Igad Climate Prediction and Applications Centre
Recent publications
Extreme Rainfall is crucial for Crop production and food security in Eastern Africa. This paper seeks to investigate the changes and variability in wet days and dry spells over the IGAD region. Data used are Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS). Several statistical methods and wet days and dry spells thresholds at ≥ 1mm applied on decadal (10 years), 20, 30 and 41 years. The results show that decrease in the number of wet days lead to longer dry spells. The majority of districts in Uganda, southwestern South Sudan, southwestern zones in Ethiopia, highlands of western and Nyanza counties in Kenya observed the highest number of wet days (50–70 days) and lowest consecutive dry spells (0–1 spells). Uganda and South Sudan are the two countries with lowest variability on wet days (highest variability in dry spells). Again, South Sudan and Uganda, most parts of Ethiopia, highlands of western Kenya observed 90–100% probability of exceeding 7 and 14 days (1 and 2 spells) during March-May (MAM), June-August (JJA) and September-November (SON). Northeastern Kenya and Somalia, southeastern Ethiopia, most parts of Eritrea and Djibouti observed less than 5% of probability under 7, 14,21,28 days (1,2,3,4 spells). In addition, most parts of the region observed decreased number of wet days in the 1980s and 1990s, while the last decade (2011–2020) experienced an increase during MAM and JJA. These findings are important for rain-fed agriculture, supplementary irrigation planning and food security in the IGAD region.
Drought is a persistent hazard that impacts the environment, people's livelihoods, access to education and food security. Adaptation choices made by people can influence the propagation of this drought hazard. However, few drought models incorporate adaptive behavior and feedbacks between adaptations and drought. In this research, we present a dynamic drought adaptation modeling framework, ADOPT-AP, which combines socio-hydrological and agent-based modeling approaches. This approach is applied to agropastoral communities in dryland regions in Kenya. We couple the spatially explicit hydrological Dryland Water Partitioning (DRYP) model with a behavioral model capable of simulating different bounded rational behavioral theories (ADOPT). The results demonstrate that agropastoralists respond differently to drought due to differences in (perceptions of) their hydrological environment. Downstream communities are impacted more heavily and implement more short-term adaptation measures than upstream communities in the same catchment. Additional drivers of drought adaptation concern socio-economic factors such as wealth and distance to wells. We show that the uptake of drought adaptation influences soil moisture (positively through irrigation) and groundwater (negatively through abstraction) and, thus, the drought propagation through the hydrological cycle.
Background In India, acute respiratory infections (ARIs) are a leading cause of mortality in children under 5 years. Mapping the hotspots of ARIs and the associated risk factors can help understand their association at the district level across India.Methods Data on ARIs in children under 5 years and household variables (unclean fuel, improved sanitation, mean maternal BMI, mean household size, mean number of children, median months of breastfeeding the children, percentage of poor households, diarrhea in children, low birth weight, tobacco use, and immunization status of children) were obtained from the National Family Health Survey-4. Surface and ground-monitored PM2.5 and PM10 datasets were collected from the Global Estimates and National Ambient Air Quality Monitoring Programme. Population density and illiteracy data were extracted from the Census of India. The geographic information system was used for mapping, and ARI hotspots were identified using the Getis-Ord Gi* spatial statistic. The quasi-Poisson regression model was used to estimate the association between ARI and household, children, maternal, environmental, and demographic factors.ResultsAcute respiratory infections hotspots were predominantly seen in the north Indian states/UTs of Uttar Pradesh, Bihar, Delhi, Haryana, Punjab, and Chandigarh, and also in the border districts of Uttarakhand, Himachal Pradesh, and Jammu and Kashmir. There is a substantial overlap among PM2.5, PM10, population density, tobacco smoking, and unclean fuel use with hotspots of ARI. The quasi-Poisson regression analysis showed that PM2.5, illiteracy levels, diarrhea in children, and maternal body mass index were associated with ARI.Conclusion To decrease ARI in children, urgent interventions are required to reduce the levels of PM2.5 and PM10 (major environmental pollutants) in the hotspot districts. Furthermore, improving sanitation, literacy levels, using clean cooking fuel, and curbing indoor smoking may minimize the risk of ARI in children.
