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Recent Development of the JMA Global Spectral Model

Recent Development of the JMA Global Spectral Model. Masayuki Nakagawa JMA/NPD, visiting NCEP/EMC Nov. 10, 2009. Outline of the Presentation. Overview of JMA Operational NWP models at JMA Recent development in global NWP Global Spectral Model Ensemble Prediction System Future plan.

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Recent Development of the JMA Global Spectral Model

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  1. Recent Development of the JMA Global Spectral Model Masayuki Nakagawa JMA/NPD, visiting NCEP/EMC Nov. 10, 2009

  2. Outline of the Presentation • Overview of JMA • Operational NWP models at JMA • Recent development in global NWP • Global Spectral Model • Ensemble Prediction System • Future plan

  3. Overview of JMA

  4. Structure of Central Government of Japan JMA is placed as an extra-ministerial bureau of the Ministry of Land, Infrastructure, Transport and Tourism. Total staff: ~5700 Budget: approx. $700 million/yr

  5. Organizational Structure of JMA

  6. Observation Networks (1) • Surface observations • 156 manned weather stations • 1337 automatic weather stations • Radars • 11 Doppler radars • 9 conventional radars

  7. Observation Networks (2) • Upper air observations • 16 radiosonde stations • 31 wind profilers • Satellite observations • Geostationary meteorological satellite (MTSAT-1R) picture from the WMO homepage (modified)

  8. Organization of NPD Numerical Prediction Division (74) • Administration Section (5) • Programming Section (11) • Management of NWP system • Development of data decoding system • Numerical Analysis and Modeling Section (46) • Development of NWP models and analysis systems • Chief (1) • Global Modeling Group (17) • Mesoscale Modeling Group (13) • Observation Group (15) • Application Section (12) • Development of applications (guidance, graphics, …)

  9. Operational NWP models at JMA

  10. Operational NWP Models at JMA (1) • Mesoscale model • Horizontal Resolution: 5 km • Updates: 8 times a day • Forecast domain: • Japan and its surrounding areas • Global model • Horizontal • Resolution: 20 km • Updates: 4 times a day • Forecast domain: • Global

  11. Operational NWP Models at JMA (2)

  12. Framework of GSM • Resolution TL959, reduced Gaussian grid 0.1875 deg. / 1920 (equator) – 6 deg. / 60 (closest to pole) x 960, roughly 20km 60 unevenly spaced sigma-p hybrid levels (surface to 0.1 hPa) • Dynamics 2-time level, semi-Lagrangian time integration Time step = 600 sec • Cumulus Prognostic Arakawa-Shubert • Cloud Prognostic cloud water • PBL Mellor and Yamada level II • Radiation(L) k-distribution method and table look-up method • Radiation(S) Lacis and Hansen (1974) • Gravity wave o(1-10km), o(100km) • Land SiB • Assimilation 4D-Var

  13. Operational Global Objective Analysis Early Analysis: Analysis for weather forecast. The data cut off time is very short. Cycle Analysis: Analysis for keeping quality of global data assimilation system. This analysis is done after much observation data are received.

  14. Roles of GSM • Basic information for a short- and medium-range, one week, one month and seasonal forecasts • Basic information for typhoon track and intensity forecasts • Assist of aviation and ship routing forecasts • Provision of lateral boundary condition for Mesoscale Model • Input data for ocean wave model • Input data for ocean data assimilation • Wind information for input of chemical transport model

  15. Recent development in global NWP - GSM -

  16. Ocean mixing layer model Reduced Gaussian grid JMA/NWP – Update & Plan Major Forecast Models in JMA FY2003 FY2004 FY2005 FY2006 FY2007 FY2008 FY2009 FY2010 GSM(T213) GSM(TL319) 60km Horizontal Resolution RSM GSM(TL959) 20km (NH)MSM 10km MSM Extend Forecast Time (NH)MSM 5km Data Assimilation Systems FY2003 FY2004 FY2005 FY2006 FY2007 FY2008 FY2009 FY2010 3DVAR 4DVAR 4DVAR GSM (T159) (TL319) (T106) (T63) (T106) Objective Analysis for 4DVAR(40km) RSM :RSM operation was finished 4DVAR(20km) (NH)4DVAR(10km) MSM HPC System Upgrade * Japanese Fiscal Year : Start from April and End in March

  17. Upgrade of GSM in Nov. 2007

  18. 20km-GSM TL1023L40 2002.7.9.00Z FT=24 60km-GSM T213L40 2002.7.9.00Z FT=24 Simulated Infrared Image GMS-5 observation 00UTC Jul. 10 2002

  19. Orography of Operational Models at JMA GSM TL959 (20km) MSM (5km) Orographic effects are better captured by higher resolution models. The surface parameters such as temperatures and winds, might be predicted more realistically by those models. GSM TL319 (60km)

  20. Sigma-P Hybrid Vertical Level of GSM 0.1 hPa about 65 km Stratosphere （25 layers） finer in lower atmosphere Troposphere （35 layers） lowest level about 20 m

