Hydrological Research Letters 2, 36 40 (2008)
Published online in J-STAGE (www.jstage.jst.go.jp/browse/HRL). DOI: 10.3178/HRL.2.36

A 59-year (1948 2006) global near-surface meteorological data set
for land surface models. Part I: Development of daily forcing
and assessment of precipitation intensity
Yukiko Hirabayashi1, 5, Shinjiro Kanae2, Ken Motoya3, Kooiti Masuda4 and Petra D
oll5
1
Interdisciplinary Graduate School of Medicine and Engineering, University of Yamanashi,
4-3-11 Takeda, Kofu, Yamanashi 400-8511, Japan
2
Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan
3
Faculty of Education and Human studies, Akita University,
1-1 Tegata-Gakuenmachi, Akita 010-8502, Japan
4
Frontier Research Center for Global Change, Japan Agency for Marine-earth Science and Technology,
3173-25 Showa-machi, Kanazawa-ku, Yokohama, Kanazawa 236-0001, Japan
5
Institute of Physical Geography, Frankfurt University,
P.O. Box 111932, 60054 Frankfurt am Main, Germany

Long-term variations in terrestrial water and energy
budgets are essential for understanding the global environmental system, especially in the face of potential
climate change. These variables are often estimated byland surface models (LSMs) driven in an off-line modewith atmospheric forcing data due to the limitation of
direct observations.
To drive LSMs, previous studies have createdseveral decadal time series of forcing data, including
precipitation, temperature, humidity, and radiation, at
daily to several-hourly timescales. Most of these products have been based on reanalysis data such as those
provided by the National Centers for Environmental
Prediction/National Center for Atmospheric Research(NCEP/NCAR) or the European Centre for Mediumrange Weather Forecasts (ECMWF). Since reanalysis

data sets contain errors in model-simulated atmosphericforcings, the products based on reanalysis data areusually corrected against globally available observations. For example, Ngo-Duc et al. (2005) estimated 53
years (1948 2000) of 6-hourly forcing data from NCEP/
NCAR reanalysis data with correction of precipitation
and radiation. They scaled the monthly precipitation
amount to fit the monthly precipitation product by the
Climate Research Unit (CRU) and scaled the monthly
mean longwave and shortwave radiations to fit thethose of the Surface Radiation Budget (SRB) project.
Sheffield et al. (2006) and Qian et al. (2006) followed
frameworks similar to that of Ngo-Duc et al. (2005). Berget al. (2005) obtained 15-year (1979 1993) 6-hourly
forcing data from ECMWF reanalysis data by scaling
temperature, dew point temperature, precipitation, andlong- and shortwave radiations to the monthly observations of those variables.
Although the above studies scaled the variables
based on monthly observations, atmospheric forcings
based on reanalysis products still contain some specificbiases at shorter timescales such as daily precipitation
intensity and number of precipitation days. The hydrological processes over the land surface, such as interception by leaves, water infiltration into the soil, andsaturation excess runoff, are sensitive to daily precipitation values even if the total monthly precipitation
amount is the same (e.g., Sheffield et al., 2004; Hirabayashi et al., 2005). Therefore, creating forcing data
sets in which the daily statistics are similar to those ofobservations is important.
Several large-scale atmospheric forcing data sets
were developed without using reanalysis products. For
example, Nijssen et al. (2000) estimated 14-year global
atmospheric forcing data with 2° horizontal resolutions.
However, only limited years of forcing can be createdusing their methodology, because daily observations are
required. Hirabayashi et al. (2005) estimated 1° × 1°
global 100-year (1901 2000) atmospheric forcing databy combining equations similar to those of Nijssen et al.
(2000). However, they extended the data period by using
a stochastic weather generator to statistically create
daily atmospheric forcing from monthly precipitationand temperature observations by the CRU (Mitchell andJones, 2005), applying statistical parameters derived

Correspondence to: Yukiko Hirabayashi, Interdisciplinary
Graduate School of Medicine and Engineering, University of
Yamanashi, 4-3-11 Takeda, Kofu, Yamanashi 400-8511, Japan,
E-mail: hyukiko@yamanashi.ac.jp ©2008, Japan Society ofHydrology and Water Resources.

