The COSMO-NExT NWP system for the Alpine region incorporates

• the Local Ensemble Transform Kalman Filter (LETKF)-based ensemble data assimilation system,
• the deterministic COSMO-1, with a grid box size of 1.1 km, and
• the probabilistic COSMO-E, with a grid box size of 2.2 km.

COSMO-1 and COSMO-E have both become operational in spring 2016.

COSMO-1

Main properties:

• Deterministic forecasts with very high spatial resolution (1.1 km mesh-size) for the Alpine region
• Fast update cycle for short-range forecasts: seven times a day up to +33 hrs, at 03 UTC out to +45  hrs
• Initial conditions currently provided by a nudging assimilation cycle, will be replaced by an LETKF (see data assimilation)-based analysis
• Boundary conditions from ECMWF IFS-HRES forecasts

Output:

• Best available prediction of short-range evolution of the three-dimensional atmospheric conditions

Primary rationale for 1.1 km grid boxes:

• Better resolution of topography - important for surface weather in complex topography (see figure 1)
• Large-scale effects of deep convection converge at resolutions of O (1 km)
• Uncertainties avoided through parameterisation (e.g. convection scheme, SSO)
• Good compatibility with the resolution of many gridded datasets (e.g. radar, satellite)

Challenges:

• Stability of the dynamical core in the presence of steep orography
• Subgrid-scale modelling of turbulence

COSMO-E

• Main aims:
• Ensemble forecasting for the Alpine region which permits explicit convection modelling (2.2 km mesh-size)
• Forecasts twice a day up to + 120 hrs.
• 21 Ensemble members
• Initial condition perturbations from LETKF (see data assimilation)
• Boundary condition perturbations from ECMWF IFS-ENS
• Stochastically Perturbed Physics Tendencies (SPPT) used to simulate model uncertainties

Information on the forecast confidence as reflected by e.g., the consistency of the individual ensemble members (see figure 2) is a substantial added value of an ensemble forecasting system.

Data assimilation

Main aims:

• New ensemble data assimilation system based on the Local Ensemble Transform Kalman Filter (LETKF)
• Quasi-optimal and weather-dependent combination of observations and model predictions based on the relevant error statistic (see figure 3)
• Assimilation of observations not directly connected with the model variables, with input from observation operators
• Offers ensemble initial conditions for COSMO-E and deterministic initial conditions for COSMO-1

Which observations will be assimilated?

• Initially: conventional observations (TEMP, SYNOP, AMDAR, WINDPROFILER, SHIPS, BUOYS)
• Later: new remote sensing data, e.g. Mode-S data, RADAR volume data, data from ground-based remote sensing systems, GPS, satellite radiances

Output:

• Ensemble from 3-hourly analyses (probability density function)
• It thus provides not only the best guess (ensemble mean), but also the analysis uncertainty (ensemble standard deviation)

Infrastructure

Main aims:

• Operating requirements for COSMO-NExT
  • COSMO-1: + 33 hr forecast in