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JP10: 59-year 10 km dynamical downscaling of re-analysis over Japan
Hideki Kanamaru, Kei Yoshimura, Wataru Ohfuchi, Kozo Ninomiya and Masao Kanamitsu
Climate Change and Bioenergy Unit, Viale delle Terme di Caracalla 00153 Rome, Italy. Hideki.Kanamaru@fao.org
1. Introduction
To date, multi-decadal dynamical downscaling simulations
driven by the Reanalysis data have been completed over
many regions (e.g., Vidale et al., 2003), but only a few are
in very high resolution, which is required for application
studies such as agricultural study or energy and water
resources management. As one such very high resolution
long-term simulation, Kanamitsu and Kanamaru (2007;
KK07 hereafter) conducted a 10 km resolution run over
California for a half-century. KK07 revealed that these
simulations had better skill than the regional Reanalysis
product, NARR, whose spatial resolution (32 km) is coarser
than the dynamical downscaling simulations (10 km),
because the current data assimilation system is incapable of
effectively utilizing high-density near-surface observations,
and places more weight on the initial guess produced by the
regional high-resolution numerical model.
In this study, we conduct a similar long-term high resolution
dynamical downscaling as CaRD10 over Japan. The product
is hereafter called JP10. The major objective is to
demonstrate that the dynamical downscaling is capable of
reproducing small scale detail which agrees better with
station observations than the coarse resolution analysis,
without injecting small scale observation. To do so, the
accuracy of JP10 will be compared with independent high
density in-situ observation data, and how JP10 reproduces
some historical extreme events will be also investigated.
2. Model and Observation
The Regional Spectral Model (RSM; Juang and
Kanamitsu 1994) originates from the one used at the
National Centers for Environmental Prediction (NCEP), but
the code was updated with greater flexibility and much
higher efficiency (Kanamitsu et al. 2005) at the Scripps
Institution of Oceanography. The RSM utilizes a spectral
method (with sine and cosine series) in two dimensions. A
unique aspect of the model is that the spectral decomposition
is applied to the difference between the full field and the
time-evolving background global analysis field. The model
configuration and the downscaling method in this study are
basically the same as that of CaRD10 (10 km California
Reanalysis Downscaling; Kanamitsu and Kanamaru, 2007)
but for a domain covering Japan Islands (22.123°–49.163°N
and 119.960°–151.577°E; shown in Figure 1a with
topography) for 1948 to 2006, and with narrower lateral
boundary nudging zones that extends only 2.5% of the total
width in each of four lateral boundaries instead of 11.5% in
CaRD10 to increase the useable domain.
As same as CaRD10, a spectral nudging scheme, i.e.,
scale selective bias correction (SSBC, Kanamaru and
Kanamitsu 2007), is applied to the Reanalysis large scale
thermodynamic fields for a 10 km horizontal resolution
downscaling simulation, to reduce the growth of large-scale
error spanning the regional domain. The scheme consists of
three components: 1) dampening the large-scale (more than
1000 km scale) part of the wind perturbation toward zero,
with dampening coefficient of 0.9, 2) removing the area
average perturbation of temperature and moisture at every
model level, and 3) adjusting the area mean perturbation
logarithm of surface pressure to the corresponding
difference of logarithm of surface pressure due to the area
mean difference in the global and regional topography.
For verification of the JP10 dataset, this study uses
hourly precipitation, surface temperature, and surface
wind direction and speed from the AMeDAS re-statistic
dataset (JMBSC, 2007), which is a surface meteorological
observation dataset of 29 years (1976-2004) from over
1,300 Japanese automated meteorological data-acquisition
system (AMeDAS) sites. These observation sites are
distributed across Japan at intervals of about 20 km on
average. The locations of all the observatories are shown
in Figure 1b. These about 1300 sites are divided into 9
groups in accordance to the geographical divisions in
Japan Meteorology Agency (JMA).
Figure 1. (a) Domain and topography and (b)
AMeDAS observatories location. Nine regions are
classified as N-Hokkaido (red), S-Hokkaido (green),
Tohoku (blue), Kanto (yellow), Chubu (pink), Kinki
(sky blue), Shikoku (orange), Kyushu (purple), and
Okinawa (light green).
3. Results
Climatology
Figure 2 shows monthly climatology variations averaged
over each of the nine AMeDAS regions. It is notable that
there is significant systematic over estimation of
precipitation all over Japan whereas air temperature is
almost perfectly agreed. By intensive investigations, we
found that this is due to the over correction of humidity
field by this version of SSBC.
Daily variations
As shown in Figure 2, downscaled daily variations of
surface meteorology at one of the AMeDAS observatories
in Okinawa Island are well reproduced, particularly for
wind and air temperature. Precipitation includes errors in
amount and timing. In Figure 3, daily air temperature in a
month at each observatory is compared with those of JP10,
and the distributions of their correlation coefficients are
shown for four months (Feb., May, Aug., and Nov.) in
1976. This figure indicates that high frequency variations
of the air temperature associated with the synoptic scale
weather changes are accurately captured in the
downscaled product (in average, more than 0.8 of
correlation coefficient), whereas the original coarse scale
Reanalysis could not reproduce them in this degree of
accuracy. Note that the accuracy is relatively low in