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Name: JMA Ensemble

1. Ensemble version

Ensemble identifier code: JMA GEPS1701

Short Description: Global ensemble system that simulates initial uncertainties using the Local Ensemble Transform Kalman Filter (LETKF), the singular vectors and lagged averaging forecasts, model uncertainties due to physical parameterizations using a stochastically perturbed physics tendencies (SPPT) scheme and uncertainties on surface boundary conditions using sea surface temperature perturbation. Ensembles are based on 50 members: run once a week (Tuesday and Wednesday at 00Z and 12Z) up to day 34.

Research or operational: Operational

Data time of first forecast run: 22/03/2017

2. Configuration of the EPS

Is the model coupled to an ocean model? No

If yes, please describe ocean model briefly including frequency of coupling and any ensemble perturbation applied: N/A

Is the model coupled to a sea Ice model? No

If yes, please describe sea-ice model briefly including any ensemble perturbation applied: N/A

Is the model coupled to a wave model? No

If yes, please describe wave model briefly including any ensemble perturbation applied: N/A

Ocean model: N/A

Horizontal resolution of the atmospheric model: TL479 (about 40 km) up to 18 days, TL319 (about 55 km) after 18 days.

Number of model levels: 100

Top of model: 0.01 hPa

Type of model levels: hybrid (sigma-p) coordinate

Forecast length: 34 days (816 hours), but archived up to 32.5 days (780 hours)

Run Frequency: once a week (combination of Tuesday and Wednesday at 00Z and 12Z)

Is there an unperturbed control forecast included?: Yes

Number of perturbed ensemble members: 46 (4 controls from each initial date), but archived as 49 (1 control from each initial date)

Integration time step: 12minutes up to 18 days and 20 minutes after 18 days

 

3. Initial conditions and perturbations

Data assimilation method for control analysis: 4D Var

Resolution of model used to generate Control Analysis: TL959L100

Ensemble initial perturbation strategy: LETKF + singular vectors (initial SV; Northern Hemisphere, Tropics and Southern Hemisphere) + Lagged Average Forecasting

Horizontal and vertical resolution of perturbations: TL319L100 (LETKF), T63L40 (SV)

Perturbations in +/- pairs: Yes (SV), No (LETKF), No (SST)

Initialization of land surface:

What is the land surface model (LSM) and version used in the forecast model, and what are the current/relevant references for the model? Are there any significant changes/deviations in the operational version of the LSM from the documentation of the LSM?

LSM is the JMA SIB. The JMA SIB is based on the Simple Biosphere (SiB) developed by Sellers et al.(1986) and implemented by Sato et al.(1989a) and Sato et al.(1989b) for the land surface process in forecast model, and overall specifications are comprehensively updated and refined schemes are introduced. Significant changes from SiB are;

  • replacement the force-restore method for soil temperature prediction with a multilayer soil heat and water flux model and separate layers for snow

  • introduction of an equation of heat conduction with seven soil levels

  • consideration of the release or absorption of latent heat from phase change for soil temperature prediction

  • introduction of a new snow model with up to four layers with consideration for thermal diffusion, increased density from snow compaction and reduction of albedo due to snow aging

The Simple Biosphere (SiB) developed by Sellers et al.(1986), Sato et al.(1989a)

How is soil moisture initialized in the forecasts? (climatology / realistic / other)? Is there horizontal and/or vertical interpolation of initialization data onto the forecast model grid? If so, please give original data resolution(s). Does the LSM differentiate between liquid and ice content of the soil? If so, how are each initialized? If all model soil layers are not initialized in the same way or from the same source, please describe.

Soil moisture is initialized with climatology derived from the offline simulations of the LSM with resolution of TL959. Soil moisture climatology and other initialization data are interpolated from the resolution of the deterministic system (TL959) to the resolution of the EPS forecast model grid (TL479). No vertical interpolation is applied. The LSM defines two soil moisture state variables, liquid and ice. The amounts of liquid and ice are determined by soil temperature and the ratio of the two soil moisture state variables cycled from the model forecast in the JMA Global Analysis (GA)

How is snow initialized in the forecasts? (climatology / realistic / other)? Is there horizontal and/or vertical interpolation of data onto the forecast model grid? If so, please give original data resolution(s) Are snow mass, snow depth or both initialized? What about snow age, albedo, or other snow properties?

