Assessing Climate-system Historical Forecast Project (CHFP) seasonal forecast skill over Central Africa

Predictive skill of North American Multi‐Model Ensemble seasonal forecasts for the climate rainfall over Central Africa

“…However, a wet bias exists and is observable in the majority of the NMME models. The results are compatible with those of Vizy et al (2013) who identified a common wet bias over all of Africa in the Coupled Model Intercomparison Project 5 models and Tanessong et al (2020) for the Climate‐system Historical Forecast Project models over CA. In addition, using the Climate Hazards Group InfraRed Precipitation with Station dataset and Global precipitation Center (GPCC), Shukla et al (2016) found a large bias of the NMME models greater than 4 mm/day over EA, also a similar bias of the MME about 5 mm/day over CA was identified by Tchinda et al (2022) using GPCC and NMME CPC PRATE observation datasets.…”

“…The improvement of forecasting for these water resources in CA countries is crucial nowadays, as their economies depend on it (Biman et al, 2004). In addition, agriculture in CA is mainly rain‐fed and sensitive to climate fluctuations (Clover, 2003), which can affect income sources, food security, and national economic development (Fotso‐Nguemo et al, 2017; Tanessong et al, 2020). Due to the importance of seasonal and interannual meteorological variability in the region, useful long‐lead forecasts of meteorological conditions (Ogallo & Oludhe, 2009) are necessary to provide guidance for timely action (Hillbrumer & Moloney, 2012) to mitigate potential humanitarian disasters.…”

Assessing precipitation seasonal forecasts in Central Africa using North American Multimodel Ensemble (NMME)

“…On the other hand, analysis of the quality of MME rainfall forecasts shows that the performance of the forecasting system is better for the first month and from 3 to 5 months, measurements seem less good and the performance of the model remains relatively constant. This follows results of literature review, which indicate good performance of seasonal rainfall forecasts up to a few weeks or even a few months before the beginning of the seasons (Crochemore et al 2016;Tanessong et al 2020). the data used to calculate the correct percentage only covers a 28-year period, the threshold for being statistically significant is closer to 40\% (Kharin et al 2001).…”

Assessing precipitation seasonal forecasts in Central Africa using North American Multimodel Ensemble (NMME)

“…On the other hand, analysis of the quality of MME rainfall forecasts shows that the performance of the forecasting system is better for the first month and from 3 to 5 months, measurements seem less good and the performance of the model remains relatively constant. This follows results of literature review, which indicate good performance of seasonal rainfall forecasts up to a few weeks or even a few months before the beginning of the seasons (Crochemore et al 2016;Tanessong et al 2020). PODs (Fig.…”

“…The skill of NMME is evaluated using retrospective forecasts of 11 dynamic models for the period 1982-2009. MME was examined by first designing ensemble means of every individual model for each season and then averaging the ensemble means of all models as in Shukla et al (2016) and Tanessong et al (2020). The strengths of this method lie in a procedure for optimizing deterministic forecast and evaluating uncertainty due to model imperfections.…”