key for achieving

the best forecast. Rain gauge–based, daily gridded pre-

cipitation data across India produced better short-term and

seasonal forecasts in that country (Krishnamurti et al.

2009a,b; Chakraborty and Krishnamurti 2009).

The Asian Precipitation–Highly-Resolved Observa-

tional Data Integration Toward Evaluation of Water

Resources (APHRODITE) project created daily grid

*Current affiliation: Solar-Terrestrial Environment Laboratory,

Nagoya University, Nagoya, Japan.

Corresponding author address: Akiyo Yatagai, Solar-Terrestrial

Environment Laboratory, Nagoya University, Furo-cho, Chikusa-

ku, Nagoya 464-8601, Japan.

E-mail: akiyoyatagai@stelab.nagoya-u.ac.jp

MONTH 2014 YATAGA I ET AL . 1

DOI: 10.1175/JCLI-D-13-00332.1

� 2014 American Meteorological Society

precipitation data over the entire Asian domain (Yatagai

et al. 2009, 2012). The initial purpose of APHRODITE

was to formulate reliable, rain gauge–based, high-resolution

products. These were for validating high-resolution cli-

mate model simulations (Yatagai et al. 2005) and for

statistical downscaling of relatively course climate sim-

ulation outputs, to make localized precipitation fore-

casts according to future climate change resulting from

the anthropogenic greenhouse effect. APHRODITE

products are used for such purposes because they con-

tain substantial rain gauge data and use an interpolation

method that considers orographic effects (see next sec-

tion). Hence, they represent a useful database for trans-

forming biased model precipitation patterns into more

realistic ones, especially in mountainous areas. Thus,

APHRODITE can furnish training data in the super-

ensemble method, as well as data to use in downscaling

(Kumar and Krishnamurti 2012).

Satellite products are also used for superensembles.

Satellite estimates usually have higher spatiotemporal

sampling rates and fewer missing values than rain gauges,

and are hence convenient for use as a downscaling tool.

However, for surface precipitation, model and satellite

estimates should be validated against rain gauge data

(Chen et al. 2008). Above all, an advantage of satellite

products over rain gauges is near-real-time delivery.

Very few countries have dense gauge networks and can

collect their data in a timely fashion and apply quality

control and gridding in near–real time. It is also difficult

to do so beyond national boundaries, although this may

sometimes be necessary (e.g., to determine precipitation

in the headwaters of an international river).

Hence, it is useful to adjust satellite products with

APHRODITE data during periods of mutual data avail-

ability. The superensemble technique requires 1) at least

four or five model results, 2) observation data for training,

and 3) observation data for validation. The second and

third items must be independent (usually different years).

Because current APHRODITE products have temporal

coverage through 2007 and becausemodels should cover

the same period as in items 2 and 3 except when applying

climate superensemble (Yun et al. 2003), we did per-

formance tests of the adjustment using currently avail-

able datasets. We compare the results of superensemble

forecasting for summer monsoon precipitation using

APHRODITE and Tropical Rainfall Measuring Mis-

sion (TRMM) 3B43 products.

2. Data

a. APHRODITE precipitation data

As reference rain gauge–based precipitation, we

used a daily gridded precipitation dataset created by

the APHRODITE project (Yatagai et al. 2012), with

0.258 resolution over the Asian monsoon region

(APHRO_MA_V0902; APHRO hereafter). The data

period is 1998–2004 (7 yr), and the coverage is over

08–558N, 608–1558E. Maximum input data were from

1998, with a gradual decrease through 2004 (Yatagai et al.

2009). We used APHRO after formulating monthly total

precipitation.

b. TRMM 3B43 precipitation data

We used TRMM 3B43, a 0.258 monthly precipitation

product. It is a combined product of microwave satellite

imagery, including that from the TRMM Microwave

Imager (TMI), geostationary satellite infrared sensors,

and rain gauges. For the latter data type in Asia, only

data from the Global Telecommunication System (GTS)

were used. Figure 1 shows the distribution of gauge sta-

tions used by APHRO and GTS. The APHRODITE

project collected data from 2.3 to 5.5 times the number of

rain gauges of GTS (this quantity varied with the year).

There were substantially more input data around the

Himalayas and Southeast Asia, which give precipitation

estimates exceeding 1000mmyr21 (Yatagai et al. 2012).

