HARMONIE

a common effort of HIRLAM, ALADIN and LACE onhigh resolution modelling

Jeanette OnvleeCOSMO General Meeting

Cracow, 20080916

Outline

• Background and scope of the HARMONIE cooperation

• HARMONIE mesoscale modelling developments; status and plans

• Ensemble forecasting activities and plans• Other developments

History• Background: Wish to join forces to meet the challenges of

mesoscale modelling– Common ambitions– Play to each partner’s strengths– Good experiences in previous research cooperation

• Goals of cooperation: full code cooperation on common mesoscale model in IFS/Arpege code framework, joint development of short-range ensemble forecasting system

• A new name:– HARMONIE: HIRLAM-ALADIN Research on Mesoscale Operational

NWP In Euromed• Common activities:

– joint scientific/strategic planning– setup of ALADIN code-based systems in HIRLAM institutes– joint meetings and working/training weeks– from “division of labour” increasingly to interacting, mixed research

teams• ECMWF increasingly also involved (common stakes in IFS system,

research cooperation in physics, dynamics and EPS)

HARMONIE components and cooperation:

Present status of HARMONIE mesoscale

• Data assimilation:– 3D-VAR/FGAT– use of obs: basically everything which ECMWF assimilates is available;

in practice: SYNOP/SHIP/BUOY, TEMP, AMDAR, AMSU-A/B, GEO/AMVs, wind profiler, radar winds

– Var-QC• Forecast model

– dynamics: NH-ALADIN (spectral, SLSI)– Upper air model physics: three flavours

• AROME/ EDMF, explicit deep convection, ECMWF radiation• ALARO/ Bougeault/TKE, 3MT, ALADIN radiation• HIRLAM/ CBR, KFB, RK/CAM3, Saavijarvi radiation (baseline only)

• Surface model/DA: – Externalized scheme SURFEX: ISBA soil, canopy/forest, snow, urban,

lake– OI assimilation scheme– INCA analysis system for nowcasting, validation

• Monitoring, validation and verification tools

The Arome-France model config

Arome-France current domain

Aladin-France domain

AROME’s resolved convection : a deep change for bench forecasters’ expertise job

Arome

Aladin

Radar observation

The ‘application side’ of the ‘double penalty’ syndrome for verification: details of AROME bring good information about the structure

of the field but they might be more misleading about the timing-position than their ALADIN counterparts at the latter’s

scale

Some other HARMONIE suites...

NRT suites with AROME/ALARO by almost all HIRLAM partners and some

more (Hungary...)Operational suites with ALARO/3MT in

several LACE countries

In total ~ 13 systems, typically 2.5km, 40 levels

Data assimilation: algorithms • 3D-VAR:

– Different ways of blending in info from larger scale 4D-VAR– LACE: implementation of 3D-VAR operationally in all LACE countries – Experimentation with rapid update cycling

• 4D-VAR in preparation; – Flow dependency by wavelets, ensemble assimilation; – Take in concepts from HIRLAM 4D-VAR experience: Jdfi, Jk, multiple

outer loops, ...• Experimentation with hybrid variational / ensemble assimilation

techniques - ETKF, Lorenc

• Surface: replace OI with EKF scheme • Initialization with scale-selective DFI

Data assimilation: use of observations

• Use of observations: Increase range of observations to be assimilated: – HIRLAM: Comprehensive observation impact studies (CIS)

• Now on synoptic scale (AMSU-A/B over sea/land/ice, GEO and MODIS AMV, scatterometer over Atlantic/Arctic, initial results positive; starting on convection exps with SEVIRI, radar winds, GPS)

• Transfer to mesoscale, focus on summer convection and radar– High-resolution sat data: e.g. SEVIRI, IASI, ADM; cloud– and

land-contaminated data, varBC, tuning of obs error stats and impact studies

– Radar winds/precip, GPS, BUFR TEMP• Radar processing inhomogeneity, data exchange, beam blocking

critical issues– Surface: screen level parameters, (scatterometer) soil moisture,

SST/sea ice, lake, snow, snow on ice

Impact of additional sat obs over N.Atl.:

winter month

Psurf

T Hu

FF

DD

Assimilation of radar winds: Neutral to slightly positive scores in AROME-France:

Precip

R0

CNTRL RADAR

Forecast model: Dynamics

– NH core validation and comparison with hydrostatic: LACE, ECMWF

– Vertical Finite Element formulation– Variable map factor for use of SISL over large

domains – Improved quality of LBC – Nesting experiments with various configurations– More mass-conserving (less diffusive) SL

interpolators (also for coupling to chemistry)

Forecast model: upper air physics• AROME:

– Highest priority: solve presently remaining problems: overestimate of severe convection, extreme precipitation, negative wind bias over steep orography, minimum temp under very stable conditions

• Physics-dynamics interactions• Impact of additional deep convection parametrization (either within

EDMF or separate) • 3D-turbulence scheme• Tuning of microphysics• Representation of orographic roughness• Stable boundary layer modifications to turbulence scheme

– Surface: compare AROME/HIRLAM snow/forest schemes, add snow on ice parametrization

• ALARO/3MT:– Now operational in several LACE institutes– Comparison with AROME on 5km scale ongoing in HIRLAM

• Convergence between various physics options? Different coding strategies make common interfacing at low level in source code difficult.

