Motivated by the increasing needs for reliable seasonal climate forecasts for enhanced living and protection of property, this study evaluates the predictive skill of the European Center for Medium-range Weather Forecast's Sub-seasonal to Seasonal (ECMWF-S2S) precipitation forecasts during the peak of West Africa Monsoon in Nigeria. It investigates the ability of the ECMWF-S2S model to reproduce the atmospheric dynamics that influence the monsoon variability in West-Africa. Rain gauge values of 46 meteorological stations and 10-member ensemble of ECMWF-S2S forecasts from the Ensemble Prediction System (EPS) version of the ECMWF were subjected to quantitative statistical analyses. Results show that the model has weak capability in predicting wind strength at 700 mb level to depict the African Easterly Jet (AEJ). However, irrespective of the ENSO phases, ECMWF-S2S model is capable of adequately and reliably predicting the latitudinal positions of the Inter-Tropical Discontinuity (ITD), mean sea level pressure component of the thermal lows and sea surface temperature (SST) anomalies over the Pacific and Atlantic Oceans. On inter-annual time-scales, results also show that ECMWF-S2S model performs best over the Savannah in forecasting of rainfall anomalies (synchronization = 75%) and over the Sahel in the prediction of rainfall accumulation. The model may however not be able to forecast extreme precipitation reliably because the disagreement between the model's ensemble members increases as higher rainfall accumulation values are attained. The implication here is that the reproducibility of the atmospheric dynamic by the model is a better measure of rainfall prediction than the actual quantitative rainfall forecasts especially in areas south of latitude 10°N. The study therefore suggests considering some climate driving mechanisms as predictability sources for the ECMWF-S2S model to enable the atmospheric dynamics to be better represented in the model.

Subseasonal-to-seasonal (S2S) forecasts are bridging the gap between weather forecasts and long-range predictions. Decisions in various sectors are made in this forecast timescale, therefore there is a strong demand for this new generation of predictions. While much of the focus in recent years has been on improving forecast skill, if S2S predictions are to be used effectively, it is important that along with scientific advances, we also learn how best to develop, communicate and apply these forecasts.In this presentation, we present recent progress in the applications of S2S forecasts. We summarise case studies from a recently-published applications community review paper in the Bulletin of the American Meteorological Society (BAMS), covering sectoral applications of S2S predictions from around the world, including public health, disaster preparedness, water management, telecommunications, energy and agriculture. Involving over 60 authors and drawing from the recent advances and experience of researchers and users working with S2S forecasts globally, we explore the value of applications-relevant S2S predictions through a series of sectoral cases where uptake is starting to occur.From across 12 case studies, we show that:The S2S forecasting timescale is a new concept for many users. While the additional value of S2S forecasts for decision-making is increasingly gaining interest among users, incorporating probabilistic ensemble S2S forecasts into existing operations is not trivial. Barriers to widespread adoption of S2S forecasts include lack of access to the forecasts and the co-production to tailor forecasts to user needs, as well as varying ‘in house’ expertise in how to interpret and effectively apply them. This can create a ‘knowledge-value’ gap in some instances. S2S forecasts do not produce a ‘go/no go’ answer of how a user should respond to a potential hazard; instead they provide additional, supplementary ‘situational awareness’ information that can be used to support decision-making on S2S timescales. While S2S forecasting is still a maturing discipline globally, this publication marks a significant step forward in moving from potential to actual S2S forecasting applications – a collective body of evidence demonstrating both skill and utility across sectors that places user needs and applications at the forefront of S2S forecast development.Our paper, ‘Advances in the application and utility of subseasonal-to-seasonal predictions’, is available from BAMS as an open access publication: https://doi.org/10.1175/BAMS-D-20-0224.1.

Rainfall onset has a lot of implications on the sustainability of the socio-economic activities in Nigeria. This study assesses the skills of CMA, ECMWF, and UKMO sub-seasonal-to-seasonal (S2S) models in predicting monsoon onset and its variability over Nigeria. It also investigates the global drivers modulating the variability and their teleconnections with rainfall onset anomaly. All the models, their ensemble members, and the observations were subjected to quantitative statistical analyses from 1998 to 2016. Results show that the three models are able to simulate the Northwards migration of the onset dates adequately with inherent biases and unique characteristics. They are also able to capture the evolution and variability of the global drivers modulating the monsoon onset. While CMA and the ECMWF models improve progressively towards the Sahel, the UKMO model performance is best over the Gulf of Guinea. In addition, despite the fairly poor performance of the models in predicting the variability of onset dates over the Gulf of Guinea and the Sahel, there is a considerable improvement in the correlation skill of the models over the Savannah. Furthermore, results show that only the ECMWF model was able to produce the strength of both the African Easterly Jets (AEJ) and the Tropical Easterly Jet (TEJ) in spatio-temporal mode. These are two of the crucial global drivers modulating the dynamics of West African monsoon. It was also found out that most global drivers, especially the Inter-tropical Discontinuity (ITD) and the Sea Surface Temperature (SST) over the Central Pacific, exhibit direct teleconnection with the onset anomaly. This direct relationship is shown to be strongest over both the Gulf of Guinea and the Sahel. Although the CMA model might have the least skill, it, however, showed that all the S2S models, despite the inherent biases, are able to predict rainfall onset over Nigeria, within the sub-seasonal timescale. Finally, the results show that improvements in multi-model ensembles are valuable added information able to significantly improve model performance.

