NTCM is a simple and relatively faster global climatological model that is developed, using ionospheric VTEC measurements from unevenly globally distributed GNSS receivers, to mitigate ionospheric effects on GNSS applications. As it is climatological, its ionospheric weather characteristic specification capability is limited. Also, its nowcasting and forecasting performance of the East-African equatorial ionosphere has not yet been tested. Therefore, this paper demonstrates techniques to change NTCM to ionospheric weather model to nowcast and forecast VTEC for East-Africa. NTCM has been changed to ionospheric weather model through adapting it to quiet days VTEC from nineteen GNSS receivers in East-Africa in 2013 and 2014. Adaptation has been done by calculating its driver, effective ionization level, when NTCM modeled VTEC fits the best with observed VTEC. Then, performances of the model in nowcasting and forecasting VTEC, before and after adaption, have been investigated compared to observed VTEC. It is found that NTCM, after adaptation, represents observed features of diurnal variations of VTEC and equatorial ionization anomaly (EIA) much better than before adaptation. After adaptation, the mean and standard deviation of daily mismodelings are found comparable with the mean and standard deviation of yearly mismodelings. It is demonstrated also that NTCM after adaptation to a reference station performs better than before adaptation at the stations nearby the reference station; however, its performance decreases at the locations far away from the reference station. In addition, after adaptation, one hour ahead prediction of NTCM is found to be the best compared to its longer hours prediction.

Different ionospheric climatological models such as NeQuick2 and NTCM have been developed to mitigate the ionosphere impact on the trans-ionosphere propagating radio wave. Moreover, the Neural Network (NN) is used to model and characterize the ionosphere. However, no one has compared the performances of NeQuick2, NTCM, and NN after adapting to GPS TEC. This study evaluates their performances in the East-African region in 2013 and 2015. It has been done by computing their drivers (effective ionization level, Az for NeQuick2 and ionization driving index, Id for NTCM) through least-square fitting to TEC observation sense. NN-algorithm has also been trained and tested with observed TEC used for NeQuick2 and NTCM adaptation. The annual performance test has shown that the correlation coefficient (R) values between observed and NTCM modeled TEC, after an adaption, are better than the corresponding values obtained from NeQuick2 and NN. It also shows that the standard deviations (STD) and root-mean-square errors (RMSE) obtained for NTCM are smaller than the STD and RMSE computed for NeQuick2 and NN. On the other hand, the daily performance test of now-casting and predicting showed that the NN performs the best, followed by NTCM. However, the 1-hour prediction test showed that NTCM performs the best among the models considered in this study.