Meteorological Analysis of Floods in Ghana

S. O. Ansah; M. A. Ahiataku; C. K. Yorke; F. Otu-Larbi; Bashiru Yahaya; P. N. L. Lamptey; M. Tanu

doi:10.1155/2020/4230627

Meteorological Analysis of Floods in Ghana

Ansah, S. O.;Ahiataku, M. A.;Yorke, C. K.;Otu-Larbi, F.;Yahaya, Bashiru;Lamptey, P. N. L.;Tanu, M. 2020-03-24 00:00:00 Hindawi Advances in Meteorology Volume 2020, Article ID 4230627, 14 pages https://doi.org/10.1155/2020/4230627 Research Article 1 1 1 1 1 S. O. Ansah , M. A. Ahiataku, C. K. Yorke, F. Otu-Larbi, Bashiru Yahaya , 2 1 P. N. L. Lamptey, and M. Tanu Ghana Meteorological Agency, Accra, Ghana Ghana Space Science and Technology Institute, Ghana Atomic Energy Commission, Kwabenya, Ghana Correspondence should be addressed to S. O. Ansah; ansahsamuelowusu2014@gmail.com Received 12 September 2019; Revised 5 January 2020; Accepted 30 January 2020; Published 24 March 2020 Academic Editor: Eduardo Garc´ıa-Ortega Copyright © 2020 S. O. Ansah et al. ,is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. ° ° ,e 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). ,e 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. ,e 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. migrate to other communities after a 2007 ﬂood event that 1. Introduction aﬀected Uganda, Ethiopia, Sudan, Togo, Mali, Niger, and Floodis a major hazard and source of human vulnerability Burkina Faso, which displaced millions of people [5]. which can lead to high mortality rate. Other impacts include Floods in West Africa in 2009 after torrential rains af- outbreak of diseases, disruption of energy supply, com- fected 600,000 people in sixteen West African countries [6]. munication, and transport infrastructure, and interference ,e most aﬀected countries were Burkina Faso, Senegal, in public service delivery. ,e African continent is the Niger, and Ghana. In 2012, Nigeria experienced one of the second hardest hit by ﬂoods in terms of number of events, most devastating ﬂooding events ever recorded [7]. ,is ﬂooding incident led to the deaths and displacement of 363 area aﬀected, and number of people killed after Asia [1, 2]. Flash ﬂoods are frequent across the continent resulting from and 2.3 million people, respectively, and the destruction of intense localized thunderstorm activity, slow-moving 59,000 houses and also aﬀected large tracts of farmland and thunderstorms, or squall lines mostly accompanied by livestock. lightning [3]. Various countries in Africa have experienced Urban ﬂooding has been a frequent occurrence in ﬂood-related disasters in the recent past. In February and Ghana since 1930 [8]. At least 18 out of the last 50 years March 2000, Mozambique experienced heavy rains and have recorded signiﬁcant ﬂooding incidences in which cyclones that caused the worst ﬂooding in 50 years, which lives and properties have been lost [8–11]. Since 1995, the led to widespread devastation in some cities [4]. Over 500 frequency of ﬂooding has increased in the coastal areas of lives were also lost and several thousands were forced to Ghana [4]. ,e same study found that the ability of people 2 Advances in Meteorology Ghana, resulting in the deaths of 14 people, displacement of to prepare for possible ﬂoods had become more diﬃcult due to increasing variability in rainfall patterns. Flooding 34,076 others, and damaged properties estimated at $168,289 according to the National Disaster Management in Ghana often occurs in the aftermath of intense and/or continuous rainfall, which results in high run-oﬀs. Possible Organization (NADMO). ,e heavy rainfall events aﬀord us reasons given for occasional ﬂooding in Ghana include the opportunity to conduct a unique case study focusing on climate variability and change [10, 12] and poor physical the meteorological explanation of the causes of the event. planning and ﬂaws in the drainage network [8]. Flooding is ,e aim of this paper is to document the meteorological ranked the second highest natural disaster after epidemics conditions that led to the initiation and propagation of the in Ghana according to [13]. Between the years 1900 and weather systems that caused the heavy precipitation events in Accra and Kumasi by analyzing the synoptic and me- 2014, the economic loss as a result of ﬂooding was ap- proximately US$ 780,500,000 [14]. soscale weather charts. ,e study area and data are described in Section 2. Section 3 provides meteorological analysis and ,e most devastating ﬂooding event in Ghanaian history occurred on the 3rd of June 2015, when most parts discussion. ,e key conclusions from the results and analysis are presented in Section 4. of southern Ghana experienced heavy thunderstorms and rain. ,e active spot of the storm was centered over Accra, where 212.8 mm of rainfall was recorded leading to 2. Materials and Methods ﬂooding over many areas in the city. ,is ﬂooding incident and an explosion of a fuel ﬁlling station at Kwame 2.1. Study Area. Ghana is a tropical West African country ° ° ° Nkrumah Circle, a suburb of Accra, claimed over 150 lives located between latitudes 4 N to 12 N and longitudes 1.5 E to and destroyed lots of properties while displacing hundreds ° 3.5 W. Its climate is dominated by the wet and the dry of people. seasons. ,ese seasons are modulated by the intertropical Various studies have looked at diﬀerent aspects of boundary (ITB) and the two main high-pressure systems, rainfall over the West African region in general and Ghana namely, St. Helena high-pressure system located in the in particular. ,ese include studies focusing on dynamics of Southern hemisphere and the Azores high-pressure system the West African Monsoon [15] and onset, cessation, and located over the Atlantic Ocean. Mechanisms of how the ITB length of rainfall season [16–18]. ,e potential impact of and high pressure systems aﬀect rainfall patterns over West climate change on precipitation and weather patterns over Africa are explained in various literatures [16, 33–36]. ,e West Africa has also been extensively studied (e.g., see wet season is characterized by the intensiﬁcation of the St. [19–23]). ,ese studies have identiﬁed and explained the Helena high-pressure system, which induces moisture inﬂux roles of key drivers of variability in West African rainfall into the country through the south westerly (SW) winds such as the Madden Julian Oscillation [24], the movement of (maritime air mass) which is the major wind that prevails the intertropical convergence zone [25, 26], the African during this season. ,e intensiﬁcation of the Azores high- Easterly Jet and African Easterly Wave [27, 28], and El Niño- pressure system with the north easterly (NE) winds (con- Southern Oscillation [29, 30]. tinental air mass) dominating induces dryness and dust On the contrary, there are very few case studies focusing particles over the country during the dry season. ,e on the meteorological dynamics of heavy precipitation northward and the southward oscillation of the ITB control events within the rainfall season, although there are studies the rainfall pattern in Ghana where the southern half (below on the impacts such weather events have on livelihoods and ° 9 N) of the country experiences bimodal rainfall pattern. society (e.g., see [8, 14]). One of the reasons for the limited April to July is classiﬁed as the major rainy season and research on extreme rainfall in West Africa is the diﬃculty in September to November is the minor rainy season. In this accessing data [31]. However, a case study by [32] identiﬁed study, two ﬂood cases that occurred on the 18th of June 2018, La Nina event in the tropical Paciﬁc, anomalous heating in ° ° identiﬁed as case A in Accra (5.6 N and 0.17 W), and the the tropical Atlantic, and enhanced activity of African 28th of June 2018, identiﬁed as case B in Kumasi (6.72 N and easterly waves as possible causes of anomalous heavy pre- ° 1.6 W), as shown in Figure 1, during the peak rainy season cipitation and ﬂooding over sub-Saharan Africa in 2007. (June) of southern sector of Ghana have been investigated In addition to the factors identiﬁed by Paeth et al. [32], focusing on the meteorological conditions that led to the the occurrence of heavy rainfall also depends on mesoscale heavy rainfall. and localized convective processes. ,ese convective pro- cesses are inﬂuenced by the topography, vegetation cover, and water bodies as well as land surface energy ﬂuxes. 