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

Overview of JMA

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

Organizational Structure of JMA

Observation Networks (1)

• Surface observations – 156 manned weather stations – 1337 automatic weather stations • Radars – 11 Doppler radars – 9 conventional radars

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)

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, …)

Operational NWP models at JMA

Operational NWP Models at JMA (1)

• Global model • Horizontal Resolution: 20 km • Updates: 4 times a day • Forecast domain: Global • Mesoscale model • Horizontal Resolution: 5 km • Updates: 8 times a day • Forecast domain: Japan and its surrounding areas

Operational NWP Models at JMA (2)

Purposes Forecast domain Grid size/ Number of grids Vertical levels/ Top Forecast hours (initial time) Analysis Global Model (GSM) Mesoscale Model (MSM) Typhoon Ensemble Model One-week Ensemble Model One-month Ensemble Model Three month Ensemble Model Warm/Cold season Ensemble Model Short- and medium range forecast Global 0.1875deg./ 1920x960 (TL959) 60 / 0.1hPa

Warnings and very short- range forecast Japan and its surrounding areas 5km/ 721x577 Typhoon forecast Global 0.5625deg./ One week forecast 640x320 (TL319) One month forecast 1.125deg./ 320x160 (TL159) Three month forecast 1.875deg./ 192x96 (TL95) Warm/Cold season outlook 50 / 21800m 60 / 0.1hPa

84 hours (00, 06, 18 UTC), 216 hours (12 UTC) 4D-Var 15 hours (00, 06, 12, 18 UTC), 33 hours (03, 09, 15, 21 UTC) 4D-Var 132 hours (00, 06, 12, 18 UTC) 11 members 9 days (12 UTC) 51 members 34 days (12 UTC; Wed. & Thu.) 25 members x2 120 days (12 UTC; once a month) 31 members 150-210 days (12 UTC; 5 times a year (Feb., Mar., Apr., Sep. & Oct.) 31 members Global analysis with ensemble perturbations

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 • Dynamics (surface to 0.1 hPa) 2-time level, semi-Lagrangian time integration Time step = 600 sec • Cumulus • Cloud Prognostic Arakawa-Shubert 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

Operational Global Objective Analysis

Cut-off time Initial Guess Grid form, resolution and number of grids Levels Analysis variables Methodology Data Used Initialization 2h20m for early run analyses at 00, 06, 12 and 18 UTC, 11h35m for cycle run analyses at 00 and 12 UTC, 5h35m for cycle run analyses at 06 and 18 UTC 6-hour forecast by GSM Reduced Gaussian grid, 0.1875 degree, 1920x960 for outer model Standard Gaussian grid, 0.75 degree, 480x240 for inner model 60 forecast model levels up to 0.1 hPa + surface Surface pressure, temperature, winds and specific humidity Four-dimensional variational (4D-Var) scheme on model levels SYNOP, SHIP, BUOY, TEMP, PILOT, wind profiler, AIREP, SATEM, ATOVS, SATOB, surface wind data from scatterometer on the QuikSCAT satellite and MODIS wind data from Terra and Aqua; Typhoon bogussing applied for analysis Non-linear normal mode initialization and a vertical mode initialization for inner model Early Analysis: Cycle Analysis: Analysis for weather forecast. The data cut off time is very short.

Analysis for keeping quality of global data assimilation system. This analysis is done after much observation data are received.

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

Recent development in global NWP - GSM -

JMA/NWP – Update & Plan

60km FY2003 FY2004 GSM(T213) Major Forecast Models in JMA FY2005 FY2006 FY2007 FY2008 GSM(TL319) FY2009 FY2010 20km 10km MSM 5km RSM (NH)MSM GSM(TL959) Extend Forecast Time (NH)MSM GSM FY2003 3DVAR (T106) FY2004 FY2005 Data Assimilation Systems FY2006 FY2007 FY2008 (T63) 4DVAR (T106) (T159) FY2009 4DVAR FY2010 (TL319) RSM MSM 4DVAR(40km) 4DVAR(20km) :RSM operation was finished (NH)4DVAR(10km) HPC System Upgrade

* Japanese Fiscal Year : Start from April and End in March

Upgrade of GSM in Nov. 2007

Forecast time Horizontal resolution Vertical resolution Time integration orography/ mask Sea surface temperature Sea ice concentration Snow depth previous 36 （ 06,18 ） / 90 （ 00 ） / 216 （ 12 ） Approximately 60 km （ TL319 40 layers （ highest 0.4 hPa ） 3-time level （ Δt=900 sec ） ） Equivalent to 60 km resolution Daily analysis (1 degree resolution) Climatology (1 degree resolution) Daily analysis (1 degree resolution) current 84 （ 00,06,18 ） / 216hours （ 12UTC ） Approximately 20 km （ TL959 ） 60 layers （ highest 0.1 hPa ） 2-time level （ Δt=600 sec ） Equivalent to 20 km resolution Daily analysis (0.25 degree resolution) Daily analysis (0.25 degree resolution) 6 hourly analysis (higher resolution over Japan area)

