MESOSCALE DATA ASSIMILATION FOR THE COSMO MODEL: STATUS AND PERSPECTIVES AT THE IMS

Massimo Bonavita, Lucio Torrisi, Antonio Vocino and Francesca Marcucci

CNMCA, Italian Meteorological Service, Pratica di Mare (ROMA)

bonavita@meteoam.it, torrisi@meteoam.it,vocino@meteoam.it, marcucci@meteoam.it

Overview

During 2007 a number of changes and upgrades have been performed on the Italian Meteorological Service (IMS) Numerical Weather Prediction System. Many of these changes have stemmed from the need to increase the NWP system spatial resolution and to use a larger amount of observational information which is nowadays available from satellite platforms. These changes have resulted in improvements to both analysis and forecast skill. In the process, some interesting results on the relevance of current and experimental objective analysis techniques have emerged.

---------------------

The NWP System at the IMS is composed of a regional and a local component. The regional component, whose integration domain and characteristics are illustrated in Fig.1 and Table 1, is initialized with a 3DVar (Bonavita and Torrisi, 2005) atmospheric objective analysis. This IMS 3DVar makes use of a large and increasing number of asynoptic observations, as reported in Tab.2

Fig. 1 Integration domain of EURO_HRM model Tab. 1 Characteristics of EURO-HRM

Domain size 769x513Grid spacing 0.125 Deg (~14 km)Number of layers 40Time step 150 secForecast range 72 hrsInitial time of model 00/12 UTCL.B.C. IFSL.B.C. update 3 hrsInitial state CNMCA 3DVARInitialization Incremental DFIExternal analysis NoneStatus OperationalHardware IBM (ECMWF)N. of processors used 32 (Model), 90 (An.)

Page 104 / 189

Tab. 2 Observation usage statistics in IMS 3DVar

The main changes applied to the analysis system have been:

move from 6 to 3-hourly analysis update cycle; doubling of horizontal resolution of EURO_HRM model (to 0.125 Deg., 14 Km); FGAT treatment of observation increments in 3DVar analysis step; NMC evaluation of background error matrix on 0.125 Deg. Grid; use of Incremental Digital Filter Initialization (IDFI) introduction of Schrodin and Heise multi-layer soil model in EURO_HRM.

The impact of these changes has been positive at all forecast ranges for most variables. Particularly noticeable has been the impact of the increase in horizontal resolution of the prognostic model (EURO_HRM), fig.2, and the switch from 6 to 3-hourly cycle (fig. 3). The main reason for these results seems to lie in the fact that both changes have improved the first guess quality, thus allowing a better use of available observations.

Fig. 2 Impact on wind vector +36h forecast skill of doubling prognostic model spatial resolution

Daily observation usage stats.

Synoptic Asynoptic

RAOB ~19000 - AIREP ~5500

PILOT ~250 - AMDAR ~38000

SYNOP ~5500 - ACAR ~8500

SHIP,BUOY ~1200 - WIND PROF ~1200

- QSCAT/ERS2/ASCAT ~5800

- AMV (MET9/MET7/MODIS)~14000

- AMSU-A Rad. (NOAA1X) ~14000

Synoptic Obs ~26000 Asynoptic Obs ~87000

Total ~ 113000 obs/day

Page 105 / 189

Fig. 3 Impact on Mean Sea Level Pressure forecast skill of doubling data assimilation system refresh time.

The positive impact of these changes has cascaded down to the non-hydrostatic model COSMO-ME which is nested in the EURO_HRM and is initialized with the EURO-HRM interpolated initial conditions (see Fig. 4 and Table 3).

Fig.4 Integration domain of COSMO-ME model Table 3 Characteristics of COSMO-ME model

To verify the impact of the improved analysis fields on the 7-Km COSMO Model, an inter-comparison has been performed between the COSMO-ME implementation described above and another implementation of the COSMO Model which is run by the IMS in collaboration with two Italian regional weather services and whose main characteristics are summarized in Fig. 5 (COSMO-IT). Apart from different integration domains, the main difference between the two implementations lies in the different initialization techniques. COSMO-ME initial conditions come from the interpolation of the EURO-HRM 3DVar analysis, while the initialization of COSMO-I7 is performed through a standard Newtonian Nudging algorithm.