The global snow depth with 1.0◦ latitude/longitude resolution is analyzed using SYNOP snow depth data on the day. A two-dimensional optimum interpolation (OI) is employed for the analysis method. The analyzed snow depth is interpolated to the EPS forecast model grid (TL479). In addition, snow depth on Japan land girds are replaced with interpolated latest observation value of AMeDAS (Automated Meteorological Data Acquisition System) snow depth as well as Japanese SYNOP. The analyzed snow depth is converted to snow mass using snow density cycled from the GA model forecast and the snow mass is used as an initial condition for the LSM. Snow age is cycled from the GA model forecast and albedo is calculated with the snow age.

How is soil temperature initialized in the forecasts? (climatology / realistic / other) Is the soil temperature initialized consistently with soil moisture (frozen soil water where soil temperature ≤0°C) and snow cover (top layer soil temperature ≤0°C under snow)? Is there horizontal and/or vertical interpolation of data onto the forecast model grid? If so, please give original data resolution(s) If all model soil layers are not initialized in the same way or from the same source, please describe.

Soil temperature cycled from the GA model forecast with resolution of TL959 is used as an initial condition for the LSM. It is interpolated to the EPS forecast model grid. The liquid and ice content of the soil initialized in the forecasts are determined by the soil temperature cycled from the GA model forecast

How are time-varying vegetation properties represented in the LSM? Is phenology predicted by the LSM? If so, how is it initialized? If not, what is the source of vegetation parameters used by the LSM? Which time-varying vegetation parameters are specified (e.g., LAI, greenness, vegetation cover fraction) and how (e.g., near-real-time satellite observations? Mean annual cycle climatology? Monthly, weekly or other interval?)

There is no consideration about time-varying of vegetation properties. The climatology for each vegetation based on Dorman and Sellers(1989) is set as outer parameters (Some parameters are monthly data, such as LAI, the ratio of green leaves, the ratio of vegetation)

What is the source of soil properties (texture, porosity, conductivity, etc.) used by the LSM?

Soil thermal and hydraulic properties are calculated using soil texture from the HWSD (Harmonized World Soil Database).

If the initialization of the LSM for re-forecasts deviates from the procedure for forecasts, please describe the differences.

Land surface values are estimated with the land-surface model in the Global EPS using atmospheric forcing from JRA-55 (Kobayashi et al. 2015)

 

4. Model Uncertainties perturbations:

Is model physics perturbed? If yes, briefly describe methods: Stochastically perturbed physics tendencies (SPPT) scheme

Do all ensemble members use exactly the same model version? Same

Is model dynamics perturbed? No

Are the above model perturbations applied to the control forecast? No

 

5. Surface Boundary perturbations:

Perturbations to sea surface temperature? Yes

Perturbation to soil moisture? No

Perturbation to surface stress or roughness? No

Any other surface perturbation? No

Are the above surface perturbations applied to the Control forecast? No

Additional comments: None

 

6. Other details of the models:

Description of model grid: Linear grid

List of model levels in appropriate coordinates: See appendix.

What kind of large scale dynamics is used? Spectral semi-lagrangian

What kind of boundary layer parameterization is used? Mellor and Yamada level 2 and diffusive coefficients based on Han and Pan (2011) for stable BL, Yonehara et al. (2014)

What kind of convective parameterization is used? Arakawa and Schubert (JMA 2013), Yonehara et al. (2014), Yonehara et al. (2017)

What kind of large-scale precipitation scheme is used?: Sundqvist (1978), Yonehara et al. (2017)

What cloud scheme is used?: Smith (1990), Kawai and Inoue (2006), Yonehara et al. (2017)

What kind of land-surface scheme is used? JMA-SIB, Yonehara et al. (2017)

How is radiation parametrized?

Longwave radiation: Yabu (2013)

Shortwave radiation: JMA(2013), Nagasawa (2012)

Other relevant details?

Gravity wave drag scheme : Iwasaki et al. (1989)

Non-orographic gravity wave forcing parametrization : Scinocca (2003)

 

7. Re-forecast Configuration

Number of years covered: 32 years (1981-2012)

Produced on the fly or fix re-forecasts?: fixed re-forecasts in advance

Frequency: The re-forecasts consists of a 5-member ensemble starting the 10th, 20th, the last dates of calendar months.