Simulated Infrared Image

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

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)

Sigma-P Hybrid Vertical Level of GSM

0.1 hPa about 65 km Stratosphere （ 25 layers ） finer in lower atmosphere lowest level about 20 m Troposphere （ 35 layers ）

Introduction of Reduced Gaussian Grid

Miyamoto (2007) 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.

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)

Convection Triggering Mechanism

Xie and Zhang (2000) defined DCAPE (dynamic CAPE generation rate) as DCAPE  [ CAPE 

T

* ,

q

*   CAPE   ] 

t

CAPE 

z TOP



z LFC g T v u T v



T v dz

(

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.

Precipitation (Typhoon)

TL959L60 TL319L40 T0610 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.

RMSE and Bias of Typhoon Central Pressure

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.

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.

Precipitation Scores against Raingauge Observation (Aug. 2004)

Bias score Threat score Threshold [mm/12h] FT=36 ～ 48 hrs, 80 km grid average over Japan GSM tends to overestimate week precipitation areas and to underestimate strong precipitation areas in summer.

Threshold [mm/12h] : TL959L60 : TL319L40 : RSM (retired)

Precipitation Scores against Raingauge Observation (Aug. 2004)

Bias score

0 12 0 12

[JST]

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.

Forecast hour [h] 80 km grid average over Japan Threshold: 1mm/3h : TL959L60 : TL319L40 : RSM (retired)

Northern Hemisphere RMSE

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

TL959L60 ： TL319L40 ： Psea z500

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.

Pie chart showing the relative cost of various components for 84 hours forecast

Disk access (20%) INOUT 1 4 % OT HER 6 % PRODUC T (OT HER) 8 % M ODEL(OT HER) 7 % PHY SIC S 6 % GRID 3 % SEM ILAG 6 % Resolution: TL959L60 Computer: HITACHI SR11000 70nodes(140MPIs) Real Time: 31min24sec (fastest case: 29min39sec) C OLLEC T 1 3 % W M - ZM 1 % ZM - ZY 5 % ZY - XY 7 % SL- XY 9 % SHT 1 3 % SPEC T RAL 1 % ADVUM B 1 % Calculation (44%) Communication (36%) After Miyamoto (2008)

Recent development in global NWP - EPS -

Upgrade of 1W-EPS in Nov. 2007

previous current Horizontal resolution Vertical resolution Time integration orography/ mask Method to make initial perturbations Approximately 120km 40 layers （ highest 0.4hPa

3 time level （ （ TL159 Δt=1200sec ） ） ） Equivalent to 120km resolution Breeding of Growing Mode method Approximately 60km 60 layers （ highest 0.1hPa

2 time level （ （ TL319 Δt=1200sec Equivalent to 60km resolution Singular Vector method Perturbed area Northern hemisphere and tropical zone (20S – 90N) Ensemble size 51 members ） ） ）

Specification of Typhoon EPS (Feb. 2008)

Purpose Forecast domain Grid size/ Number of grids Vertical levels/Top Improve both deterministic and probabilistic forecasts of tropical cyclone (TC) movement Global 0.5625 deg./ 640x320 (TL319) Forecast hours Ensemble size Method to make initial perturbations 60 / 0.1 hPa 132 hours (00, 06, 12, 18 UTC) Runs when TCs of TS/STS/TY intensity exist in the responsibility area of RSMC Tokyo - Typhoon Center (0N-60N, 100E-180E) or are expected to move into the area within the next 24 hours 11 members Singular Vector (SV) method Linear combination of SVs targeted on both TCs (up to three TCs in one forecast event) and a mid-latitude region 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.

Example of Typhoon Ensemble forecasts (1)

Forecast by GSM T0607 (MARIA) Typhoon Ensemble forecasts (11 members; blue line: control run) 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.

Example of Typhoon Ensemble forecasts (2)

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

Future plan (GSM)

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

Future Resolution Upgrade Plan (next supercomputer system)

• Deterministic forecast – TL959L60 → TL959L100 Upgrade model dynamics and physics Introduce new satellite data • Probabilistic forecast – 1WEPS TL319L60M51 → TL479L100M51 Improve representation of smaller scale phenomena Improve forecast skill of severe weather – TEPS TL319L60M11 → TL479L80M25 Improve probabilistic forecast skill of tropical cyclone movement Improve forecast skill of severe weather associated with tropical cyclones

Thank you!