Considering the potential application of this study

to operational seasonal forecasting, an observation

dataset and multiple model results are required. Hence,

optimizing the use of satellite-based data is also expec-

ted. Therefore, we prepared three training datasets:

1) APHRO; 2) TRMM 3B43 (TRMM hereafter); and

3) modified TRMM 3B43 with APHRO (m-TRMM

hereafter). The time (7 yr) and space coverage is the

same as described above. We used all monthly data, but

show only results for summer (June–August).

c. Model data

The multiple superensemble requires at least four

model datasets. Thus, we used four coupled general

circulation model (CGCM) outputs those are the part

of Climate Prediction and Its Application to Society

(CliPAS) project (Wang et al. 2009). These are from the

Geophysical Fluid Dynamics Laboratory (GFDL) of

the National Oceanic and Atmosphere Administration

(NOAA;Delworth et al. 2006), the National Centers for

Environmental Prediction (NCEP) and NOAA (Saha

et al. 2005), Seoul National University (SNU) in Korea

(Kug et al. 2005), and the University of Hawaii (Fu and

Wang 2001). Each model consists of different sets of

atmospheric or oceanic GCMs. Forecast precipitation

data from the four GCMs were converted to monthly

precipitation for the seven years.

CGCM results do not reflect the dynamic field in a

year. Hence, the climate superensemble technique (Yun

et al. 2003, 2005) uses empirical orthogonal function

2 JOURNAL OF CL IMATE VOLUME 00

(EOF) analysis to compare the 7-yr summer model data

with observation data prior to the construction of a

multimodel superensemble.

3. Method of analysis

a. Preparation of m-TRMM

Monthly m-TRMM data were created by the follow-

ing equation:

RAPHRO5 aRTRMM 1 b , (1)

where RAPHRO and RTRMM are APHRO monthly pre-

cipitation and TRMM, respectively. The regression co-

efficients a and b are the slope and intercept of the least

squares fit. Figure 2 shows a and b patterns for July.

Large values appear around the Himalayas and Tianshan

Mountains and Southeast Asia, where GTS rain gauges

are sparse (Fig. 1). Here, a (slope) is the ratio of APHRO

to TRMM.

Figure 3 shows time series of the coefficient of

spatial correlation between TRMM (m-TRMM) and

APHRODITE and root-mean-square error (RMSE)

between TRMM (m-TRMM) and APHRODITE, which

was calculated monthly over the entire domain de-

scribed above (Fig. 2). There was large error in summer

relative to other seasons because summer is the rainy

season across most of the domain. Correlation coeffi-

cients improved with m-TRMM compared with TRMM

itself, because of the related adjustment.

FIG. 1. Distribution of APHRODITE rain gauges across Asian monsoon region.

FIG. 2. Pattern of a (slope) and b (intercept), defined in Eq. (1).

Fig(s). 1,2 live 4/C

MONTH 2014 YATAGA I ET AL . 3

b. Preparation of model data and downscaling

Some CGCM results are the average of several mem-

bers within a single model. After attaining monthly pre-

cipitation values, eachmodel result was interpolated onto

the 0.258 grid by bilinear interpolation. Then, the re-

gression coefficients c and d were defined by

Robs 5 cRmodel 1 d , (2)

where Robs and Rmodel are observed and interpolated

model forecasts of precipitation, respectively. The co-

efficients c and d are as defined in the preceding sub-

section, and are calculated using Eq. (2) at each grid

point for every month of the year. However, following

the cross-validation principle, the year for which re-

gression coefficients are calculated is separated and the

remaining years are used to determine the regression

coefficients.

The regression coefficients contain information on

model biases (Kumar and Krishnamurti 2012), but this

information is not shown here. We defined parameter

sets c and d for the three types of observation (APHRO,

m-TRMM, and TRMM) and for the four models de-

scribed in section 2c. The observation data used for

downscaling [Eq. (2)] and for teacher data are the same.

c. Synthetic superensemble

Themultimodel superensemble approach (Krishnamurti

et al. 1999) yields high skill relative to that of partici-

pating member models. The approach consists of two

phases: ‘‘training’’ and ‘‘forecast.’’ In the former, a pa-

rameter matrix is defined so that error between multiple

models and observation is minimized. The parameter is

defined grid by grid and month by month. For climate

forecasts, a slightly modified superensemble method

called the synthetic superensemble (SSE) is constructed

(Yun et al. 2003; Chakraborty and Krishnamurti 2009)

because aCGCMdoes not represent the circulation field

in a year. In this method, expansion of the forecast and

observation fields in time is done using principal com-

ponents (PCs) and spatial EOFs. From the 7-yr dataset

(1998–2004), one year of data is separated for use in

forecasting and validation.

