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Numerical weather prediction: current state and perspectives M.A.Tolstykh Institute of Numerical Mathematics RAS, and Hydrometcentre of Russia.

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Presentation on theme: "Numerical weather prediction: current state and perspectives M.A.Tolstykh Institute of Numerical Mathematics RAS, and Hydrometcentre of Russia."— Presentation transcript:

1 Numerical weather prediction: current state and perspectives M.A.Tolstykh Institute of Numerical Mathematics RAS, and Hydrometcentre of Russia

2 What is the global atmospheric model Atmospheric equations ~ averaged Navier- Stokes equations on the rotating sphere. Processes on unresolved scales are parameterized. Currently, numerical solution of the equations for resolved dynamics accounts for ~30 % of total computations time, the rest is for parameterizations

3 Main ways to increase an accuracy of numerical weather prediction 1) Increasing the horizontal and vertical resolution of atmospheric models Requires masssively parallel computations => development of new dynamical cores (new governing equations, new numerical techniques) 2) Development of new parameterizations of subgrid-scale processes 3) Improvement of initial conditions

4 RMS error of 3-day H500 forecast

5 Current state of global NWP models Typical horizontal resolution at the end of 2009 – 20-30 km Japan is the leader with 20 km, next year ECMWF will be the leader with 15 km

6 The increase of the processor number necessary for operational implementation of the SL-AV model 70 km, 28 levels – 4 processors 37 km, 50 levels – 40 processors 20 km, 50 levels - about 350 processors 10 km, 100 levels – supposedly 6000 processors

7 Development of new dynamical cores for global NWP models Currently, a half of global NWP models us based on spectral techniques It scales up to~0.5N_harm* N_openmp(*N_lev) processors, ~5000 for Т1279.



10 Forecast Centre 2009201020112012 2013 2014 (Country) ECMWF T L 799 L91T L 1279 L91T L 1279 L140 tbd (Europe) Met Office 25 km L70 20 km L90tbd (UK) Météo France T799c2.4 L70 T1240c2.4 L90tbd (France) DWD 30 km L60 15 km L70 tbd (Germany) HMC T169 L31; T339 L63; tbd (Russia) 0.72°x0.9° L500.37°x0.45° L500.19˚x0.225˚L60 NCEP T382 L64 (7.5)T878 L91 (7.5) 25 km L90 (USA) T190 L64 (16)T574 L91 (16) CMC (0.45°x0.3°) L80 (0.45x0.3°) L80(0.45°x0.3°) L80(0.3°x0.2°) L90(0.3°x0.3°) L90 (Canada) CPTEC/INPE 20 km L96 10 km L9610 km L128tbd (Brazil) JMA T L 959 L60 tbd (Japan) CMA T L 639 L60 tbd (China) GRAPES 50 km L31 25 km L31 26 km L31 KMA T426 L4040 km L5025 km L70 25 km L90tbd (Korea) BoM ACCESS ~80 km L50 ~40 km L5025 km L7025 km L90tbd (Australia)

11 New dynamical cores of atmospheric models High parallel efficiency, locality of data A grid on the sphere with quasiconstant resolution Computational efficiency of numerical algorithm (sufficiently long time-step) Nonhydrostatic formulation (includes sound waves)

12 Choice of the grid Traditional lat-lon grids have condensed meridians near the poles (from presentation by W.Skamarock, NCAR)

13 Evolution of p s, day 9 (Jablonowski test) hPa GEOS-FVGEOS-FVCUBEGME HOMMEICONOLAM BQ (GISS) CAM-FV-isen CAM-EUL with  =0°, resolution ≈ 1°  1°L26

14 Reduced latitude-longitude grid Routinely used in models based on spectral approach. It is possible to use it in finite-difference/finite volume models with specific formulation Advantages - High-order approximations are easily possible - Easy to code and parallelize

15 Shallow-water model

16 Developments in parameterizations of subgrid-scale processes Parameterizations depend on horizontal resolution (examples: deep convection, microphysics) Taking into account exchanges with adjacent horizontal grid cells (currenly, most of parameterizations are 1D in vertical)

17 New and advanced parameterizations of subgrid-scale processes Advanced land surface parameterization accounting for hydrology, evolution of snow cover, freezing/melting, bogs, … Deep convection parameterization for partially resolved case Explicit description of microphysical processes in clouds Lake parameterizations Boundary layer parameterizations in the case of strongly stable stratification

18 Land surface parameterization «Tile» approach (subcells describing water, low and high vegetation, etc) New directions: Soil hydrology taking into account adjacent grid cells Biogeochemistry (carbon cycle, dynamical leaf area index …)

19 Slide 19 H-TESSEL surface parameterization scheme (ECMWF) The revised hydrology includes spatial variability related to topography (runoff) and soil texture (drainage)

20 Slide 20 ECMWF: New microphysics parameterization WATER VAPOUR CLOUD Liquid/Ice PRECIP Rain/Snow Evaporation Autoconversion Evaporation Condensation CLOUD FRACTION Current Cloud Scheme New Cloud Scheme

21 Impact of initial data on model forecasts

22 Data assimilation Weight optimally observations and short- range forecast from previous initial conditions to create initial conditions for the model Current approaches: 4D-Var and ensemble Kalman filter

23 Some directions of development for the global semi-Lagrangian model SL-AV Increasing the scalability of the code from ~300 to 5000 processors Replacement of 3D solvers by divide-and conquer algorithms Nonhydrostatic dynamical core More advanced land surface parameterization (bogs, carbon cycle, multilayer soil, soil hydrology…)

24 Conclusions Challenges of the nearest decade – development and implementation of global atmospheric models with the horizontal resolution 1-10 km. New approaches to develop new dynamical cores and parameterizations This requires efficient parallel implementation on ~ 10000 processors ============================= We shorten the distance with leading centres in the field of global NWP

25 Thank you for attention!

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