Received 11 April 2008
Accepted 20 June 2008

Abstract:
This paper describes the development and assessment of global 0.5° near-surface atmospheric data from1948 to 2006 at daily (for precipitation, snowfall, andspecific humidity) to 3-hourly (for temperature, shortwave radiation, and longwave radiation) time scales,
which can be used to drive land surface models. Using
newly available monthly precipitation and temperature
data extending to recent years, the variables werecreated by statistical methods, the parameters of whichwere obtained from available daily to 3-hourly observations. The daily precipitation developed in this paper
produces reasonable numbers of precipitation days andheavy precipitation days, different from previous longterm meteorological data sets based on reanalysis.
Together with its relatively high spatial resolution(0.5°) and availability of recent years, the newly
obtained data may be preferred to other forcing data
sets in case of hydrological and climate change studies,in particular if the study results are sensitive to daily
variations in atmospheric conditions.
KEYWORDS global atmospheric forcing; LSM

INTRODUCTION― 36 ―

A 59-YEAR (1948 2006) GLOBAL NEAR-SURFACE METEOROLOGICAL DATA SETfrom available daily or 3-hourly observations.
The goal of this study was to create a 59-year (19482006) near-surface meteorological data set (hereafter
called as H08) with daily to 3-hourly timescales. H08
represents an improvement of the product by Hirabayashi et al. (2005). The enhancements in the methodology include 1) finer (0.5°) spatial resolution using new
global gridded monthly observation product of precipitation and temperature, and gridded daily precipitation
products over India and East Asia; 2) new estimations of
the statistical parameters of a stochastic weather generator from new global daily to 3-hourly observation
products; 3) improved methods for estimating dew pointtemperature and spatial distribution of daily precipitation; 4) correction of gauge undercatch of precipitation
based on rain/snow phase detection; and 5) data for2001 2006, with the ability to extend to future years.
H08 was statistically created from monthly observations of precipitation and temperature using daily statistics obtained from daily observations of precipitation,
maximum and minimum temperature, and shortwave
radiation. An expected advantage of these newly
created data is that they should contain statistical characteristics similar to observations.
This paper describes the overall process to create
H08 and comparison of precipitation data with otherpublished data. Comparisons of daily statistics of temperature and shortwave radiation with other data sets
and impact of the gauge correction to estimate snowfall
amount will be included in a companion paper.

DATA AND METHODOLOGY
The global 0.5° near-surface meteorological data set
for the period 1948 2006 was created based on themethod of Hirabayashi et al. (2005). The method of
Hirabayashi et al. (2005) enables estimation of daily to
3-hourly atmospheric forcing when monthly means ofprecipitation and temperature are available. The monthly mean of precipitation is statistically disaggregatedinto daily time steps. Using the obtained daily precipitation and monthly mean temperature, a stochastic
weather generator creates maximum and minimum
temperature and incoming shortwave radiation at land
surface at daily time steps. From these daily variables,
other meteorological data (specific humidity and longwave radiation) are obtained using an empirical equa-tion model. We obtained all the required parameters forthe method from available observations at shorter
timescales but over limited periods. Table I lists the data
sets used to create H08. A schematic diagram of theprocess is shown in Supplement 1.

Precipitation
We used the 0.5° global monthly precipitation product by PREC/L (Chen et al. 2002) as the base product.
PREC/L is one of the best precipitation products currently available in terms of the number of gauges used,careful selection of the interpolation algorithm, androutine updates. The number of gauges reaches amaximum in the 1960s (14480 in 10-year average) anddecreases after the middle of the 1990s. The interpolation algorithm of PREC/L overcomes the bias of smaller
peak values in the annual cycle seen in other precipitation products (Chen et al. 2002). Given the importance ofstudying recent hydrological change under possible
human impact on climate changes, the consistent andcontinuously updated PREC/L product is useful for
future research extensions, even though the number ofgauges used decreased to less than 4000 in the 2000s.
The monthly precipitation of PREC/L was disaggregated into daily values using a gamma-distribution
algorithm (Groisman et al. 1999). The two gamma parameters were obtained from observed daily precipitation of the Global Telecommunication System (GTS) of
the World Meteorological Organization from 1978 to2006. The GTS data used here is a 0.5° grid product indaily time step created by the National Oceanic andAtmospheric Administration (Xie P. 2007, personal communication). Because the number of gauges registeredin the GTS is much lower (about 4000 6000) than thatof the PREC/L before 2000, we used the monthly totalof PREC/L as the base data for precipitation amountrather than using the daily GTS data directly as the precipitation product.
The method of Hirabayashi et al. (2005) does notinclude the spatial organization of the precipitation distribution because the occurrence of the daily precipitation at each grid is generated independently fromneighboring grids. To overcome this problem, we usedprecipitation data from NCEP/NCAR product correctedby the CRU monthly precipitation (NCC; Ngo-Duc et al.
2005) (1948 1977) and the GTS (after 1978) to obtain
information on the spatial distribution patterns of precipitation. The daily precipitation intensity in a month