  21. Introduction of Reduced Gaussian Grid A reduced Gaussian grid was implemented in GSM as a new dynamical core in August 2008. On the standard Gaussian grid, the longitudinal interval between two grid points at the high latitudes is smaller than that at the low latitudes. Hence, it is redundant to use an equal number of grid points for all given latitudes in global model. The total number of grid-points is reduced by about 30% in the reduced Gaussian grid, thus saving the computational throughput. Miyamoto (2007)

  22. Moist Parameterization in GSM • Cumulus convection • Arakawa-Schubert scheme (Arakawa and Shubert 1974; Moorthi and Suarez 1992; Randall and Pan 1993) • Convection triggering mechanism proposed by Xie and Zhang (2000) (DCAPE)was introduced to improve the rainfall forecast • Clouds and large-scale precipitation • Prognostic cloud water scheme (Sommeria and Deardorff 1977; Smith 1990) • Marine stratocumulus • Stratocumulus scheme (diagnostic) (Slingo 1980, 1987; Kawai and Inoue 2006)

  23. Xie and Zhang (2000) defined DCAPE (dynamic CAPE generation rate) as (T*, q*) are (T, q) plus the change due to the total large-scale advection over a time interval Δt (integration time step used in the model). They are equal to (T, q) just after the calculation of model dynamics. Xie and Zhang (2000) showed a strong relationship between deep convection and positive DCAPE. In TL959L60 GSM, deep convection (cloud top < 700hPa) is assumed to occur only when DCAPE> -1/300 (J/kg/s) , which corresponds to dynamic warming or moistening in the lower troposphere. Convection Triggering Mechanism

  24. Precipitation (Typhoon) T0610 TL959L60 TL319L40 Radar 6 hour accumulated precipitation valid at 12UTC 18 August 2006. The initial time of the forecasts is 12UTC 17 August 2006. The gray area in right panel indicate an absence of analysis. Typhoon T0610 (WUKONG) was moving northward over Kyushu Island. Both models predicted its position well. TL319L40 GSM could not predict the detailed distribution of precipitation and strong rainfall over land. TL959L60 GSM simulated the distribution and intensity of precipitation better then TL319L40 GSM, including orographic precipitation and heavy rainfall near the center of the typhoon.

  25. RMSE and Bias of Typhoon Central Pressure TL319L40 GSM predicted weak typhoons compared to the best track analyzed by RSMC-Tokyo Typhoon Center because of its low horizontal resolution. TL959L60 GSM predicted the typhoon intensity better then TL319L40 GSM. 0 24 48 72 Forecast time (hour) TYM: 24-km resolution regional model covering a tropical cyclone and its surrounding areas. Its operation was terminated in November 2007.

  26. Precipitation Scores against Raingauge Observation (Aug. 2004) Bias score Threat score Threshold [mm/12h] Threshold [mm/12h] FT=36～48 hrs, 80 km grid average over Japan : TL959L60 : TL319L40 : RSM (retired) GSM tends to overestimate week precipitation areas and to underestimate strong precipitation areas in summer.

  27. : TL959L60 : TL319L40 : RSM (retired) Precipitation Scores against Raingauge Observation (Aug. 2004) Bias score The Introduction of convection triggering mechanism proposed by Xie and Zhang (2000) (DCAPE) reduced the tendency of GSM to overestimate weak precipitation areas especially from local noon to late afternoon. 0 12 0 12 [JST] Forecast hour [h] 80 km grid average over Japan Threshold: 1mm/3h

  28. Northern Hemisphere RMSE Aug. – Sep. 2004 TL959L60： TL319L40： RMSE of Psea and z500 decreased slightly in both summer and winter season. Psea z500 Dec. 2005 – Jan. 2006 TL959L60： TL319L40： Psea z500

  29. Verification Score RMSE of 24, 48 and 72 hour forecasts by GSM for 500 hPa geopotential height against analysis in NH (20N – 90N). Curves: monthly means, horizontal lines: yearly means.

  30. Pie chart showing the relative cost of various components for 84 hours forecast Resolution: TL959L60 Computer: HITACHI SR11000 70nodes(140MPIs) Real Time: 31min24sec (fastest case: 29min39sec) Disk access (20%) Calculation (44%) Communication (36%) After Miyamoto (2008)

  31. Recent development in global NWP - EPS -

  32. Upgrade of 1W-EPS in Nov. 2007

  33. Specification of Typhoon EPS (Feb. 2008) It is possible to obtain reliability of typhoon track forecast from the ensemble spread of typhoon track forecasts by Typhoon EPS. In addition, alternative track scenarios to an ensemble mean track are available.

  34. Example of Typhoon Ensemble forecasts (1) T0607 (MARIA) Typhoon Ensemble forecasts (11 members; blue line: control run) Forecast by GSM Analyzed track Possibility of recurvature of the typhoon is represented in Typhoon Ensemble forecasts. Ensemble spread is large, which indicates the reliability of the forecasts is relatively low.

  35. Example of Typhoon Ensemble forecasts (2) T0416 (CHABA) Typhoon Ensemble forecasts (11 members, blue line: control run) Forecast by GSM Analyzed track Ensemble spread is quite small, which indicates the reliability of the forecasts is relatively high.

