WORLD METEOROLOGICAL ORGANIZATION
____________________
GPC-SIF/Doc. 5.10
(7.II.2003)
_________
COMMISSION FOR BASIC SYSTEMS
WORKSHOP OF GLOBAL PRODUCERS OF
SEASONAL TO INTERANNUAL FORECASTS
ITEM: 5.10
Original: ENGLISH
Geneva, 10-13 February 2003
Ensemble Prediction System (EPS) for seasonal forecast at JMA
(Submitted by Mr Nobutaka MANNOJI)
GPC-SIF/Doc. 5.10
Ensemble Prediction System (EPS) for seasonal forecast at JMA
1.
EPS for four-month/seven-month forecast
Operation of the EPS for the four-month forecast in JMA started in January 2003,
while the EPS for seven-month forecast is planned to start in September 2003. The numerical
weather prediction (NWP) model for the EPS is a T63 version of JMA's Global Spectral Model
(GSM0103, T213) used for the short range forecast. The four-month EPS is conducted every
month while seven-month EPS, that is an extension of four-month EPS, is conducted to
forecast summer season (JJA) or winter season (DJF). Therefore, seven-month EPS will be
conducted in February, March and April for summer, and September and October for winter.
The specifications of the model are shown in Table 1.
Table 1 The specification of GSM for the long-range EPS
Horizontal
resolution
Vertical levels
Physical
processes
T63 (about 1.875-degree Gaussian grid) ~180km
40 (Surface to 0.4hPa)
same as those of the short-range forecast model except for the coefficients
of gravity wave drag parameterization
Moist
Prognostic Arakawa-Schubert cumulus parameterization
processes
and large-scale condensation
Radiation
Shortwave radiation computed every hour
Longwave radiation computed every three hours
Cloud
Prognostic cloud water, cloud cover diagnosed from
moisture and cloud water
Gravity
wave Longwave scheme for troposphere and lower
drag
stratosphere, shortwave scheme for lower troposphere
Planetary
Mellor-Yamada level-2 closure scheme and similarity
boundary layer theory for surface boundary layer
Land
surface Simple Biosphere Model (SiB). The analyzed snow depth is used for the
process
initial value. Predicted values are used for the initial soil temperature and
soil moisture
Forecast Time
four months or more, up to seven months
Executing
Once a month (four-month prediction)
frequency
Five times a year (February, March, April, September and October) (five- to
seven- month predictions for JJA and DJF)
Ensemble size
31 members
Perturbation
Singular Vector method
method
SST
Two-tiered method.
Combination of persisted anomalies, climate and prediction
SST anomalies used as the lower boundary condition of the atmospheric model are
persisted for the four-month integration. For five- to seven-month integration, a two-tiered
method is employed for SST: combination of persisted anomalies, climatology and prediction
with an atmosphere-ocean coupled model operated in JMA. The prediction of SST anomalies
with the coupled model is used mainly in the equatorial Pacific region, while persisted
GPC-SIF/Doc. 5.10, p. 2
anomalies or climatology are used dominantly in the middle and high latitudes.
The issuance time for public is the 25th day or earlier of each month, and the initial
time of the model is a certain day in the middle of the month considering the computer
resources available for SI forecast and the working days of forecasters.
Hindcast experiments of 120-day prediction were performed prior to the operational
use. The experiments start from every month from 1984 to 2001 (18 years), five members
each with SV method. Fig. 1 shows anomaly correlation of height of 500hPa after systematic
errors are removed (without using its own forecast) for four areas. Forecast time is 90days
with no lead time.
Climate data used to calculate anomaly correlation are average of
1971-2000 used for operational forecast. Solid lines indicate anomaly correlation of 13-month
running mean, while black dots indicate months of which anomaly correlation are more than
0.5. Thick black lines and gray lines along the bottom axis, respectively, indicate that El Niño
and La Niña events occurred at the initial time. Anomaly correlation is generally large when
the El Niño or La Niña occurs at the initial time in every area. On the contrary, in the 90's when
the SST anomaly is small, anomaly correlation is generally small. The skill differs from area to
area. The skill is generally high in the North Pacific sector and east Asia, where SST
anomalies in the tropics have large effects. On the contrary, the skill is generally low over the
Eurasian sector where SST anomalies in the tropics have small effects. The fact that the skill
Fig.1 Anomaly correlation of height of 500hPa of hindcast for 18 years. Forecast time is
90days with no lead time. Solid lines indicate anomaly correlation of 13-month running
mean, while black dots indicate months of which anomaly correlation are more than 0.5.
Thick black lines and gray lines along the bottom axis, respectively, indicate that the El Niño
and La Niña events occurs at the initial time. The diagram shows four different areas; Upper
left: Northern Hemisphere (0°-360°, 20°N-90°N), Upper right: Eurasian sector (0 °-180°E,
20°N-90°N), Lower left: Northern Pacific sector (90°E-90°W, 20°N-90°N), Lower right:
Eastern Asia (100°E-170°E, 20°N -60°N).