Table I. Data sets used.
Variables

Grid

PeriodPREC/L (Chen et al. 2002)

0.5°

1948 2006

CRU TS 2.1(Mitchell and Jones, 2005)

0.5°

1948 1977

GTS grid product by NOAA

0.5°

1978 2006

GTS grid product by NOAA

0.5°

1978 2006

EA (Xie et al. 2007)

0.5°

1978 2003

65 155°E, 5 60N°

IMD (Goswani et al. 2006)

1°

1951 2004

over India

GHCN/CAMS (Fan and van den Dool 2008)

0.5°

1948 2006

CRU TS 2.1 (Mitchell and Jones 2005)

0.5°

1948 2002

GTS grid product by NOAA

0.5°

2003 2006

Daily max. and min. temperature

GTS grid product by NOAA

0.5°

1986 1995

Daily shortwave radiation

NASA Langley SRB (Gupta et al. 2006)

0.5°

1984 2004

Monthly precipitation

Product name (reference)

Note

Monthly rain days

Daily precipitation

Monthly temperature
Monthly temperature range― 37 ―

Y. HIRABAYASHI ET AL.
obtained by the gamma distribution was distributed
within a month, in the same order as the NCC or GTS. If
H08 showed more precipitation days than indicated by
NCC or GTS, the occurrences of precipitation days wereobtained by a first-order Markov chain model (Gabrieland Neuman, 1962), and were randomly distributed.
Next, the obtained daily precipitation were replacedby two regional daily gauge-based precipitation products when and where they were available. The first
product was a daily precipitation product over East
Asia (EA; 5 60°N, 65 155°E) with 0.5° grid resolution
for the 26-year period from 1978 to 2003, produced by
Xie et al. (2007). The number of the gauge incorporated
in the product over the region (1400 2000) is more than
twice those used in the PREC/L. The second productwas 1° daily precipitation data by the India Meteorological Department (IMD) from 1951 to 2000 (Goswami
et al. 2006). The number of gauges used in the IMD isabout 1600 before the 1980s and more than 500 even in
the 2000s, which is much higher than that of PREC/L(50 350 after the 1970s).
During periods when EA or IMD data were unavailable, the monthly mean precipitation over those regions
was scaled using the ratio of the monthly climatology.
The ratio of the monthly climatology of EA to that of
PREC/L was estimated by averaging the monthly precipitation from 1978 to 1990; more recent years were
not included in this average because a relatively low
number of gauges have been used for PREC/L since1990. The ratio of the monthly climatology of IMD tothat of PREC/L was obtained from averages of monthly
precipitation from 1951 to 1990.
Finally, snowfall amount was estimated. Because
gauge undercatch error is particularly large in case ofsnowfall, we distinguished solid precipitation using anequation for the wet-bulb temperature suggested by
Yamazaki et al. (2001) and then corrected rainfall andsnowfall amounts separately with the undercatch correction factor based on gauge types. The wind velocity
data of ECMWF’s 40-year reanalysis (ERA40) (Betts andBeljaars 2003) was used in the method. Wind data from1988 to 1996 and from 1983 to 1986 were subjectively
selected and used to estimate the correction factors for1948 1956 and 2003 2006, respectively, assuming thatthe impact of interannual change of wind velocity was
small.

