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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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slide1

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/

slide19
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

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:

approach
Approach

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

Predictors: SOLAR

simple radiation scheme1
Simple radiation scheme:

Predictors INFRARED

approach1
Approach

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
  • 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)

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

21June 2004

with adaptive scheme smoother error curves

smoother developing of model variables with time
Smoother developing of model variables with time

Surface temperature

21 June 2004

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

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