The first episodes of floods caused by heavy rainfall during the major rainy season in 2018 occurred in Accra (5.6°N and 0.17°W), a coastal town, and Kumasi (6.72°N and 1.6°W) in the forest region on the 18th and 28th of June, respectively. We applied the Weather Research and Forecasting (WRF) model to investigate and examine the meteorological dynamics, which resulted in the extreme rainfall and floods that caused 14 deaths, 34076 people being displaced with damaged properties, and economic loss estimated at $168,289 for the two cities according to the National Disaster Management Organization (NADMO). The slow-moving thunderstorms lasted for about 8 hours due to the weak African Easterly Wave (AEW) and Tropical Easterly Jet (TEJ). Results from the analysis showed that surface pressures were low with significant amount of moisture influx aiding the thunderstorms intensification, which produced 90.1 mm and 114.6 mm of rainfall over Accra and Kumasi, respectively. We compared the rainfall amount from this event to the historical rainfall data to investigate possible changes in rainfall intensities over time. A time series of annual daily maximum rainfall (ADMR) showed an increasing trend with a slope of 0.45 over Accra and a decreasing trend and a slope of –0.07 over Kumasi. The 95th percentile frequencies of extreme rainfall with thresholds of 45.10 mm and 42.16 mm were analyzed for Accra and Kumasi, respectively, based on the normal distribution of rainfall. Accra showed fewer days with more heavy rainfall, while Kumasi showed more days with less heavy rainfalls.

Periodic climate zoning is an essential classification of land cover to account for anthropogenic activities resulting from population increase and urbanization that affect key climate response parameters. Rainfall, relative humidity (RH), maximum (T max ) and minimum temperature (T min ) data from the Ghana Meteorological Agency were used to zone Ghana by adopting cluster and PCA analysis methods and verifying the groupings with the seasonal trend and Tukey Honestly Significance Difference (HSD) analysis. The cluster analysis grouped the synoptic stations into four major homogenous clusters while the PCA distributed them into three sub-divisions with reference to 1976-2018. Rainfall, RH, T max and T min were characterized by five, three, two and three factors with factor loadings in the range of 0.71-0.78, 0.53-0.70, 0.54-0.74 and 0.50-0.72, respectively. HSD found transition stations like Bole and Kete Krachi in cluster 1 and 2 to have no significant difference with cluster 1, while Wenchi, Sunyani, Sefwi Bekwai and Koforidua in cluster 2 had no significant difference with cluster 3. Accra station which was classified in cluster 3 showed the seasonal pattern of cluster 4 and was confirmed by HSD to belong in cluster 4. Therefore, Ghana-based on-point analysis is climatically grouped into Savannah (11 0 0 0 00 N-7 46 0 11 00 N), Forest (from 7 46 0 11 00 N to the coast) and Coastal (about 30 km from the Gulf of Guinea coastline) based on the assessed parameters.These findings are vital for planners and decision-makers especially for industries that depend on weather and climatic conditions for their activities.

The monitoring of rainfall variability over recent decades has become a necessity due to its devastating effects such as floods and droughts, which render humans vulnerable across different parts of the West African region. The current study seeks to provide a good understanding of variability within the minor rainfall season over southern Ghana by employing statistical tools to quantify variability in rainfall. Daily rainfall data from 1981 to 2018 for seventeen (17) synoptic weather stations across southern Ghana are used for this analysis. We perform trend and descriptive statistics of rainfall amount and extreme indices intending to identify the areas with the greatest variability in rainfall. Further, for five recent years (2014–2018), we do an interpolation of the ground station rainfall data and compute anomalies. We find increasing trends of rainfall in the minor rainy season for 16 out of the 17 stations, with rainfall increasing between 0.10 mm and 4.30 mm each season. For extreme rainfall indices, the 17 stations show nonsignificant trends of very wet and extremely wet days. We also find that the middle parts of Ghana have the highest rainfall amounts (262.7 mm/season–400.2 mm/season), while the East Coast has the lowest (125.2 mm/season–181.8 mm/season). Over the whole of southern Ghana, we find high variability in rainfall amount with the coefficient of variations (CV) between 25.3% and 70.8% and moderate to high variability in rainfall frequency (CV = 14.0%–48.8%). The results of rainfall anomalies show that the middle parts had an above-normal rainfall amount. In the same period, the transition areas experienced below-normal rainfall. Our finding of high variability in the minor rainfall season has implications for agricultural productivity in Ghana and countries in the West African region, which rely heavily on rain-fed agriculture. Hence, this study recommends more research to understand the causes of variability in the West African monsoon and how this will change in the region.

Testbeds have become integral to advancing the transfer of knowledge and capabilities from research to operational weather forecasting in many parts of the world. The first high-impact weather testbed in tropical Africa was recently carried out through the African SWIFT program, with participation from researchers and forecasters from Senegal, Ghana, Nigeria, Kenya, the United Kingdom, and international and pan-African organizations. The testbed aims were to trial new forecasting and nowcasting products with operational forecasters, to inform future research, and to act as a template for future testbeds in the tropics. The African SWIFT testbed integrated users and researchers throughout the process to facilitate development of impact-based forecasting methods and new research ideas driven both by operations and user input. The new products are primarily satellite-based nowcasting systems and ensemble forecasts at global and regional convection-permitting scales. Neither of these was used operationally in the participating African countries prior to the testbed. The testbed received constructive, positive feedback via intense user interaction including fishery, agriculture, aviation, and electricity sectors. After the testbed, a final set of recommended standard operating procedures for satellite-based nowcasting in tropical Africa have been produced. The testbed brought the attention of funding agencies and organizational directors to the immediate benefit of improved forecasts. Delivering the testbed strengthened the partnership between each country’s participating university and weather forecasting agency and internationally, which is key to ensuring the longevity of the testbed outcomes.

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