Using the ECMWF OpenIFS model and state-of-the-art training techniques in meteorological education

Abstract. The OpenIFS programme of the European Centre for Medium-Range Weather Forecasts (ECMWF) maintains a version of the ECMWF forecast model (IFS; Integrated Forecasting System) for use in education and research at universities, national meteorological services and other institutes. The OpenIFS model can be run on high-performance computing systems, desktop or laptop computers to produce weather forecasts in a similar way to the operational forecasts at ECMWF. Application of OpenIFS as a training tool is wide ranging. At several universities, masters students are taught modelling aspects via sensitivity studies, such as numerical stability, impact of spatial resolution and physical parameterisation settings on the forecast quality. The OpenIFS single column model is used to study a subset of physical processes in the atmosphere. Participants of the OpenIFS user workshops are trained through selected weather events on interpretation of different forecasts, for example ensemble forecasts, probabilistic information, seasonal forecasts. The OpenIFS user meetings and training events demonstrate advanced and easy-to-use graphical tools and training technologies. Metview is developed to analyse, visualise and evaluate the forecast outputs. OpenIFS and Metview “virtual machines” relieve the tutors from the difficulties often found in installing this software on the local computing environment. They provide data, applications and documents in a package tested in-house and deployed easily to another site. A further step on virtualisation is utilising cloud servers, ensuring the computational resources demanded by model runs are available in the cloud space. This paper shows the education activity in the OpenIFS programme with some examples.

[1]  K. Emanuel,et al.  Optimal Sites for Supplementary Weather Observations: Simulation with a Small Model , 1998 .

[2]  Richard Neale,et al.  A standard test for AGCMs including their physical parametrizations. II: results for the Met Office Model , 2000 .

[3]  Lisa Bengtsson,et al.  The HARMONIE-AROME Model Configuration in the ALADIN-HIRLAM NWP System , 2017 .

[4]  D. Mironov.  Parameterization of Lakes in Numerical Weather Prediction. Description of a Lake Model , 2020 .

[5]  William White,et al.  A Proposal , 2008, Moon, Sun, and Witches.

[6]  K. Fraedrich,et al.  Climate anomalies in Europe associated with ENSO extremes , 1992 .

[7]  R. Hogan,et al.  787 ECRAD : A new radiation scheme for the IFS , 2016 .

[8]  Céline Lutoff,et al.  HYMEX , a 10-year Multidisciplinary Program on the mediterranean water cycle. , 2014 .

[9]  B. Hurk,et al.  A Revised Hydrology for the ECMWF Model: Verification from Field Site to Terrestrial Water Storage and Impact in the Integrated Forecast System , 2009 .

[10]  Edward N. Lorenz,et al.  Designing Chaotic Models , 2005 .

[11]  Mats Hamrud,et al.  A new grid for the IFS , 2016 .

[12]  I. Roulstone,et al.  Royal Meteorological Society discussion meeting on ‘New directions in mathematical modelling in numerical weather prediction’, 16th February, 2000. , 2000 .

[13]  E. Richard,et al.  Vortex–vortex interaction between Hurricane Nadine (2012) and an Atlantic cut‐off dropping the predictability over the Mediterranean , 2016 .

[14]  Richard Neale,et al.  A standard test for AGCMs including their physical parametrizations: I: the proposal , 2000 .

[15]  A. Sterl,et al.  EC-Earth A Seamless earth-System Prediction Approach in Action , 2010 .

[16]  G. Shutts.  A kinetic energy backscatter algorithm for use in ensemble prediction systems , 2005 .

[17]  M. Suárez,et al.  A proposal for the intercomparison of the dynamical cores of atmospheric general circulation models , 1994 .

[18]  Simon T. K. Lang,et al.  Stochastic representations of model uncertainties at ECMWF: state of the art and future vision , 2017 .

[19]  J. Morcrette,et al.  Impact of a New Radiation Package, McRad, in the ECMWF Integrated Forecasting System , 2008 .

[20]  T. Palmer,et al.  Stochastic representation of model uncertainties in the ECMWF ensemble prediction system , 2007 .