165

Development of a Long-term Daily Gridded Temperature Dataset and
Its Application to Rain/Snow Discrimination of Daily Precipitation
Natsuko YASUTOMI1*, Atsushi HAMADA2 and Akiyo YATAGAI3
1

Center for Coordination, Promotion and Communication, Research Institute for Humanity and Nature, Japan
457-4, Kamigamo-Motoyama, Kita-ku, Kyoto 603-8047, Japan
2
Atmosphere and Ocean Research Institute, the University of Tokyo, Japan
3
Faculty of Life and Environmental Sciences, University of Tsukuba, Japan*e-mail: yasutomi@chikyu.ac.jp

Abstract
We created a daily mean gridded temperature dataset of monsoon Asia (15˚S-55˚N, 60˚E-155˚E) for theperiod of 1973-2007, with a 0.50 x 0.50 degree grid. We analyzed this dataset based on station observations
collected and a quality control and interpolation system developed through the activities of the Asian
Precipitation – Highly Resolved Observational Data Integration Towards Evaluation of Water Resources(APHRODITE) project (Yatagai et al., 2009, 2012). The number of stations is up to 1.5-3 times the number ofstations based on the Global Telecommunication System (GTS), which have been used to obtain other gridded
temperature products. The monthly means and climatology of our product are comparable to the monthly
means of those products. This is the only product in Asia that has high resolution both temporally and spatiallyas the APHRODITE precipitation product is known for.
The ability to discriminate between rain and snow is added to the APHRODITE daily precipitation product by using daily mean temperature and relative humidity (RH). Relative humidity is derived from a reanalysis product, because RH observation data are collected insufficiently for interpolation. We found use of thetemperature product of this study and the RH derived from the reanalysis product to be adequate fordetermining whether precipitation was rain or snow. Our estimated solid precipitation amount using rain/snow
discrimination for late fall to early spring (October to March) is consistent with satellite observations.
This dataset is available on the APHRODITE website <http://www.chikyu.ac.jp/precip>. The combination of daily mean temperature, precipitation and rain/snow information in this high- resolution gridded
format would be useful as input to river-flow models, crop models and many other situations where water
resources must be estimated.
Key words: climate change, daily temperature, quality control, rain/snow discrimination

1. Introduction
Many temperature datasets based on surface observations have been developed to estimate recent increases insurface temperature and to assess the impact of global
warming. Changes in temperature affect vegetation,
agriculture, the spread of infectious diseases and manyother aspects of human life. Moreover, it has been
pointed out that global warming will bring heavier rainfall or drought, or changes of snowfall into rainfall.
Whether the precipitation is solid or liquid is critical for
those who live in mountainous areas or the subarctic zone
because rain may damage permafrost ground, mountainglaciers or perpetual snow by causing them to melt.
Surface temperatures simulated in climate models are
dependent on the complexity of physical processes suchas the solar radiation balance, fluxes, surface topography
Global Environmental Research
15/2011: 165-172
printed in Japanand weather. Moreover, the resolution of climate models
has been developing rapidly recently. Therefore, a surface temperature dataset which is highly resolved both
spatially and temporally, based on surface observations isneeded for validation of the output data of highresolution climate models.
Currently a number of gridded temperature datasets
based on surface observations are available. A monthly
time series of gridded terrestrial air temperatures has
been released from the University of Delaware (UDel)(Legates and Willmott, 1990; Willmott and Robeson,
1995). HadCRUT3 (Brohan et al., 2006) is a monthly
gridded temperature dataset created by the Hadley Centre,
UK Met Office, in collaboration with the Climate Research Unit (CRU) of the University of East Anglia. The
CRU has also released a high-resolution monthly temperature dataset, CRU_TS2.1 (Mitchell et al., 2004;©2011 AIRIES166

N. YASUTOMI et al.

Mitchell and Jones, 2005). The E-OBS dataset has beenreleased by the EU-FP6 project ENSEMBLES (Haylock
et al., 2008) for European daily gridded temperatures.
The Hadley Centre and US National Climatic Data
Center (NCDC) have created the HadGHCND daily
gridded temperature dataset based upon maximum andminimum temperature observations (Caesar et al., 2006).
Table 1 gives detailed information such as spatial andtemporal resolution and data periods for each dataset.
The APHRODITE project has released a highresolution daily gridded precipitation dataset covering
nearly the whole Eurasian continent (Yatagai et al., 2009).
Since 2006, we have made continuous efforts to collectrain-gauge data, build good relationships with many
Asian countries and improve our quality control andinterpolation in the project (Yatagai et al., 2011, 2012. In
this study, we have developed a high-resolution gridded
daily mean temperature product for Asia by making useof APHRODITE project achievements.

