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Application of an adaptive radiative transfer parameterisation in a mesoscale numerical weather prediction model. DWD Extramural research. Annika Schomburg 1) , Victor Venema 1) , Felix Ament 2) , Clemens Simmer 1) 1) Department of Meteorology, University of Bonn, Germany

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Application of an adaptive radiative transfer parameterisation in a mesoscale numerical weather prediction model

DWD Extramural research

Annika Schomburg1) , Victor Venema1), Felix Ament2), Clemens Simmer1)

1) Department of Meteorology, University of Bonn, Germany

2) University of Hamburg


Outline
Outline

  • The adaptive radiative transfer scheme

    • General idea

    • Implementation

  • Results

    • 3in1 runs

    • Single runs

    • Preliminary new result

  • Outlook


Adaptive parameterizations
Adaptive parameterizations

  • Accurate parameterization

    • Process-based

    • Computationally expensive

  • Fast parameterization

    • Less processes (statistical)

    • Typically: biased

  • Adaptive scheme

    • Combine: accurate and fast parametrization

    • Accurate one corrects biases of fast one


Adaptive rt spatial scheme
Adaptive RT: Spatial scheme

  • Uses spatial correlations

  • Update every 2.5 minutes one out of 5x5 columns

  • For other 24 columns: search for similar column in the vicinity (search region 5x5 pixels)

  • Similarity index to be minimised:


Implemented in cosmo 4 0
Implemented in COSMO 4.0

  • Adaptive scheme

    • Called every 2.5 minutes

  • Reference high-resolution

    • δ-two-stream approximation (Ritter & Geleyn)

    • Full field computed every 2.5 min

  • Comparison

    • Coarse-scheme COSMO-DE (2x2 columns)

    • Called every 15 minutes





Scale dependent errors
Scale dependent errors

Surface net flux

Atmospheric heating rate





Spread single runs
Spread single runs

Surface pressure

Total precipitation

2m-Temperature


Selection column accurate computation
Selection column accurate computation

  • Optimized pattern: as before in this talk

  • Global difference: largest difference in full field

  • Local difference: largest difference in 5x5 regions

  • Spiral pattern: regular pattern, close together

    Preliminary new results


Conclusions
Conclusions

  • Adaptive radiative transfer makes computations more accurate (or efficient)

  • Employs spatial and temporal correlations in atmosphere

     in error fields of simplified computations


Outlook
Outlook

  • Develop a temporal spatial adaptive scheme

    • Improve our results for heating rates

  • Question: what is a good error measure?

    • Bias & RMSD

    • Scales (temporal, spatial)

    • Locations (layers, regions)

    • Heating rates, fluxes & PAR

  • Other parameterizations

    • Surface module (looking for 2 PhD students)

    • Aerosols, etc.


References
References

Schomburg, A., V. Venema, F. Ament, and C. Simmer. Application of an adaptive radiative transfer scheme in a mesoscale numerical weather prediction model. Quarterly Journal of the Royal Meteorological Society, accepted 2011.

Venema, V.K.C., A. Schomburg, F. Ament, and C. Simmer. Two adaptive radiative transfer schemes for numerical weather prediction models. Atmospheric Chemistry and Physics, 7, 5659-5674, doi: 10.5194/acp-7-5659-2007, 2007.

Download:

http://www2.meteo.uni-bonn.de/venema/articles/


Errors in the solar heating rates (W m-2) in the LM at the surface for 12.30 h UTC.

(a) The two-stream calculation of the solar surface flux is the reference field

(b) Cloud cover of low clouds

(c) Total cloud cover

(d) the 1-h persistence assumption,

(e) the adaptive perturbation scheme,

(f) the adaptive search scheme. The corresponding errors are shown in the same order in the third row.


The idea adaptive parameterisation
The Idea: Adaptive parameterisation surface for 12.30 h UTC.

Grid points where…

Recalculate

radiation

fluxes with exact

scheme

calculate error-

estimator based on

a simple

radiation scheme for

each grid point

…Δ‘large‘

…Δ ‘small‘

Apply

„perturbation method“

for surface fluxes

Perturbation method:


Rmse perturbation methods
RMSE perturbation methods surface for 12.30 h UTC.


Approach
Approach surface for 12.30 h UTC.

solar

cloud free

infrared

cloud free

solar

cloudy

infrared

cloudy

  • Simple radiation scheme:

    → Multivariate linear regression

  • Predictands:

    • longwave:

    • shortwave: transmissivity:

  • Distinction of 4 categories,

    with different sets of predictors:


Simple radiation scheme
Simple radiation scheme surface for 12.30 h UTC.

Predictors: SOLAR


Simple radiation scheme1
Simple radiation scheme surface for 12.30 h UTC. :

Predictors INFRARED


Approach1
Approach surface for 12.30 h UTC.

Implementation of adaptive scheme into LM

  • First tests and configuration on PC (small model domain)

  • After successfull implementation: exemplary cases on LMK-domain on parallel machine at DWD

  • Horizontal resolution: 2.8 km

  • Frequency of call to adaptive scheme: 2.5 min


  • Approach2
    Approach surface for 12.30 h UTC.

    • Problem: Radiation of 3 separate model runs not comparable due to different evolution of cloud field → Development of a model version „3 in 1“:

      • Calculation of the radiation fluxes

        • hourly

        • adaptive

        • frequently (every 2.5 min)

          ... in the same model run

      • Dynamics only influenced by frequent radiation

    • Test for 3 summer days characterised with much

    • convection


    Correlation lengths for error fields 15 30 utc
    Correlation lengths for error fields (15:30 UTC) surface for 12.30 h UTC.

    Hourly

    Adaptive

    Hourly

    Adaptive

    The covariance functions of the errors in the solar (a) and infrared (b) fluxes at the surface.


    Instantaneous rmse
    Instantaneous RMSE surface for 12.30 h UTC.

    21June 2004

    with adaptive scheme smoother error curves


    Smoother developing of model variables with time
    Smoother developing of model variables with time surface for 12.30 h UTC.

    Surface temperature

    21 June 2004

    Adaptive approach prevents „wavy“ structure of developing of variables with time


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