Ensemble size: 5 members

Initial conditions: JRA-55 (TL319L60) + land surface values estimated with the land-surface model in the Global EPS using atmospheric forcing from JRA-55

Is the model physics and resolution the same as for the real-time forecasts: Yes

If not, what are the differences: N/A

Is the ensemble generation the same as for real-time forecasts? No

If not, what are the differences: LETKF perturbations are not used and singular vectors (initial SV + evolved SV) are used

Other relevant information:

The JMA re-forecasts dataset is a "fixed" dataset which means that the re-forecasts are produced once from a "frozen" version of the model and are used for a number of years to calibrate real-time forecast. The JMA re-forecasts consist of a 5-member ensemble running three times a month from 1981 to 2012. The start dates correspond to 1st / 11th and 21st of each month at 00Z minus 12 hours (28 February instead of 29 February). here is the complete list of re-forecast start dates:

10/20/31 January - 10/20/28 February - 10/20/31 March - 10/20/30 April - 10/20/31 May - 10/20/30 June - 10/20/31 July - 10/20/31 August - 10/20/30 September - 10/20/31 October - 10/20/30 November and 10/20/31 December 1981-2012

The S2S database contains the complete JMA re-forecast dataset.

 

The JMA real-time ensemble forecasts include 4 start dates (Tuesdays at 00Z and 12Z + Wednesdays at 00Z + 12Z). The ensemble size is 13 members for Tuesdays 12Z and Wednesdays 00 and 12Z and 11 members for Tuesdays 00Z. For user convenience, ensemble data from 4 start dates are archived as a single 50-member ensemble starting on Wednesdays at 12Z. List of initialize date and index of archived member in final 50 member ensemble are as follows;

  • Wednesday 12 Z : 0,1, …, 12

  • Wednesday 00 Z : 13,14, …, 25

  • Tuesday 12 Z : 26, 27, …, 38

  • Tuesday 00 Z : 39, 40, …, 49

Note that member 0, 13, 26 and 39 are control forecast from each initial date, however, they are encoded as pf for user convenience.

 

The JMA re-forecasts are archived in the S2S database with 2 date attributes:

  • hdate which corresponds to the actual starting date of the re-forecast

  • date which correspond to the ModelVersionDate. Since the JMA re-forecasts are "fixed" re-forecasts this ModelVersiondate is the same for all the re-forecasts and equal to 20170131. This variable will change when a new version of the JMA model for extended-range forecasts will be implemented.

 

Dorman, J. L. and P. J. Sellers, 1989: A global climatology of albedo, roughness length and stomatal resistance for atmospheric general circulation models as represented by the simple biosphere model (SiB). J. Appl.Meteor., 28, 833–855.

Han, J. and H-L, Pan. 2011: Revision of convection and vertical diffusion schemes in the NCEP global forecast system. Weather and Forecasting, 26.4, 520-533.

Iwasaki, T., S. Yamada and K. Tada. 1989: A parameterization scheme of orographic gravity wave drag with two different vertical partitionings. J. Meteor. Soc. Japan. Ser. II, 67(1), 11-27.

Japan Meteorological Agency, 2013: Outline of the operational numerical weather prediction at the Japan Meteorological Agency.

Kobayashi, S., Y. Ota, Y. Harada, A. Ebita, M. Moriya,H. Onoda, K. Onogi, H. Kamahori, C. Kobayashi, H. Endo, K. Miyaoka, and K. Takahashi, 2015: The JRA-55 reanalysis: General specifications and basic characteristics. J. Meteor. Soc. Japan. 93, 5-48.

Nagasawa, R., 2012: The Problem of Cloud Overlap in the Radiation Process of JMA's Global NWP Model. CAS/JSC WGNE Research Activities in Atmospheric and Oceanic Modelling, 42, 04.15-04.16.

Sato, N., P. J. Sellers, D. A. Randall, E. K. Schneider, J. Shukla, J. L. Kinter III, Y-T Hou, and E. Albertazzi, 1989a: Effects of implementing the simple biosphere model (SiB) in a general circulation model. J. Atmos. Sci., 46, 2757–2782.

Sato, N., P. J. Sellers, D. A. Randall, E. K. Schneider, J. Shukla, J. L. Kinter III, Y-T Hou, and E. Albertazzi, 1989b: Implementing the simple biosphere model (SiB) in a general circulation model: Methodologies and results. NASA contractor Rep. 185509, NASA. 76pp.

Scinocca, J. F. 2003: An accurate spectral nonorographic gravity wave drag parameterization for general circulation models. J. Atmos. Sci., 60(4), 667-682.

Sellers, P. J., Y. Mintz, Y. C. Sud, and A. Dalcher, 1986: A simple biosphere model (SiB) for use within general circulation models. J. Atmos. Sci., 43, 505–531.

Verseghy, D., 1991: CLASS—A Canadian Land Surface Scheme for GCMs. I. Soil model, Int. J. Climate., 13, 111-133.

Yabu, S. 2013: Development of longwave radiation scheme with consideration of scattering by clouds in JMA global model. CAS/JSC WGNE Research Activities in Atmospheric and Oceanic Modelling, 43, 04.07–04.08.

