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The provision of weather and climate information (WCI) can help the most at-risk communities cope and adapt to the impacts of extreme events. While significant progress has been made in ensuring improved availability of WCI, there remain obstacles that hinder the accessibility and use of this information for adaptation planning. Attention has now focused on the “usability gap” to ensure useful and usable WCI informs practise. Less attention has however been directed on barriers to the active production and use of WCI. In this study, we combine two frameworks through a bottom-up approach to present a more coordinated institutional response that would be required to ensure a better flow of information from information providers to users at community level and vice versa. The bottom-up approach was designed in form of Farmers Agri-Met Village Advisory Clinics (FAMVACs) and Listening Groups (LG) and was initiated by Uganda Meteorological Authority (UNMA) as a way of ensuring connections between the information providers, the disseminators, and the communities to specifically give voice to the communities to provide feedback on the use of WCI in coping with flood risks. This approach is used to identify the barriers and opportunities in the production/provision and use of WCI for flood risk preparedness for a case study in Eastern Uganda. First, a use-case is developed for Katakwi District where smallholder farming communities have recorded their coping practises and barriers to the use of WCI in practise. Second, online interviews with practitioners from disaster management institutions are used to identify barriers to the production and provision of WCI to local farming communities. Findings show that for providers, barriers such as accessibility and completeness of data hinder the production of useful WCI. In situations where useful information is available, technical language used in the format and timeliness in dissemination hinder usability by local farmers. Useful and usable WCI may not be acted on in practise due to factors such as costs or market availability e.g., lack of access to improved seeds. Further, the study highlights possible solutions to bridge the identified gaps and they include capacity building, fostering data collaborations across sectors, data translation to simple advisories, among others. The study also presents the FAMVACs approach which shows the importance of a more coordinated response with a shift of focus from the users of information only, to a more inclusive understanding of the data and information gaps across the wider provider-user landscapes. We argue that this would contribute to more effective disaster management at both the national and local levels.

Rainfall information is essential for many applications in developing countries, and yet, continually updated information at fine temporal and spatial scales is lacking. In Africa, rainfall monitoring is particularly important given the close relationship between climate and livelihoods. To address this information gap, this paper describes two versions (v2.0 and v3.0) of the TAMSAT daily rainfall dataset based on high-resolution thermal-infrared observations, available from 1983 to the present. The datasets are based on the disaggregation of 10-day (v2.0) and 5-day (v3.0) total TAMSAT rainfall estimates to a daily time-step using daily cold cloud duration. This approach provides temporally consistent historic and near-real time daily rainfall information for all of Africa. The estimates have been evaluated using ground-based observations from five countries with contrasting rainfall climates (Mozambique, Niger, Nigeria, Uganda, and Zambia) and compared to other satellite-based rainfall estimates. The results indicate that both versions of the TAMSAT daily estimates reliably detects rainy days, but have less skill in capturing rainfall amount—results that are comparable to the other datasets.

Climate data are used in a number of applications including climate risk management and adaptation to climate change. However, the availability of climate data, particularly throughout rural Africa, is very limited. Available weather stations are unevenly distributed and mainly located along main roads in cities and towns. This imposes severe limitations to the availability of climate information and services for the rural community where, arguably, these services are needed most. Weather station data also suffer from gaps in the time series. Satellite proxies, particularly satellite rainfall estimate, have been used as alternatives because of their availability even over remote parts of the world. However, satellite rainfall estimates also suffer from a number of critical shortcomings that include heterogeneous time series, short time period of observation, and poor accuracy particularly at higher temporal and spatial resolutions. An attempt is made here to alleviate these problems by combining station measurements with the complete spatial coverage of satellite rainfall estimates. Rain gauge observations are merged with a locally calibrated version of the TAMSAT satellite rainfall estimates to produce over 30-years (1983-todate) of rainfall estimates over Ethiopia at a spatial resolution of 10 km and a ten-daily time scale. This involves quality control of rain gauge data, generating locally calibrated version of the TAMSAT rainfall estimates, and combining these with rain gauge observations from national station network. The infrared-only satellite rainfall estimates produced using a relatively simple TAMSAT algorithm performed as good as or even better than other satellite rainfall products that use passive microwave inputs and more sophisticated algorithms. There is no substantial difference between the gridded-gauge and combined gauge-satellite products over the test area in Ethiopia having a dense station network; however, the combined product exhibits better quality over parts of the country where stations are sparsely distributed.

African societies are dependent on rainfall for agricultural and other water-dependent activities, yet rainfall is extremely variable in both space and time and reoccurring water shocks, such as drought, can have considerable social and economic impacts. To help improve our knowledge of the rainfall climate, we have constructed a 30-year (1983–2012), temporally consistent rainfall dataset for Africa known as TARCAT (TAMSAT African Rainfall Climatology And Time-series) using archived Meteosat thermal infra-red (TIR) imagery, calibrated against rain gauge records collated from numerous African agencies. TARCAT has been produced at 10-day (dekad) scale at a spatial resolution of 0.0375°. An intercomparison of TARCAT from 1983 to 2010 with six long-term precipitation datasets indicates that TARCAT replicates the spatial and seasonal rainfall patterns and interannual variability well, with correlation coefficients of 0.85 and 0.70 with the Climate Research Unit (CRU) and Global Precipitation Climatology Centre (GPCC) gridded-gauge analyses respectively in the interannual variability of the Africa-wide mean monthly rainfall. The design of the algorithm for drought monitoring leads to TARCAT underestimating the Africa-wide mean annual rainfall on average by −0.37 mm day−1 (21%) compared to other datasets. As the TARCAT rainfall estimates are historically calibrated across large climatically homogeneous regions, the data can provide users with robust estimates of climate related risk, even in regions where gauge records are inconsistent in time.