Rainfall Onset Dates (ROD), Rainfall Cessation Dates (RCD) and Length of rainy Season (LRS) are crucial for Crop production and food security in Eastern Africa yet scantily documented. This paper seeks to investigate the spatial patterns of these parameters. Data used are Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and National Oceanic and Atmospheric Administration (NOAA) gridded temperature. Threshold of 0.1mm for rainy day, 20mm over 5 days with at least 3 rain days and dry spell not exceeding 7 days in the next 21 days were used to determine RODs, while Potential Evapotranspiration (PET) and Water Balance (WB) criteria were computed to determine RCDs then differences between ROD and RCD were used in calculating LRS. The results showed early rainfall cessation over more than 30 counties in Kenya lead to shortened rainy season by 10–20 days during MAM season. Similarly, 20–40 days early onset dates are observed in most counties in upper Nile, Unity and Jonglei states in South Sudan, while 20–40 days delayed rainfall onset was observed in Khartoum and southern parts of Nile state western Darfur, eastern and Aljazeera states in Sudan, most parts of Ethiopia and Eritrea districts during JJA season. Highlands of western and Nyanza region in Kenya, most parts of Uganda observed rainfall onset by March and no sign of cessation before November. Early cessation over northern Uganda districts was behind shortened LRS, furthermore, the early RODs over western and southwestern Uganda districts increased LRS. Prolonged dry conditions over northern Sudan, southeastern parts of South Sudan, northern Kenya, central Somalia, northern Darfour, Kordofan and northern parts of Sudan exacerbated by significant delayed onset and early cessation of rainfall. These findings are important for rain-fed agricultural planning and food security in the IGAD region of Eastern Africa.
Measurements provided by Next Generation Weather Radar and operational thunderstorm monitoring instruments at the Kennedy Space Center and the Air Force Eastern Range have been examined to determine the initial electrical development of 13 isolated, air mass thunderstorms. The same instruments were used to examine the surface potential gradient prior to and following 13 long, horizontal discharges that propagated into the area. The motivation and primary objective for this work was to evaluate the safety of the existing lightning‐related launch constraints associated with surface potential gradient and precipitation radar measurements. The onset of cloud electrification as seen by a large‐area surface electric field mill network was detected 3.7–14.6 min before the first lightning discharge, with lead‐times that depended on proximity to the storm. In 11 of 13 cases, the first detectable field change was a positive excursion in potential gradient close to the storm, likely indicating initial development of lower positive charge. Lead‐times for the radar‐derived cloud tops (0 dBZ) reaching −20°C were longer than those for early electrification in all but two cases. Surface potential gradients above +500 V/m or below −100 V/m “warning thresholds” were exceeded before the first lightning flash in all cases. Radar reflectivities >35 dBZ above the −10°C level provided 3–14 min of lead time for lightning. Potential gradients just prior to and near long horizontal discharges exceeded 3 kV/m at most sites and were typically positive. Measurements at a single field mill site would not have provided adequate warning.
A significant proportion of the population in Sub-Saharan Africa are vulnerable to extreme climatic conditions, hence there is a high demand for climate information. In response to this need, the Global Challenges Research Fund African Science for Weather Information and Forecasting Techniques has been undertaking a two-year testbed to co-produce tailored forecasts for different sectors using the sub-seasonal to seasonal forecast datasets from the sub-seasonal to seasonal Real Time Pilot Initiative project. Sub-seasonal forecasts are essential for early warning and informed decision-making in the agriculture and food security sector. This study summarises the co-production process of climate services between the Intergovernmental Authority on Development (IGAD) Climate Prediction and Applications Centre and the Food Security and Nutrition Working Group for Eastern and Central Africa, highlighting the importance of efficient communication as well as the lessons learnt and challenges faced in the co-production process. A case study approach is utilised to evaluate the model performance. Two contrasting case studies, one for an extreme rainfall event in week three in April and another for the evolution of tropical cyclone Gati were conducted for the year 2020. Skillful and timely climate information and services co-produced has the potential to increase the uptake, ownership, and appropriate use of sub-seasonal forecasts for resilience building in Eastern Africa. Practical Implication. In the past decades Eastern Africa has been plagued by numerous climate related disasters including flooding and drought. Eastern Africa has a relatively dry tropical climate with a high percentage of the region being arid or semi-arid. To properly plan for these events there is need for provision of weather and climate forecasts. Traditionally forecasts have been mostly issued out at short range and seasonal timescales. Creating a glaring gap in the provision of forecasts between the short-range and seasonal forecasts, thus raising the need for sub-seasonal