Hare-run : JMA

’

s mascot Hare: Japanese word for “fine weather.”

Replacement of JMA Supercomputer

Previous System Mar 2001-Feb 2006 Current System Mar 2005 Mar 2006 50nodes 80nodes HITACHI SR8000E1-80nodes 768Gflops HITACHI SR11000J1 -210nodes 27.5Tflops

80nodes

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.

Early Analysis in hurry to issue forecast Da18 84 hour forecast Ea00 Da00 Cycle Analysis Ea06 Da06 84 hour forecast The first guesses for Ea06 and Ea18 are supplied from Ea00 and Ea12, respectively.

Da12 in hurry to issue forecast 216 hour forecast 84 hour forecast Ea18 Ea12 Early Analysis

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.

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.

Physical Properties

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

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)

Reduced Gaussian grid

The number of longitudinal grid points …

Standard Gaussian grid

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.

Longitudinal grid interval (km)

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 Water vapor condensation evaporation Long-wave radiation upward mass flux detrainment Cloud water Conversion from cloud droplets Cumulus convection re-evaporation precipitation entrainment convective downdraft compensative downdraft

Simple Biosphere model

lowest level of the atmospheric model soil layer canopy

sw rad.

sensible heat latent heat lw rad.

grass 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

bare ground

thin skin layer

conductive heat

(evaluated with force restore method)

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

Introduction of new convection triggering function to Arakawa Schubert scheme

Moist parameterization in GSM



Cumulus convection



Arakawa-Schubert scheme

 Convection triggering function  Rainwater and cloud water budget 

Clouds and large-scale precipitation



Cloud water scheme



Marine stratocumulus



Stratocumulus scheme

Convection triggering function (1)

Radar observation GSM forecast

GSM tends to predict convective precipitation too early with too wide areas in summer daytime. In order to improve the rainfall forecast, a new convection triggering mechanism is introduced.

Xie and Zhang (2000) showed a strong relationship between deep convection and positive

DCAPE (dynamic CAPE generation rate)

which is determined by the large scale advective tendencies.

6 hour accumulated precipitation, 12UTC 18 July 2005 initial, FT=18 (15 local time).

Convection triggering function (2)

Xie and Zhang (2000) defined DCAPE (dynamic CAPE generation rate)

DCAPE

 as

[ CAPE



T

*

,

q

*  

CAPE

 

]



t

CAPE



z TOP



z LFC g T v u T v



T v dz

(

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 ( after the calculation of model dynamics.

T

,

q

) just

Radar obs.

40 10 1 DCAPE GSM w/o DCAPE GSM with DCAPE

6 hour accumulated precipitation and DCAPE valid at 12 UTC 18 July 2005. Initial time of forecasts is 12UTC 17 July 2005.

0.1

0

Precipitating area is closely related to the area where

DCAPE>0

, which suggests the capability of DCAPE as the triggering function of deep convection.

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.

The threshold value depends on horizontal resolution.

Case study (thunderstorm)

GSM w/o DCAPE GSM with DCAPE Radar obs.

6 hour accumulated precipitation valid at 12 UTC 9 August 2004. Initial time of forecasts is 12 UTC 8 August 2004.

GSM without DCAPE predicts too weak and wide precipitation.

GSM with DCAPE simulates the areas and the intensity of thunderstorm better than that without DCAPE.

Case study (Typhoon T0416)

GSM w/o DCAPE GSM with DCAPE Radar obs.

T0416

6 hour accumulated precipitation valid at 00 UTC 30 August 2004. Initial time of forecasts is 12 UTC 28 August 2004.

GSM without DCAPE predicts too weak precipitation.

GSM with DCAPE simulates the areas and the intensity of heavy precipitation better than that without DCAPE.

Statistics

Bias and equitable threat scores of 3 hour accumulated precipitation forecasts against raingauge observation over Japan for August 2004.

Horizontal axis: forecast time.

Bias score for weak precipitation (1mm/3hour) of GSM without DCAPE ( blue ) is larger than 1 and shows strong diurnal variation.

The variation is reduced substantially in GSM with DCAPE ( red ), though the bias is still large.

Summary

 The convection triggering mechanism proposed by Xie and Zhang (2000) (DCAPE) was introduced to the A-S scheme to improve the rainfall forecast.

 GSM with DCAPE simulated the area and the intensity of heavy precipitation associated with thunderstorm and typhoon better than GSM without DCAPE.

 The tendency of GSM to overestimate weak precipitation areas especially from local noon to late afternoon is also reduced.  DCAPE is implemented to the operational GSM in November 2007.