2.2. Data and Methodology. Datasets used in this study for Understanding the dynamics of meteorological conditions the evaluation were in situ rainfall data (45 stations), and features that inﬂuence heavy rainfall will greatly im- Global Satellite Mapping of Precipitation (GSMaP), prove extreme weather forecasts and help to mitigate their Ghana Meteorological Agency (GMet), GMet Weather eﬀect. Early identiﬁcation of meteorological features that Research and Forecasting (WRF) model output, and could lead to extreme weather event such as torrential rain Meteo France Arpege 0.5 mean sea level pressure (MSLP), can help inform early warning systems, which will reduce a product from the Meteosat Second Generation (MSG) fatalities and the economic losses. Preparation for Use of MSG in Africa (PUMA) project. On the 18th and 28th of June 2018, heavy rains and ,e rainfall datasets are daily cumulative rainfall amounts thunderstorms caused ﬂooding in the two biggest cities in recorded at the various stations measured with rain Advances in Meteorology 3 Southern ghana showing ashanti and greater accra regions Northern N Ghana Brong Ahafo Volta Southern Ghana Ashanti Eastern 024 81216 kilometers Greater Accra Western African continent Central 03 6 1218 24 kilometers 0 1530 60 90 120 kilometers Ashanti region Greater Accra Region Kumasi Accra 03 6 1218 24 kilometers 03 6 1218 24 kilometers Kumasi 28th June 2018 ﬂooding Accra, 18th June 2018 ﬂooding Figure 1: Map of study area. Location of cases A and B ﬂood events and the distribution of the Ghana Meteorological Agency’s weather observation stations (red dots). gauges. Sources of error that may aﬀect rainfall mea- track the genesis, development, dissipation, and the active surement or data according to Mensah et al. [37] include spots of the thunderstorms as they propagated zonally for evaporation, wetting, wind induced errors, and instru- cases A and B, respectively. For case A, the thunderstorm ment reading errors on the part of observers. In order to aﬀected the coast of Ghana and lasted for about 17 hours avoid some of these errors, wind shielded rain gauges are before dissipating. At 18 UTC, the slow-moving thunderstorm hit the used by GMet. To track the evolution of the rainfall events for this study, eastern coast of Ghana (Figure 2(d)) and after three hours it the hourly GSMaP images (Figures 2 and 3) were used to had reached Accra as it intensiﬁed (Figure 2(e)). ,e storm 4 Advances in Meteorology GSMAP_NRT 09Z18JUN2018 GSMAP_NRT 12Z18JUN2018 GSMAP_NRT 15Z18JUN2018 GSMAP_NRT 18Z18JUN2018 12N 12N 12N 12N 11N 11N 11N 11N 10N 10N 10N 10N 9N 9N 9N 9N 8N 8N 8N 8N 7N 7N 7N 7N 6N 6N 6N 6N 5N 5N 5N 5N 4N 4N 4N 4N 3N 3N 3N 3N (a) (b) (c) (d) GSMAP_NRT 21Z18JUN2018 GSMAP_NRT 00Z19JUN2018 GSMAP_NRT 03Z19UN2018 GSMAP_NRT 06Z19JUN2018 12N 12N 12N 12N 11N 11N 11N 11N 10N 10N 10N 10N 9N 9N 9N 9N 8N 8N 8N 8N 7N 7N 7N 7N 6N 6N 6N 6N 5N 5N 5N 5N 4N 4N 4N 4N 3N 3N 3N 3N (e) (f) (g) (h) th Figure 2: GSMaP satellite products. ,ree hourly precipitation intervals from 09 UTC of the 18th of June to 06 UTC of the 19 of June 2018 for case A. GSMAP_NRT 12Z28JUN2018 GSMAP_NRT 14Z28JUN2018 GSMAP_NRT 16Z28JUN2018 GSMAP_NRT 18Z28JUN2018 12N 12N 12N 12N 11N 11N 11N 11N 10N 10N 10N 10N 9N 9N 9N 9N 8N 8N 8N 8N 7N 7N 7N 7N 6N 6N 6N 6N 5N 5N 5N 5N 4N 4N 4N 4N 3N 3N 3N 3N (a) (b) (c) (d) GSMAP_NRT 20Z28JUN2018 GSMAP_NRT 22Z28JUN2018 GSMAP_NRT 00Z29JUN2018 GSMAP_NRT 02Z29JUN2018 12N 12N 12N 12N 11N 11N 11N 11N 10N 10N 10N 10N 9N 9N 9N 9N 8N 8N 8N 8N 7N 7N 7N 7N 6N 6N 6N 6N 5N 5N 5N 5N 4N 4N 4N 4N 3N 3N 3N 3N (e) (f) (g) (h) Figure 3: GSMaP satellite products. Two hourly precipitation intervals from 12 UTC of the 28th of June 2018 to 02 UTC of the 29th of June 2018 for case B. ﬁnally moved westwards from Accra after 06 UTC on the about –90 C (estimated using the Meteorological Product 19th of June, lasting for about 9 hours. As the storm moved Extraction Facility (MPEF) from the Synergie), an indication slowly westwards across the eastern coast, the cloud top of its intensiﬁcation which lasted for about 8 hours temperature was observed to change from about –75 C to (Figure 2(e)). Rainfall amounts recorded over some stations 5W 5W 5W 5W 4W 4W 4W 4W 3W 3W 3W 3W 2W 2W 2W 2W 1W 1W 1W 1W 0 0 0 0 1E 1E 1E 1E 2E 2E 2E 2E 3E 3E 3E 3E 4E 4E 4E 4E 5E 5E 5E 5E 5W 5W 5W 5W 4W 4W 4W 4W 3W 3W 3W 3W 2W 2W 2W 2W 1W 1W 1W 1W 0 0 0 0 1E 1E 1E 1E 2E 2E 2E 2E 3E 3E 3E 3E 4E 4E 4E 4E 5E 5E 5E 5E 5W 5W 5W 5W 4W 4W 4W 4W 3W 3W 3W 3W 2W 2W 2W 2W 1W 1W 1W 1W 0 0 0 0 1E 1E 1E 1E 2E 2E 2E 2E 3E 3E 3E 3E 4E 4E 4E 4E 5E 5E 5E 5E 5W 5W 5W 5W 4W 4W 4W 4W 3W 3W 3W 3W 2W 2W 2W 2W 1W 1W 1W 1W 1E 1E 1E 1E 2E 2E 2E 2E 3E 3E 3E 3E 4E 4E 4E 4E 5E 5E 5E 5E Advances in Meteorology 5 in the east coast were 124.8 mm, 90.1 mm, and 109.0 mm at Accra’s ﬁrst three highest ADMR values were higher than Saltpond, Accra, and Pokuase, respectively. those of Kumasi. For the ﬂooding event in Kumasi, a localized convective It is worth noting that these records occurred at the peak cell started developing within the country in the afternoon of the West African Monsoon season over the respective (Figure 3(c)). It intensiﬁed over the middle sector and stations. ,is can be attributed to its location along the coast produced heavy rains lasting for a period of about 8 hours where rich moisture from the Gulf of Guinea fuels thun- th before dissipating at approximately 02 UTC on the 29 of derstorms development as well as land and sea surface June (Figure 3(f)). Kumasi had the highest rainfall of temperature variations producing heavy rainfall [39]. ,e 114.6 mm and Kwadaso, a suburb of Kumasi, had 81.0 mm. frequency of these extreme rainfall events using the 95th ,e spatial distributions of rainfall for the two cases are percentile threshold with a value of 45.10 mm and 42.16 mm shown in Figure 4. over the two areas shows a positive slope (Figure 8) of 0.02 ,e WRF model product used in this study includes the (Kumasi) and a negative slope of –0.02 (Accra). ,is indi- wind, relative humidity (RH), and the skew-T for the second cates that the chance of extreme rainfall over Kumasi is domain. ,e atmospheric forcing data used for the initial increasing, while that of Accra is decreasing based on the and lateral boundary condition of the model was the global respective threshold values. forecasting system (GFS), which was dynamically down- Further analysis on the ADMR distribution and fre- scaled for two domains with resolutions