forecasts. Sub-seasonal forecasts bridge the gap between the short-range and long-range forecasts and are critical for informed decision making in the agricultural and disaster risk reduction sectors over Eastern Africa. Here we propose utilisation of a co-production process to increase sub-seasonal forecast uptake over Eastern Africa. The co-production is implemented through a two-year testbed under the Global Challenges Research Fund African Science for Weather Information and Forecasting Techniques. The IGAD Climate Prediction and Applications Centre collaborated with the Food Security Nutrition Working Group (FSNWG) for Eastern and Central Africa in the co-production process. The FSNWG coordinates regional food security, and nutrition updates to planners and decision-makers (e.g in disaster and risk reduction, agriculture, livestock sectors). In the region the main drivers of food insecurity include climate, conflict and macro-economic drivers. The European Centre for Medium Range Weather Forecasts (ECMWF) model outputs are utilized to derive the forecast information. The co-produced products include weekly total rainfall, rainfall anomalies, probability of exceedance, soil moisture anomalies, maximum and minimum temperature anomalies and also the maximum wet and dry spells. The forecast information is disseminated through bulletins and also during the monthly FSNWG plenary sessions. Sharing of the sub-seasonal forecasts in the monthly meetings allows for further direct interaction between the climate information users and producers. The forecasts are mostly used for crop choice, planting timing, drought risk, flood risk, disease outbreaks, early assistance appeals, disaster relief preparation, and early warning with drought and flood risk tied on top decisions made. One major challenge that is often faced by climate producers and users is the communication of the forecasts. In this study the challenge is addressed by incorporating a communication and user service team based at ICPAC. The communication and user service team is composed of social scientists, climate information experts and journalists. Improved communication is fundamental in increasing the uptake of the sub-seasonal forecasts and appropriate use of these climate products by climate information users. In consequence the communication and user services team at ICPAC simplifies the language that is utilized in the forecast bulletins and also improves on the layout of the bulletins. This improves the readability and usage of the forecast outputs. For example, initially forecast bulletins were written in paragraph format, which potentially makes the readability of the document harder. Hence, it was suggested that the forecast bulletins be produced in bullet point form. To evaluate the model a case study approach is utilized for two extreme events that occurred in 2020. One case focused on an extreme rainfall event in week 3 in April and another for the evolution of tropical cyclone Gati. Tropical cyclones that make landfall over Somalia are rare during the October-December season. Results showed that the model is able to capture the wet anomalies for both case studies, hence giving an indication to stakeholders of potential flood risk. However, the model underestimates the rainfall intensity over the region thus use of anomalies might provide more information on the risk of flooding or extended dry spells in comparison to the total rainfall. In conclusion this study has shown that the S2S forecast information have a potential to provide early warning systems and hence, increase the Eastern Africa community resilience. However, to ensure long term viability of the co-production process there is need for continued support in access to the real time S2S forecast datasets.
The Leeds Africa Climate Hackathon aimed to generate user‐relevant narratives of possible future climate in East and West Africa relevant to hydroelectric power generation and agriculture respectively. Here we discuss how the virtual hackathon was organised, present the results, and examine the lessons learned from running such a hackathon. We found East African hydroelectric power generation will need to store more water during heavier rain events and cope with longer drought periods in future. Agriculture in Ghana will face a much greater possibility of severe droughts by mid‐century especially if 1.5 degC global warming targets are not met.
Compared with well documented and frequent occurrence of multi-year La Niña, double-year El Niño is less frequent and has not been well investigated. Both of them are a discrepancy from the cyclic behavior of the El Niño-Southern Oscillation and deserve investigation. During 1950-2021, 75% of El Niño events persist for one year, and 25% of them last for two years. Both central and eastern Pacific type El Niños occur in the single-year and double-year El Niños with various strengths. Compared with the single-year El Niños, the averaged warm water volume (WWV) is larger in the peak and declines much slower for the double-year El Niños, suggesting that a persistently recharged heat condition of the equatorial Pacific is a precondition for the emergence of a second-year El Niño. The faster decline of WWV in the single-year El Niños is associated with the in-phase decrease of its intraseasonal-interseasonal and interannual components, while the slower decline of WWV in the double-year El Niños is determined by the interannual component. In addition, the single-year and double-year El Niño may have different impacts on regional climate.
