Hydrological Research Letters 2, 65 69 (2008)
Published online in J-STAGE (www.jstage.jst.go.jp/browse/HRL). DOI: 10.3178/HRL.2.65

A 59-year (1948 2006) global meteorological forcing data set
for land surface models. Part II: Global snowfall estimation
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

Terrestrial snowfall and its storage are importantcomponent of the global water cycle. However, there are
not many observations on global snowfall and its variability, even though they are sensitive to climatechange as global warming. For example, Trenberth et al.
(2007) used global data sets to estimate elements in the
global water cycle but not snowfall. Oki and Kanae(2006) presented global estimated terrestrial snowfall,
but they did not show its annual variations due to theshort investigation period.
Two factors can affect snowfall estimation: themethod to estimate the percentage of snowfall in total
precipitation, and the method to correct gauge undercatch. Common approaches in many hydrological andland surface models to obtain snowfall from total precipitation are 1) to use snowfall or the ratio of snowfall
to total precipitation obtained from reanalysis data (e.g.,

Ngo-Duc et al., 2005) and 2) to estimate snowfall usingnear-surface climate data (e.g., temperature).
Systematic undercatch of snowfall measurements
caused by wind effects is one of the problems of gaugebased precipitation observations. Utsumi et al. (2008)
found that for simulating river discharge in Japan, correction of wind-induced undercatch of snowfall is more
important than correction for orographic effect. Tianet al. (2007) showed that the correction of precipitation
has large effects on hydrological simulations, especiallyin cold regions.
Previous studies estimated gridded undercatch correction coefficients by interpolating correction coefficients obtained from available meteorological station
data and gauge types. Due to the limitation of available
daily meteorological data, monthly climatologies of theundercatch correction coefficients, obtained frommonthly means of long-term meteorological data (e.g.,
Legates and Willmott, 1990) or from daily meteorological data during limited period (e.g., 5 years from 1994 to1998 by Adam and Lettenmeier, 2003), are commonlyused to correct global precipitation data sets. Yang et al.
(2005) estimated daily precipitation undercatch for a
relatively long (1973 2004) period over the northernhigh latitude, instead of using a climatology of undercatch correction coefficients. For investigating snowfall
variability over long time-scales, use of a climatology ofmonthly means of undercatch correction coefficients, asreported by previous studies (e.g., Hirabayashi et al.,2005), is problematic.
Hanasaki et al. (2008) estimated 3-hourly undercatchcorrection coefficients in 1° grid cells from 1986 to 1995
using the method by Motoya et al. (2002) on whichmuch of our methodology is based. Motoya et al. (2002)
used gauge type data and daily meteorological data toestimate undercatch correction coefficients at each grid.
When gauge-based precipitation observations areused to drive hydrological models, it is essential toobtain the phases of precipitation (rainfall or snowfall)
and to correct gauge records for undercatch using thephase information. Previous studies (e.g., Tian et al.,2007) have discussed the effect of gauge correction onhydrological cycle estimation mainly in terms of increased total precipitation; however, it is also importantto understand whether the effect is due to the correction

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 4 September 2008
Accepted 13 December 2008

Abstract:
Global terrestrial snowfall was estimated for 59
years from 1948 to 2006 by applying gauge undercatchcorrection for snowfall and rainfall based on daily meteorological data and gauge type. Following gauge correction, global annual snowfall estimation increased from
9.4 to 12.3 × 103 km3, while annual terrestrial precipitation increased from 112.8 to 119.6 × 103 km3. Percentage
of snowfall in total precipitation increased from 8.3 to10.3% with gauge correction. The snowfall distinction
method using wet-bulb temperature produced larger
values for snowfall than those obtained using a 0°C
threshold method and those from reanalysis-based
products. In contrast, the increase in total precipitation
was half of the increase obtained by using a climatology
of correction coefficients from previous studies. The estimated 59-year time series of snowfall amount showeda downward trend after the mid-1980s, suggesting adecrease in snowfall associated with a warmer climate
in recent decades.
KEYWORDS global snowfall; gauge correction; LSM

INTRODUCTION― 65 ―

Y. HIRABAYASHI ET AL.
of the total precipitation or to differences in the phase(rainfall/snowfall) distribution.
Our purpose was to show the range in the amount of
global snowfall obtained from different methods for estimating snowfall and rainfall phases, and for correcting
undercatch biases. Here, the ratio of snowfall to total
precipitation estimated using a distinction equation by
Yamazaki (2001) is compared to the value obtained by asimple 0°C temperature threshold method and to tworeanalysis products. Then, differences between oursnowfall estimation and the results of using climatologies of undercatch correction coefficients are discussed. Finally, the estimated global terrestrial snowfall
is compared with that from other data sets.