of 27 km and 9 km, quency over Accra and Kumasi using the normal distri- respectively, as illustrated in Figure 5. ,e model’s conﬁg- bution shows that 68% of the area of the normal uration setup uses Betts–Miller–Janjic’s [38] convective distribution for Accra was between 53.03 mm and parameterization scheme for both domains with 49 vertical 136.81 mm of rainfall, which implies that most of the levels. ,e inner domain model products were used. rainfall events occurred within the 1st z-score distribution value, while 95% of the area of the normal distribution was found between 11.14 mm and 178.70 mm of the 2nd z-score 3. Results and Discussion distribution value. ,e mean ADMR over Accra is 3.1. Rainfall Time Series Analysis. ,e long-term (1981–2010) 94.92 mm with a standard deviation of 14.89, as shown in monthly mean rainfall amounts (MMR) for stations along Figure 9. ,e rainfall distribution for Kumasi shows that 68% of the area of normal distribution was between the coast and middle sectors were analyzed (Figure 6) and all showed bimodal rainfall pattern. However, the major dif- 64.37 mm and 108.87 mm, which implies that most rainfalls ferences between the two sectors are land cover and to- were within the 1st z-score distribution value. ,e 95% of pography of the area, which have an inﬂuence on the rainfall the area of normal distribution ranged from 42.12 mm to pattern [37]. Kumasi (286.3 m above MSL) in the middle 131.12 mm, which satisﬁed the 2nd z-score distribution sector where case B ﬂood occurred has more vegetation value. ,e mean ADMR is 86.62 mm with a standard de- cover than Accra (67.7 m above MSL) where case A ﬂood viation of 22.25, as shown in Figure 9(c), where N is 57, occurred. It was observed that all the stations along the coast representing the total number of years of data used. Table 1 and the middle sector recorded higher rainfall amount summarizes the 1st and 2nd z-score ADMR values over Accra and Kumasi. during the peak of the major season in June than that of the minor season in October except Akim Oda and Wenchi which recorded 200 mm and 170 mm, respectively, for the 3.2. Charts Analysis. ,is section reviews the atmospheric major season as compared to 220 mm and 200 mm for minor dynamics and associated physical mechanisms responsible peak, respectively. for the ﬂooding incidents that occurred on those fateful ,is anomaly could be attributed to seasonal to sub- days within southern Ghana. ,e 12 UTC and 18 UTC seasonal variations. Axim (37.8 m above MSL), the wettest prognostic charts used for the 18th and 28th of June 2018 rainfall station in Ghana along the western coast, recorded forecast preparation were retrieved and examined. Gen- the highest MMR of 500 mm and 230 mm for peak of the erally, the quasi-static high pressure systems and their major and minor seasons, respectively. ,is could be as- consequent eﬀect on the ITB determine the prevailing sociated with the orientation of its coastline and the vege- weather within the West Africa subregion. During the rainy tation cover. Accra and Kumasi which are the main focus of season, the country is inﬂuenced by the maritime air mass this study recorded MMR of 180 mm and 215 mm during the due to the intensiﬁcation of northward ridging of the St. peak of the major season, while the peak of the minor season Helena high-pressure system, which drives moisture into recorded 60 mm and 170 mm, respectively. the subregion, while the Azores high-pressure system ,e time series of the ADMR variability (1961–2017) weakens. over Accra has a positive slope of 0.45 and Kumasi has a negative slope of –0.07 as shown in Figure 7. ,e top three highest ADMR amounts ever recorded in Accra are 3.2.1. Mean Sea Level Pressure (MSLP). From the Meteosat 243.9 mm, 212.3 mm, and 175.3 mm which occurred on the Second Generation (MSG) Preparation for Use of MSG in 3rd of July 1995, 3rd of June 2015, and 22nd of June 1973, Africa (PUMA) station, MSLP charts at 12 UTC for case A respectively, while Kumasi recorded 167.9 mm, 145.8 mm, showed that St. Helena had a center value of 1027 hPa, while st Azores was 1030 hPa with the equatorial trough (ET) ex- and 125.2 mm on the 21 of July 1966, 13th of June 1986, and 5th of May 1997, respectively. It is interesting to note that tended zonally to the east of the continent as shown in 6 Advances in Meteorology 18 June 2018 (mm) 29 June 2018 (mm) 12°N 12°N 300 300 200 200 150 150 10°N 10°N 130 130 70 70 8°N 8°N 30 30 6°N 6°N 4°N 4°N 4°W 3°W 2°W 1°W 0° 1°E 2°E 4°W 3°W 2°W 1°W 0° 1°E 2°E (a) (b) Figure 4: Distribution of rainfall (mm). (a) Case A and (b) case B. 20°N 15°N d02 10°N 5°N 0° 5°S 10°S 15°W 10°W 5°W 0°W 5°E 10°E Figure 5: Two domain conﬁgurations for GMET’s WRF model. Resolutions of 27 km and 9 km (white cell), respectively. Figure 10(a). ,e country was between the pressures of ,e Azores and St. Helena high-pressure centers for case B were 1022 hPa and 1027 hPa, respectively, as at 12 UTC. ,e 1012 hPa to the north and 1016 hPa to the south. By 18 UTC, the St. Helena and Azores center pressures dropped to ET extended covering most parts of Niger (excluding the 1025 hPa and 1029 hPa, respectively, with zonal and me- southern fringes and whole of Burkina Faso), northern half ridional expansion of the ET where the country’s pressure of Mali, and eastern Mauritania through to southern half of was between 1015 hPa and 1010 hPa. Algeria. ,e isobar over Ghana ranged between 1012 hPa to Low pressure centers of 1005 hPa and 999 hPa were the north and 1014 hPa to the south (Figure 11(a)). At 18 UTC, observed, respectively, between the borders of Mauritania there was a 2 hPa drop in the core values of the Saint Helena and Mali as well as central Chad as shown from Figure 10(b). high-pressure system, while that of Azores was maintained. ,is Advances in Meteorology 7 550 300 Month Month Axim Accra Akim Oda Ho Takoradi Tema Koforidua Abetiﬁ Saltpond Ada Akuse Kumasi Akatsi Sunyani Sefwi Bekwai Wenchi (a) (b) Figure 6: MMR (1981–2010). Bimodal rainfall pattern within Southern Ghana: six and eleven stations along the coast (a) and over the middle sector (b), respectively. Accra Slope = 0.45 (1995, 243.9 mm) (2015, 212.8 mm) (1973, 175.3 mm) 1960 1970 1980 1990 2000 2010 2020 Years Kumasi Slope = –0.07 (1986, 145.8 mm) (1966, 167.9 mm) (1997, 125.4 mm) 1960 1970 1980 1990 2000 2010 2020 Years Figure 7: ADMR (1961–2017). Rainfall variability over Accra and Kumasi. condition triggered both zonal and meridional expansions of the sector. ,e Africa Easterly Jet (AEJ) at 700 hPa level ET with the lower portions stretching and covering north (steering level) which had southerly components was weak –1 (Nigeria, Benin, Togo, Ghana, Ivory Coast, Guinea, and Sene- (i.e., speed between 5knots (2.5 ms ) and 15 knots –1 gal). ,e isobar over Ghana ranged between 1010 hPa to the (7.5 ms )); hence, it inﬂuenced the slow propagation of north and 1012 hPa to the south (Figure 11(b)). the storm as marked by the broken red oval shape in Figures 12(b) and 12(e). In case B, signiﬁcant amount of moisture (RH> 80%) 3.2.2. Wind and Moisture. ,e GMet WRF model was able was also observed at the 850 hPa and the 700 hPa levels, to forecast the moisture laden winds at the 850 hPa level which reﬂects the extent of moisture depth within the at- (Figures 12(a) and 12(d)) with speed ranging between 10 –1 –1 mosphere. ,e south westerly winds ﬁlled a cyclonic vortex knots (5 ms ) and 20 