Yonehara, H, M. Ujiie, T. Kanehama, R. Sekiguchi, and Y. Hayashi, 2014: Upgrade of JMA’s Operational NWP Global Model, CAS/JSC WGNE Research Activities in Atmospheric and Oceanic Modelling, 44, 06.19-6.21.

Yonehara, H., T. Tokuhiro, R. Nagasawa, M. Ujiie, A. Shimokobe, M. Nakagawa, R. Sekiguchi, T. Kanehama, H. Sato, and K. Saitou, 2017: Upgrade of Parameterization Schemes on JMA’s Operational Global NWP Model, CAS/JSC WGNE Research Activities in Atmospheric and Oceanic Modelling, submitted.

 

Appendix. Hybrid coordinates

 GEPS_model_description_appendix.pdf (includes equations)

Table A. Model level from 1 to 100 (half-level and full-level pressures with a surface pressure of 1000hPa)

k

A[Pa]

B

Ph [Pa]

Pf [Pa]

1

0.000000000000

1.000000000000

100000.000000000000

99904.290819480000

2

0.381960371274

0.998082302320

99808.612192413300

99670.173146094300

3

2.282912116885

0.995295152540

99531.798166091900

99347.919438176400

4

7.263035741060

0.991568910490

99164.154084739200

98932.523764289200

5

17.501423784709

0.986835726509

98701.074074667800

98419.771991070100

6

35.837818528753

0.981028998386

98138.737657170300

97806.230067624800

7

65.788588562684

0.974083102672

97474.098855798800

97089.233648303900

8

111.534494496255

0.965933418157

96704.876310209600

96266.878411635700

9

177.878560017639

0.956516652201

95829.543780106500

95338.010474994700

10

270.173200177349

0.945771472535

94847.320453676100

94302.216328606000

11

394.216660935207

0.933639438313

93758.160492246800

93159.811724711600

12

556.119785479916

0.920066214680

92562.741253454200

91911.828970647500

13

762.145131558889

0.905003045132

91262.449644765300

90560.002083076000

14

1018.521502093580

0.888408445709

89859.366073000600

89106.749280559000

15

1331.237994182840

0.870250074835

88356.245477677100

87555.152318963900

16

1705.822687785370

0.850506722823

86756.494970038600

85908.932215338800

17

2147.112046884750

0.829170356011

85064.147648024700

84172.420962542400

18

2659.017949071050

0.806248142726

83283.832221671900

82350.528909056100

19

3244.299946519710

0.781764382176

81420.738164133200

80448.707567340400

20

3904.350843037290

0.755762253571

79480.576200176800

78472.907717980000

21

4639.003900351110

0.728305301549

77469.534055300000

76429.532794641800

22

5446.369919361920

0.699478575927

75394.227512075600

74325.387662497900

23

6322.712044687760

0.669389349082

73261.646952875500

72167.623038412900

24

7262.365392218190

0.638167343135

71079.099705731800

69963.675938907600

25

8257.707494428910

0.605964411538

68854.148648278300

67721.206678283200

26

9299.184110690550

0.572953635479

66594.547658560000

65448.033068336100

27

10375.393194275100

0.539327814317

64308.174625936400

63152.062588666700

28

11473.227799960100

0.505297350453

62002.962845277100

60841.223396792200

29

12578.076531849900

0.471087551766

59686.831708492700

58523.395126843200

30

13674.077863475800

0.436935398115

57367.617674957400

56206.340479565500

31

14744.422417575000

0.403085841256

55053.006543217000

53897.638632826500

32

15771.695184622600

0.369787728735

52750.468058108900

51604.621498241300

33

16738.247801019900

0.337289460678

50467.193868787900

49334.313815716200

34

17626.589506536500

0.305834502976

48210.039804087600

47093.378013777600

35

18419.784348312200

0.275656890050

45985.473353357000

44888.064671945600

36

19101.841666029400

0.246976854698

43799.527135832300

42724.169304322200

37

19658.086923129000

0.219996720901

41657.759013273400

40606.996045923500

38

20075.528020764800

0.194896913328

39565.219353552600

38541.328669130100

39

20348.204534314600

0.171782212581

37526.425792387600

36531.409193246100

40

20482.864577903400

0.150624810943

35545.345672247000

34580.924180921600

41

20488.764993118100

0.131366211745

33625.386167611000

32692.998647295100

42

20375.968557002400

0.113934233832

31769.391940169000

30870.197346917800

43

20155.123908885900

0.098245261029

29979.650011778900

29114.533055043500

44

19837.250236607800

0.084206511655

28257.901402076100

27427.481328390100

45

19433.531725236600

0.071718272305

26605.358955724400

25810.001119634600

46

18955.126107165600