DATA AND METHODS
Methods to distinguish rainfall and snowfall
The global atmospheric data set used (daily precipitation, temperature, shortwave radiation, and humidity)
was a global 0.5° daily product described in a companion paper (Hirabayashi et al., 2008; hereafter, H08). We
compared two methods for distinguishing snowfall andrainfall phases. The first was a simple method that uses
daily mean temperature as a threshold to distinguishrainfall and snowfall phases. Because of limitations in
the global availability of atmospheric forcing data, thissimple method is still frequently used in global hydrological models. We chose a threshold of 0°C daily
surface temperature to determine whether the precipitation was rainfall or snowfall, which is used in someglobal hydrological models (e.g., WaterGAP2; Doll et al.,2003). When surface air temperature is below 0°C, precipitation on this day is assumed to completely occur assnowfall, while precipitation is assumed to fall as rain
only when temperature is larger than 0°C. We selected a
0°C as threshold temperature as according to themethod using web-bulb temperature (see below), almost
all precipitation falls as snow, if temperature is smaller
than 0°C.
The second method uses daily precipitation and wetbulb temperature as follows (Yamazaki, 2001):1 0.5 exp ( 2.2 (1.1 TW)1.3),
s(TW)  1.3
0.5 exp ( 2.2 (TW 1.1) ),Correction factors for gauge undercatch
To correct the undercatch of precipitation gauges,
three different correction coefficients were applied tothe daily precipitation data. The first method used themonthly climatology of undercatch correction coefficients obtained from a revised version of the global precipitation product of Legates and Willmott (1990;
hereafter LW90; version 2.01; http://climate.geog.udel.
edu/~climate/). The global monthly climatology of thegauge correction coefficients was estimated using themonthly climatology of precipitation before and after
gauge correction on a 0.5° grid.
The second data set was the monthly climatology ofgauge correction coefficients reported by Adam andLettenmaier (2003; hereafter Ad03). The global 0.5° correction coefficients of Ad03 were obtained by interpolating monthly correction values that were estimated forapproximately 8000 daily meteorological station datafrom 1994 to 1998; the correction incorporated wetting
losses and wind-induced precipitation undercatch basedon the gauge type provided by the World Meteorological Organization (WMO; Goodison et al., 1998). The
data also included an adjustment for orographic effects,
as described in Adam et al. (2006).
The third type of correction coefficient was obtainedusing the method of Motoya et al. (2002). This correction
coefficient, which was used in our product (H08), wasbased on global gauge-type classification data over 36
countries. Gauge type was determined for 25 countries
from Sevruk and Klemm (1989), and for the other 11
countries by information provided by WMO. Goodison
et al.’s (1998) equations to obtain correction factors fordifferent gauge types were simplified as an exponentialfunction with two parameters (Motoya et al., 2002), andthe number of gauge types included in Goodison et al.
(1998) was reduced to eight by assigning gauges withsimilar undercatch properties to the same type.
Correction factors were obtained separately forsnowfall and rainfall as follows:
a exp (bU)

50.0 exp ( 0.182U)

CRsnow  + 50.0 exp ( 0.112U)

TW < 1.1
 (1)TW 1.1

where s (TW) is the proportion of snowfall in total daily
precipitation, and TW is daily wet-bulb temperature (°C).
TW was obtained from daily mean temperature and dew
point temperature at each grid cell. Dew point temperature was derived from maximum and minimum temperature and shortwave radiation of H08 by anempirical equation. The method of Yamazaki (2001)
using TW is potentially better for estimating snowfall
than the method which uses surface temperature only,
since snowfall is observed at temperatures above 0°C especially under dry conditions (Fuchs et al., 2001). Snow
depth and temperature in eastern Siberia that were estimated by a one-dimensional land surface model using
this method showed good correlation with observations(Yamazaki, 2001), even though Equation (1) was derived
from observations at 30 stations in Japan.
To estimate the daily rainfall/snowfall phase, dayto-day variation in the meteorological data is important.
Daily precipitation statistics of H08 showed reasonablevariation (Hirabayashi et al., 2008). Standard deviationsof the daily temperature and shortwave radiation dataof H08 are similar to those of observation-based data
and better than those of reanalysis-based products(Supplements 1 and 2).