We computed the correlation (COR), bias, and equi-

table threat score (ETS) between forecast precipitation

of the SSE and independent dataset of the training phase.

d. Study flow

The flowchart of downscaling, SSE training phase, and

forecast and evaluation is shown in Fig. 3 of Kumar and

Krishnamurti (2012). Here, we summarize the steps

taken in the present work.

1) Coarse-resolution precipitation data from four cou-

pled climate models (resolution 2.58) were bilinearly

interpolated to the 0.258 grid.2) Regression coefficients c and d were obtained by

a least squares linear fit of model-interpolated pre-

cipitation with that of the high-resolution observa-

tional datasets (separately for APHRO, m-TRMM,

and TRMM).

FIG. 3. Time series of (top) correlation coefficients and (bottom)RMSE, betweenAPHROand

TRMM (black) and between APHRO and m-TRMM (green).

Fig(s). 3 live 4/C

4 JOURNAL OF CL IMATE VOLUME 00

3) A cross-validation technique was adopted, in which

the year to be forecast was excluded from calcula-

tions of the regression coefficients. Coefficients var-

ied spatially and monthly during the study years.

4) These regression coefficients were applied to the

forecast year to obtain downscaled model forecasts

for that year.

5) The above steps (1–4) were repeated for each year of

1998–2004 to obtain downscaled forecasts of individ-

ual models.

6) The final outcome was monthly precipitation fore-

casts on 0.258 grids across the greater monsoon

region, from the four coupled models.

To assess the performance of SSE, we did the following

(7 and 8) calculations separately.

7) We also computed ensemble means of seasonal fore-

casts from our suite of multiple models, which did not

use TRMM or gauge datasets. The seasonal precipita-

tion forecasts were validated against the dense gauge-

based seasonal datasets over the greater monsoon

region. Metrics for forecast validation included the

standardRMS errors, ETS, and bias against APHRO.

8) The same metric was applied for the three observa-

tion data, and validation is cross checked with other

observation data.

FIG. 4. Precipitation pattern (mmday21) in summer (June–August) 1998 from (a)APHRODITEand (b)m-TRMM.

(c) Ensemblemean of summer 1998 precipitation forecast by the fourmodels; (d) summer 1998 precipitation simulated

by best performing of the four models; and SSE results with (e) m-TRMM and (f) APHRO used for training.

Fig(s). 4 live 4/C

MONTH 2014 YATAGA I ET AL . 5

4. Results

a. Precipitation pattern and time series

Figure 4 shows precipitation patterns of APHRO and

m-TRMM, along with results of forecasts for summer

1998 (June–August). The superensemble and other fore-

cast experiments were executed monthly, and the figure

shows the three-month sum. The correlation between

APHRO and m-TRMM is 0.96, and that of RMSE with

APHRO is 0.43 (Fig. 4b).

Figures 4c–f describe the results of an ensemble

mean, a model achieving best performance, SSE with

m-TRMM, and SSE with APHRO, respectively. Sta-

tistics (spatial correlation and RMSE) shown in the

figures are versus APHRO (Fig. 4a).

Downscaling was applied in Figs. 4c–f, so even Figs. 4c

and 4d show orographic rainfall along the Himalayas

and over South and Southeast Asia realistically, com-

pared with the model climatology/seasonal patterns

[precipitation patterns simulated by CGCMs, including

the four models used here, were displayed in Kumar

and Krishnamurti (2012) and Krishnamurti and Kumar

(2012)].

Spatial correlation in the areas shown in Fig. 4 reveals

that the SSE with APHRO had the best correlation,

followed by the SSE with m-TRMM. In terms of RMSE,

the SSE with APHRO had the least error. RMSE time

series for the four results are shown in Fig. 5a. This

shows results consistent with Fig. 4, the example plot,

throughout the years. The SSEwithAPHROperformed

best, followed by SSE with m-TRMM. In the summer

monsoon months, the difference among the four [en-

semble mean (EM), best model, SSE with m-TRMM,

SSE with APHRO] was large compared with winter. In

summer, the EM and best model performed similarly;

SSE with m-TRMM showed better performance, with

about half the error of the best model and SSE with

APHRO.

