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Abstract We expand the assessment study of modeling capabilities in the prediction of foF2 and hmF2 for the ionospheric climatology (Tsagouri et al., 2018,https://doi.org/10.1029/2018sw002035) by using updated empirical (IRI and MIT Empirical model) and physics‐based models (CTIPe, WACCM‐X, and TIE‐GCM) as well as the additional observations in the southern hemisphere. Monthly medians of foF2 and hmF2 are considered to evaluate the model performance for the entire year of 2012. For quantitative evaluation, we employ several metrics including the correlation coefficient (R), coefficient of determination (R²), root‐mean square error (RMSE), mean error (ME), and mean relative error (MRE). The linear regression analysis shows that the empirical models perform much better than physics‐based models for foF2 but to a lesser degree for hmF2. There are negligible hemispheric differences in the predictions from empirical models. All the physics‐based models show relatively good correlations with the observations for foF2 in the northern hemisphere compared to the southern hemisphere, but the hemispheric differences are small for hmF2. The results of the study indicate that recent versions of empirical models tend to perform better than old versions of the models, but this is not always true for physics‐based models. 

Abstract This paper uses a regional simulation of plasma convective instability in the postsunset equatorial ionosphere together with a global atmosphere/ionosphere/plasmasphere GCM (WAM‐IPE) to forecast irregularities associated with equatorial spreadF(ESF) for 1–2 hr after sunset. First, the regional simulation is initialized and forced using ionosphere state parameters derived from campaign data from the Jicamarca Radio Observatory and from empirical models. The irregularities produced by these simulations are found to be quantitatively similar to those observed. Next, the aforementioned state parameters are replaced with parameters from WAM‐IPE, and the resulting departures between the simulated and observed irregularities are noted. In one of five cases, the forecast failed to accurately predict ESF irregularities due to the late reversal of the zonal thermospheric winds. In four of five cases, significant differences between the observed and predicted prereversal enhancement (PRE) of the background vertical drifts resulted in degraded forecast accuracy. This highlights the need for improved PRE forecasting in the global‐scale model.