model for prediction across the scales-atmosphere (mpas-a ... · dynamics nonhydrostatic horizontal...

Model for Prediction Across Scales-Atmosphere (MPAS-A)

A first view in the Mediterranean basin

Alex Montornès

7th International Conference on Meteorology and Climatology of the Mediterranean 2019

Montornes et. al, [email protected] Vortex

MPAS-A: A first view in the Mediterranean basin


Atmospheric modeling 90s and 2000s: LAM nesting

1 m 10 m100 m

1 km10 km

100 km

1.000 km

10.000 km

1 h

1 day

1 week

1 Month

1 Year

10 Years

100 Years

1000 Years

10 min

1 min

10 s

1 s

<0.1 s

Synoptic scale

Mesoscale

Planetary waves

Seasonal

ENSO

100.000 km

Climate variations

Microscale

Climate

models

Global

models

Mesoscale

models

Small eddies

Molecular diffusion

ClimateMeteorology

Fluid dynamics

10 cm1 cm

<1 mm

Large Eddy

Simulation

SGS

Characteristic length

Chara

cte

ristic

tim

e

Dynamicaldownscaling




Structured Cgrid staggering

uu

u uu

u

v

v

vv

v

v

Limited area models as WRF

Mercator Lambert conformal Polar stereographic

Projections





uu

u uu

u

v

v

vv

v

v



Projections

GlobalWRF

Source: MPAS tutorial





uu

u uu

u

v

v

vv

v

v



Projections

GlobalWRF

Source: MPAS tutorial

Deformation due to projections.

Nesting approach :: seamless model limitation

Poor interaction between scales (1way/2way nesting)

Polar isses




Unstructured grid: a different approach

u

u

u

u

u

u u

u

u

u

u

uu

u

u

Unstructured Cgrid staggering Variable resolution

Dx

Dx




Unstructured grid: a different approach

u

u

u

u

u

u u

u

u

u

u

uu

u

u

Unstructured Cgrid staggering Variable resolution

Dx

Dx

Projection is not required

Seamless modeling chain (without nesting)

No polar issues




MPAS: Model Prediction Across Scales.

Earthsystem component models collectively known as MPAS:

All MPAS components use centroidal Voronoi tessellations for their horizontal meshes.

Common software framework between components and partially with WRF

MPASOcean(LANL)

MPASAtmosphere(NCAR)

MPASLand/Sea Ice(Albany U, LANL,..)




DYNAMICS

Nonhydrostatic

Horizontal velocity is defined as the normalvelocity on Voronoi cell faces.

State variables are defined at the cell centers.

For the vertical coordinate a structured grid isassumed, the model can use traditional sigmacoordinates as well as a Hybrid Coordinatesystem as in last versions of WRF.

MPASAtmosphere

PHYSICS

Surface Layer: MoninObukhov as in WRF 3.8.1,MYNN as in WRF 3.6.1.

PBL: YSU as in WRF 3.8.1, MYNN as in WRF3.6.1.

LSM: Noah as in WRF 3.3.1.

Gravity Wave Drag: YSU GWDO as in WRF3.6.1.

Convection: KainFritsch as in WRF 3.2.1,Tiedtke as in WRF 3.3.1, New Tiedtke as in WRF(3.8.1), GrellFreitas as in WRF 3.6.1.

Microiphysics: WSM6 as in WRF 3.8.1,Thompson as in WRF 3.8.1, Kessler

Radiation: RRTMG sw as in WRF 3.8.1, RRTMGlw as in WRF 3.8.1, CAM as in WRF 3.3.13.5

MESHES

Global model version.

Quasiuniform and variable resolution grids




Applications

WRF MPAS

Urban meteorology

LES modeling

Obs/grid nudging

applications

Fire model coupling

Global NWP

Integrated

global/regional NWP

Global atmospheric

chemistry

Global/regional

climate modeling

Seasonal prediction

Regional NWP

Tropical cyclone/hurricane

prediction

Convection permitting hazardous

forecasting

Regional atmospheric chemistry

Ensemble (EnKf), variational and

Hybrid DA

Idealized simulations accross

scales

Regional air quality




MPAS-A: Strengths and Weaknesses

BENEFITS

All scales are solved at the same time.

Common physical packages with WRF (NCAR UnifiedAtmosphere Modeling Roadmap).

Scaling improvement.

LIMITATIONS

High memory consumption

Quasiuniform mesh at 60 km: 163.842 cellsVariable resolution grid 9225 km: 163.842 cells

~30 GB

Variable resolution grid 6010 km: 999.426 cellsVariable resolution grid 153 km: 6.488.066 cells




ExperimentsExperiments

Reanalysis: ERA5

Initialization: 20151210 18:00 h

Spinup: 6 hours

Effective horizon: 24 hours

Output: hourly

LBC: updated every hour

WRF 3.6.1

Domain: 120x120 = 14,400 cells x 38 levels

Resolution: 60 km

Physics:

SW: RRTMGLW: RRTMGSfc layer: ETASimilarityLSM: 5layer thermal diffusionPBL: YSUCumulus: KainFritschMicrophysics: WSM6





Reanalysis: ERA5


Spinup: 6 hours


Output: hourly

LBC: none (global model)

MPASA 6.0

Domain: 163,842 cells x 38 levels (global)

Resolution: 60km quasiuniform mesh

Physics:

SW: RRTMGLW: RRTMGSfc layer: ETASimilarityLSM: 5layer thermal diffusionPBL: YSUCumulus: KainFritschMicrophysics: WSM6





Reanalysis: ERA5


Spinup: 6 hours


Output: hourly

MPASA 6.0

WRF 3.6.1

Reanalysis: ERA5


Spinup: 6 hours

15º 45º

55º

20º

0.5º

Regridding




Results: 2meter temperature




Results: 2meter water vapor mixing ratio




Next steps

Tests with variable resolution meshes

Full validation: Long period Different regions 3D fields Model options Explore applications New products

Regional MPAS with LBC (expected 2019)



Model for Prediction Across Scales-Atmosphere (MPAS-A)

A first view in the Mediterranean basin

Alex Montornès




model for prediction across the scales-atmosphere (mpas-a ... · dynamics nonhydrostatic horizontal...

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