WG 3: Overview status report

COSMO General Meeting21 September 2005

Marco Arpagaus

Detailed status reports available on the COSMO web-site

Acknowledgments: WG 3 members …

WP 3.1: Planetary boundary layer

3.1.1: Surface transfer scheme and turbulence scheme 3.1.1.1 (top): Single column model, etc. 3.1.1.2 (top): Reorganisation of the turbulence scheme 3.1.1.3 (top): Extended documentation 3.1.1.4: … 3.1.1.5: … 3.1.1.6 (top): Merge with 3-d approach

3.1.2: Special boundary layer matters

COSMO Zürich 2005

The single column (SC) framework for LM - A testing tool : •SC-framework is a set of modules that can be added to a subset of existing LM-modules

•The LM-modules are mainly those below ‘organise_physics.f90’

•The additional modules contain a complex data structure, which include pointers to the LM variable fields

•The SC-framework enables the following functionality:

•A SC-model run, using the LM-routines for physical calculations including the vertical flux divergence (slow_tendencies.f90) and using the INPUT-files for controlling the physics

•The SC-run is hydrostatic with moving vertical -levels and geostrophic wind forcing

•The model level configuration can be done by a configuration file specifying arbitrary pressure levels for the atmosphere and height levels of the soil. The configuration pressure levels define the corresponding -levels

•Initial variable profiles and those for possible model forcing are extracted from a measurement file

•The data in the measurement file include variable names and -units

•Variable values of the measurement file are converted if possible to model variables and model units by the help of a iterative variable- and unit conversion tool forcing by not-model-variables

•Iterative calculation of pressure values if only height based measurements are present

Matthias Raschendorfer

COSMO Zürich 2005

•Variables that are chosen for forcing or needed for the initial state are vertically interpolated on model levels and stored in internal time series

•The time series are interpolated by cubic splines on model time

•Assimilation by either overwriting the model values by the values of the interpolated time series

•or by combination with model values in order to minimise the resulting error

•This includes an estimate of the interpolation error and the model time step error

•and implicit error propagation for variable conversion and model calculations that are located within the framework

•Chosen subroutines of LM may be shifted towards the framework with a modified declaration header to participate on the error propagation facility sensitivity tests

•Model forcing at different assimilation points regarding to the variable kind (prognostic, diagnostic, external, time tendency, etc.)

•ASCII model output in the form of a measurement file for arbitrary forcing with model data

•Output in structured ASCII files for visualisation (at the moment by UNIRAS)

•Diagnostic levels (0m, 2m, 10m, 850hPa, ...) are implicitly considered for input and output

•Calculation of correction tendencies belonging to the forcing by the grid point output of a 3D LM run and forcing an other SC-run with those 3D correction tendencies (not yet ready)

•Calculation of cost functions for predefined forcing variables (only in preparation) variational parameter optimisation

Matthias Raschendorfer

•Current problem: The whole thing has to be carefully tested before safe use!!

WP 3.1: Planetary boundary layer

3.1.1: Surface transfer scheme and turbulence scheme 3.1.1.1 (top): Single column model, etc. 3.1.1.2 (top): Reorganisation of the turbulence scheme 3.1.1.3 (top): Extended documentation 3.1.1.4: … 3.1.1.5: … 3.1.1.6 (top): Merge with 3-d approach

3.1.2: Special boundary layer matters

WP 3.2: Soil processes

3.2.1: Multi-layer soil model 3.2.1.1 (top): Operational implementation of the multi-

layer soil model 3.2.1.2: Usage of satellite derived (weekly updated) plant

cover 3.2.1.3 (top): Revision of external parameters for plants

3.2.2: Implementation of lake model into the LM 3.2.3: Intercomparison of soil models in the

framework of soil moisture validation

WP 3.2: Soil processes

3.2.1: Multi-layer soil model 3.2.1.1 (top): Operational implementation of the multi-

layer soil model 3.2.1.2: Usage of satellite derived (weekly updated) plant

cover 3.2.1.3 (top): Revision of external parameters for plants

3.2.2: Implementation of lake model into the LM 3.2.3: Intercomparison of soil models in the

framework of soil moisture validation

WP 3.2: Soil processes

3.2.1: Multi-layer soil model 3.2.1.1 (top): Operational implementation of the multi-

layer soil model 3.2.1.2: Usage of satellite derived (weekly updated) plant

cover 3.2.1.3 (top): Revision of external parameters for plants

3.2.2: Implementation of lake model into the LM 3.2.3: Intercomparison of soil models in the

framework of soil moisture validation

WP 3.2.3 (Loglisci)

