TY - GEN
KW - workshop
KW - model uncertainty
AU - Lauriane Batte
AU - Michel Déqué
AB - <p>
  To account for atmospheric model uncertainties in the seasonal forecasting system based on CNRM-CM (Voldoire et al. 2013), two stochastic perturbation methods have been introduced in the ARPEGE-Climate atmospheric model, namely:</p>

<ul><li>stochastic dynamics (Batté and Déqué, 2016): prognostic variables (T, q, ψ) are perturbed with random simultaneous corrections (δX) of model errors estimated by nudged seasonal runs over the hindcast period;</li><li>SPPT (Palmer et al. 2009): multiplicative physical parameterization tendencies perturbations using a random spectral pattern generator. In ARPEGE-Climate only u,v tendencies are perturbed.</li></ul>
<p>
  We present here separate assessments of the impact of these methods on seasonal forecast quality.</p>

C1 - Events
DA - 2016
LA - English
N2 - <p>
  To account for atmospheric model uncertainties in the seasonal forecasting system based on CNRM-CM (Voldoire et al. 2013), two stochastic perturbation methods have been introduced in the ARPEGE-Climate atmospheric model, namely:</p>

<ul><li>stochastic dynamics (Batté and Déqué, 2016): prognostic variables (T, q, ψ) are perturbed with random simultaneous corrections (δX) of model errors estimated by nudged seasonal runs over the hindcast period;</li><li>SPPT (Palmer et al. 2009): multiplicative physical parameterization tendencies perturbations using a random spectral pattern generator. In ARPEGE-Climate only u,v tendencies are perturbed.</li></ul>
<p>
  We present here separate assessments of the impact of these methods on seasonal forecast quality.</p>

PY - 2016
TI - Representing atmospheric model uncertainties: Applications in seasonal forecasts with CNRM-CM
ER -