0.060676045775

25022.730684665700

24262.560532586700

47

18412.990881865800

0.050972569937

23510.247875545100

22785.166942447800

48

17817.729937538000

0.042499672253

22067.697162859300

21377.400668987200

49

17179.462446459200

0.035149932979

20694.455744310800

20038.451378590600

50

16507.715059741200

0.028818138556

19389.528915388800

18767.156403317900

51

15811.337630045400

0.023402514934

18151.589123493200

17562.040198551200

52

15098.441971265300

0.018805738142

16979.015785418600

16421.354213004000

53

14376.362544194500

0.014935726284

15869.935172620800

15343.116512473300

54

13651.637451362600

0.011706222916

14822.259742954800

14325.150578084500

55

12930.007739834300

0.009037186420

13833.726381873900

13365.122787431100

56

12216.432748330100

0.006855003584

12901.933106779500

12460.578179237800

57

11515.119089025100

0.005092547928

12024.373881805000

11608.974195704400

58

10829.560814360000

0.003689104692

11198.471283537700

10807.712188287600

59

10162.588370980400

0.002590184771

10421.606848056500

10054.166559642400

60

9516.424069790310

0.001747249438

9691.149013591930

9345.711494310320

61

8892.741985017770

0.001117366638

9004.478648797760

8679.745301339560

62

8292.730417742580

0.000662818044

8359.012222132530

8053.712451632740

63

7717.155302649380

0.000350674188

7752.222721456430

7465.123439718090

64

7166.423184127830

0.000152352890

7181.658473176600

6911.572632502810

65

6640.642624128240

0.000043174116

6644.960035722230

6390.754284962960

66

6139.683116216300

0.000001922353

6139.875351495750

5900.476903411930

67

5664.273333449310

0.000000000000

5664.273333449310

5438.676119685850

68

5216.156035873420

0.000000000000

5216.156035873420

5003.426203215030

69

4793.669512829290

0.000000000000

4793.669512829290

4592.950281621950

70

4395.113401275500

0.000000000000

4395.113401275500

4205.629263972720

71

4018.949179441300

0.000000000000

4018.949179441300

3840.009364797400

72

3663.806945739750

0.000000000000

3663.806945739750

3494.808013775610

73

3328.490442759910

0.000000000000

3328.490442759910

3168.917810183670

74

3011.979920608280

0.000000000000

3011.979920608280

2861.408050530980

75

2713.432303648210

0.000000000000

2713.432303648210

2571.523234060480

76

2432.178008474700

0.000000000000

2432.178008474700

2298.677850371330

77

2167.713677654230

0.000000000000

2167.713677654230

2042.446697702930

78

1919.690067136030

0.000000000000

1919.690067136030

1802.549995199940

79

1687.894382929320

0.000000000000

1687.894382929320

1578.832666188020

80

1472.226533650630

0.000000000000

1472.226533650630

1371.237409874010

81

1272.669075953680

0.000000000000

1272.669075953680

1179.771567623220

82

1089.251096446150

0.000000000000

1089.251096446150

1004.468335371320

83

922.006895759386

0.000000000000

922.006895759386

845.343562014634

84

770.931090588804

0.000000000000

770.931090588804

702.350160062845

85

635.932565397326

0.000000000000

635.932565397326

575.332957157731

86

516.790485034353

0.000000000000

516.790485034353

463.987514226638

87

413.116183588936

0.000000000000

413.116183588936

367.826876121908

88

324.325010406765

0.000000000000

324.325010406765

286.160240721826

89

249.621981177447

0.000000000000

249.621981177447

218.086991815740

90

188.004232058539

0.000000000000

188.004232058539

162.508363909331

91

138.281777767921

0.000000000000

138.281777767921

118.157227370720

92

99.116030468172

0.000000000000

99.116030468172

83.644276546337

93

69.073197343268

0.000000000000

69.073197343268

57.516594593799

94

46.687430698176

0.000000000000

46.687430698176

38.322587662240

95

30.526919677562

0.000000000000

30.526919677562

24.676080758333

96

19.255419160479

0.000000000000

19.255419160479

15.312303379225

97

11.682278394236

0.000000000000

11.682278394236

9.129699478265

98

6.795854571503

0.000000000000

6.795854571503

5.213807249093

99

3.777949571397

0.000000000000

3.777949571397

2.842405790613

100

2.000000000000

0.000000000000

2.000000000000

1.000000000000

101

0.000000000000

0.000000000000

0.000000000000

 

 

 

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