WP 3.3: Convection

3.3.1 (top): Implementation and testing of alternative (deep) convection schemes

3.3.2 (top): Shallow convection on the meso- scale

WP 3.3: Convection

3.3.1 (top): Implementation and testing of alternative (deep) convection schemes

3.3.2 (top): Shallow convection on the meso- scale

WP 3.3.2 (Seifert) poster

LMK

WP 3.3.2 (Seifert) poster

LMK

WP 3.3.2 (Seifert) poster

WP 3.4: Microphysics

3.4.1: Three-category ice scheme

WP 3.5: Clouds

3.5.1: Validation of boundary layer clouds 3.5.2 (top): Testing sub-grid scale cloudiness

approaches

WP 3.5.1 (Heise) poster There is a long lasting problem of LM to rapidly dissolve low

level stratus or stratocumulus in late autumn and in winter. According to earlier experiments by Dmitrii Mironov the

problem might be fixed by setting back the minimum value of the vertical diffusion coefficient to zero.

This coefficient was introduced some years ago to avoid long lasting overcast situations over water areas. Following a reduction of evaporation over water in April 2004 this minimum vertical diffusion might turn up to be unnecessary.

The effect of a zero minimum vertical diffusion was tested in a parallel experiment for the period 1 October 2004 to 31 December 2004.

WP 3.5.1 (Heise) posterDecember 2004 Reference Experiment

Frequency bias (opt=1)total cloud cover (0-2/8) 1.64 1.12total cloud cover (7-8/8) 0.82 1.01low level cloud cover (0-2/8) 1.31 0.93low level cloud cover (7-8/8) 0.75 1.05Percent correct (opt=100)total cloud cover 63.0 71.0low level cloud cover 58.7 65.4mid level cloud cover 68.6 67.2high level cloud cover 77.7 77.02m temperature 63.9 64.22m dew point 76.4 78.0Root mean square error (K)2m temperature 2.67 2.702m dew point 2.89 2.85True skill statistics (opt=100)precip > 0.1 mm/6h 60.7 47.0precip > 2.0 mm/6h 63.9 63.9precip > 10.0 mm/6h 44.6 42.7Equitable threat score (opt=100)gusts > 12 m/s 34.2 35.5gusts > 15 m/s 29.8 29.8gusts > 20 m/s 20.2 16.3gusts > 25 m/s 8.7 4.2

large improvements for cloud cover (good!)

negligible changes for 2m temperature and dew point (not so good!)

drastic degradation for (weak) precipitation and (turbulent) gusts (very bad!)

WP 3.5: Clouds

3.5.1: Validation of boundary layer clouds 3.5.2 (top): Testing sub-grid scale cloudiness

approaches

WP 3.5.2 (Avgoustoglou) poster

Aim: Validate (and tune) statistical cloud scheme,

particularly the interaction with the radiation scheme.

Start with tuning the ‘cloud cover at saturation’ (zclc0) and the ‘critical value of the saturation deficit’ (zq_crit) parameters.

WP 3.5.2 (Avgoustoglou) poster

Conclusions: The forecasted low cloud cover was sensitive to the

parameters zq_crit and zclc0 of the statistical cloud scheme.

Vertical diffusion coefficients did not show any effect to this particular test case.

The results, although versatile, look realistic, leaving space for possible tuning.

Due to the more general impact the results might have to the physics of the model further understanding and testing must be considered before any operational implementation of the statistical cloud scheme.

WP 3.6: Radiation

3.6.1: Cloud-radiation interaction 3.6.2: Evaluate possible inclusion of 3-d effects in

current scheme Gridscale parameterization of topographic effects on

radiation Radiation calculation on a coarser grid – very first results

with LMK

WP 3.6.1 (Bozzo et al.)