Timing of the rainy season is essential for a number of climate sensitive sectors over Eastern Africa. This is particularly true for the agricultural sector, where most activities depend on both the spatial and temporal distribution of rainfall throughout the season. Using a combination of observational and reanalysis datasets, the present study investigates the atmospheric and oceanic conditions associated with early and late onset for Eastern Africa short rains season (October to December). Our results indicate enhanced rainfall in October and November during years with early onset and rainfall deficit in years with late onset for the same months. Early onset years are found to be associated with warmer sea surface temperatures (SSTs) in the western Indian Ocean, and an enhanced moisture flux and anomalous low‐level flow into Eastern Africa from as early as the first dekad of September. The late onset years are characterised by cooler SSTs in the western Indian Ocean, anomalous westerly moisture flux and zonal flow limiting moisture supply to the region. The variability in onset date is separated into the interannual and decadal components, and the links with SSTs and low‐level circulation over the Indian Ocean basin are examined separately for both timescales. Significant correlations are found between the interannual variability of the onset and the Indian Ocean dipole mode index. On decadal timescales the onset is shown to be partly driven by the variability of the SSTs over the Indian Ocean. Understanding the influence of these potentially predictable SST and moisture patterns on onset variability has huge potential to improve forecasts of the East African short rains. Improved prediction of the variability of the rainy season onset has huge implications for improving key strategic decisions and preparedness action in many sectors, including agriculture.
Both climate change and rapid urbanization accelerate exposure to heat in the city of Kampala, Uganda. From a network of low-cost temperature and humidity sensors, operational in 2018-2019, we derive the daily mean, minimum and maximum Humidex in order to quantify and explain intra-urban heat stress variation. This temperature-humidity index is shown to be heterogeneously distributed over the city, with a daily mean intra-urban Humidex Index deviation of 1.2°C on average. The largest difference between the coolest and the warmest station occurs between 16:00 and 17:00 local time. Averaged over the whole observation period, this daily maximum difference is 6.4°C between the warmest and coolest stations, and reaches 14.5°C on the most extreme day. This heat stress heterogeneity also translates to the occurrence of extreme heat, shown in other parts of the world to put local populations at risk of great discomfort or health danger. One station in a dense settlement reports a daily maximum Humidex Index of >40°C in 68% of the observation days, a level which was never reached at the nearby campus of the Makerere University, and only a few times at the city outskirts. Large intra-urban heat stress differences are explained by satellite earth observation products. Normalized Difference Vegetation Index (NDVI) has the highest (75%) power to predict the intra-urban variations in daily mean heat stress, but strong collinearity is found with other variables like impervious surface fraction and population density. Our results have implications for urban planning on the one hand, highlighting the importance of urban greening, and risk management on the other hand, recommending the use of a temperature-humidity index and accounting for large intra-urban heat stress variations and heat-prone districts in urban heat action plans for tropical humid cities.
South–North research collaborations are now commonly used in the field of climate and development to advance knowledge, inform decision-making and strengthen capacity in the global South. Southern leadership within these collaborations is widely seen as instrumental to the lasting impact. This study examines how Southern leadership and capacity were promoted in the Future Climate for Africa (FCFA) programme, a five-year initiative that sought to enhance resilience to climate change in Africa. Drawing on interview and survey data from programme participants, document analysis and experiential insights from the author team, we examine how Southern leadership was pursued within the programme, and the barriers that constrained action at a range of scales. Most climate and development initiatives, like FCFA, sit at the intersection of multiple social, political and research systems. To disrupt the structures that sustain the power of Northern institutions and obstruct change, funders must go beyond programme-level interventions such as funding and distribution of roles, and consider deeper leverage points of change. We propose how shifts in mindsets and metrics in relation to Southern leadership and capacity can contribute to this change. © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
The lives and livelihoods of people around the world are increasingly threatened by climate-related risks as climate change increases the frequency and severity of high-impact weather. In turn, the risk of multiple hazards occurring simultaneously grows and compound impacts become more likely. The World Meteorological Organization (WMO) proposed the use of multi-hazard impact-based forecasting (IbF) to better anticipate and reduce the impacts of concurrent hazards, but as yet, there are few operational examples in the humanitarian sector. Drought is particularly susceptible to multi-hazard influences. However, challenges encountered in the development of drought IbF systems – including poor understanding of compound impacts and specific hazard-focused mandates – raise important questions for the feasibility of multi-hazard IbF as envisioned by the WMO. With these challenges in mind, we propose an interim approach in which real-time assessment of dynamic vulnerability provides a context for drought-based IbF. The incorporation of dynamic vulnerability indicators account for the local effects of non-drought hazards, whilst the use of a drought-based system facilitates effective intervention. The proposed approach will improve our understanding of compound events, enhance adoption of IbF in the humanitarian sector, and better mitigate the impacts of concurrent hazards.
