Gauge data from a West African network of 920 stations are used to assess Tropical Rainfall Measuring Mission (TRMM) satellite and blended rainfall products for 1998. In this study, mean fields, scattergrams, and latitudinal transects for the months of May-September and for the 5-month season are presented. Error statistics are also calculated. This study demonstrates that both the TRMM-adjusted Geostationary Observational Environmental Satellite precipitation index (AGPI) and TRMM-merged rainfall products show excellent agreement with gauge data over West Africa on monthly-to-seasonal timescales and 2.5Њ ϫ 2.5Њ latitude/longitude space scales. The root-mean-square error of both is on the order of 0.6 mm day Ϫ1 at seasonal resolution and 1 mm day Ϫ1 at monthly resolution. The bias of the AGPI is only 0.2 mm day Ϫ1 , whereas the TRMM-merged product shows no bias over West Africa. Performance at 1.0Њ ϫ 1.0Њ latitude/longitude resolution is also excellent at the seasonal scale and good for the monthly scale. A comparison with standard rainfall products that predate TRMM shows that AGPI and the TRMM-merged product perform as well as, or better than, those products. The AGPI shows marked improvement when compared with the GPI, in reducing the bias and in the scatter of the estimates. The TRMM satellite-only products from the precipitation radar and the TRMM Microwave Imager do not perform well over West Africa. Both tend to overestimate gauge measurements.
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.
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