Figures 5b and 5c show bias and ETS according to

rainfall intensity threshold, respectively. In every rain-

fall class, SSE with APHRO gave better performance,

followed by m-TRMM, best model, and EM. In terms

of bias (Fig. 5b), SSE with APHRO and SSE with

m-TRMM were almost the same, but the other two

showed large biases for weak (2mmday21) and heavy

(.20mmday21) rainfall.

b. Metrics against other observations

The above three metrics are relative to benchmark

APHRODITE data. It may be that forecast precipitation

looks like training data, not real values. To evaluate

this effect, we derived statistics versus m-TRMM, and

TRMM versus APHRO. Results are listed in Table 1.

It is clear that correlation coefficients are higher in the

case in which the forecast was evaluated with bench-

mark data used for training (shown in italics). The same

was true for RMSE and ETS. RMSEs are lower in the

case of evaluation against the data used for training, and

ETS was larger in the couple that is evaluated against

the same with the training data.

Interestingly, compared with the value itself, the

correlation of APHRO (0.91) is the highest among the

same pairs (m-TRMMhad 0.88 and TRMMhad 0.82). It

may be expected that APHRO had a low correlation

because it has very precise patterns that may produce

bias. The RMSE revealed excellent performance, simi-

lar to the use of APHRO. The RMSE of APHRO was

the smallest at 0.64; m-TRMM yielded 0.80 and TRMM

FIG. 5. (a) Time series of RMSE computed against benchmark

APHRODITE data for ensemble mean (green), best model (blue),

SSE by m-TRMM (purple), SSE by APHRO (red). (b) Bias and

(c) ETS computed against APHRO. Colors in (b),(c) are as in (a).

Fig(s). 5 live 4/C

6 JOURNAL OF CL IMATE VOLUME 00

gave 1.0. The result of ETS was a slightly different. The

TRMM pair showed the best value (0.65) followed by

APHRO (0.62), but this is a slight difference.

These results demonstrate that high-resolution pre-

cipitation data from a dense network of rain gauges are

essential for improving seasonal rainfall estimation over

the Asian monsoon region.

5. Conclusions

Using APHRODITE precipitation data (APHRO),

the TRMM 3B43 product, and four CGCMs, we per-

formed an experiment combining an SSE with downscal-

ing. The results were compared with ensemble forecasts

lacking observation data and singular (best) model out-

puts. This revealed the following:

1) The dense rain gauge network dataset (APHRO-

DITE) considerably improved seasonal summer

monsoon precipitation forecasts. An m-TRMM

product, which was created by modifying TRMM

3B43 using APHRO data, gave similar results. Skill

scores of these two experiments weremuch superior to

the best single model and ensemble forecast lacking

observation data.

2) Availability of a dense rain gauge network is imper-

ative to success of the seasonal forecast. It is con-

firmed by cross-evaluation of superensemble with

using other observation data than the teacher data

used in the training phase.

3) The method using satellites can be effective in real-

time application.

Acknowledgments. This paper is a contribution to the

APHRODITE project, supported by the Global Envi-

ronment Research Fund of the Ministry of the Envi-

ronment, Japan. This work was also supported by NASA

GPM Grant Number NNX13AF75G. Part of this work

was supported by a research project on Human Life,

Aging, and Disease in High-Altitude Environments ad-

ministrated in the Research Institute for Humanity and

Nature and a Research Collaboration with Disaster

Prevention Research Institute, Kyoto University.

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TABLE 1. Evaluation of score statistics against the references (APHRO,m-TRMM, and TRMM). Values in italics denote the forecast was

evaluated with benchmark data used for training. Boldface values indicate the best score statistics.

Observations used

in downscaling/training

Validation against APHRODITE Validation against m-TRMM Validation against TRMM

COR RMSE ETS COR RMSE ETS COR RMSE ETS

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m-TRMM 0.73 1.2 0.60 0.88 0.80 0.60 0.51 1.4 0.59

TRMM 0.45 1.9 0.46 0.75 0.97 0.50 0.82 1.0 0.65

MONTH 2014 YATAGA I ET AL . 7

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