Anthrax, an acute disease of homeotherms caused by soil-borne Bacillus anthracis is implicated in dramatic declines in wildlife mainly in sub-Saharan Africa. Anthrax outbreaks are often localized in space and time. Therefore, understanding predictors of the spatial and temporal occurrence of anthrax in wildlife areas is useful in supporting early warning and improved response and targeting measures to reduce the impact of epizootic risk on populations. Spatial localization of anthrax is hypothesized to be driven by edaphic factors, while the temporal outbreaks are thought to be driven by extreme weather events including temperature, humidity, rainfall, and drought. Here, we test the role of select edaphic factors and normalized difference vegetation index (NDVI) metrics driven by vegetation structure and climate variability on the spatial and temporal patterns of wildlife mortality from anthrax in key wildlife areas in Kenya over a 20-year period, from 2000 to 2019. There was a positive association between the number of anthrax outbreaks and the total number of months anthrax was reported during the study period and the nitrogen and organic carbon content of the soil in each wildlife area. The monthly occurrence (timing) of anthrax in Lake Nakuru (with the most intense outbreaks) was positively related to the previous month’s spatial heterogeneity in NDVI and monthly NDVI deviation from 20-year monthly means. Generalized linear models revealed that the number of months anthrax was reported in a year (intensity) was positively related to spatial heterogeneity in NDVI, total organic carbon and cation exchange capacity of the soil. These results, examined in the light of experimental studies on anthrax persistence and amplification in the soil enlighten on mechanisms by which these factors are driving anthrax outbreaks and spatial localization.
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.
Drought and food security crises heighten risks to lives and livelihoods in East Africa. In recent years, a shift towards acting in advance of such events has gained momentum, notably among the humanitarian and development community. This shift is premised on tools that link climate forecasts with pre-agreed actions and funding, known as Forecast-based Action (FbA), or anticipatory action more widely. While FbA approaches have been developed by a number of humanitarian agencies, the key to scaling-up is mainstreaming these approaches into national risk management systems. This paper addresses this gap in the context of drought risk management in Kenya. We analyse Kenya's current drought management system to assess the potential usability of climate forecast information within the existing system, and outline steps towards improved usability of climate information. Further, we note the critical importance of enabling institutions and reliable financing to ensure that information can be consistently used to trigger early action. We discuss the implications of this for scaling-up FbA into national risk management systems.
The subseasonal-to-seasonal (S2S) predictive timescale, encompassing lead times ranging from 2 weeks to a season, is at the frontier of forecasting science. Forecasts on this timescale provide opportunities for enhanced application-focused capabilities to complement existing weather and climate services and products. There is, however, a ‘knowledge-value’ gap, where a lack of evidence and awareness of the potential socio-economic benefits of S2S forecasts limits their wider uptake. To address this gap, here we present the first global community effort at summarizing relevant applications of S2S forecasts to guide further decision-making and support the continued development of S2S forecasts and related services. Focusing on 12 sectoral case studies spanning public health, agriculture, water resource management, renewable energy and utilities, and emergency management and response, we draw on recent advancements to explore their application and utility. These case studies mark a significant step forward in moving from potential to actual S2S forecasting applications. We show that by placing user needs at the forefront of S2S forecast development – demonstrating both skill and utility across sectors – this dialogue can be used to help promote and accelerate the awareness, value and co-generation of S2S forecasts. We also highlight that while S2S forecasts are increasingly gaining interest among users, incorporating probabilistic S2S forecasts into existing decision-making operations is not trivial. Nevertheless, S2S forecasting represents a significant opportunity to generate useful, usable and actionable forecast applications for and with users that will increasingly unlock the potential of this forecasting timescale.
