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

Project

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.

Project start01.01.2012
Project end31.12.2016
RegionInternational
StatusCompleted projects
  • Research & cooperation
  • Weather
  • Climate

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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 <=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.

About us

MeteoSwiss is the Federal Office for Meteorology and Climatology. We keep our finger on the pulse of the weather and climate, so it’s all clear as day to you.

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