knots (10 ms ) at 12 UTC and then –1 –1 with speed between 5 and 10 kts (3–5 ms ) located north of it increased to 25 knots (13 ms ) by 18 UTC for the coastal Rainfall (mm) Jan Feb Mar Apr May Jun Rainfall (mm) Rainfall (mm) Jul Aug Sep Oct Nov Dec Rainfall (mm) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 8 Advances in Meteorology Accra Slope = –0.02 1960 1970 1980 1990 2000 2010 2020 Years Kumasi Slope = 0.02 1960 1970 1980 1990 2000 2010 2020 Years Figure 8: Annual rainfall frequency (ARF). Accra and Kumasi ARF based on the 95th percentile thresholds of 45.10 and 42.16 mm, respectively. Probability distribution of rainfall in Accra 18 0.8 0.7 Mean = 94.92 mm 0.6 Std. Dev. = 41.89 N = 57 0.5 0.4 0.3 0.2 0.1 0 50 100 150 200 250 –3 –2 –1 012345 Rainfall (mm) Standard deviation Rainfall Standardized data Normal distribution Normal distribution GEV (a) (b) Probability distribution of rainfall in Kumasi 18 0.6 Mean = 86.92 mm 0.5 Std. Dev. = 22.25 N = 57 12 0.4 0.3 6 0.2 0.1 0 0 –3 –2 –1 01 2 3 4 5 0 20 40 60 80 100 120 140 160 180 Standard deviation Rainfall (mm) Rainfall Standardized data Normal distribution Normal distribution GEV (c) (d) Figure 9: Normal and standardized rainfall distribution. Plots (a) and (b) and (c) and (d) for cases A and B, respectively. Frequency Frequency Frequency Frequency Density Density Advances in Meteorology 9 MON 18/06/2018 MSILP 18UTC MON 18/06/2018 MSILP 12UTC 1010hPa isobar 1010hPa isobar (a) (b) Figure 10: Meteo France Arpege 0.5 MSLP charts. (a) 12 UTC and (b) 18 UTC for case A. Table 1: ,e 68% and 95% of the area of normal distribution for cases A and B from annual daily maximum rainfall (mm), respectively. 68% area of normal distribution 95% area of normal distribution Event μ – ε μ + σ μ – 2σ Μ + 2σ Case A 53.03 136.81 11.14 178.7 Case B 64.37 108.87 42.12 131.12 THUR 28/06/2018 MSILP 18UTC THUR 28/06/2018 MSILP 12UTC 1010hPa isobar 1010hPa isobar (a) (b) Figure 11: Meteo France Arpege 0.5 MSLP charts. (a) 12 UTC and (b) 18 UTC for case B. Benin at 12 UTC (Figure 13(a)). At 18 UTC (Figure 14(b)), 25 kts (Figures 12(b), 12(e), and 13(b)). ,e upper level jet –1 the speed increased to15 kts (8 ms ), especially for the coast. (200 hPa) was direct easterlies with speed around 35 kts. ,ere were direct AEJ at 700 hPa with speeds between 15 and ,e convective activities observed in Figure 3 were a result 10 Advances in Meteorology 20°N 20°N 20°N 90 90 15°N 15°N 15°N 80 80 80 70 70 10°N 10°N 10°N 60 60 5°N 5°N 5°N 50 50 40 40 40 0° 0° 0° 30 30 20 20 5°S 5°S 5°S 10 10 10°S 10°S 10°S 15°W 10°W 5°W 0° 5°E 10°E 15°W 10°W 5°W 0° 5°E 10°E 15°W 10°W 5°W 0° 5°E 10°E (a) (b) (c) 20°N 20°N 20°N 90 90 90 15°N 15°N 15°N 80 80 80 70 70 70 10°N 10°N 10°N 60 60 60 5°N 5°N 5°N 50 50 50 40 40 40 0° 0° 0° 30 30 30 20 20 20 5°S 5°S 5°S 10 10 10 10°S 10°S 10°S 15°W 10°W 5°W 0° 5°E 10°E 15°W 10°W 5°W 0° 5°E 10°E 15°W 10°W 5°W 0° 5°E 10°E (d) (e) (f) Figure 12: WRF wind speed and direction. For 850 hPa, 700 hPa, and 200 hPa at 12 UTC ((a), (b), and (c)) and 18 UTC ((d), (e), and (f)) for case A. 20°N 20°N 20°N 90 90 15°N 15°N 15°N 80 80 70 70 10°N 10°N 10°N 60 60 5°N 5°N 5°N 50 50 40 40 0° 0° 0° 30 30 30 20 20 20 5°S 5°S 5°S 10 10 10°S 10°S 10°S 15°W 10°W 5°W 0° 5°E 10°E 15°W 10°W 5°W 0° 5°E 10°E 15°W 10°W 5°W 0° 5°E 10°E (a) (b) (c) 20°N 20°N 20°N 90 90 90 15°N 15°N 15°N 80 80 80 70 70 70 10°N 10°N 10°N 60 60 60 5°N 5°N 5°N 50 50 50 40 40 40 0° 0° 0° 30 30 30 20 20 20 5°S 5°S 5°S 10 10 10 10°S 10°S 10°S 15°W 10°W 5°W 0° 5°E 10°E 15°W 10°W 5°W 0° 5°E 10°E 15°W 10°W 5°W 0° 5°E 10°E (d) (e) (f) Figure 13: WRF wind speed and direction for 850 hPa, 700 hPa, and 200 hPa at 12 UTC ((a) (b), and (c)) and 18 UTC ((d), (e), and (f)) for case B. Relative humidity (%) Relative humidity (%) Relative humidity (%) Relative humidity (%) Relative humidity (%) Relative humidity (%) Relative humidity (%) Relative humidity (%) Relative humidity (%) Relative humidity (%) Relative humidity (%) Relative humidity (%) Advances in Meteorology 11 Accra (KIA) at 2018-06-18_12:00:00 Accra (KIA) at 2018-06-18_18:00:00 Picl = 911 Ticl[C] = 19 Shox = 0 Pwat[cm] = 6 Cape[j] = 2282 Picl = 029 Ticl[C] = 20 Shox = 0 Pwat[cm] = 5 Cape[j] = 1030 100 100 16 16 15 15 14 14 700 3 850 1 –30 –20 –10 0 10 20 30 40 –30 –20 –10 0 10 20 30 40 Temperature (°C) Temperature (°C) (a) (b) Figure 14: WRF skew-T diagram showing atmospheric dynamics for case A (a) at 12 UTC and (b) at 18 UTC. of local meteorological features where orographic forcing water-saturated cloud layer from which deep moist con- and vegetation cover as in Kumasi (above 286.3 m) have vection subsequently evolves [42]. been established for many years by both research and local ,e dew point (blue lines) and the temperature (red forecasters experience [33] to have signiﬁcant inﬂuence on lines) proﬁles showed high levels of humidity (>70%) and convective initiations. ,e main cause of this triggering is temperature proﬁles of the atmosphere for more cloud the diurnal heating over higher elevations coupled with the formations especially between 925 hPa and 850 hPa where moisture within the atmosphere. ,ese characteristics the convective condensation level (CCL) can be estimated. contributed to the development and spread of the con- ,is is in agreement with the relative humidity estimates seen on the WRF output in Figures 12 and 13. ,e con- vective cells, resulting in the heavy rainfall experienced in Kumasi. vective available potential energy (CAPE) at 12 UTC for cases A and B was 2282 J/kg and 2439 J/kg, respectively, Meanwhile, the African Easterly Wave (AEW) troughed along Togo-Benin coast and then extended to the coast of while at 18 UTC it dropped to 1039 J/kg and 1997 J/kg, Ghana and deepened by 18 UTC (Figures 12(b) and 12(e)). respectively. Tajbakhsh et al. [43] used the Miller checklist to From Figures 12(c) and 12(f), an anticyclonic vortex ob- categorize the severity of thunderstorms. From this served at 200 hPa level, located close to the western border of checklist, it is observed that most of the parameters available Mali, ridged over the subregion with weak Tropical Easterlies to determine the strength in both cases implied that the Jets (TEJ). ,ese conditions ahead of the system supported storms were weak (Table 2). the westward propagation of the storm, thereby playing Comparing the daily rainfall amounts recorded for cases signiﬁcant role in its kinetics. It was observed that an ap- A and B, they were not exceptional and not even close to the 3rd highest of the ADMR amounts which caused the ﬂoods preciable amount of moisture (RH) above 80% at 850 hPa and the 700 hPa was within the atmosphere indicat- and resulted in casualties and damage of properties. As ing saturation for storm formation at the 850 hPa levels climate varies geographically, the deﬁnition of extreme (Figure 12). Moreover, tidal waves on that day were high, weather thresholds will also vary. ,is was observed for the which were aided by low surface pressure over the coastal 95th percentile threshold values over Accra and Kumasi. For area for the entire period of the event. ,e convective best practices globally, [44] established a tasked team on the available potential energy (CAPE) in the WRF skew-T deﬁnition of extreme weather and climate events which (Figures 14 and 15) showed enhanced instability in the classiﬁed heavy rainfall threshold greater than 50 mm atmosphere for convective processes. It has been established (>50 mm/24 hr.) per day. It was observed that most rainfalls that low level convergence (i.e., at 925 hPa pressure level) of for the ﬂood events fell below the threshold over Accra and moisture and upper level divergence are good indicators for Kumasi (Figure 16). Possible reasons for these ﬂooding cases can be related to large scale precipitation [40, 41]. However, localized mois- ture convergence and mesoscale lifting could also produce a nonmeteorological factors which included poor drainage P (hPa) Height (km) P (hPa) Height (km) 12 Advances in Meteorology Kumasi (KSI) at 2018-06-18_12:00:00 Kumasi (KSI) at 2018-06-18_18:00:00 Picl = 892 Ticl[C] = 19 Shox = 0 Pwat[cm] = 5 Cape[j] = 2439 Picl = 945 Ticl[C] = 22 Shox = 0 Pwat[cm] = 5 Cape[j] = 1997 100 100 16 16 15 15 14 14 150 150 13 13 12 12 200 200 11 11 250 250 10 10 300 300 9 9 8 8 400 400 7 7 500 500 5 5 4 4 700 3 700 3 2 2 850 850 1 1 1000 1000 0 0 –30 –20 –10 0 10 20 30 40 –30 –20 –10 0 10 20 30 40 Temperature (°C) Temperature (°C) (a) (b) Figure 15: WRF skew-T diagram showing atmospheric dynamics for case B (a) at 12 UTC and (b) at 18 UTC. Table 2: Miller checklist of some selected parameters for severe weather adopted from Tajbakhsh et al. [43]. Case A Case B Miller checklist Parameter 12 UTC 18 UTC 12 UTC 18 UTC Weak Moderate Strong Surface pressure 1015 hPa 1014 hPa 1014 hPa 1012 hPa >1010 hPa 1010–1005 hPa <1005 hPa ° ° ° ° ° ° ° Dew point 22 C 23 C 21 C 22 C <2.8 C 12.8–17.8 C 18.3 C CAPE 2282 J/kg 1039 J/kg 2439 J/kg 1997J/kg 800–1500 J/kg 1500–2500 J/kg >2500 J/kg Mean RH 90% 70% 90% 80% 70–80% or 40–50% 50–70% 50–70% 850–500 hPa wind shear 5–35 kts 5–35 kts 5–25 kts 5–20 kts 15–25 kts 26–35 kts >35 kts ultimately end up in the sea. City dwellers occupy riparian systems, building on water ways, improper disposal of re- fuse, limited roof-top rain harvesting in urban area, land zones and wet-lands leading to ﬂood initiation when cover change, less lawn, and limited tree planting. In their moderate rains occur. It is a great challenge for city work also conﬁrmed that intensive and unplanned human planners to control and regulate infrastructural activities in settlements in ﬂood-prone areas played a major role in the area. ,ese play a greater role in causing ﬂood events increasing ﬂood risk in Africa as observed in most parts of with moderate rainfall. Accra (e.g., Avenor and Kaneshie) and Kumasi (e.g., Buokrom and Anloga junction) [6]. 4. Conclusion Generally, the month of June is the peak of the major rainy season over southern Ghana. Consecutive days of ,is study investigated the meteorological dynamics for the rainfall over the high grounds and the inland areas were heavy rainfall that resulted in ﬂood over Kumasi and Accra. continually drained oﬀ to the sea. Prior to these ﬂood events, Qualitative analysis was done using graphical representation signiﬁcant amount of rainfall was experienced. For case B, of the thunderstorm propagation from GSMaP and global the city being a coastal town and at comparatively low al- and regional model outputs results. In order to establish the titude receives run-oﬀs from the high grounds and dis- uniqueness of the event as a result of its damage, a com- charges oﬀ to the sea. parative analysis was done with historical ﬂood occurrence. Accra is one of the most densely populated cities in the ,e results from meteorological analysis indicate that MSLP country, with high rural-urban migration resulting in was low with signiﬁcant moisture inﬂux for thunderstorm unplanned settlements. ,e waste generated mostly ends system growth and development. It was observed that the up in the drains and is washed into water bodies which thunderstorm thrived under weak synoptic features, even P (hPa) Height (km) P (hPa) Height (km) Advances in Meteorology 13 Accra Data Availability ,e in situ rainfall and WRF data used to support the ﬁndings of this study are available from the corresponding author upon request. Conflicts of Interest ,e authors declare that there are no conﬂicts of interest Flood date regarding the publication of this paper. 95th % of 45.1 mm Rainfall (mm) References Kumasi [1] R. Few, “Flooding, vulnerability and coping strategies: local responses to a global threat,” Progress in Development Studies, vol. 3, no. 1, pp. 43–58, 2003. [2] P. Tschakert, R. Sagoe, G. Ofori-Darko, and S. N. Codjoe, “Floods in the sahel: an analysis of anomalies, memory, and anticipatory learning,” Climatic Change, vol. 103, no. 3-4, pp. 471–502, 2010. [3] D. J. Parker, Floods, Routledge, Abingdon, UK, 2000. Flood date [4] I. Douglas, K. Alam, M. Maghenda, Y. Mcdonnell, L. McLean, 95th % of 42.16 mm and J. Campbell, “Unjust waters: climate change, ﬂooding and Rainfall (mm) the urban poor in Africa,” Environment and Urbanization, vol. 20, no. 1, pp. 187–205, 2008. Figure 16: Accra and Kumasi 95th percentile threshold. ,is shows [5] BBC, 2007, http://news.bbc.co.uk/1/hi/world/africa/6998651.stm. most ﬂood events below the threshold. [6] G. D. Baldassarre, A. Montanari, H. Lins, D. Koutsoyiannis, and L. Brandimarte, “Flood fatalities in Africa: from diagnosis though the moisture was enough for its survival. ,is led to to mitigation,” Journal of Geophysical Research Letters, vol. 37, no. 22, 2010. its slower movement and subsequent stagnation. Moreover, [7] T. G. Amangabara and M. Obenade, “Flood vulnerability it was also observed that the weak AEJ (at 12 UTC and 18 assessment of Niger delta states relative to 2012 ﬂood disaster UTC) contributed to its weak propagation. To quantify the in Nigeria,” American Journal of Environmental Protection, frequency of anomalous rainfall events and to determine the vol. 3, no. 3, pp. 76–83, 2015. strength of the thunderstorm, we conducted trend analysis [8] N. K. Karley, “Flooding and physical planning in urban areas that showed an increasing trend for Accra with slope of 0.045 in west Africa: situational analysis of Accra, Ghana,” 2eo- and a decreasing trend for Kumasi with slope of –0.07. ,is retical and Empirical Researches in Urban Management, vol. 4, also coincides with the less days with heavy rainfall for Accra no. 13, pp. 25–41, 2009. using the 95th percentile threshold of 45.10 mm and more [9] Y. A. Twumasi and R. Asomani-Boateng, “Mapping days with less heavy rainfall for Kumasi using the 95th seasonal hazards for ﬂood management in Accra, Ghana using GIS,” in Proceedings of the Geoscience and Remote percentile threshold of 42.16 mm. Sensing Symposium: IGARSS Apos; 02 IEEE Interna- Miller checklist for severe weather categorization was tional, vol. 5, pp. 2874–2876, Toronto, Canada, February used to assess the strength of the thunderstorms for some selected meteorological parameters. From the results as [10] D. Rain, R. Engstrom, C. Ludlow, and S. Antos, “Accra Ghana: summarized in Table 2, the thunderstorms that led to the a city vulnerable to ﬂooding and drought-induced migration: heavy rainfalls were weak. ,is study concludes that the case study prepared for cities and climate change,” Global meteorological conditions that aided the formation and Report on Human Settlements 2011, http://www.unhabitat. propagation of the weather systems that produced the heavy org/grhs/2011, 2011. rainfall events resulting in the ﬂoods that occurred over the [11] C. Amoako and E. Frimpong Boamah, “,e three-dimen- study areas were nonmeteorologically induced. Anthropo- sional causes of ﬂooding in Accra, Ghana,” International