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COSMO-NExT - the next-generation system for numerical weather prediction

Project start 01.01.2012
Project end 31.12.2016
Topics Research & cooperation
Weather
Region Alps
Status Current projects

MeteoSwiss initiated the COSMO-NExT project in 2012 to further develop the system for numerical weather prediction (NWP) with a forecast range of up to five days. After four years of research and development, the new system, which includes the ensemble-based data assimilation system, COSMO-1 and COSMO-E, is operational since spring 2016.

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

Enlargement: Figure 1: Comparison of topography models of the Bernese Oberland with resolutions of 2.2 km and 1.1 km.
Figure 1: Comparison of topography models of the Bernese Oberland with resolutions of 2.2 km and 1.1 km.

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

Enlargement: Figure 2: 21 COSMO-E forecasts for the heavy precipitation event of June 16, 2016. Depicted is the forecast of the 24h precipitation sum for 16.06.2016 with a lead-time of 3 days.
Figure 2: 21 COSMO-E forecasts for the heavy precipitation event of June 16, 2016. Depicted is the forecast of the 24h precipitation sum for 16.06.2016 with a lead-time of 3 days.
  • 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

Enlargement: Figure 3: Model first guess ensemble temperature probability distribution as a proxy for the uncertainty of the first guess.
Figure 3: Model first guess ensemble temperature probability distribution as a proxy for the uncertainty of the first guess.

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 <=30 minutes
    • COSMO-E: + 120 hr forecast in <=120 minutes
    • LETKF cycle: + 1 hr analysis in <=10 minutes
  • Major upgrade of computer resources required (factor of ~40 in comparison to the old forecasting system)
  • Back-up machine of the same size to ensure high level of reliability (failover)
  • Cost-efficient system, maximisation of acceleration capability through GPUs (Cray CS-Storm with two cabinets, eached packed with 12 hybrid computing nodes for a total of 96 NVIDIA Tesla K80 GPU accelerators and 24 Intel Haswell CPUs, installed in autumn 2015)
  • As far as we know, MeteoSwiss is the first national Met Service to operationally run their NWP system on a GPU platform.

Further information

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