  36. Future plan (GSM)

  37. Focus of NPD’s recent efforts Model bias Temperature, moisture, … Spin-up Precipitation, … Land-sea contrast in precipitation Precipitation over tropical eastern Pacific Global circulation Formation of Typhoon Size of Typhoon Maximum wind radius Intensity of Typhoon Ocean mixing layer model

  38. Future Resolution Upgrade Plan(next supercomputer system) Deterministic forecast TL959L60 → TL959L100 Upgrade model dynamics and physics Introduce new satellite data Probabilistic forecast 1WEPSTL319L60M51 → TL479L100M51 Improve representation of smaller scale phenomena Improve forecast skill of severe weather TEPSTL319L60M11 → TL479L80M25 Improve probabilistic forecast skill of tropical cyclone movement Improve forecast skill of severe weather associated with tropical cyclones

  39. Thank you! Hare-run: JMA’s mascot Hare: Japanese word for “fine weather.”

  40. Replacement of JMA Supercomputer Current System Previous System Mar 2005- Mar 2006- Mar 2001-Feb 2006 80nodes 50nodes HITACHI SR8000E1-80nodes 80nodes HITACHI SR11000J1 -210nodes 768Gflops 27.5Tflops

  41. Early Analysis and Cycle Analysis Early Analysis: Analysis for weather forecast. The data cut off time is very short. Cycle Analysis: Analysis for keeping quality of global data assimilation system and for supplying the first guess to early analysis. This analysis is done after much observation data are received. 84 hour forecast Early Analysis Ea00 84 hour forecast Ea06 in hurry to issue forecast The first guesses for Ea06 and Ea18 are supplied from Ea00 and Ea12, respectively. Da00 Da18 Da06 Cycle Analysis Da12 in hurry to issue forecast 216 hour forecast Ea12 84 hour forecast Ea18 Early Analysis

  42. Numerical/Dynamical Properties (1) • Horizontal representation • Spectral (spherical harmonic basis functions) with transformation to a reduced Gaussian grid for calculation of nonlinear quantities and most of the physics. • Horizontal resolution • Spectral triangular TL959 (deterministic), TL319 (EPS) • Vertical representation • Finite differences in sigma-pressure hybrid coordinates. • Vertical domain • Surface to 0.1 hPa. • Vertical resolution • There are 60 unevenly spaced hybrid levels.

  43. Numerical/Dynamical Properties (2) • Time integration scheme • A two-time level semi-implicit semi-Lagrangian scheme is used for the time integration. • A constant time step length 600 sec. is used for the deterministic (TL959) model. • Equations of state • Primitive equations for dynamics in a spectral semi-Lagrangian framework are expressed in terms of wind components, temperature, specific humidity, cloud water and surface pressure. • Diffusion • A linear fourth-order horizontal diffusion is applied on the hybrid sigma-pressure surfaces in spectral space.

  44. Physical Properties • Cumulus Prognostic Arakawa-Shubert • Cloud Prognostic cloud water • PBL Mellor and Yamada level II • Radiation(L) k-distribution method and table look-up method • Radiation(S) Lacis and Hansen (1974) • Gravity wave o(1-10km), o(100km) • Land SiB

  45. Reduced Gaussian grid Standard Gaussian grid Latitude Longitudinal grid interval (km) Reduced Gaussian Grid (Aug. 2008) There are a large number of redundant grid-points and insignificant wavenumber components in the standard Gaussian grid. The total number of grid-points is reduced by about 30% in the reduced Gaussian grid. After Miyamoto (2007) The number of longitudinal grid points … must be the multiples of the number of longitudinal sub-domains. must be the composite numbers of the radices of FFT kernels. should be the multiple numbers of the longitudinal interval of the radiation process.

  46. Convection and precipitation • deep convection - Arakawa and Schubert 1974 • conversion of cloud droplets to precipitation • moisture detrainment from top of the cumulus • re-evaporation of stratiform precipitation Short-wave radiation Long-wave radiation upward mass flux detrainment condensation evaporation Water vapor Cloud water Cumulus convection Conversion from cloud droplets re-evaporation entrainment convective downdraft precipitation compensative downdraft

  47. Simple Biosphere model lowest level of the atmospheric model sensible heat latent heat canopy sw rad. lw rad. bare ground grass thin skin layer Snowmass is not treated explicitly and is regarded as an iced water on the grass or bare ground. Upper 5cm snow is accounted in heat budget soil layer conductive heat (evaluated with force restore method)

  48. Transition Steps • Algorithm development • Preliminary testing • Low resolution (TL319L60) forecast/assimilation experiment, summer and winter • High resolution (TL959L60) single forecast experiment (no assimilation) • Pre-Implementation testing • High resolution (TL959L60) forecast/assimilation experiment, at least summer and winter • Systematic error, RMSE, anomaly correlation, typhoon track and intensity, precipitation, … • Implementation

  49. Introduction of new convection triggering function to Arakawa-Schubert scheme

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