gauge type known
gauge type unknown ,(2)CRrain  100.0 1.51U 0.21U2
regardless of gauge types ,(3)

where CRsnow and CRrain are catch ratios of the precipitation gauge for snowfall and rainfall, respectively; U iswind speed (m s 1) at a height of 2 m; and a and b are parameters that depend on the gauge type. Since the a andb values for the Hellman type gauges by Motoya et al.
(2002), which were obtained from the HungarianHellmann gauge, lead to erroneously high correction
amounts under high wind velocity, new a and b valuesfor Hellmann type gauges were obtained from a correction equation of the German Hellmann gauge (Goodison
et al., 1998). Supplement 3 summarizes the parametervalues a and b. The average compensation equation of
Kondo (1994) for snowfall was applied when the gauge
type was not specified. Because the difference of rainfall
undercatch between different gauges is smaller thanthat of snowfall undercatch (Sevruk and Hamon, 1984),
a single equation (Kondo, 1994) was applied to estimate
the undercatch ratio of rainfall (Equation (3)), assumingthat rainfall undercatch does not depend on the gauge
type. Undercatch correction coefficients for rainfall andsnowfall were obtained as 1/CRrain and 1/CRsnow . To
avoid excessively large correction values, the maximum
wind speed to estimate correction coefficients for― 66 ―

A 59-YEAR (1948 2006) GLOBAL NEAR-SURFACE METEOROLOGICAL DATA SET: PART II
rainfall and snowfall was subjectively set to 6 m s 1 as
Goodison et al. (1998) ensures the applicability of theirequations for wind velocities up to 6 m s 1.
Wind velocity data were obtained from the 40-year
reanalysis (ERA40) product of the European Centre for
Medium-Range Weather Forecasts (ECMWF; Betts andBeljaars, 2003) from 1957 to 2002 by interpolating theoriginal ERA40 data to a 0.5° grid. Wind data from 1988
to 1996 and from 1983 to 1986 were subjectively
selected and used to estimate the correction factors for1948 to 1956 and 2003 to 2006, respectively. Based on asensitivity test in which not the wind velocity of thesame year was used to correct for undercatch but windvelocity data from other years, we conclude that total
correction amount of snowfall on land is underestimated by up to 5% during the periods 1948 1956 and2003 2006. This is related to the inconsistencies of wetdays between the years with wind velocity and precipitation data, as wind velocities on wet days tend to belarger than on dry days.
Because the equations of Motoya et al. (2002) assume
wind velocities at 2 m height, the 2-m wind velocity
used was obtained from the wind velocity of ERA40 at
10 m height, assuming the following logarithmic profile:




0

0

z ln z 

U2  U10 × ln

(4)where U2 and U10 are wind velocity (m s 1) at 2 m and 10
m height, respectively, and z0 is roughness length (m).
Goodison et al. (1998) suggested z0 of 0.01 m for winterand 0.03 m for summer. We set z0 to 0.03 m, which is
close to the roughness of a grass, because wind velocities estimated using equation (4) differed by only 4%
between z0 of 0.01 and 0.03 m and precipitation gauges
are usually located on grass. Thus for a global product,a roughness length of grass condition (0.03 m) is more
representative than a roughness length of snow condition (0.01 m).
Because our method to estimate undercatch depends
on gauge types and wind velocity, we may overestimate
correction amounts in the stations where precipitation
gauges are located at wind-protected places, as is often
the case e.g., in Germany.