2. Data and Analysis Procedures
2.1 Station data sources
Station daily air temperature data were compiled fromthe following kinds of sources,
1) GTS-based datasets,- Global Summary of the Day (GSOD) (data are
available at the NCDC FTP site <ftp://ftp.ncdc.noaa.
gov/pub/data/gsod> [last accessed on 20 June 2011])- Global Historical Climatology Network (GHCN2),(Peterson and Vose, 1997)
2) Pre-compiled datasets from the following organizations or groups,- Global Energy and Water Cycle Experiment(GEWEX) Asian Monsoon Experiment (GAME)
Tropics <http://hydro.iis.u-tokyo.ac.jp/GAME-T>[last accessed on 20 June 2011]- Association of Southeast Asian Nations (ASEAN)
Compendium of Climatic Statistics project

Table 1 List of gridded temperature datasets based on station observations. Spatial resolution denotes resolution in (longitude) ×(latitude). All datasets consist of terrestrial surface air temperatures. The reference paper for each dataset is presented in
Section 2.
DatasetCRU_TS2.1
HadCRUT3
UDel

RegionGlobalGlobalGlobalVariable(s)Tmx/mn/ave
Tave(anomaly)
Tave

E-OBS

EuropeTmx/mn/ave

HadGHCND
APHRODITE

GlobalAsia

Tmx/mnTave

Resolution (spatial)
0.5deg × 0.5deg
5.0deg × 5.0deg
0.5deg × 0.5deg
0.5deg × 0.5deg
0.25deg × 0.25deg
3.75deg × 2.5deg
0.5deg × 0.5deg(temporal)
MonthlyMonthlyMonthly

Period1901-2002
1850-present
1900-2008

Daily

1950-present

Daily
Daily

1950-2007
1973-2007

Fig. 1 Distribution of observation stations which passed our QC system for the year 1987. Blue: GTS based datasets.
Green: pre-compiled datasets. Red: individual collections by the APHRODITE project. The orange boundary
indicates the area of the Monsoon Asia gridded dataset, corresponding to the APHRO_MA precipitation dataset.

Development of a Long-term Daily Gridded Temperature Dataset

3) Individual collection by the APHRODITE project,- Archives of national meteorological and hydrologicalservices (NMHs) or individuals in Japan, China,
Mongolia, Russia, Taiwan and Nepal.
GHCN and Nepal country data consist of daily maximum and minimum temperatures. We estimated dailymean temperature using Equation (1),Tmean = (Tmax + Tmin)/2(1),

where Tmin/max/mean are the daily minimum/maximum/mean temperature, respectively. Estimated dailymean temperature data are also used in the product andtreated the same as daily mean temperature.
Figure 1 shows the distribution of temperature observations for 1987, in which a large number of daily reports
were collected. The region analyzed was 60°E-155°E,
15°S-55°N, corresponding to APRHO_MA (MonsoonAsia) of the APHRODITE gridded daily precipitation
product. Observations are dense, especially in Japan,
Nepal and Taiwan. We collected 1.5 to 3 times theamount of data compared to GTS-based reports, uponwhich most available gridded temperature datasets are
based. A time series of the number of observation records
is shown in Fig. 2. The number of reports varies among
years and seasons. It is constant from April to October,and then gradually decreases to a minimum in December
and January since some stations are closed due to severe
winter weather during that season. The number of reports
is no more than half that of the warm seasons. It was
smaller in 1951-1972 than in recent years. Reports in
India before 1973 are found to be missing entirely. In thisstudy, interpolation is applied to 1973-2007.
2.2 Quality control of observation data
A number of erroneous data have been found in collected observation datasets. Detection of errors is essential to the accuracy of the gridded product. We developed
an automated quality control (QC) system for observed
precipitation (Yatagai et al., 2009, 2010; Hamada et al.,2011). The system has been modified to detect erroneoustemperature data. We did not apply comparison of timeseries of nearby stations, in our processes for detecting
errors, since differences arising from elevation of thestations could be detected. Our QC process, modified
from Hamada et al. (2011) is as follows.
1) Detection of outliers
Temperature outliers are removed by identifying dayson which temperature exceeded six standard deviations
from the mean with reference to all days within sevendays of that calendar day over all available years. For
example, to test the observation on 12 January 1975, we
calculate the mean and standard deviation using observations from 5 January to 19 January for all available
years. This method is based on the application by
Haylock et al. (2008) to the observation data archive ofEurope (E-OBS). They detected temperature outliers
using five standard deviations from the mean within fivedays of daily temperatures in Europe. We used six stan-167

