Recent Development of the JMA Global Spectral Model by JKVN5u16

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									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)




                             • Mesoscale model
                             • Horizontal
  • Global model               Resolution: 5 km
  • Horizontal               • Updates: 8 times a day
    Resolution: 20 km        • Forecast domain:
  • Updates: 4 times a day     Japan and its
  • Forecast domain:           surrounding areas
    Global
           Operational NWP Models at JMA (2)
                                                                                               Three-          Warm/Cold
                                                 Typhoon        One-week      One-month
                 Global Model    Mesoscale                                                     month           season
                                                 Ensemble       Ensemble      Ensemble
                 (GSM)           Model (MSM)                                                   Ensemble        Ensemble
                                                 Model          Model         Model
                                                                                               Model           Model
                 Short- and      Warnings
                                                                                               Three           Warm/Cold
                 medium-         and very        Typhoon        One week      One month
Purposes                                                                                       month           season
                 range           short- range    forecast       forecast      forecast
                                                                                               forecast        outlook
                 forecast        forecast
                                 Japan
Forecast                         and its
                 Global                          Global
domain                           surrounding
                                 areas
Grid size/       0.1875deg./                     0.5625deg./                  1.125deg./       1.875deg./
Number of        1920x960        5km/ 721x577                                 320x160
grids            (TL959)                         640x320 (TL319)              (TL159)          192x96 (TL95)

Vertical
                 60 / 0.1hPa     50 / 21800m     60 / 0.1hPa
levels/ Top
                                                                                                               150-210
                                 15 hours (00,                                                                 days
                 84 hours (00,                                                34 days (12
                                 06, 12, 18      132 hours                                     120 days (12    (12 UTC; 5
Forecast         06, 18 UTC),                                   9 days (12    UTC; Wed.
                                 UTC),           (00, 06, 12,                                  UTC; once a     times a year
hours                                                           UTC)          & Thu.)
                 216 hours       33 hours (03,   18 UTC)                                       month)          (Feb., Mar.,
(initial time)                                                  51 members    25 members
                 (12 UTC)        09, 15, 21      11 members                                    31 members      Apr., Sep. &
                                                                              x2                               Oct.)
                                 UTC)
                                                                                                               31 members
Analysis         4D-Var          4D-Var          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
               (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
      Operational Global Objective Analysis
                           2h20m for early run analyses at 00, 06, 12 and 18 UTC,
Cut-off time               11h35m for cycle run analyses at 00 and 12 UTC,
                           5h35m for cycle run analyses at 06 and 18 UTC
Initial Guess              6-hour forecast by GSM
Grid form, resolution      Reduced Gaussian grid, 0.1875 degree, 1920x960 for outer model
and number of grids        Standard Gaussian grid, 0.75 degree, 480x240 for inner model
Levels                     60 forecast model levels up to 0.1 hPa + surface
Analysis variables         Surface pressure, temperature, winds and specific humidity
Methodology                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
Data Used
                           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
Initialization
                           for inner model

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.
                  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
                                                         Major Forecast Models in JMA
                                FY2003   FY2004     FY2005    FY2006     FY2007     FY2008                 FY2009   FY2010
Horizontal Resolution




                                                                                                                      Ocean mixing
                                                                                      Gaussian grid




                                                                                                                      layer model
                                                                                        Reduced
                         60km      GSM(T213)             GSM(TL319)


                         20km                   RSM                                       GSM(TL959)

                         10km   MSM            (NH)MSM                   Extend Forecast Time

                         5km                                                        (NH)MSM


                                                           Data Assimilation Systems
                                FY2003   FY2004     FY2005    FY2006      FY2007    FY2008                 FY2009   FY2010
Objective Analysis for




                         GSM       3DVAR                        4DVAR                                      4DVAR
                                   (T106)             (T63)     (T106)                            (T159)             (TL319)


                         RSM                4DVAR(40km)                         :RSM operation was finished

                         MSM                      4DVAR(20km)                                              (NH)4DVAR(10km)


                                                              HPC System Upgrade

                                                                 * Japanese Fiscal Year : Start from April and End in March
                Upgrade of GSM in Nov. 2007

                           previous                                  current
Forecast time   36(06,18)/ 90(00)/ 216(12)             84(00,06,18)/ 216hours(12UTC)

Horizontal
resolution
                Approximately 60 km(TL319)             Approximately 20 km(TL959)
Vertical
resolution
                40 layers(highest 0.4 hPa)             60 layers(highest 0.1 hPa)
Time
integration
                3-time level(Δt=900 sec)               2-time level(Δt=600 sec)
orography/
mask
                Equivalent to 60 km resolution         Equivalent to 20 km resolution
Sea surface
temperature
                Daily analysis (1 degree resolution)   Daily analysis (0.25 degree resolution)
Sea ice
concentration
                Climatology (1 degree resolution)      Daily analysis (0.25 degree resolution)
                                                       6 hourly analysis (higher resolution over
Snow depth      Daily analysis (1 degree resolution)
                                                       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
                               Troposphere
 lowest level                  (35 layers)
 about 20 m
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
             Miyamoto (2007)   grid, thus saving the
    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 , q ] t
                      zTOP     Tvu  Tv
       CAPE      zLFC
                             g
                                  Tv
                                        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)