Temperature and shortwave radiation
Daily temperature and shortwave radiation werecreated by Richardson’s (1981) stochastic weather generator. The parameters of the stochastic weather generator, the means and standard deviations of the maximum
and minimum temperature, and shortwave radiation,
were separately obtained for wet and dry days fromdaily precipitation, maximum and minimum temperature from GTS from 1986 to 1995, and daily shortwave
radiation product of the Surface Radiation Budget (SRB)
project (Release 2.8, Gupta et al. 2000) (http://eosweb.
larc.nasa.gov/) from 1984 to 2004.
The estimated daily maximum and minimum temperatures were scaled using monthly mean temperatures of Fan and van den Dool (2008) which was createdfrom a higher number of gauges (4000 8000 stations)than previous similar products, and monthly means ofdaily temperature range provided by CRU (1948 2002)
and GTS (after 2002). The 3-hourly temperature was estimated by fitting a sine curve to the daily maximum
and minimum temperature. Fan and van den Dool(2008) used a least squares distance weighting method
to interpolate station data to grid cell, including ananomaly interpolation approach for topographic adjust-ment based on the temperature lapse rate obtained from
NCEP/NCAR reanalysis. Like the monthly PREC/L precipitation data, this product is available for recent years
and will be continually updated in the future.
The obtained shortwave radiation was scaled using
monthly mean shortwave radiation of the SRB. During
the period when SRB was unavailable, monthly mean ofshortwave radiation was scaled using the ratio of themonthly climatologies of SRB and H08 obtained asmeans from 1984 to 2004. The daily shortwave radiation was then disaggregated into 3-hourly values basedon the ratio of the 3-hour average to the daily average
decided by the solar angle.

Specific humidity and downward longwave radiation
Daily specific humidity and downward longwave
radiation were calculated as a function of daily precipitation, maximum and minimum temperature, andshortwave radiation using an empirical equation model.
The original model by Hirabayashi et al. (2005) estimated dew point temperature by iteratively calculatingsets of empirical equations until convergence wasachieved. The original model, however, show unrealistic
values when annual precipitation of the grid is verysmall. The coefficient to obtain dew point temperaturein our model was therefore obtained from 1986 1995 atmospheric forcing data (Dirmeyer et al. 2006).

ASSESSMENT OF DAILY PRECIPITATIONSTATISTICS
The mean annual precipitation without gauge
undercatch correction from 1986 to 1995 in H08 shows
similar spatial distribution of those in CRU and Global
Precipitation Climatology Center (GPCP; Fuchs et al.
2007) data (Supplement 2). Differences of mean annual
precipitation between products are large over low latitudes, where the available number of gauge is limited.
The spatial distribution of daily precipitation in
Hirabayashi et al. (2005) shows unrealistic patterns,
since the occurrence and order of the intensity of daily
precipitation were randomly obtained at each grid. The
snapshot of the daily precipitation of H08 shows better
spatial distribution than those of Hirabayashi et al.
(2005), due to the improved method on the spatial distribution of daily precipitation (Supplement 3).
The reanalysis-based daily precipitation of ERA40(Betts and Beljaars, 2003) and the NCC (Ngo-Duc et al.
2005) were compared with daily precipitation of H08,
because these data sets are frequently applied as atmospheric forcings for LSMs. The GTS precipitation product and the satellite-observed daily precipitation datafrom the Global Precipitation Climatology Project OneDegree Daily Precipitation Data Set (GPCP-1DD;
Huffman et al. 2001) were also used for the comparison,even though the available periods are limited in recent
years. The comparisons of daily precipitation focusedon the number of precipitation days and number of daywith more than 20 mm/day, because existing atmospheric forcing data have commonly been scaled withmonthly observations.
Figure 1 compares the zonal means of the number ofprecipitation days that showed any precipitation (> 0.5mm/day) and of the number of heavy precipitation days(> 20 mm/day) for January and July. All values are 10year means from 1986 to 1995, except for the GPCP1DD product, which shows the 10-year mean from 1997
to 2006. Since the 1997 2006 means of GTS and H08 aresimilar to those of 1985 1996 (not shown), differences of
GPCP-1DD from other data sets due to the means of― 38 ―

A 59-YEAR (1948 2006) GLOBAL NEAR-SURFACE METEOROLOGICAL DATA SET

Figure 1. Zonal means of number of precipitation days(> 0.5 mm/day) (top) and of number of heavy precipitation days (> 20 mm/day) (bottom) for January (left) andJuly (right). All values are means from 1986 to 1995.