Domain size 641x401 Grid spacing 0.0625 (~7 km) Number of layers 40time step and scheme 40 s LF Forecast range 72 hrs Initial time of m. run 00/12 UTC Lateral bound. condit. IFS L.B.C. update freq. 3 hrs

Interp. 3DVAR Initial state Initialization Digital FilterExternal analysis NoneSpecial features Filtered topography Status Operational Hardware IBM (ECMWF) N° of processors 192

Page 106 / 189

Results of this comparison (an example is shown in Fig. 6) point to a clear advantage of using a variational analysis scheme at the model spatial resolution (7 Km). The in-built quasi-geostrophic analytical balance constraints present in the 14 Km 3DVar seem to hold at the 7 Km scale, producing balanced initial conditions of statistically significant better quality than the corresponding fields derived from the nudging scheme. For a fair assessment, it must be stressed that the 3DVar analysis is ingesting a larger number of observations, especially satellite radiances in the microwave part of the spectrum, which the nudging scheme is not able to assimilate directly.

Fig.5 Characteristics of COSMO-I7 model.

Fig. 6 Wind speed mean error (dashed) and mean absolute error (solid), t+24h forecast, of COSMO-ME (red) and COSMO-I7 (blue)verified against radiosonde network

2 run per day starting at 00 and 12 UTC

Forecast length + 72 hours

Horizontal resolution about 7 km

40 vertical levels

3-hourly boundary conditions from IFS/ECMWF forecast

Initial Conditions through continuous assimilation cycle based on nudging

COSMO I7

Page 107 / 189

The initialization of the 2.8 Km COSMO Model implementation operationally run at the IMS (Fig. 7 and Table 4) has also been the subject of some experimentation in order to test which assimilation strategy would work best at this scale. For a month two parallel cycles have been run, one with initial conditions directly interpolated from the 3DVar 14 Km analyses and the other from the COSMO model nudging scheme.

Fig.7 Integration domain for COSMO-IT model Table 4 Characteristics of COSMO-IT model

Forecast skill for the two systems was found to be comparable (not shown) in a statistical sense, but the forecast initialized from the 3DVar analysis showed clear symptoms of unbalanced initial conditions, with precipitation spin-up in the first 6 hour of forecast (an example is shown in Fig. 8,9). For this reason the initialization of the COSMO-IT is currently performed with the nudging scheme.

Fig. 8 500 hPa Analysis, 30-05-2007 00UTC

Domain size 542 x 604 Grid spacing 0.025 (~2.8 km) Number of layers 50 Time step and scheme 25 s RK Forecast range 48 hrs Initial time of model run 00 UTC Lateral bound. condit. COSMO-ME L.B.C. update frequency 1 hr Initial state Observation Nudging Initialization NoneConvective paramet. Only shallow convection Special features Filtered topography Status Operational Hardware IBM (ECMWF) N° of processors 352

Page 108 / 189

Fig.9 Cumulated precipitation, t+6h COSMO-IT forecast, with 3DVar interpolated Initial conditions (left) and nudging initial conditions (right).

Future Directions

As it has been showed, data assimilation for the high resolution COSMO Model implementations run at the IMS is based on two separate approaches: variational for the 7Km COSMO-ME, nudging for the 2.8Km COSMO-IT. While one would like to use the variational approach for the mesoscale too, given the coherent framework it provides for the ingestion of observations non linearly related to the state variables, the current 14Km implementation is not able to provide balanced initial conditions for the COSMO-IT model. Possible solutions to the problem include the use of digital filter (or incremental digital filter) smoothing of initial conditions and are being investigated. On a longer time frame, the use of strongly non linear observations (i.e. radar reflectivities), the complex nature of the model (and nature!) balance relationships at the mesoscale and the need to move towards ensemble forecasting at the mesoscale, are all factors which clearly point to the need of migrating towards an ensemble based data assimilation system. On this path, tests are undergoing at the IMS to test the operational feasibility of using an Ensemble Kalman Filter scheme, both to supersede the current 3DVar system for the deterministic analysis, and to provide optimal initial perturbations for an ensemble forecasting system at the mesoscale.

Page 109 / 189