Climate models are useful tools that aid in short to long term prediction of the evolution of climate. In this study we assess how CMIP6 models represent coupling between processes over the land and atmosphere, based on terrestrial and atmospheric indices, to show the nature and strength of the coupling relative to the ERA5 datasets over Africa, with a particular focus on the March-May season. Characterization of the annual cycle indicates that model biases are highest during the peak of the rainfall season, and least during the dry season, while soil moisture biases correspond with rainfall amounts. Models show appreciable sensitivity to regional characteristics; there was model consensus in representing East Africa as a limited soil moisture regime, while major differences were noted in the wet regime over Central Africa. Most CMIP6 models tend to over-estimate the strength of the terrestrial and atmospheric pathways over East and Southern Africa. Inter-model differences in coupling indices could be traced to their inter-annual variability rather than to the mean biases of the variables considered. These results are good indicators towards scientific advancement of land surface schemes in the next generation of climate models for better applications in Africa.
Ionosphere delay is one of the main sources of noise affecting Global Navigation Satellite Systems, operation of radio detection and ranging systems and, very-long-baseline-interferometry. One of the most important and common methods to reduce this phase delay is to establish accurate nowcasting and forecasting ionospheric total electron content models. For forecasting models, compared to mid-to-high latitudes, at low latitudes, an active ionosphere leads to extreme differences between long-term prediction models and the actual state of the ionosphere. To improve the problem of low accuracy for long-term prediction models at low latitudes, this article provides a low-latitude, long-term ionospheric prediction model based on a multi-input-multi-output, long short-term memory neural network. To verify the feasibility of the model, we first made predictions of the vertical total electron content data 24 and 48 hours in advance for each day of July 2020 and then compared both the predictions corresponding to a given day, for all days. Furthermore, in the model modification part, we selected historical data from June 2020 for the validation set, determined a large offset from the results that were predicted to be active, and used the ratio of the mean absolute error of the detected results to that of the predicted results as a correction coefficient to modify our multi-input-multi-output long short-term memory model. The average root mean square error of the 24-hour-advance predictions of our modified model was 4.4 TECU, which was lower and better than 5.1 TECU of the multi-input-multi-output, long short-term memory model and 5.9 TECU of the IRI-2016 model.
Forecasting volatility from econometric datasets is a crucial task in finance. To acquire meaningful volatility predictions, various methods were built upon GARCH-type models, but these classical techniques suffer from instability of short and volatile data. Recently, a novel existing normalizing and variance-stabilizing (NoVaS) method for predicting squared log-returns of financial data was proposed. This model-free method has been shown to possess more accurate and stable prediction performance than GARCH-type methods. However, whether this method can sustain this high performance for long-term prediction is still in doubt. In this article, we firstly explore the robustness of the existing NoVaS method for long-term time-aggregated predictions. Then, we develop a more parsimonious variant of the existing method. With systematic justification and extensive data analysis, our new method shows better performance than current NoVaS and standard GARCH(1,1) methods on both short- and long-term time-aggregated predictions. The success of our new method is remarkable since efficient predictions with short and volatile data always carry great importance. Additionally, this article opens potential avenues where one can design a model-free prediction structure to meet specific needs.