genic activities such as buildings on water ways and choked Journal of Urban Sustainable Development, vol. 7, no. 1, pp. 109–129, 2015. drainage systems were responsible for the ﬂoods. Accurate [12] K. Appeaning Addo and M. Adeyemi, “Assessing the impact and timely weather forecast is needed in planning the day- of sea-level rise on a vulnerable coastal community in Accra, to-day activities and to serve as early warning system. ,is Ghana,” Jamb ` a: ´ Journal of Disaster Risk Studies, vol. 5, no. 1, study has shown some of the main synoptic features that p. 8, 2013. produced the heavy rainfall events in Ghana and could serve [13] O. Cred, 2e Ofda/cred International Disaster Database, Em- as a reference for forecasters in Ghana and the West African dat, Brussels, Belgium, 2009. subregion as far as forecasting heavy rainfall event is con- [14] S. Asumadu-Sarkodie, P. A. Owusu, and M. P. C. Jayaweera, cerned. Further studies must be carried out over other areas “Flood risk management in Ghana: a case study in Accra,” within the country to determine the 95th threshold for Advances in Applied Science Research, vol. 6, no. 4, pp. 196– extreme rainfall, especially the ﬂood-prone zones. 201, 2015. Rainfall (mm) Rainfall (mm) 4-Feb 27-Feb 3-Mar 24-Mar 12-Mar 14-Mar 16-Mar 20-Apr 17-Mar 20-Mar 24-Mar 2-May 26-Mar 4-Apr 15-Apr 8-May 18-Apr 22-Apr 25-Apr 17-May 27-Apr 29-Apr 3-May 5-May 25-May 10-May 11-May 12-May 6-Jun 13-May 17-May 21-May 14-Jun 25-May 26-May 28-May 18-Jun 29-May 3-Jun 28-Jun 20-Jun 29-Jun 3-Jul 4-Jul 14 Advances in Meteorology [15] S. Janicot, G. Caniaux, F. Chauvin et al., “Intraseasonal [32] H. Paeth, K. Born, R. Podzun, and D. Jacob, “Regional dy- variability of the west african monsoon,” Atmospheric Science namical downscaling over west Africa: model evaluation and Letters, vol. 12, no. 1, pp. 58–66, 2011. comparison of wet and dry years,” Meteorologische Zeitschrift, vol. 14, no. 3, pp. 349–367, 2005. [16] L. Amekudzi, E. Yamba, K. Preko et al., “Variabilities in rainfall onset, cessation and length of rainy season for the [33] D. J. Parker, Meteorology of Tropical West Africa: 2e Fore- various agro-ecological zones of Ghana,” Climate, vol. 3, no. 2, casters’ Handbook, p. 33, John Wiley & Sons, Hoboken, New pp. 416–434, 2015. Jersey, USA, 2017. [17] J. B. Omotosho, “Long-range prediction of the onset and end [34] M. I. Lele and P. J. Lamb, “Variability of the intertropical front of the rainy season in the west African Sahel,” International (ITF) and rainfall over the west African Sudan-sahel zone,” Journal of Climatology, vol. 12, no. 4, pp. 369–382, 1992. Journal of Climate, vol. 23, no. 14, pp. 3984–4004, 2010. [18] J. B. Omotosho, A. A. Balogun, and K. Ogunjobi, “Predicting [35] C. Flamant, P. Knippertz, D. J. Parker et al., “,e impact of a monthly and seasonal rainfall, onset and cessation of the rainy mesoscale convective system cold pool on the northward season in west Africa using only surface data,” International propagation of the intertropical discontinuity over west Journal of Climatology, vol. 20, no. 8, pp. 865–880, 2000. Africa,” Quarterly Journal of the Royal Meteorological Society, [19] B. J. Abiodun, Z. D. Adeyewa, P. G. Oguntunde, A. T. Salami, vol. 135, no. 638, pp. 139–159, 2009. and V. O. Ajayi, “Modeling the impacts of reforestation on [36] T. O. Odekunle, “An assessment of the inﬂuence of the inter- tropical discontinuity on inter-annual rainfall characteristics future climate in west Africa,” 2eoretical and Applied Cli- matology, vol. 110, no. 1-2, pp. 77–96, 2012. in Nigeria,” Geographical Research, vol. 48, no. 3, pp. 314–326, [20] I. Diallo, M. B. Sylla, F. Giorgi, A. T. Gaye, and M. Camara, 2009. “Multimodel GCM-RCM ensemble-based projections of [37] C. Mensah, L. K. Amekudzi, N. A. B. Klutse, J. N. A. Aryee, temperature and precipitation over west Africa for the early and K. Asare, “Comparison of rainy season onset, cessation 21st century,” International Journal of Geophysics, vol. 2012, and duration for Ghana from RegCM4 and GMet datasets,” Article ID 972896, 19 pages, 2012. Atmospheric and Climate Sciences, vol. 6, no. 2, pp. 300–309, [21] M. B. Sylla, F. Giorgi, J. S. Pal, P. Gibba, I. Kebe, and 2016. M. Nikiema, “Projected changes in the annual cycle of high- [38] S. S. Vaidya and S. S. Singh, “Applying the betts-miller-janjic intensity precipitation events over west Africa for the late scheme of convection in prediction of the Indian monsoon,” twenty-ﬁrst century,” Journal of Climate, vol. 28, no. 16, Weather and Forecasting, vol. 15, no. 3, pp. 349–356, 2000. pp. 6475–6488, 2015. [39] F. Mounier, S. Janicot, and G. N. Kiladis, “,e west African [22] A. Dosio and H. J. Panitz, “Climate change projections for monsoon dynamics. Part III: the quasi-biweekly zonal dipole,” Journal of Climate, vol. 21, no. 9, pp. 1911–1928, 2008. CORDEX-Africa with COSMO-CLM regional climate mode land diﬀerences with the driving global climate models,” [40] W. Ma, W. Huang, Z. Yang, B. Wang, D. Lin, and X. He, Climate Dynamics, vol. 46, no. 5-6, pp. 1599–1625, 2016. “Dynamic and thermodynamic factors associated with dif- [23] N. Kumi and B. J. Abiodun, “Potential impacts of 1.5 C and ferent precipitation regimes over south China during pre- 2 C global warming on rainfall onset, cessation and length of monsoon season,” Atmosphere, vol. 9, no. 6, 2018. rainy season in west Africa,” Environmental Research Letters, [41] S. Minobe, A. Kuwano-Yoshida, N. Komori, S.-P. Xie, and vol. 13, no. 5, Article ID 055009, 2018. R. J. Small, “Inﬂuence of the Gulf stream on the troposphere,” [24] A. Sossa, B. Liebmann, I. Blade´ et al., “Statistical connection Nature, vol. 452, no. 7184, pp. 206–209, 2008. between the madden-julian oscillation and large daily pre- [42] C. L. Ziegler, T. J. Lee, and R. A. Pielke, “Convective initiation cipitation events in west Africa,” Journal of Climate, vol. 30, at the dryline: a modeling study,” Monthly Weather Review, no. 6, pp. 1999–2010, 2017. vol. 125, no. 6, pp. 1001–1026, 1997. [25] E. D. Maloney and J. Shaman, “Intraseasonal variability of the [43] S. Tajbakhsh, P. Ghafarian, and F. Sahraian, “Instability in- west african monsoon and atlantic ITCZ,” Journal of Climate, dices and forecasting thunderstorms: the case of 30 April vol. 21, no. 12, pp. 2898–2918, 2008. 2009,” Natural Hazards and Earth System Sciences, vol. 12, [26] S. E. Nicholson, “A revised picture of the structure of the no. 2, pp. 403–413, 2012. “monsoon” and land ITCZ over west Africa,” Climate Dy- [44] TT-DEWCE WMO, Guidelines on the Monitoring of Extreme namics, vol. 32, no. 7-8, pp. 1155–1171, 2009. Weather and Climate Events, Draft Version-First Review by [27] M. Diop and D. I. F. Grimes, “Satellite-based rainfall esti- TT-DEWCE, TT-DEWCE WMO, Geneva, Switzerland, 2015. mation for river ﬂow forecasting in Africa. II: African easterly waves, convection and rainfall,” Hydrological Sciences Journal, vol. 48, no. 4, pp. 585–599, 2003. [28] J. Cretat, ´ E. K. Vizy, and K. H. Cook, “,e relationship be- tween African easterly waves and daily rainfall over west Africa: observations and regional climate simulations,” Cli- mate Dynamics, vol. 44, no. 1-2, pp. 385–404, 2015. [29] S. Janicot, V. Moron, and B. Fontaine, “Sahel droughts and ENSO dynamics,” Geophysical Research Letters, vol. 23, no. 5, pp. 515–518, 1996. [30] S. Janicot, “Impact of warm ENSO events on atmospheric circulation and convection over the tropical Atlantic and west Africa,” Annales Geophysicae, vol. 15, no. 4, pp. 471–475, 1997. [31] G. Panthou, T. Vischel, and T. Lebel, “Recent trends in the regime of extreme rainfall in the central Sahel,” International Journal of Climatology, vol. 34, no. 15, pp. 3998–4006, 2014. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Advances in Meteorology Hindawi Publishing Corporation http://www.deepdyve.com/lp/hindawi-publishing-corporation/meteorological-analysis-of-floods-in-ghana-2HABaYI7dJ

