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

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

Victor Venema, [email protected], COSMO General meeting, Rome, 5th September 2011, #2

Outline

• The adaptive radiative transfer scheme– General idea– Implementation

• Results– 3in1 runs– Single runs– Preliminary new result

• Outlook


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

• 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:

dwtwwIWVwLWPwCCTwCCLw 7654321


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


Diurnal cycle error net fluxB

ias

SW LW

RM

SD


Diurnal cycle error heating ratesB

ias

SW LW

RM

SD


Error height profile heating ratesB

ias

SW LW

RM

SD


Scale dependent errors

Surface net flux Atmospheric heating rate


Errors net flux for COSMO-EU

SW LW


Physical consistency: LWP

SW LW


Consistency: diurnal cycle

SW LW


Spread single runs

Total precipitation 2m-TemperatureSurface pressure


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 togetherPreliminary new results


Conclusions

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

• Employs spatial and temporal correlations in atmosphere

in error fields of simplified computations


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

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/


(a) (b) (c)

(d) (e) (f)

(g)Bias: 5 W m-2RMS: 77 W m-2

(h)Bias: 6 W m-2RMS: 43 W m-2

(i)Bias: 2 W m-2RMS: 31 W m-2

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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

calculate error-estimator based on

a simple radiation scheme for

each grid point

Grid points where…

…Δ ‘large‘

…Δ ‘small‘

Apply „perturbation method“

for surface fluxes

Recalculate radiation

fluxes with exactscheme

Perturbation method:

)()(

)()(

tFttFF

FtFttFsimplesimplesimple

simple


RMSE perturbation methods


Approach

• Simple radiation scheme:

→ Multivariate linear regression

• Predictands:

– longwave:

– shortwave: transmissivity:

• Distinction of 4 categories,

with different sets of predictors:

surL

ec

solarcloud free

infraredcloud free

solarcloudy

infraredcloudy


Simple radiation scheme

Cloudfree Cloudy

Cosine of solar zenith angle LWP

IWV CLCL

Surface pressure CLCT

Continental aerosols Cosine of solar zenith angle

Cloud thickness

IWV

Temperature at cloud base

Predictors: SOLAR


Simple radiation scheme:

Cloudfree Cloudy

IWV IWV

Surface temperature Temperature at cloud base

Cosine of zenith angle Surface temperature

CLCT

Cloud thickness

CLCL

Cosine of zenith angle

LWP

Predictors INFRARED


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


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)

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

Hourly

Adaptive

Hourly

Adaptive


Instantaneous RMSE

with adaptive scheme smoother error curves

21June 2004


Smoother developing of model variables with time

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

Surface temperature

21 June 2004

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

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