analysis products were NCC, a product of the National
Centers for Environmental Prediction/National Center
for Atmospheric Research (NCEP/NCAR) corrected bymonthly observations (Ngo-Duc et al., 2005), andECMWF’s ERA40 product. Because the gauge correction coefficient data of Ad03 and the precipitation andsnowfall of NCC do not cover Antarctica, all values inTable I represent total values over land excluding
Antarctica. We selected the period from 1986 to 1995
because time series of snowfall in ERA40 are problematic before the 1980s, and because the number of observation data to create H08 decreases after the mid 1990s.
Snowfall of H08 estimated using the equations ofYamazaki (2001) was larger than that estimated using
the 0°C threshold method. When the 0°C threshold wasused to distinguish snowfall from precipitation, theannual snowfall was 9.57 × 103 km3 , whereas the H08
value was 11.64 × 103 km3. Differences between the two
estimations were large in places with heavy snowfall
and gauge types with a large undercatch, including
western North America, United Kingdom, the Alps, andthe Tibetan Plateau (Figure 1). With the thresholdmethod, computed snowfalls strongly depends on thethreshold values. Using a threshold of 2°C (percentage
of snowfall in precipitation is approximately 50% at asurface temperature of 2°C), estimated global snowfall
increases to 11.70 × 103 km3, which is close to the H08
value.
After the gauge correction was applied to the precipitation data, precipitation and snowfall of H08 in-

RESULTS
We compared mean annual terrestrial precipitationand snowfall from 1986 to 1995 estimated from two different methods for distinguishing snowfall and rainfall
phases, three different undercatch correction methods,
and from two reanalysis products (Table I). The re-Figure 1. Difference between 1986 1995 mean annual
snowfall (mm/year) as computed with a) 0°C thresholdmethod, b) LW90 and c) Ad03, and H08 snowfall.

Table I. Global terrestrial total precipitation and snowfall from 1986 to 1995 (excluding Antarctica). Units are 103 km3/year. Small islands in NCC, where data are
missing, are supplemented using H08.
Precipitation(correction
amount %)Undercatchcorrection

Phase detection

No correction

H08 (Yamazaki, 2001)

102.3

8.92 ( 8.7%)

H08

H08 (Yamazaki, 2001)

108.3 ( +5.9%)

11.64 (10.7%)

H08
LW90
Ad03

0°C threshold
H08 (Yamazaki, 2001)
H08 (Yamazaki, 2001)

108.0 ( +5.5%)
114.9 (+12.3%)
112.5 ( +9.9%)

9.57 ( 8.9%)
11.59 (10.1%)
11.36 (10.1%)

ERA40

Reanalysis

Reanalysis

110.8

7.67 ( 6.9%)

NCC

Reanalysis

Reanalysis

102.0

6.48 ( 6.4%)

Data

H08

― 67 ―

Snowfall (% oftotal precip.)Y. HIRABAYASHI ET AL.
creased by 5.9% (6.0 × 103 km3) and 30% (2.7 × 103 km3),
respectively. The percentage of snowfall in precipitation
reported by H08 increased from 8.7 to 10.7% after windcorrection was applied because the correction coefficient for snowfall was larger than that for precipitation.
Precipitation correction in H08 (+5.9%) was smaller thanin LW90 (+12.3%) and Ad03 (+9.9%). The differences inthe total corrected precipitation values resulted in correction amount of rainfall.
Snowfall amounts differed regionally (Figure 1),
even though total global snowfall amount was close forall three precipitation corrections. Corrected snowfall of
H08 was larger than that of Ad03 and LW90 in the
United Kingdom and Germany where Hellman-type
gauges, which have the largest correction coefficients ofall gauge types, were used to estimate the correction
amount of H08.
Snowfall correction in LW90 was higher in thenorthern part of Eurasia compared to estimates by H08
and Ad03. The difference between LW90 and H08 (andAd03) arose from the catch ratio used by LW90. Adamand Lettenmaier (2003) explained that the undercatchbias of snowfall used by Legates (1987), which was thesame method used by LW90, was larger at higher windspeeds than WMO’s regression for Tretyakov-type
gauges; this led to higher correction amounts especiallyover Russia.
The gauge correction coefficients of Ad03 wereobtained by the interpolation of monthly climatologies(1994 1998) of correction coefficients from daily meteorological gauging station observations, with additional gauge information in Canada, whereas thecorrection amount of H08 was estimated based ongauge types and daily meteorological data at each grid.
Ad03 includes a correction for topographic effects(Adam et al., 2006) that uses a simple topographic regression method and leads to excessive precipitation on
the lee sides of mountainous regions (K. Yoshimura,personal communication, 2008), e.g. east of the Sierra
Mountains in North America. These methodological differences led to differences in the correction amountbetween H08 and Ad03, even though both use the equations of Goodison et al. (1998).
Because Ad03 only corrected snowfall in countries
in which at least half of the land area experiences snow
during the coldest month, the correction amounts of
H08 were larger than those of Ad03 in many regions;the difference was especially large in the United Kingdom and Germany, where Ad03 did not correct snowfalland H08 included gauge types with a large snowfall
undercatch correction. For Canada, Ad03 developed
their own correction ratio instead of using Goodison
et al.’s equations (1998), resulting in estimation differences between H08 and Ad03 for this area.
Snowfall amounts from reanalysis products (ERA40
and NCC) were smaller than the H08 estimate (Table I).
The percentages of snowfall in the total precipitation of
the reanalysis data sets were also lower (6.9 and 6.4%)
than those estimated by H08 and the 0°C thresholdmethod, indicating that snowfall in reanalysis products
is underestimated compared to observations. Corrected
mean annual terrestrial (including Antarctica) precipitation of H08 during the period 1948 2006 was 119.6 ×
103 km3, as compared to 112.8 × 103 km3 of annual terrestrial precipitation without gauge correction. Annual
snowfall increased from 9.4 to 12.3 × 103 km3 with gauge
correction.
H08 snowfall shows a downward trend (Figure 2).
This downward trend in snowfall was not the result ofa decrease in total precipitation because the amount ofsnowfall estimated by applying the fixed snowfall ratio(10% of precipitation) did not show a notable trend. The

