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.