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Publisher: Hindawi Publishing Corporation
Copyright: Copyright © 2020 S. O. Ansah et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
ISSN: 1687-9309
eISSN: 1687-9317
DOI: 10.1155/2020/4230627
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Abstract

Hindawi Advances in Meteorology Volume 2020, Article ID 4230627, 14 pages https://doi.org/10.1155/2020/4230627 Research Article 1 1 1 1 1 S. O. Ansah , M. A. Ahiataku, C. K. Yorke, F. Otu-Larbi, Bashiru Yahaya , 2 1 P. N. L. Lamptey, and M. Tanu Ghana Meteorological Agency, Accra, Ghana Ghana Space Science and Technology Institute, Ghana Atomic Energy Commission, Kwabenya, Ghana Correspondence should be addressed to S. O. Ansah; ansahsamuelowusu2014@gmail.com Received 12 September 2019; Revised 5 January 2020; Accepted 30 January 2020; Published 24 March 2020 Academic Editor: Eduardo Garc´ıa-Ortega Copyright © 2020 S. O. Ansah et al. ,is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. ° ° ,e 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). ,e 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. ,e 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. migrate to other communities after a 2007 ﬂood event that 1. Introduction aﬀected Uganda, Ethiopia, Sudan, Togo, Mali, Niger, and Floodis a major hazard and source of human vulnerability Burkina Faso, which displaced millions of people [5]. which can lead to high mortality rate. Other impacts include Floods in West Africa in 2009 after torrential rains af- outbreak of diseases, disruption of energy supply, com- fected 600,000 people in sixteen West African countries [6]. munication, and transport infrastructure, and interference ,e most aﬀected countries were Burkina Faso, Senegal, in public service delivery. ,e African continent is the Niger, and Ghana. In 2012, Nigeria experienced one of the second hardest hit by ﬂoods in terms of number of events, most devastating ﬂooding events ever recorded [7]. ,is ﬂooding incident led to the deaths and displacement of 363 area aﬀected, and number of people killed after Asia [1, 2]. Flash ﬂoods are frequent across the continent resulting from and 2.3 million people, respectively, and the destruction of intense localized thunderstorm activity, slow-moving 59,000 houses and also aﬀected large tracts of farmland and thunderstorms, or squall lines mostly accompanied by livestock. lightning [3]. Various countries in Africa have experienced Urban ﬂooding has been a frequent occurrence in ﬂood-related disasters in the recent past. In February and Ghana since 1930 [8]. At least 18 out of the last 50 years March 2000, Mozambique experienced heavy rains and have recorded signiﬁcant ﬂooding incidences in which cyclones that caused the worst ﬂooding in 50 years, which lives and properties have been lost [8–11]. Since 1995, the led to widespread devastation in some cities [4]. Over 500 frequency of ﬂooding has increased in the coastal areas of lives were also lost and several thousands were forced to Ghana [4]. ,e same study found that the ability of people 2 Advances in Meteorology Ghana, resulting in the deaths of 14 people, displacement of to prepare for possible ﬂoods had become more diﬃcult due to increasing variability in rainfall patterns. Flooding 34,076 others, and damaged properties estimated at $168,289 according to the National Disaster Management in Ghana often occurs in the aftermath of intense and/or continuous rainfall, which results in high run-oﬀs. Possible Organization (NADMO). ,e heavy rainfall events aﬀord us reasons given for occasional ﬂooding in Ghana include the opportunity to conduct a unique case study focusing on climate variability and change [10, 12] and poor physical the meteorological explanation of the causes of the event. planning and ﬂaws in the drainage network [8]. Flooding is ,e aim of this paper is to document the meteorological ranked the second highest natural disaster after epidemics conditions that led to the initiation and propagation of the in Ghana according to [13]. Between the years 1900 and weather systems that caused the heavy precipitation events in Accra and Kumasi by analyzing the synoptic and me- 2014, the economic loss as a result of ﬂooding was ap- proximately US$ 780,500,000 [14]. soscale weather charts. ,e study area and data are described in Section 2. Section 3 provides meteorological analysis and ,e most devastating ﬂooding event in Ghanaian history occurred on the 3rd of June 2015, when most parts discussion. ,e key conclusions from the results and analysis are presented in Section 4. of southern Ghana experienced heavy thunderstorms and rain. ,e active spot of the storm was centered over Accra, where 212.8 mm of rainfall was recorded leading to 2. Materials and Methods ﬂooding over many areas in the city. ,is ﬂooding incident and an explosion of a fuel ﬁlling station at Kwame 2.1. Study Area. Ghana is a tropical West African country ° ° ° Nkrumah Circle, a suburb of Accra, claimed over 150 lives located between latitudes 4 N to 12 N and longitudes 1.5 E to and destroyed lots of properties while displacing hundreds ° 3.5 W. Its climate is dominated by the wet and the dry of people. seasons. ,ese seasons are modulated by the intertropical Various studies have looked at diﬀerent aspects of boundary (ITB) and the two main high-pressure systems, rainfall over the West African region in general and Ghana namely, St. Helena high-pressure system located in the in particular. ,ese include studies focusing on dynamics of Southern hemisphere and the Azores high-pressure system the West African Monsoon [15] and onset, cessation, and located over the Atlantic Ocean. Mechanisms of how the ITB length of rainfall season [16–18]. ,e potential impact of and high pressure systems aﬀect rainfall patterns over West climate change on precipitation and weather patterns over Africa are explained in various literatures [16, 33–36]. ,e West Africa has also been extensively studied (e.g., see wet season is characterized by the intensiﬁcation of the St. [19–23]). ,ese studies have identiﬁed and explained the Helena high-pressure system, which induces moisture inﬂux roles of key drivers of variability in West African rainfall into the country through the south westerly (SW) winds such as the Madden Julian Oscillation [24], the movement of (maritime air mass) which is the major wind that prevails the intertropical convergence zone [25, 26], the African during this season. ,e intensiﬁcation of the Azores high- Easterly Jet and African Easterly Wave [27, 28], and El Niño- pressure system with the north easterly (NE) winds (con- Southern Oscillation [29, 30]. tinental air mass) dominating induces dryness and dust On the contrary, there are very few case studies focusing particles over the country during the dry season. ,e on the meteorological dynamics of heavy precipitation northward and the southward oscillation