The study uses numerical weather prediction models to predict the occurrence of heavy convective rainfall associated with the passage of the African Easterly Wave (AEW) during the period 23–26 August 2017 over Nigeria. Fraction skill score (FSS) and method for object‐based diagnostic evaluation (MODE) verification techniques were applied to verify how well the models predict the high‐impact event and to demonstrate how these tools can support operational forecasting. Ensemble model forecasts at a convective scale from UK Met Office Unified Model (MetUM) and a one‐way nested weather research and forecasting (WRF) model were compared with the integrated multisatellite retrievals for global precipitation measurement (IMERG GPM). The purpose is to examine skills of improved model resolution and ensemble in reproducing rainfall forecasts on useful scales and how the skill varies with spatial scale. WRF 2 and 6 km model forecasts show comparable skill at smaller grid scales. The skill of MetUM improves dramatically when the verification statistics are applied to the ensemble mean of the binary fields of the individual member forecast. The object‐based analysis reveals a similar structure as observed, although displaced eastwards. Most improvement occurred for heavier rainfall events associated with the passage of the AEW. WRF 6 km compares reasonably well with WRF 2 km in terms of shape and structure of rainfall underscoring the ability of the model to reasonably represent convection at 6 km horizontal resolution. The ensemble members in MetUM explicitly reproduce convection at 4 km resolution but are displaced at about 166 km behind observed rainfall.

The frequency of flash floods resulting from heavy rainfall over West Africa has increased in recent years with serious socio‐economic consequences. Therefore, the need to utilize numerical weather prediction models to forecast heavy rainfall events reliably is also rising at many operational meteorological centres in West Africa. This paper evaluates the performance of the Consortium for Small‐scale Modelling (COSMO) model of the German Meteorological Services (DWD) in predicting rainfall over West Africa for high‐impact rainfall events that occurred between 19 and 26 August 2017. The paper aims to investigate the synoptic forcings modulating daily rainfall variability during that period. Results show that COSMO simulates adequately the spatio‐temporal variability of rainfall distribution over West Africa, though with inherent biases. COSMO displays a decreasing skill in producing spatial rainfall distribution as rainfall amounts tend to 30 mm and above. Additionally, areas of heavy rainfall, mostly about 100–300 km southwest of the core of the Africa Easterly Jet (AEJ), often coincide with areas of decreasing mean sea level pressure of at least 0.6 hPa and areas of increasing convective available potential energy of at least 500 J/kg. Although not in all cases, the trough of the Africa Easterly Wave (AEW) is always located to the east of these areas. We show that not every storm, especially east of the prime meridian, is associated with an AEW trough. COSMO is able to reproduce the atmospheric dynamics modulating the daily rainfall variability, in addition to capturing the daily propagation of the AEW trough, and the core of the AEJ. However, the reproducibility skill of the model in predicting atmospheric dynamics may not transform into the predictive skill of the model in producing rainfall. Nevertheless, operational forecasters may be able to determine likely areas of heavy rainfall by estimating the position of the AEJ core based on the position of areas of the least falling pressure from COSMO. Finally, the incorporation of the fractions skill score metric based on the neighbourhood approach could also assist operational forecasters to decide at which scale a severe weather alert can be issued.

Due to the continuing large number of malaria-related deaths in tropical Africa, the need to develop a robust Malaria Early Warning System (MEWS) for effective action is growing to guide cost-effective implementation of interventions. This study employs a two-stage hierarchical evaluation technique to evaluate the ability of the VECTRI malaria model to simulate malaria dynamics at seasonal time scale (1 - 7 months) over Nigeria and West Africa. Two sets of malaria simulations are considered. The first set is based on VECTRI simulations driven by observed rainfall and temperature datasets (hereafter referred to as control run). The second is based on malaria simulations driven by the European Centre for Medium-Range Weather Forecasting (ECMWF) System5 ensemble seasonal forecasting system (hereafter referred to as Forecast run). Different metrics are employed to assess the skill of the VECTRI malaria model. Results based on the control run indicate that the model can reproduce hyper-endemic zones and the evolution of malaria cases, particularly the observed increase in cases with decreasing population density. Despite having significant biases and low correlation, the model successfully predicts annual anomalies in malaria cases across Nigeria, particularly in the savannah region that experience large malaria burden. Annual correlations between the VECTRI Forecast run and the VECTRI Control run are relatively low at all lead times (LT) and for each start date (SD) across West Africa, although correlation generally increases from the Gulf of Guinea to the Sahel. Despite low correlations, the Rank Probability Skill Score (RPSS) reveals that the model has a statistically significant skill in predicting malaria occurrences across all categories of malaria cases, regardless of start date or lead time. While the Guinea Forest has the strongest RPSS, the increase or decrease in skill from the first to seventh lead time varies significantly across the region. In addition, the VECTRI malaria model has a good ability to discriminate variability in malaria cases across all regions, with an average Area Under the Relative Operating Characteristics (ROC) Curve (AUC) of approximately 0.62. Our findings suggest that the VECTRI malaria model could be used as a reliable Malaria Early Warning System (MEWS), particularly for identifying malaria hyper-endemic zones in West Africa at seasonal time scale.