                                                                                    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.
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.
      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
GSM tends to overestimate week precipitation               : RSM (retired)
areas and to underestimate strong precipitation
areas in summer.
      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
    0       12     0       12   [JST]
                                        areas especially from local noon to
                  Forecast hour [h]     late afternoon.
80 km grid average over Japan
Threshold: 1mm/3h
        : TL959L60
        : TL319L40
        : RSM (retired)
Northern Hemisphere RMSE
       Aug. – Sep. 2004
                                 TL959L60:
                                 TL319L40:




                                    RMSE of Psea
Psea                   z500       and z500 decreased
   Dec. 2005 – Jan. 2006          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
                                                                             Resolution: TL959L60
Disk access (20%)                          PRODUC T (OT HER)
                                                 8%
                                                                             Computer: HITACHI SR11000
                             OT HER
                               6%
                                                                                       70nodes(140MPIs)
                                                  M ODEL(OT HER)
                 INOUT                                  7%
                                                                             Real Time: 31min24sec
                   14%                               PHY SIC S               (fastest case: 29min39sec)
                                                        6%
                                                          GRID
                                                           3%
                                                         SEM ILAG
          C OLLEC T
                                                            6%
             13%

            W M - ZM
               1%
                                                    SHT             Calculation (44%)
                                                    13%
               ZM - ZY
                                               SPEC T RAL
                 5%                   SL- XY      1%
                       ZY - XY          9%
                                               ADVUM B
                         7%
                                                 1%


   Communication (36%)

                                                                             After Miyamoto (2008)
Recent development
   in global NWP
     - EPS -
              Upgrade of 1W-EPS in Nov. 2007

                           previous                                  current
Horizontal
resolution
                 Approximately 120km(TL159)           Approximately 60km(TL319)
Vertical
resolution
                 40 layers(highest 0.4hPa)            60 layers(highest 0.1hPa)
Time
integration
                 3 time level(Δt=1200sec)             2 time level(Δt=1200sec)
orography/
mask
                 Equivalent to 120km resolution       Equivalent to 60km resolution

Method to
make initial     Breeding of Growing Mode method      Singular Vector method
perturbations

Perturbed area   Northern hemisphere and tropical zone (20S – 90N)
Ensemble size    51 members
Specification of Typhoon EPS (Feb. 2008)
                         Improve both deterministic and probabilistic forecasts of tropical
Purpose
                         cyclone (TC) movement
Forecast domain          Global
Grid size/ Number of
                         0.5625 deg./ 640x320 (TL319)
grids
Vertical levels/Top      60 / 0.1 hPa
                         132 hours (00, 06, 12, 18 UTC)
Forecast hours           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
Ensemble size            11 members
                         Singular Vector (SV) method
Method to make initial
perturbations            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)
                          T0607 (MARIA)
        Forecast by GSM                    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)
                       T0416 (CHABA)
     Forecast by GSM                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          Current System

 Mar 2001-Feb 2006   Mar 2005-            Mar 2006-




                            50nodes                80nodes


 HITACHI
 SR8000E1-80nodes                                  80nodes
                     HITACHI SR11000J1 -210nodes
   768Gflops
                                 27.5Tflops
        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                                   84 hour forecast
                                   Ea00
                                                            Ea06          84 hour forecast
 in hurry to                                                                   The first guesses
 issue forecast                              Da00                              for Ea06 and
                                                                               Ea18 are
                                                                               supplied from
                       Da18          Cycle Analysis                Da06        Ea00 and Ea12,
                                                                               respectively.


                                             Da12                              in hurry to
                                                                               issue forecast
             216 hour forecast
                                                        Ea12
   84 hour forecast            Ea18
                                                                          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    Prognostic Arakawa-Shubert
•   Cloud      Prognostic cloud water
•   PBL        Mellor and Yamada level II
•              k-distribution method and table look-up
    Radiation(L)
               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 …
                                                  must be the multiples of the number of
 Latitude




               Standard
             Gaussian grid
                                                   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               Long-wave radiation
                                                               upward
                                                               mass flux
                                           detrainment
Water     condensation        Cloud
vapor      evaporation        water
                           Conversion        Cumulus
                           from cloud        convection
                             droplets
                                                 entrainment
          re-evaporation
                                                         convective
                           precipitation                 downdraft compensative
                                                                    downdraft
            Simple Biosphere model
          lowest level of the atmospheric model


        canopy                          sensible      latent
                                          heat         heat

                             sw rad.                           lw rad.


             grass                      bare ground
                                          thin skin layer
           Snowmass is not treated
           explicitly and is regarded
 soil      as an iced water on the
layer      grass or bare ground.              conductive heat
           Upper 5cm snow is                   (evaluated with
           accounted in heat budget
                                            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 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
GSM forecast
                      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) as

      DCAPE  [CAPET , q  CAPET , q ] t
                                    *   *

                   zTOP       Tvu
                                  Tv
     CAPE     zLFC
                          g
                                Tv
                                      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.
                                                        Precipitating area is closely
                          40                            related to the area where
                                                  0.1   DCAPE>0, which suggests the
                          10                            capability of DCAPE as the
                                                   0    triggering function of deep
                          1
                                                        convection.
         Radar obs.                       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.
   GSM w/o DCAPE               GSM with DCAPE
                                                        The threshold value depends
6 hour accumulated precipitation and DCAPE
valid at 12 UTC 18 July 2005. Initial time of           on horizontal resolution.
forecasts is 12UTC 17 July 2005.
                 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.

								
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