Download presentation

Presentation is loading. Please wait.

Published byRichard Fleming Modified about 1 year ago

1
Introduction to parametrization Introduction to Parametrization of Sub-grid Processes Anton Beljaars (anton.beljaars@ecmwf.int room 114) What is parametrization? Processes, importance and impact. Testing, validation, diagnostics Parametrization development strategy

2
Introduction to parametrization Why 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.

3
Introduction to parametrization 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) What is parametrization and why is it needed

4
Introduction to parametrization Reynolds decomposition e.g. equation for potential temperature: advectionsourcemolecular diffusion Reynolds decomposition: Averaged (e.g. over grid box): : source term (e.g. radiation absorption/emission or condensation) : subgrid (Reynolds) transport term (e.g. due to turbulence, convection)

5
Introduction to parametrization Space and time scales

6
Introduction to parametrization Space and time scales 1 hour100 hours0.01 hour

7
Introduction to parametrization Climate models 500 km 1000 m100 years Global weather prediction 50 km500 m10 days Limited area weather pred. 10 km 500 m2 days Cloud resolving models 500 m500 m1 day Large eddy models 50 m50 m5 hours Hor. scales Vert. Scalestime range Different models need different level of parametrization Numerical models of the atmosphere

8
Introduction to parametrization Parametrized processes in the ECMWF model

9
Introduction to parametrization Applications and requirements Applications of the ECMWF model –Data assimilation T799L91-outer and T95L91/T255L91-inner loops: 12-hour 4DVAR. –Medium range forecasts at T799/L91 (25 km): 10 days from 00 and 12 UTC. –Ensemble prediction system at T399L62 (50 km): 10 days, 2x(50+1) members. –Short range at T799L91 (25 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, 50+1 members. –Fully coupled ocean wave model. –Interim reanalysis (1989-current), under test (possibly T255L91, 4DVAR). Basic requirements –Accommodate different applications. –Parametrization needs to work over a wide range of spatial resolutions. –Time steps are long (from 15 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.

10
Introduction to parametrization Importance 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.

11
Introduction to parametrization Global energy and moisture budgets EnergyMoisture 5 mm/day =P-E Hartman, 1994. Academic Press. Fig 6.1 and 5.2

12
Introduction to parametrization Sensitivity of cyclo-genesis to surface drag controlP centre =918 hPa analysisP centre =925 hPa difference P centre =923 hPa

13
Introduction to parametrization T-tendencies

14
Introduction to parametrization T-Tendencies

15
Introduction to parametrization T-Tendencies

16
Introduction to parametrization T-Tendencies

17
Introduction to parametrization T-tendencies

18
Introduction to parametrization T-Tendencies Imbalance (physics – dynamics) due to: (a) Fast adjustment to initial state (“data assimilation problem”); (b) Parametrization deficiencies

19
Introduction to parametrization Validation and diagnostics Compare with analysis daily verification systematic errors e.g. from monthly averages Compare with operational data SYNOP’s radio sondes satellite Climatological data ERBE, ISCCP ocean fluxes Field experiments TOGA/COARE, PYREX, ARM, FIFE,...

20
Introduction to parametrization Day-5, T850 errors Viterbo and Betts, 1999. JGR, 104D, 27,803-27,810

21
Introduction to parametrization Diurnal cycle over land

22
Introduction to parametrization History of 2m-T errors over Europe

23
Introduction to parametrization Surface SW-JJA (model climate - DaSilva climatology) era15 model

24
Introduction to parametrization PYREX experiment

25
Introduction to parametrization PYREX mountain drag (Oct/Nov 1990) Lott and Miller, 1995. QJRMS, 121, 1323-1348

26
Introduction to parametrization TOGA-COARE fluxes (0-24 h fcsts. vs. ship-observ.) era15 model Model Obs

27
Introduction to parametrization Cloud fraction (LITE/ECMWF model) Model LITE

28
Introduction to parametrization Latent heat flux at IMET-buoy

29
Introduction to parametrization Parametrization 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

30
Introduction to parametrization Measure diffusion coefficients stableunstable

31
Introduction to parametrization Convert to empirical function Diffusion coefficients based on Monin Obukhov similarity: Operational CY30R2 MO-scheme (less diffusive) Operational CY30R2 MO-scheme (less diffusive)

32
Introduction to parametrization Column test Cabauw: July 1987, 3-day time series Observations versus model (T159, 12-36 hr forecasts)

33
Introduction to parametrization Test in 3D model: Data assimilation + daily forecasts over 32 days March 2004 Effect of MO-stability functions

34
Introduction to parametrization Concluding remark Enjoy the course and Don’t hesitate to ask questions

Similar presentations

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google