Title: Introduction to Parametrization of Sub-grid Processes Anton Beljaars (anton.beljaars@ecmwf.int room 114)
1Introduction to Parametrization of Sub-grid
Processes Anton Beljaars(anton.beljaars_at_ecmwf.
int room 114)
- What is parametrization?
- Processes, importance and impact.
- Testing, validation, diagnostics
- Parametrization development strategy
2Why parametrization
- Small scale processes are not resolved by large
scale models, because they are sub-grid. - The effect of the sub-grid process on the large
scale can only be represented statistically. - The procedure of expressing the effect of
sub-grid process is called parametrization.
3What is parametrization and why is it needed
- The standard Reynolds decomposition and
averaging, leads to co-variances that need
closure or parametrization - Radiation absorbed, scattered and emitted by
molecules, aerosols and cloud droplets play an
important role in the atmosphere and need
parametrization. - Cloud microphysical processes need
parametrization
- Parametrization schemes express the effect of
sub-grid processes in resolved variables. - Model variables are U,V,T,q, (l,a)
4Reynolds decomposition
5Space and time scales
6Space and time scales
1 hour
100 hours
0.01 hour
7Numerical models of the atmosphere
Hor. scales Vert. Scales time range
- Climate models 200 km 500 m 100 years
- Global weather prediction 20 km 200 m 10 days
- Limited area weather pred. 10 km 200 m 2
days - Cloud resolving models 500 m 500 m 1 day
- Large eddy models 50 m 50 m 5 hours
Different models need different level of
parametrization
8Parametrized processes in the ECMWF model
9Applications and requirements
- Applications of the ECMWF model
- Data assimilation T1279L91-outer and
T95/T159/T255-inner loops 12-hour 4DVAR. - Medium range forecasts at T1279/L91 (16 km) 10
days from 00 and 12 UTC. - Ensemble prediction system at T639L62 (32 km) for
10 days, and T319 (65 km) up to day 15
2x(501) members. - Short range at T1279L91 (15 km) 3 days, 4 times
per day for LAMs. - Seasonal forecasting at T95L40 (200 km) 200 days
ensembles coupled to ocean model. - Monthly forecasts (ocean coupled) at T159L61
Every week, 501 members. - Fully coupled ocean wave model.
- Interim reanalysis (1989-current) is under way
(T255L60, 4DVAR).
- Basic requirements
- Accommodate different applications.
- Parametrization needs to work over a wide range
of spatial resolutions. - Time steps are long (from 10 to 60 minutes)
Numerics needs to be efficient and robust. - Interactions between processes are important and
should be considered in the design of the schemes.
10Importance of physical processes
- General
- Tendencies from sub-grid processes are
substantial and contribute to the evolution of
the atmosphere even in the short range. - Diabatic processes drive the general
circulation. - Synoptic development
- Diabatic heating and friction influence synoptic
development. - Weather parameters
- Diurnal cycle
- Clouds, precipitation, fog
- Wind, gusts
- T and q at 2m level.
- Data assimilation
- Forward operators are needed for observations.
11Global energy and moisture budgets
Hartman, 1994. Academic Press. Fig 6.1 and 5.2
12Sensitivity of cyclo-genesis to surface drag
13T-tendencies
14T-Tendencies
15T-Tendencies
16T-Tendencies
17T-tendencies
18T-Tendencies
Imbalance (physics dynamics) due to (a) Fast
adjustment to initial state (data assimilation
problem) (b) Parametrization deficiencies
19Validation and diagnostics
- Compare with analysis
- daily verification
- systematic errors e.g. from monthly averages
- Compare with operational data
- SYNOPs
- radio sondes
- satellite
- Climatological data
- CERES, ISCCP
- ocean fluxes
- Field experiments
- TOGA/COARE, PYREX, ARM, FIFE, ...
20Day-5, T850 errors
Viterbo and Betts, 1999. JGR, 104D, 27,803-27,810
21Diurnal cycle over land
22History of 2m T-errors over Europe in the ECMWF
model (step60/72)
RMS (day/night)
Bias (day/night)
LESS STABLE BL DIFFUSION
- Model temperature errors are influenced by many
processes. - Observations at process level are needed to
disentangle their effect
23TSR JJA 24 hour forecasts (CY31R1/23R4 - CERES)
CY31R1 - CERES
CY23R4 - CERES
24Lat. Heat Flux-DJF (ERA_40_Step_0_6 vs. DaSilva
climatology)
ERA40 model
25PYREX experiment
26PYREX mountain drag (Oct/Nov 1990)
Lott and Miller, 1995. QJRMS, 121, 1323-1348
27Latent heat flux ERA-40 vs. IMET buoy
28Cloud fraction (LITE/ECMWF model)
Cross section over Pacific 45N/120E 45S/160W
(16-09-1994)
Model
LITE
29Parametrization development strategy
- -Invent empirical relations (e.g. based on
theory, similarity arguments or physical insight) - To find parameters use
- Theory (e.g. radiation)
- Field data (e.g. GATE for convection PYREX for
orographic drag HAPEX for land surface Kansas
for turbulence ASTEX for clouds BOREAS for
forest albedo) - Cloud resolving models (e.g. for clouds and
convection) - Meso scale models (e.g. for subgrid orography)
- Large eddy simulation (turbulence)
- Test in stand alone or single column mode
- Test in 3D mode with short range forecasts
- Test in long integrations (model climate)
- Consider interactions
30Measure diffusion coefficients
stable
unstable
31Convert to empirical function
Diffusion coefficients based on Monin Obukhov
similarity
MO-scheme (less diffusive)
Operational CY30R2
MO-scheme (less diffusive)
Operational CY30R2
32Column test
Cabauw July 1987, 3-day time series
Observations versus model (T159, 12-36 hr
forecasts)
33T2-difference DJF (ensemble of 6 integrations)
Effect of MO-stability functions instead of LTG
Contours at 1, 3, 5, K
34Test in 3D model Data assimilation daily
forecasts over 32 days March 2004
Effect of MO-stability functions
35Concluding remark
and Dont hesitate to ask questions