2nd AROME training course, Lisbon, March 2008

16

AROME performance : low-level scores

• Objective scores of AROME-France using French automatic surface obs network (hourly data every ~30km)

• Beats ALADIN-France in most respects

Scores over France for 5-18 February 2008 (Arome in pink Aladin in blue)

10m windspeed

2m Temperatureforecast range (h)

forecast range (h) forecast range (h)

2m Humidity

3MT’s sampling of the ‘grey-zone’ (ALARO-0)

A0 with 3MT =>

A0 without 3MT =>

‘Resolved’ convection =>

Observed precipitations =>

Δx=9.0 km (2x) Δx=4.5 km (2x) Δx=2.3 km (3x)

Forecast model: other aspects

• Numerical efficiency and portability: profiling studies• Diagnostics, validation and verification:

– Exchange started, but should be intensified– HIRLAM: routine monitoring/verification system, increasingly

enhance with appropriate mesoscale diagnostics tools (incl from COSMO)

– ALADIN/LACE: Catching up with the state of the art – Exchange with other consortia within SRNWP/ Verification

project Programme.

• System aspects:– HIRLAM/LACE: Explore common script system?– SRNWP/Interoperability programme

Ensemble forecasting activities

Existing real-time LAM EPS systems within HIRLAM/ALADIN/LACE:

• LAEF: – Downscaling of Arpege, plus breeding, ETKF, physics perturbations;

Eur area + part Atlantic, +72h, 20km res, 16 members• NORLAMEPS:

– downscaling of ECMWF TEPS, N.Atl/Eur area, +72h, 12km resolution, 21 members

• SREPS – 5 regional models nested in 4 global models, with SLAF; NAtl/Eur

area, +72h, 27km resolution, 80 members

Under construction: GLAMEPS (others to be replaced by/integrated into this??)– Downscaling of EUROTEPS, plus HIRLAM/ALADIN SV’s, ETKF,

perturbations in physics and surface, ... – NAtl/Eur area, target resolution 10km, 40 layers, 50-60 members

Ensemble forecasting activities

LAEF:- New breeding/blending/multi-physics system

offers improvements over pure downscaling version => to be implemented operationally soon

- 1st + 2d moment calibration of pdf promising- Optimize operational setup through e.g.

introduction of SMS- Target resolution: 10km- Evaluate outcome of Beijing 2008

Verification for a two-month period: June-August 2007: Clear improvement of extended LAEF system

Impact of bias correction and 2d moment calibration

GLAMEPS

Joint multi-model EPS system for HIRLAM & ALADIN. Present status:• GLAMEPS prototype setup (version 0) implemented at ECMWF. • HIRLAM components: EUROTEPS,

physics perturbations, HIRLAM (forcing) SVs,ETKF, surface perturbations

• ALADIN components: breeding, blending,multi-physics, ETKF, ALADIN SV

• Calibration: BMA for 1st / 2d moment of pdf, Gaussian and non-Gaussian parameters

• Ongoing: configuration tests for prototype distributed GLAMEPS system (v1: March 2009). Continue parallel experimentation in laboratory

• Continue pursuit of Eur. LAMEPS coopera- tion in TIGGE-LAM

GLAMEPS

Model domain (for

HIRLAM components)

and EUROTEPS

targeting areas

for Northern,

Middle and Southern

Europe

Other (HIRLAM) developments (1)

Coupling with atmospheric chemistry Activities:• Make HIRLAM output better suited as input to ACT models

(postprocessing, vert resolution in BL, …)• Dynamics: better mass-conserving properties:

– more accurate interpolators– mass conserving SL scheme of Kaas et al.

• ENVIRO-HIRLAM coupled system being installed as HIRLAM chemistry branch at DMI

To be done: – include aerosols in microphysics– include desired physics options where necessary (e.g. radiation

scheme) – Start study of chemistry-cloud feedbacks

Current version of DMI-ENVIRO-HIRLAM modelling systems, showing the components of a forecast

WRF-CHEM

Gas-phase chemistry: RADM, RACM, CBMZAerosol dynamics: MOSAIC, SORGAMPhotolysis: MadronichCloud chemistryConvectionDepositionPlumerise

CAC-Aerosol Dynamics

Modal modelLog-normal modes: nuclei, accumulation, coarseMoment equations: Intra-modal coagulation, Intra-modal coagulation, condensation

-GEMS/TNO-EMEP

Min. number of advected quantities: RADM 2 gas-phase chemistry, no cloud chemistry, CAC aerosol dynamics ~ 50

Max. number of advected quatities: Carbon bond IV gas-phase chemistry, cloud chemistry, 8-bin Mosaic aerosol dynamics ~ 250

Other developments

• HIRLAM: Coupling with ocean model• HIRLAM/ALADIN: Simplified model version for

academia• HIRLAM/ALADIN: Models as tools for regional

climate modelling:– Update of HIRLAM as “climate branch”– Push use of ALADIN as tool for regional projections– Definition and setup of climate branch for HARMONIE

RCR 00 + 6h AROME 00 + 6h

SMHI H22 conv. prec SMHI H22 strat.prec.

Radar

Downscaling EPS with HIRLAMRKKF - cloud scheme: verif. at 2007/08/22 12utc

+42-48

+18-24

]6/5Pr[ hmmP ]6/10Pr[ hmmP

RMSE difference

U

RHU

Expected extra from 3-hourly cycling: - more SYNOPs (Ps only)

- more AMDAR

- more AMV

- more Wind Profiler

- smaller error in the innovation vector for

ATOVS (due to more frequent analysis)

Preliminary results: - improvement for all fields (see figures on the

left where red shades indicate that

3h cycling is better than 6h cycling)

Next:

- diagnose spin-up in the 3h background forecast

Aladin Rapid Update Cycle (Ald-Hun)