dard deviations from the mean within seven days todetect temperature outliers because quite a few values inthe tropics would be misjudged as temperature outliers
when using the same parameters as Haylock et al. (2008).
2) Detection of constant values
We detected repetition of constant values, including
zero, by five times or more. In cases where temperatures
were recorded in 0.5 degree centigrade increments or the
Fahrenheit scale, repetition by five times was frequently
detected. We could not distinguish which of these may
have been erroneous.
2.3 Interpolation of daily mean temperature
Our interpolation algorithm was based on the procedure for gridding observed precipitation used by Yatagai
et al. (2009). As for temperature, since we did not collectmonthly means or high resolution climate data, we tried
to interpolate based on raw observed temperatures. Theinterpolation processes modified from Yatagai et al.
(2009) is described below.
1) Temperature correction to mean sea level with a temperature lapse rate 6.0°C/km.
2) Interpolation of the corrected temperature onto
0.05 degree grids using a distance-weighting function
based on Spheremap (Willmott et al., 1985) with100 km of effective radius. The number of observations is also calculated.
3) Temperature correction to GTOPO30 elevation (U.S.
Geological Survey EROS Data Center, 1998) with atemperature lapse rate 6.0°C/km.
4) Regridding of the 0.05 degree data into a 0.50 degree
grid product using area-weighted means.

Fig. 2 Time series of the number of valid daily reports for the
Monsoon Asia region (60°E-155°E, 15°S-55°N). Blue:
number of all valid reports used in the APHRODITE
gridded dataset. Green: number of valid reports from the
GTS network. The GTS reports are included among thevalid APHRODITE reports.

168

Fig 3

N. YASUTOMI et al.

Monthly mean temperatures of January (upper panels) and July (lower panels) 1987, using the gridded datasets of APHRODITE(left) CRU_TS2.1 (center) and UDel (right). Unit: degrees centigrade.

3. Analyses of Gridded Daily Mean
Temperature
3.1 Climatology
We derived climatology data and monthly means ofdaily mean temperature. The monthly mean daily mean
temperatures of January and July 1987 derived fromthe datasets of APHRODITE (this study), CRU_TS2.1
and UDel are shown in Fig. 3. Differences between
APHRODITE and the other datasets are within 3°C in
most of the grids (figure not shown). The APHRODITE
daily mean gridded temperature is comparable to those
products based on monthly mean observations. The
distribution of temperatures over the Himalayas andTibetan Plateau resembles that of UDel. On the other
hand, our temperature data are higher than those of theUDel and CRU_TS2.1 datasets in India for both winterand summer. Those differences may have resulted fromdifferences in density of the input data. Original temperature data of Nepal and ASEAN were collected by the
APHRODITE project whereas the observation data from
NMHs of India were limited to precipitation.
3.2 Linear trends of maximum daily mean
temperature over Monsoon Asia
Daily gridded products are useful for analyzing extremes such as the maximum/minimum daily mean
temperature of the year or the number of summer days.
Figure 4 shows linear trends of the average of the highest
five daily mean temperatures each year for the period

Fig. 4 Linear trends (°C/30yr) of averages of the five highest
daily mean temperatures each year for the period
1978-2007.

1978-2007. A large positive trend is seen around
Mongolia. It can be seen that the highest temperature of
the year has been rising recently, especially in inland
regions. The increasing trend in Mongolia is also seen in
HadEX, a set of extreme indices released by the Hadley
Centre (Alexander et al., 2006). The relationshipbetween the trend over Mongolia and changes inprecipitation is an issue for future studies.