Figure 2. Time series of annual terrestrial snowfall andpercentage of snowfall in total precipitation. H08 and
ERA40 include Antarctica, and NCC excludes
Antarctica.

percentage of snowfall in total precipitation has decreased, probably related to the increase in terrestrial
temperature in recent decades. The Mann-Kendall trend
test (Kendall, 1938) shows a 99% significant trend ofsnowfall from 1981 to 2006, while there is no statistically significant trend in the precipitation time series. The
increase of surface temperature may not only be due tothe change of climate change, but also the result of urbanization of regions close to the gauges to create thesurface temperature data of H08. The rapid increase insnowfall in ERA40 stems from lower precipitation
before the 1970s, which is considered to be an artificial
trend caused by a change in the observation data used
to create the reanalysis product (Chen and Bosilovich,2007). Both ERA40 and NCC show downward trends
from approximately 1990, although these trends are
smaller than the trend of H08.

CONCLUSION
Global terrestrial snowfall was estimated for 59
years from 1948 to 2006. Observed precipitation wascorrected for wind-related gauge undercatch of rainfall
and snowfall, depending on gauge types. The precipitation phase distribution method of H08 using dew pointtemperature resulted in larger snowfall than the 0°C
threshold method, and is expected to assist more realistic snowfall estimation especially in dry regions. The
percentage of snowfall increased after the gauge correction for rainfall and snowfall was applied because thecorrection coefficient for snowfall was larger than thatfor precipitation. The correction amount for precipitation of H08 was approximately half of the amounts that
we obtained using correction coefficients of previous
studies, because of the lower correction amount in
rainfall.
Snowfall estimations with the different correction
methods differed significantly in some regions. Because
we estimated undercatch correction using daily atmospheric data, H08 snowfall allows to determine long-term
variations of snowfall more reliably than snowfall estimates that are derived using a climatology of correction
factors. The estimated 59-year time series of snowfall
amount shows a downward trend after the mid-1980s
that may be due to a warmer climate.

― 68 ―

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

ACKNOWLEDGMENTS
This study was partially supported by the Japan
Society for the Promotion of Science (JSPS) Postdoctoral
Fellowships for Research Abroad, a JSPS Research
Fellowship for Young Scientists A (20686033), and theData Integration and Analysis System (DIAS) of Japan.

SUPPLEMENTS
Supplement 1. Zonal means of the standard deviation ofdaily temperature from 1986 to 1995 of the observation-based product of the Global Teleconnection
System (GTS) by the National Oceanic and Atmospheric Administration, our product (H08), and the
ERA40 and NCC reanalysis products.Units are K.
Supplement 2. Zonal means of the standard deviation ofshortwave radiation from 1986 to 1995 of the satellite-based product of the Surface Radiation Budget(SRB) project (Release 2.8, Gupta et al., 2006; http://
eosweb.larc.nasa.gov/), our product (H08), and the
ERA40 and NCC reanalysis products. Units are W
m 2.
Supplement 3. Parameters in exponential functions ofsnowfall catch ratios (Motoya et al., 2002) for different
gauge types.

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