Preprints
https://doi.org/10.5194/egusphere-2022-524
https://doi.org/10.5194/egusphere-2022-524
 
27 Jun 2022
27 Jun 2022

Ensemble forecast of an index of the Madden Julian Oscillation using a stochastic weather generator based on circulation analogs

Meriem Krouma1,2, Riccardo Silini3, and Pascal Yiou2 Meriem Krouma et al.
  • 1ARIA Technologies, 8 Rue de la Ferme, 92100 Boulogne-Billancourt, France
  • 2Laboratoire des Sciences du Climat et de l’Environnement, UMR 8212 CEA-CNRS-UVSQ, IPSL & Université Paris-Saclay, 91191 Gif-sur-Yvette, France
  • 3Departament de Fisica, Universitat Politècnica de Catalunya, Edifici Gaia, Rambla Sant Nebridi 22, 08222 Terrassa, Barcelona, Spain

Abstract. The Madden-Julian Oscillation (MJO) is one of the main sources of sub-seasonal atmospheric predictability in the Tropical region. The MJO affects precipitation over highly populated areas, especially around Southern India. Therefore, predicting its phase and intensity is important as it has a high societal impact. Indices of the MJO can be derived from the first principal components of wind speed and outgoing longwave radiation (OLR) in the Tropics (RMM1 and RMM2 indices). The amplitude and phase of the MJO are derived from those indices. Our goal is to forecast these two indices on a sub-seasonal timescale. This study aims to provide an ensemble forecast of MJO indices from analogs of the atmospheric circulation, computed from the geopotential at 500 hPa (Z500) by using a stochastic weather generator (SWG). We generate an ensemble of 100 members for the MJO amplitude for sub-seasonal lead times (from 2 to 4 weeks). Then we evaluate the skill of the ensemble forecast and the ensemble mean using probabilistic scores and deterministic skill scores. According to score-based criteria, we find that a reasonable forecast of the MJO index could be achieved within 40-day lead times for the different seasons. We compare our SWG forecast with other forecasts of the MJO.

Meriem Krouma et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-524', Anonymous Referee #1, 25 Jul 2022
  • RC2: 'Comment on egusphere-2022-524', Anonymous Referee #2, 28 Jul 2022

Meriem Krouma et al.

Meriem Krouma et al.

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Short summary
We present a simple system to forecast the MJO. We are using atmospheric circulation as input to our system. We found a good skill forecast of the MJO amplitude within 40 days using this methodology. Comparing our results with ECMWF and machine learning forecasts confirmed the good skill of our system.