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Sustainability of African weather and climate information can only be ensured by investing in improved scientific understanding, observational data, and model capability. These requirements must be underpinned by capacity development, knowledge management; and partnerships of co-production, communication and coordination. [ABSTRACT FROM AUTHOR]

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 Global Challenges Research Fund (GCRF) African Science for Weather Information and Forecasting Techniques (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, modeling and operational prediction, and 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 semioperational forecasting environment. [ABSTRACT FROM AUTHOR]

The relevance of carrying out marine warranty surveys (MWS) as mandatory insurable practice in mitigating associated operational risks within the offshore oil and gas industry, during the Covid-19 crises, was examined against the potential health risk and the various Covid-19 restrictions resulting from newly formed regulations. The health risk concerns to the surveyor and the impact of the Covid-19 policy restrictions for the surveyor's business measured are against the risk of excluding MWS in assessing the suitability of procedures, analyses, and vessels involved in typical offshore marine operations. The purpose of any MWS undertakings ensures that all risks in operations, with potential to destroy property, life, and environment, are minimized—if not eliminated. A series of remote interviews across Africa and direct field observations were respectively conducted at the Takoradi port to develop an understanding of the MWS service product, to ascertain the conditions resulting from current challenges, and to determine contingencies and innovations to ensure full service. The study finds MWS service as paramount to the energy industry with its range of risk control checks for high-value assets and operations. Surveyors demonstrated high-levels of awareness for Covid-19 crises and regulatory policies while in adherence but noted challenges of high-cost and delays as by-products of the restrictions. Surveyors deemed site attendances as crucial in their practice at all time, though recognized the several social and technologically innovative approaches adopted industry-wide to facing the pandemic. The study highlights the need for 'essential worker' status and the introduction of innovative insurance packages. [ABSTRACT FROM AUTHOR]

The article offers information on the University Corporation for Atmospheric Research (UCAR) Africa Initiative (AI), an effort to help constrain the development of climate and weather in Africa. UCAR and AI worked together to improve the atmospheric sciences infrastructure in the continent. AI is making effort to encourage universities and government agencies to take on other efforts, such as atmospheric science capacity building, in different ways, in Africa. UCAR is expanding those activities it collaborated with AI. A weather radar network project is currently in progress as part of the effort to improve the atmospheric sciences infrastructure in the continent.

Two monthly gridded precipitation datasets of the Global Precipitation Climatology Project (GPCP; the multisatellite product) and the Global Precipitation Climatology Centre (GPCC) Variability Analysis of Surface Climate Observations (VASClimO; rain gauge data) are compared for a 22-yr period, from January 1979 to December 2000, over land areas (i.e., latitudes 4°–20°N and longitudes 18°W–15°E). The two datasets are consistent with respect to the spatial distribution of the annual and seasonal rainfall climatology over the domain and along latitudinal bands. However, the satellite generally overestimates rainfall. The inability of the GPCC data to capture the bimodal rainfall pattern along the Guinea coast (i.e., south of latitude 8°N) is an artifact of the interpolation of the rain gauge data. For interannual variability, the gridded multisatellite and gridded gauge datasets agree on the sign of the anomaly 15 out of the 22 yr (68% of the time) for region 1 (between longitude 5° and 18°W and north of latitude 8°N) and 18 out of the 22 yr (82% of the time) for region 2 (between longitude 5°W and 15°E and north of latitude 8°N). The datasets agreed on the sign of the anomaly 14 out of the 22 yr (64% of the time) over the Guinea Coast. The magnitudes of the anomaly are very different in all years. Most of the years during which the two datasets did not agree on the sign of the anomaly were years with El Niño events. The ratio of the seasonal root-mean-square differences to the seasonal mean rainfall range between 0.24 and 2.60. The Kendall’s tau statistic indicated statistically significant trends in both datasets, separately. [ABSTRACT FROM AUTHOR]