Forecasts on sub-seasonal to seasonal (S2S) timescales have huge potential to aid preparedness and disaster risk reduction planning decisions in a variety of sectors. However, realising this potential depends on the provision of reliable information that can be appropriately applied in the decision-making context of users. This study describes the African SWIFT (Science for Weather Information and Forecasting Techniques) forecasting testbed which brings together researchers, forecast producers and users from a range of African and UK institutions. The forecasting testbed is piloting the provision of real-time, bespoke S2S forecast products to decision-makers in Africa. Drawing on data from the kick-off workshop and initial case study examples, this study critically reflects on the co-production process. Specifically, having direct access to real-time data has allowed user-guided iterations to the spatial scale, timing, visualisation and communication of forecast products to make them more actionable for users. Some key lessons for effective co-production are emerging. First, it is critical to ensure there is sufficient resource to support co-production, especially in the early co-exploration of needs. Second, all the groups in the co-production process require capacity building to effectively work in new knowledge systems. Third, evaluation should be ongoing and combine meteorological verification with decision-makers feedback. Ensuring the sustainability of project-initiated services within the testbed hinges on integrating the knowledge-exchanges between individuals in the co-production process into shaping sustainable pathways for improved operational S2S forecasting within African institutions.
Africa is poised for a revolution in the quality and relevance of weather predictions, with potential for great benefits in terms of human and economic security. This revolution will be driven by recent international progress in nowcasting, numerical weather prediction, theoretical tropical dynamics and forecast communication, but will depend on suitable scientific investment being made. The commercial sector has recognized this opportunity and new forecast products are being made available to African stakeholders. At this time, it is vital that robust scientific methods are used to develop and evaluate the new generation of forecasts. The GCRF African SWIFT project represents an international effort to advance scientific solutions across the fields of nowcasting, synoptic and short-range severe weather prediction, subseasonal-to-seasonal (S2S) prediction, user engagement and forecast evaluation. This paper describes the opportunities facing African meteorology and the ways in which SWIFT is meeting those opportunities and identifying priority next steps. Delivery and maintenance of weather forecasting systems exploiting these new solutions requires a trained body of scientists with skills in research and training; modelling and operational prediction; communications and leadership. By supporting partnerships between academia and operational agencies in four African partner countries, the SWIFT project is helping to build capacity and capability in African forecasting science. A highlight of SWIFT is the coordination of three weather-forecasting “Testbeds” – the first of their kind in Africa – which have been used to bring new evaluation tools, research insights, user perspectives and communications pathways into a semi-operational forecasting environment.
The skill of precipitation forecasts from global prediction systems has a strong regional and seasonal dependence. Quantifying the skill of models for different regions and timescales is important, not only to improve forecast skill, but to enhance the effective uptake of forecast information. The sub-seasonal to seasonal prediction (S2S) database contains near real-time forecasts and re-forecasts from 11 operational centres and provides a great opportunity to evaluate and compare the skill of operational S2S systems. This study evaluates the skill of these state-of-the-art global prediction systems in predicting monthly precipitation over the Greater Horn of Africa. This comprehensive evaluation was performed using deterministic and probabilistic forecast verification metrics. Results from the analysis showed that the prediction skill varies with months and region. Generally, the models show high prediction skill during the start of the rainfall season in March and lower prediction skill during the peak of the rainfall in April. ECCC, ECMWF, KMA, NCEP and UKMO show better prediction skill over the region for most of the months compared with the rest of the models. Conversely, BoM, CMA, HMCR and ISAC show poor prediction skill over the region. Overall, the ECMWF model performs best over the region among the 11 models analyzed. Importantly, this study serves as a baseline skill assessment with the findings helping to inform how a subset of models could be selected to construct an objectively consolidated multi-model ensemble of S2S forecast products for the Greater Horn of Africa region, as recommended by the World Meteorological Organization.
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18 members
George Otieno
Titike Kassa Bahaga
  • climate diagnostics and prediction
P. Omondi
  • climate Prdiction and Diagnostics
Farah Abdulsamed
  • Drought resilience and sustainability initiative paltform
Djibouti, Djibouti