Development of a Long-term Daily Gridded Temperature Dataset

4. Rain/Snow Discrimination Using
APHRODITE Daily Gridded Precipitation
Products
In managing water resources, especially in mountainous regions, it is essential, to judge whether precipitation is liquid or solid. Solid precipitation accumulates
during winter and melts in spring. Accumulated precipitation melts and flows down in spring and the water isused by many people. Heavy rainfall in spring may cause
floods. Since APHRODITE gridded precipitation is daily,
the resulting highly resolved product with good representation of precipitation in Himalayas, together withrain/snow discrimination meet the needs of NMHs andhydrological model users.
4.1 Analysis procedure
Whether precipitation is melting or not depends on
humidity as well as surface temperature. Matsuo andSasyo (1981a, 1981b) found through observation andnumerical modeling that melting of snowflakes follows alinear function of temperature and relative humidity.
Melting versus non-melting condition is judged using thefollowing equation,
RHcri = 92.5 – 7.5T(2),

where RHcri is the critical relative humidity (%) and T issurface air temperature (°C). A line graph of Eq. (2) isshown in Fig. 5. Below the critical humidity threshold,precipitation is all snow and above it, it is rain, a mixtureof snow and rain or sleet, according to conditions. Here,sleet is classified as melting. Rain/snow discrimination ismade in accordance with the melting or non-melting
condition of precipitation. It can be seen that precipitation at as much as 10°C above freezing level might not be

Fig. 5 Rain (melting)/snow (not melting) discrimination
following Equation (2).

169

melting under dry conditions.
Most of the collected gauge data lack humidity information. Though some gauge data have humidity observations on the surface, the number of humidity records is
too small to calculate surface humidity through the analyzed area. Therefore, we substituted relative humidity
derived from a reanalysis product (2-m temperature,
specific humidity at the surface and surface pressure) for
observations in order to discriminate rain from snow. In
this study we used reanalysis data derived from theEuropean Centre for Medium-range Weather Forecast(ECMWF) Reanalysis product for the period of 1957 to2002 (ERA40; Uppala et al., 2005) and the Interim
product for the period of 2002 to 2007 (ERA-Interim;
Dee et al., 2011). Variables such as 2-meter temperatureand specific humidity were used for the period of
1973-2007, with a horizontal resolution of 1.5 degrees.
Two-meter temperatures from the model surface werecorrected to mean sea level, interpolated into a
0.05 degree grid and adjusted to GTOPO30 elevation.
RH was also interpolated into the 0.05 degree grid.
Judging from Eq. (2), the value of 1 is given in cases
where the temperature and RH of each 0.05 degree grid
satisfy the condition of snowing. The ratio of 0.05 degree
grid boxes judged as in a snowing condition for each
0.5 degree grid was then calculated, corresponding to thedaily precipitation dataset.
4.2 Total solid precipitation amounts over Asia
To validate our rain/snow discrimination based onreanalysis of RH and daily mean temperature, we calculated the total solid precipitation amount (TSP) for theextended winter season (October to March) and compared it with satellite snow cover observations. TSP is
defined as the total amount of precipitation proportional
to the snowing ratio. Melting of snow during the winterand distribution of perpetual snow or mountain glaciers
are not taken into account. Snow cover data are derived
from Northern Hemisphere EASE-Grid Weekly Snow
Cover and Sea-Ice Extent data which combine AVHRR,
GOES and other visible-band satellite data, and passive
microwave brightness temperature data of SSMR and
SSM/I (Robinson et al., 1993). We used the monthly and1-degree grid interpolated version for the period of
1971-1995 edited by the Physical Sciences Division,
Earth System Research Laboratory, US National Ocean
and Atmosphere Agency (NOAA/ESRL PSD) [downloaded from http://www.esrl.noaa.gov/psd/data/gridded/
data.snowcover.html]. Figure 6 displays TSP and snow
cover observed by satellites in the extended winter of
1985/86. The TSP derived from observed temperatures iscomparable to the snow cover observed by satellites in
the regions where temperatures in the extended winterstay continuously below freezing. The distribution of
TSP reproduces the extent of snow cover in Tibet and theHimalayas very well. The rain/snow discrimination
based on our temperature product is a good indicator formonitoring solid precipitation in mountainous regions.
On the other hand, it underestimates solid precipitation in170

N. YASUTOMI et al.

Siberia and Mongolia because the dry monsoon prevents
solid precipitation from being caught by rain gauges. TSP
is likewise overestimated in the extratropics such as
Japan and South Korea, where it snows and rains (andmelts) repeatedly during wintertime.
RH is derived from the temperature, specific humidity
and surface pressure of ERA-40 and ERA-Interim.

