1. Ensemble version
Ensemble identifier code: to be defined
Short Description: Grid point, hydrostatic, atmospheric general circulation model GLOBO. Ensemble prediction system on 40 members plus control, run once a week (Monday at 00Z) up to day 31. Initial perturbations are taken from the GEFS-NCEP operational ensemble with a mixed lagged-ensemble technique.
Research or operational: Operational
Data time of first forecast run: 19/10/2015
2. Configuration of the EPS
Is the model coupled to an ocean model ? No - a 'slab' ocean is used.
If yes, please describe ocean model briefly including frequency of coupling and any ensemble perturbation applied:-
Is the model coupled to a sea Ice model? No - Sea ice is held fixed if sea ice fraction is above (below) the climatological value in the fall-winter (spring-summer) season. Sea ice is relaxed to climatology otherwise.
If yes, please describe sea-ice model briefly including any ensemble perturbation applied: -
Is the model coupled to a wave model? No - Charnock roughness is used over sea.
If yes, please describe wave model briefly including any ensemble perturbation applied: -
Horizontal resolution of the atmospheric model: lat-lon regular grid of 0.8x0.56 degrees
Number of model levels: 54
Top of model: roughly 6.8 hPa
Type of model levels: sigma-hybrid
Forecast length: 31 days (744 hours)
Run Frequency: once a week (Monday 00Z before 19 Jan 2017; Thursdays 00Z since 19 January 2017 included)
Is there an unperturbed control forecast included?: Yes
Number of perturbed ensemble members: 40
Integration time step: 6 minutes
3. Initial conditions and perturbations
Data assimilation method for control analysis: obtained from direct interpolation of GEFS-NCEP global analyses at 0.5 degrees (see
Resolution of model used to generate Control Analysis: GEFS-NCEP operational (see above)
Ensemble initial perturbation strategy: mixed lagged-ensemble technique. 10 perturbed initial conditions every 6 hours, taken on Sunday at 00, 06, 12, 18Z respectively, with control run on Monday at 00Z.
Horizontal and vertical resolution of perturbations: GEFS-NCEP ensemble operational system (see
Perturbations in +/- pairs: No
Initialization of land surface:
- What is the land surface model (LSM) and version used in the forecast model, and what are the current/relevant references for the model? The soil scheme has been written from scratch and is inspired to the Tiled ECMWF Scheme for Surface Exchanges over Land – see IFS Documentation Cy40r1 (http://www.ecmwf.int/sites/default/files/IFS_CY40R1_Part4.pdf). See also Malguzzi et al, 2011: The Meteorological Global Model GLOBO at the ISAC-CNR of Italy Assessment of 1.5 Yr of Experimental Use for Medium-Range Weather Forecasts. Weather and Forecasting , Volume 26, Issue 6, 1045-1055. The soil database, with resolution 1/12 degree, includes 15 soil types and was obtained from the global soil map of FAO. The vegetation dataset with resolution 1/120 degrees was obtained from the Global Land Cover Facility (GLFC), University of Maryland and includes 14 land use types.
Are there any significant changes/deviations in the operational version of the LSM from the documentation of the LSM? Six prognostic soil layers are used, with climatological boundary condition at 2.5 m depth.
2. How is soil moisture initialized in the forecasts? Realistic
If “realistic”, does the soil moisture come from an analysis using the same LSM as is coupled to the GCM for forecasts, or another source? Please describe the process of soil moisture initialization. Soil moisture comes from GEFS (http://www.emc.ncep.noaa.gov/GEFS/doc.php) analyses, as well as soil moisture climatology.
Is there horizontal and/or vertical interpolation of initialization data onto the forecast model grid? If so, please give original data resolution(s). Original values are interpolated both horizontally and vertically on the soil model levels. Original resolution of GEFS data is 1.0 x 1.0 degrees.
Does the LSM differentiate between liquid and ice content of the soil? If so, how are each initialized? The LSM has a frozen fraction of soil moisture, initialized as a function of soil temperature.
If all model soil layers are not initialized in the same way or from the same source, please describe. All soil layers are initialized in the same way. No ice content is assigned to the climatological layer.
3. How is snow initialized in the forecasts? (climatology / realistic / other? Realistic.
If “realistic”, does the snow come from an analysis using the same LSM as is coupled to the GCM for forecasts, or another source? Please describe the process of soil moisture initialization. Snow is initialized from GEFS analyses.
Is there horizontal and/or vertical interpolation of data onto the forecast model grid? If so, please give original data resolution(s) Snow is interpolated on model grid. Original resolution is 1.0 x 1.0 degrees.
Are snow mass, snow depth or both initialized? What about snow age, albedo, or other snow properties? The LSM uses average values for snow density and snow albedo.
