Role of initial error growth in the extended range prediction skill of Madden-Julian Oscillation (MJO)

Abstract

The seamless forecast approach of subseasonal to seasonal scale variability has been succeeding in the forecast of multiple meteorological scales in a uniform framework. In this paradigm, it is hypothesized that reduction in initial error in dynamical forecast would help to reduce forecast error in extended lead-time up to 2-3 weeks. This is tested in a version of operational extended range forecasts based on Climate Forecast System version 2 (CFSv2) developed at Indian Institute of Tropical Meteorology (IITM), Pune. Forecast skills are assessed to understand the role of initial errors on the prediction skill for MJO. A set of lowest and highest initial day error (LIDE & HIDE) cases are defined and the error-growth for these categories are analysed for the strong MJO events during May to September (MJJAS). The MJO forecast initial errors are categorized and defined using the well-known multivariate MJO index introduced by Wheeler & Hendon (2004). The probability distribution of bivariate RMSE and error growth evolution (first order difference of index error for each successive lead days) with respect to extended range lead-time are used as metrics in this analysis. The result showed that initial error is not showing any influence in the skill of model after a lead time of 7-10 days and the error growth remains the same for both set of errors. A rapid error growth evolution of same order is seen for both the classified cases. Further the physical attribution of these errors are studied and found that the errors originate from the events with initial phase in Western Pacific and Indian Ocean. The spatial distribution of OLR and the zonal winds also confirms the same. The study emphasise the importance of better representation of MJO phases especially over Indian ocean in the model to improve the MJO prediction rather than focusing primarily on the initial conditions.

Monthly-scale extended predictions using the atmospheric model coupled with a slab ocean

“…Only at the start of the forecasts are the errors of SST in WRF-ROMS lower than those in WRF-SOM. This is because the errors in the 3-D dynamical ocean model have not spread from subsurface to the surface, and the initial condition still plays a major role (Lekshmi et al, 2022). Except for July and August, the SST error growth rate is faster in WRF-SOM over different months in the second half of the year.…”

Monthly-Scale Extended Predictions Using the Atmospheric Model Coupled with a Slab-Ocean

“…The prediction skills of SST in the WRF-SOM and WRF-ROMS have been assessed by calculating the RMSE averaged of 142 forecast cases from July to December. role (Lekshmi et al, 2022). To explore the spatial distribution of skills with different forecast periods in WRF-SOM, we use the ACC of SST anomaly to characterize the temporal and spatial predictability of SST in the WRF-SOM and WRF-ROMS (Wu et al, 2016).…”

Evolution of Double Vortices Induce Tropical Cyclogenesis of Seroja over Flores, Indonesia

“…Thus, tropical cyclogenesis comprises dynamic and thermodynamic processes and its interaction at multiple spatio-temporal scales [1]. In term of external processes, tropical cyclogenesis may be induced by large scale circulations [2] (i.e., monsoon [3]), intertropical convergence zone [5]; [6], Madden Julian Oscillation (MJO) [7]; [8]; [9], equatorial waves [7]; [9], and proven by recently previous work that the activity (i.e., MJO) determined by the dynamic of regional ocean [10] and depend on moisture advection over Indowestern Paci c [11]. As a result, predicting genesis of TC prevails uncertain [12] despite signi cant progress in understanding interaction between tropical cyclones (TC) and their environment which affect to its intensity and structure [1].…”