GPC-SIF/Doc. 5.10, p. 3
is
rela
tivel
y
high
duri
ng
Fig.2
Correlation coefficients between
observed (CMAP) and model ensemble
averaged forecast precipitation for 18
years (1984-2001). Forecast is 90-days
forecast with no lead time with initial date
of 31st of May. Areas having significant
correlation at the 5% level with t-test are
hatched, and contour interval is 0.4.
the
El
Niñ
o
and
La
Niña event suggests that the JMA model
properly predicts the influence of the SST
anomalies in the tropics on the atmospheric
Fig.3 Interannual variations of observed and
model precipitation anomaly of the
western North Pacific summer monsoon
region (110-160°E,10-20°N) in JJA. Unit
in mm/day. Solid line and dot-dashed line
indicate ensemble mean of model
forecast and analysis, respectively.
Several marks represent forecast of each
member.
circulation.
Next,
correlation
coefficient
of
precipitation forecast and analysis (CMAP by Xie and Arkin, 1997) are shown in Fig.2. Areas
with significant correlation spreads over Western to Central tropical Pacific. Fig3. shows
annual variations of correlation coefficient between observed and forecast precipitation over
the western North Pacific summer monsoon region (110-160°E,10-20°N, WNPM).
The
correlation coefficient is as high as 0.75, indicating that the model represents the interannual
variability of the precipitation over the area. Lu and Dong (2001) show that anomalies of
850hPa height at the western edge of subtropical high over northern Pacific (110-140°E,
10-30°N) are well correlated with convective activities over WNPM region. The model also
represents westward extension of the subtropical high.
GPC-SIF/Doc. 5.10, p. 4
Fig.4 Same as Fig.3, but for vorticity of
850hPa in 110-140°E,10-30°N. Unit in
1/106s.
Fig.4 shows annual variations of correlation
coefficient between analyzed and forecast
Fig.5. Schematic diagram of the operational land
data assimilation system of JMA.
vorticity over 110-140°E, 10-30°N as an index of westward extension of the subtropical high. It
is noted that negative anomaly of precipitation and consequent negative anomaly of vorticity
(indicating strong westward expansion of subtropical high) in 1998 are well predicted. These
verifications indicate that the model can properly predict the response of the atmospheric
circulation corresponding to the convective activities in the tropics.
JMA is to make several fundamental grid point values (GPVs) of operational SI
forecast available for NMHSs, that is, one-month and three-month mean and standard
deviation of Z500, T850, sea level pressure, precipitation, and 2m temperature on land,
starting from September, 2003.
Those GPVs are encoded in GRIB2 format and will be
available at the Tokyo Climate Centre's (TCC) web page ("http://cpd2.kishou.go.jp/tcc/") for
NMHSs with password protection. The verification of hindcast following SVS and necessary
GPC-SIF/Doc. 5.10, p. 5
documentation are also to be presented. JMA will gradually expand data available at TCC
web page, for example, the forecast and verification maps and scores of operational forecast.
JMA plans to upgrade physical parameterization of the model step by step: cumulus
parameterization in 2004, cloud and radiation in 2005, boundary layer in 2006, and so on.
JMA plans to conduct the seven-month forecast every month starting from early 2007 with
increased horizontal resolution from T63 to T106.
2.
Land data assimilation system
JMA started the operation of global land analysis system for NWP applying a land
surface model in April 2002 (Tokuhiro, 2003). Schematic diagram of the land data assimilation
system at JMA is depicted in Fig.5. The land surface parameters such as snow depth, soil
moisture and soil temperature are calculated with the land surface model with the radiative flux
and precipitation amount provided by the operational four-dimensional data assimilation
(4DDA) system of NWP. Only snow depths reported in SYNOP are assimilated into the
land-surface analysis system as observational data. It is found that one-month forecast is
improved by using those land parameters compared to those without SYNOP snow depth data.
The analyzed land surface parameters are used as initial conditions of the land process model
'SiB' incorporated in the Global Spectral Models.
JMA will introduce the assimilation of snow depth data retrieved from SSM/I data in
March 2003. Thereafter, JMA will study on using precipitation data retrieved from SSM/I data
as a forcing from the atmosphere instead of 4DDA output.
References
Lu, R. and B. Dong, 2001 : Westward Extension of North Pacific Subtropical High in Summer. J. Meteor..
Soc. Japan, 79, 1229-1441.
Tokuhiro, T., 2003: Validation of Land Surface Parameters from the JMA-SiB Using ERA15 Atmospheric
Forcing Data. ( to be submitted to J. Meteor. Soc. Japan )
Xie, P. and P. Arkin, 1997 : Global precipitation: A 17 year monthly analysis based on gauge
observations, satellite estimates and numerical model outputs. Bull. Amer. Meteor. Soc., 78,
2539-2558.