4. How is soil temperature initialized in the forecasts? (climatology / realistic / other Realistic.
If “realistic”, does the soil moisture come from an analysis using the same LSM as is coupled to the GCM for forecasts, or another source? Please describe the process of soil moisture initialization. Soil temperature comes from GEFS analyses, as well as climatological temperature.
Is the soil temperature initialized consistently with soil moisture (frozen soil water where soil temperature ≤0°C) and snow cover (top layer soil temperature ≤0°C under snow)? Frozen soil moisture is initialized consistently with soil temperature. Concerning soil temperature below snow, consistency with GEFS is maintained.
Is there horizontal and/or vertical interpolation of data onto the forecast model grid? If so, please give original data resolution(s Soil temperature is interpolated on the model grid. Original resolution is 1.0 x 1.0 degrees.
If all model soil layers are not initialized in the same way or from the same source, please describe. All soil layers are initialized in the same way.
5. How are time-varying vegetation properties represented in the LSM? Is phenology predicted by the LSM? If so, how is it initialized? If not, what is the source of vegetation parameters used by the LSM? Which time-varying vegetation parameters are specified (e.g., LAI, greenness, vegetation cover fraction) and how (e.g., near-real-time satellite observations? Mean annual cycle climatology? Monthly, weekly or other interval?) Vegetation fraction and LAI are computed by prescribing a climatological, annual cycle for each vegetation type.
6. What is the source of soil properties (texture, porosity, conductivity, etc.) used by the LSM? Soil texture is interpolated from the global soil map of FAO. Soil properties are defined as in Pielke, R.A., 2002: Mesoscale meteorological modeling. Academic Press, San Diego, 676 pp. The saturated hydraulic conductivity values defined by Pielke have been substantially reduced (actually, more similar to ECMWF values).
7. If the initialization of the LSM for re-forecasts deviates from the procedure for forecasts, please describe the differences. Reforecasts are initialized in the same way but from ERA Interim re-analyses. To keep consistency with the GEFS analyses used for forecasting, a fixed amount of snow is initialized over sea ice (ERA Interim does not put snow over sea ice).fff
4. Model uncertainties perturbations:
Is model physics perturbed? If yes, briefly describe methods: No
Do all ensemble members use exactly the same model version? Same
Is model dynamics perturbed? No
Are the above model perturbations applied to the control forecast? NA
5. Surface boundary perturbations
Perturbations to sea surface temperature? No
Perturbation to soil moisture? No
Perturbation to surface stress or roughness? No
Any other surface perturbation? No
Are the above surface perturbations applied to the Control forecast? NA
6. Other details of the models
Description of model grid: Grid points (lat-lon regular) with polar filter
List of model levels in appropriate coordinates: uniformly spaced in the terrain-following coordinate 0<s<1 , with dP/ds analitically prescribed
What kind of large scale dynamics is used? Eulerian, second order in space, split explicit in time (forward-backward for gravity waves, weighted average flux for advection - Billet and Toro, 1997, J.Comput Phys., 130).
What kind of boundary layer parameterization is used? 1.5 E-l closure, with Monin-Obukhov parameterization in the surface layer.
What kind of convective parameterization is used? Kain and Fritsch (revised - Kain, 2004, J.App.Meteorol., 43)
What kind of large-scale precipitation scheme is used? Kessler-type bulk microphysics with 5 water species.
What cloud scheme is used? Cloud fraction function of specific humidity and explicit clouds, with different parameters for stratiform and convective contributions. Maximum overlapping total cloud cover.
What kind of land-surface scheme is used? HTESSEL-type, with 7 soil layers
How is radiation parametrized? Ritter and Geleyn computed every 30 min, corrected with ECMWF radiation scheme (Morcrette, 1991, J.Geophys.Res., 96D) every 2 hours of simulated time.
Other relevant details? Orographic wave drag included (Baines, 1995, Cambridge Monographs on Mechanics. Cambridge Univ. Press)
7. Re-forecast configuration
Number of years covered: 30 past years, from 1981 to 2010.
Produced on the fly or fix re-forecasts? Fix
Frequency: once every 5 days in the 1981-2010 period (a total of 2190 reforecast runs at fixed calendar days).
Ensemble size: 1 member
Initial conditions: ERA interim (T255L60)
Is the model physics and resolution the same as for the real-time forecasts: Yes
If not, what are the differences: NA
Is the ensemble generation the same as for real-time forecasts? NA
The Meteorological Global Model GLOBO at the ISAC-CNR of Italy Assessment of 1.5 Yr of Experimental Use for Medium-Range Weather Forecasts
By: P.Malguzzi, A.Buzzi, O.Drofa. WEATHER AND FORECASTING Volume: 26 Issue: 6 Pages: 1045-1055.
First outcomes from the CNR-ISAC monthly forecasting system
By: D. Mastrangelo, P.Malguzzi, C.Rendina, O.Drofa, and A.Buzzi. Adv.Sci.Res., 8, 77–82, 2012