Fig 6

Figure 6 (c) displays TSP calculations based onrain/snow discrimination using reanalysis data, including
daily mean temperature, in terms of consistency. In
comparison with Fig. 6 (a), it is clear that TSP based on
ERA-40 and -Interim is overestimated, that is, thereanalysis surface temperatures are lower than theobserved ones.

(a), (c) Total solid precipitation (TSP) amount (mm) (b) satellite-observed snow cover extent (%)
averaged from October 1985 to March 1986. TSP has been calculated from APHRODITE (a) andERA40 reanalysis daily mean temperature with APHRO_MA daily precipitation and snowing ratio (c).

Development of a Long-term Daily Gridded Temperature Dataset

5. Concluding Remarks
In this study we created a daily mean gridded temperature dataset of high resolution. The input data, QC
and interpolation system developed for the APHRODITE
precipitation product work properly with some modifications. The climatology data and monthly means of the
product are comparable to those of other monthly mean
gridded temperature datasets. This dataset is useful not
only for improved evaluation of daily series of reanalysis
data, satellite data and model outputs, but also monitoring of long-term change and statistical analysis ofextreme weather.
Rain/snow discrimination is added to precipitation
data as a ratio of 0.05 degree grids in a snowing conditionin each 0.50 degree grid. The TSP amount for theextended winter season is derived for estimating thevalidity of the discrimination. It can be seen that the TSP
amount reproduces the snow cover extent observed fromsatellites well. The rain/snow information is necessary
for the accurate estimation of precipitation amounts
observed from satellites, since microwave reflections
from rainfall and snowfall differ. It also offers new
applications to many research fields such as water
resource management as use as an input in flow models.
The data periods of gridded temperature datasets
might be too short for analyzing global changes. Station
observations in East Asia (e.g., Japan, China and Taiwan)
are available for the period of 1951-2007. There is room
to extend the analysis, currently limited to East Asia, andthe data period. On the other hand, evaluation from astatistical point of view is insufficient. This will be anissue for future studies.

Acknowledgments
This study was supported by the Environment
Research and Technology Development Fund (A-0601)
of the Ministry of the Environment, Japan. The authors
thank Mr. Kenji Kamiguchi and Mr. Osamu Arakawa of
the Meteorological Research Institute of Japan Meteorological Agency for their valuable help and advice.
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Yatagai, A., A. Kitoh, K. Kamiguchi, O. Arakawa, N. Yasutomi
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Natsuko YASUTOMI
Natsuko YASUTOMI is an Assistant Professor at
the Center for Coordination, Promotion andCommunication at the Research Institute for
Humanity and Nature, Japan. She received her
Doctor of Science degree from the University ofTokyo in 2003 with a major in climatology andmeteorology. Prior to her present affiliation she studied and worked at theCenter for Climate System Research, the University of Tokyo. She isinterested in variability of the Asian monsoon in terms of observation
analyses and dynamic simulation of climate models.

Atsushi HAMADA
Atsushi HAMADA is a postdoctoral fellow at the
Atmosphere and Ocean Research Institute, theUniversity of Tokyo, Japan. He received his
Doctor of Philosophy in Science from Kyoto
University. His research has focused on thedevelopment of methods for estimating cloud andprecipitation characteristics from satellite measurements.

Akiyo YATAGAI
Dr. Akiyo YATAGAI is a climatologist and a
researcher at the Faculty of Life and Environmental Sciences, University of Tsukuba. She
received her Doctor of Philosophy in 1996 fromthe Graduate School of Geoscience, University ofTsukuba. She was a Researcher at the Earth
Observation Research Center, National Space Development Agency from1995 to 2001. She was an Assistant Professor at the Research Institute for
Humanity and Nature (RIHN), Kyoto from 2002 to 2011. She was a
Principal Investigator of the Asian Precipitation – Highly Resolved
Observational Data Integration Towards Evaluation of water resources(APHRODITE) project for 2006–2011, funded by the Global Environment
Research Fund, Ministry of the Environment, Japan.

(Received 29 June 2011, Accepted 7 October 2011)