different periods are expected to be small. The
reanalysis products (ERA40 and NCC) showed larger
total precipitation days than other products. The difference in the zonal mean of precipitation days reachedmore than 4 days over northern latitude regions. The
heavy precipitation in H08 was close to that of the
GPCP-1DD, with both overestimating heavy precipitation days. In contrast as compared to GTS, fewer heavy
precipitation days were shown by the reanalysis data
(ERA40 and NCC).
Figure 2 presents the spatial distribution of thenumber of precipitation days in July. July values areshown because the differences between GTS and H08 of
Asia (where the two regional data sets were replaced)
are large. The relatively flat variations in GPCP-1DD in
the longitudinal direction in the high latitudes arises
from the coverage (40°S 40°N) of the satellite data usedin the product (Huffman et al. 2001).
The number of precipitation days in GPCP-1DD islower over regions such as India and the Indochina peninsula, indicating that the GPCP-1DD product may
reflect difficulties in detecting cumulous small-scale andshort-time precipitation events (e.g., squalls) from satellite images. ERA40 and NCC show many more precipitation days per month than H08, GTS and GPCP-1DD.
Both ERA40 and NCC indicates more than 25 days ofprecipitation per month over many low-latitude regions,while other data sets show 10 20 precipitation days.
Figure 3 is the same as Figure 2, but for the numberof heavy precipitation days. GPCP-1DD overestimatedheavy precipitation days when the monthly precipitation was high. This indicates that GPCP-1DD tends toshow high precipitation intensity when the cloud information obtained from the satellite image is dense.
Heavy precipitation in H08 was also higher thanthat of GTS, especially over the eastern United States,
northern South America, and western to eastern
Eurasia. This result can be attributed to the method of
estimating the parameters of the gamma distribution. If
the monthly total precipitation was larger, H08 tended
to show greater precipitation intensities. Since the GTS
data were based on measurements by fewer gauges than
the PREC/L data (and H08), it is difficult to assert thatthe precipitation data and number of heavy precipitation days in GTS are always more realistic than those of
H08 or GPCP-1DD, especially over regions with a small
number of gauges. Over northern mid- to high latitudes,
both ERA40 and NCC showed many fewer heavy precipitation days compared to other data sets.
The unrealistic number of total precipitation days

Figure 2. 1986 1995 mean number of precipitation days
(days with precipitation > 0.5 mm/day) in July.

Figure 3. 1986 1995 mean number of heavy precipitation days (days with precipitation > 20 mm/day) in July.

and heavy precipitation days in reanalysis data sets is
an inevitable feature of the parameterization of precipitation process of large-scale atmospheric general circulation models (AGCMs) used to create the reanalysis
data sets. The daily precipitation product presented inthis paper (H08) therefore has an advantage compared
to data sets based on reanalysis products because H08
includes the observed number of precipitation days.

SUMMARY
Daily precipitation, snowfall and specific humidity,
and 3-hourly temperature, shortwave radiation andlongwave radiation data were developed for 59-years(1948 2006) with 0.5° resolution in a consistent manner;these data were created using parameters obtained fromdaily observations that are available in recent years.
One of the advantages of this data set is that the statistical characteristics of the created variables are independent from those of reanalysis data. Other advantages are the availability of data for recent years andthe expectation of future extensions.
Global observed daily precipitation products such as
GTS and GPCP-1DD are only available in recent years.
Although reanalysis-based products are available forlast several decades, daily precipitation products basedon reanalysis have defects on the number of precipita-― 39 ―

Y. HIRABAYASHI ET AL.
tion days and number of heavy precipitation days. The
daily precipitation developed in this paper provides
long-term period as reanalysis-based products, but
produces reasonable numbers of precipitation days andheavy precipitation days. Precipitation in H08 has advantage in high latitude comparing to the GPCP-1DD,
where the values are uncertain due to the limitation ofsatellite used to create the GPCP-1DD. Because thenumber of gauges registered in H08 is larger than thatin GTS, and because local observation in India and East
Asia are included, values of H08 is expected to be better
than that of GTS. Thus, a LSM simulation driven by the
newly developed daily precipitation is expected to
produce more reasonable long-term land surface hydrological components than those using former data sets.

ACKNOWLEDGMENTS
The authors thank Dr. Pingping Xie for providing
GTS and PREC/L products. This study was partially
supported by the Japan Society for the Promotion of
Science (JSPS) Postdoctoral Fellowships for Research
Abroad, JSPS Young Scientists A (20686033) and theData Integration and Analysis System (DIAS) in Japan.

SUPPLEMENTS
Supplement 1. Schematic figure of the processes tocreate H08. The meteorological data are created bymodels or equations specified as rectangles. The
models and equations are processed from small
numbers in the figure because of the required input at
each process. Inputs and outputs in the processes are
expressed as starts and ends of arrows.
Supplement 2. 1986 1995 mean annual precipitation
without gauge correction (mm) from H08, CRU and
GPCC data sets and differences between CRU and H08
as well as GPCC and H08.
Supplement 3. Snapshot of daily precipitation (mm) on
the 25th September 1988 of Hirabayashi et al. (2005)(top) and this study (H08) (bottom).

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