Water cloud parameterization used in GRAALS gain good results compared to “exact” multiple scattering computa-tions provided by RTX-3

Small discrepancies in clear sky profile are most probably due to out of date spectroscopic database used in GRAALS (= code used in LM)

WP 3.6: Radiation

3.6.1: Cloud-radiation interaction 3.6.2: Evaluate possible inclusion of 3-d effects in

current scheme Gridscale parameterization of topographic effects on

radiation Radiation calculation on a coarser grid – very first results

with LMK

WP 3.6.2 (Buzzi et al.) poster

Surface temperature difference, 2004 12 11 12 UTC

7 km 2 km

WP 3.6.2 (Buzzi et al.) poster

Summary and conclusions The Müller and Scherrer (2005) scheme for topographic

effects on radiation has been implemented into the LM. Some sensitivity case studies have been carried out. The impact of the topographic effects (shadowing, slope

angle, slope aspect, and sky view) is substantial at high resolution.

Some significant indirect impacts (feedbacks) related to snow melt, stability (turbulence), and low clouds, even at 7km.

Larger impact during the winter time due to sun elevation and snow conditions.

Surface thermal changes at high resolution have also secondary effects on thermal circulation.

WP 3.6.2 (Reinhardt)

Aim: call radiation code more frequently

calculate radiation on a coarser grid

LMK grid

grid for radiation

WP 3.6.2 (Reinhardt)

(Very) First results: Overall: nearly neutral impact Bottom and top radiative fluxes, averaged: neutral Verification against 24-h precipitation sums: ok Eye-inspection of visualized output: ok Verification against SYNOP data: not yet done Pointwise, of course, also bigger differences Outlook: Systematic tests

WP 3.7: Sub-km version

3.7.1: Use of LM to study intense convective precipitation events

WP 3.8: z-coordinate version

3.8.1 (N.N.): Adaptation of the parameterization schemes

WP 3.9: Validation and related matters

3.9.1: Testing the SC framework, running parameter tuning experiments, and validating (1-d) boundary layer processes

3.9.2: Validation (3-d) of the boundary layer processes

3.9.3 (top, N.N.): Development of a tool for extended parameter determination

WP 3.9.2 (Vogel et al.)

PBL too moist and too cold Sensitivity experiments

with different turbulence schemes however inconclusive, since ‘boundary conditions’ wrong: too strong radiative forcing

(due to wrong clouds) too moist soil

LM

WP 3.10: Tackle observed model deficiencies

3.10.1: Cure overestimation of low precipitation in winter

3.10.2: Improve diagnosis of convective and turbulent gusts

3.10.3: Investigate reason for cold/moist bias in PBL

3.10.4 (N.N.): Understand sharp increase in precipitation overestimation since November 2003

WP 3.10: Tackle observed model deficiencies

3.10.1: Cure overestimation of low precipitation in winter

3.10.2: Improve diagnosis of convective and turbulent gusts

3.10.3: Investigate reason for cold/moist bias in PBL

3.10.4 (N.N.): Understand sharp increase in precipitation overestimation since November 2003

WP 3.10.2 (Heise) poster

Convective gusts, changes:The effect of water loading is included in the

buoyancy determination of downdraftsThe tuning parameter responsible for distributing

the downward kinetic energy of the gusts to all horizontal directions is increased from sqrt(0.2) to sqrt(1/).

If the convective precipitation rate is below 0.015 mm/h, convective gusts are suppressed.

Improvements for test cases; verification results of test suite still pending.

WP 3.10.2 (Heise) poster

Turbulent gusts, changes: Use Brasseur (2001) method Add sqrt(TKE) of lowest layer

Results of test suite are …>12 m/s

ETS FBI POD FARref exp ref exp ref exp ref exp

Oct 25.9 24.7 1.7 1.2 66.1 53.8 59.6 55.0Nov 34.5 34.5 1.6 1.4 77.7 70.2 51.2 47.4Dec 34.2 33.6 1.7 1.3 84.6 74.1 50.4 47.4

> 20 m/sETS FBI POD FAR

ref exp ref exp ref exp ref expOct 16.0 9.5 0.7 0.4 25.7 13.3 63.4 67.8Nov 27.3 23.3 1.1 1.3 46.9 64.7 58.4 65.4Dec 20.2 18.0 1.2 1.4 38.3 38.6 64.8 70.6

… rather negative!

Need to reformulate and try again …

Joint WG3-WG5 workshop (9.3.2005)

Conditional verification (excerpt from the minutes): WG3 provides a list of criteria to use for conditional

verification. [done] WG5 is invited to provide a tool (‘data finding tool’) that

allows to easily and efficiently find days / grid-points which satisfy specific criteria.

WG5 is kindly asked to supply a conditional verification of the operational model suites on a regular basis.

Testing of the reference version should also include conditional verifications.