of the ITB control events within the rainfall season, although there are studies the rainfall pattern in Ghana where the southern half (below on the impacts such weather events have on livelihoods and ° 9 N) of the country experiences bimodal rainfall pattern. society (e.g., see [8, 14]). One of the reasons for the limited April to July is classiﬁed as the major rainy season and research on extreme rainfall in West Africa is the diﬃculty in September to November is the minor rainy season. In this accessing data [31]. However, a case study by [32] identiﬁed study, two ﬂood cases that occurred on the 18th of June 2018, La Nina event in the tropical Paciﬁc, anomalous heating in ° ° identiﬁed as case A in Accra (5.6 N and 0.17 W), and the the tropical Atlantic, and enhanced activity of African 28th of June 2018, identiﬁed as case B in Kumasi (6.72 N and easterly waves as possible causes of anomalous heavy pre- ° 1.6 W), as shown in Figure 1, during the peak rainy season cipitation and ﬂooding over sub-Saharan Africa in 2007. (June) of southern sector of Ghana have been investigated In addition to the factors identiﬁed by Paeth et al. [32], focusing on the meteorological conditions that led to the the occurrence of heavy rainfall also depends on mesoscale heavy rainfall. and localized convective processes. ,ese convective pro- cesses are inﬂuenced by the topography, vegetation cover, and water bodies as well as land surface energy ﬂuxes. 2.2. Data and Methodology. Datasets used in this study for Understanding the dynamics of meteorological conditions the evaluation were in situ rainfall data (45 stations), and features that inﬂuence heavy rainfall will greatly im- Global Satellite Mapping of Precipitation (GSMaP), prove extreme weather forecasts and help to mitigate their Ghana Meteorological Agency (GMet), GMet Weather eﬀect. Early identiﬁcation of meteorological features that Research and Forecasting (WRF) model output, and could lead to extreme weather event such as torrential rain Meteo France Arpege 0.5 mean sea level pressure (MSLP), can help inform early warning systems, which will reduce a product from the Meteosat Second Generation (MSG) fatalities and the economic losses. Preparation for Use of MSG in Africa (PUMA) project. On the 18th and 28th of June 2018, heavy rains and ,e rainfall datasets are daily cumulative rainfall amounts thunderstorms caused ﬂooding in the two biggest cities in recorded at the various stations measured with rain Advances in Meteorology 3 Southern ghana showing ashanti and greater accra regions Northern N Ghana Brong Ahafo Volta Southern Ghana Ashanti Eastern 024 81216 kilometers Greater Accra Western African continent Central 03 6 1218 24 kilometers 0 1530 60 90 120 kilometers Ashanti region Greater Accra Region Kumasi Accra 03 6 1218 24 kilometers 03 6 1218 24 kilometers Kumasi 28th June 2018 ﬂooding Accra, 18th June 2018 ﬂooding Figure 1: Map of study area. Location of cases A and B ﬂood events and the distribution of the Ghana Meteorological Agency’s weather observation stations (red dots). gauges. Sources of error that may aﬀect rainfall mea- track the genesis, development, dissipation, and the active surement or data according to Mensah et al. [37] include spots of the thunderstorms as they propagated zonally for evaporation, wetting, wind induced errors, and instru- cases A and B, respectively. For case A, the thunderstorm ment reading errors on the part of observers. In order to aﬀected the coast of Ghana and lasted for about 17 hours avoid some of these errors, wind shielded rain gauges are before dissipating. At 18 UTC, the slow-moving thunderstorm hit the used by GMet. To track the evolution of the rainfall events for this study, eastern coast of Ghana (Figure 2(d)) and after three hours it the hourly GSMaP images (Figures 2 and 3) were used to had reached Accra as it intensiﬁed (Figure 2(e)). ,e storm 4 Advances in Meteorology GSMAP_NRT 09Z18JUN2018 GSMAP_NRT 12Z18JUN2018 GSMAP_NRT 15Z18JUN2018 GSMAP_NRT 18Z18JUN2018 12N 12N 12N 12N 11N 11N 11N 11N 10N 10N 10N 10N 9N 9N 9N 9N 8N 8N 8N 8N 7N 7N 7N 7N 6N 6N 6N 6N 5N 5N 5N 5N 4N 4N 4N 4N 3N 3N 3N 3N (a) (b) (c) (d) GSMAP_NRT 21Z18JUN2018 GSMAP_NRT 00Z19JUN2018 GSMAP_NRT 03Z19UN2018 GSMAP_NRT 06Z19JUN2018 12N 12N 12N 12N 11N 11N 11N 11N 10N 10N 10N 10N 9N 9N 9N 9N 8N 8N 8N 8N 7N 7N 7N 7N 6N 6N 6N 6N 5N 5N 5N 5N 4N 4N 4N 4N 3N 3N 3N 3N (e) (f) (g) (h) th Figure 2: GSMaP satellite products. ,ree hourly precipitation intervals from 09 UTC of the 18th of June to 06 UTC of the 19 of June 2018 for case A. GSMAP_NRT 12Z28JUN2018 GSMAP_NRT 14Z28JUN2018 GSMAP_NRT 16Z28JUN2018 GSMAP_NRT 18Z28JUN2018 12N 12N 12N 12N 11N 11N 11N 11N 10N 10N 10N 10N 9N 9N 9N 9N 8N 8N 8N 8N 7N 7N 7N 7N 6N 6N 6N 6N 5N 5N 5N 5N 4N 4N 4N 4N 3N 3N 3N 3N (a) (b) (c) (d) GSMAP_NRT 20Z28JUN2018 GSMAP_NRT 22Z28JUN2018 GSMAP_NRT 00Z29JUN2018 GSMAP_NRT 02Z29JUN2018 12N 12N 12N 12N 11N 11N 11N 11N 10N 10N 10N 10N 9N 9N 9N 9N 8N 8N 8N 8N 7N 7N 7N 7N 6N 6N 6N 6N 5N 5N 5N 5N 4N 4N 4N 4N 3N 3N 3N 3N (e) (f) (g) (h) Figure 3: GSMaP satellite products. Two hourly precipitation intervals from 12 UTC of the 28th of June 2018 to 02 UTC of the 29th of June 2018 for case B. ﬁnally moved westwards from Accra after 06 UTC on the about –90 C (estimated using the Meteorological Product 19th of June, lasting for about 9 hours. As the storm moved Extraction Facility (MPEF) from the Synergie), an indication slowly westwards across the eastern coast, the cloud top of its intensiﬁcation which lasted for about 8 hours temperature was observed to change from about –75 C to (Figure 2(e)). Rainfall amounts recorded over some stations 5W 5W 5W 5W 4W 4W 4W 4W 3W 3W 3W 3W 2W 2W 2W 2W 1W 1W 1W 1W 0 0 0 0 1E 1E 1E 1E 2E 2E 2E 2E 3E 3E 3E 3E 4E 4E 4E 4E 5E 5E 5E 5E 5W 5W 5W 5W 4W 4W 4W 4W 3W 3W 3W 3W 2W 2W 2W 2W 1W 1W 1W 1W 0 0 0 0 1E 1E 1E 1E 2E 2E 2E 2E 3E 3E 3E 3E 4E 4E 4E 4E 5E 5E 5E 5E 5W 5W 5W 5W 4W 4W 4W 4W 3W 3W 3W 3W 2W 2W 2W 2W 1W 1W 1W 1W 0 0 0 0 1E 1E 1E 1E 2E 2E 2E 2E 3E 3E 3E 3E 4E 4E 4E 4E 5E 5E 5E 5E 5W 5W 5W 5W 4W 4W 4W 4W 3W 3W 3W 3W 2W 2W 2W 2W 1W 1W 1W 1W 1E 1E 1E 1E 2E 2E 2E 2E 3E 3E 3E 3E 4E 4E 4E 4E 5E 5E 5E 5E Advances in Meteorology 5 in the east coast were 124.8 mm, 90.1 mm, and 109.0 mm at Accra’s ﬁrst three highest ADMR values were higher than Saltpond, Accra, and Pokuase, respectively. those of Kumasi. For the ﬂooding event in Kumasi, a localized convective It is worth noting that these records occurred at the peak cell started developing within the country in the afternoon of the West African Monsoon season over the respective (Figure 3(c)). It intensiﬁed over the middle sector and stations. ,is can be attributed to its location along the coast produced heavy rains lasting for a period of about 8 hours where rich moisture from the Gulf of Guinea fuels thun- th before dissipating at approximately 02 UTC on the 29 of derstorms development as well as land and sea surface June (Figure 3(f)). Kumasi had the highest rainfall of temperature variations producing heavy rainfall [39]. ,e 114.6 mm and Kwadaso, a suburb of Kumasi, had 81.0 mm. frequency of these extreme rainfall events using the 95th ,e spatial distributions of rainfall for the two cases are percentile threshold with a value of 45.10 mm and 42.16 mm shown in Figure 4. over the two areas shows a positive slope (Figure 8) of 0.02 ,e WRF model product used in this study includes the (Kumasi) and a negative slope of –0.02 (Accra). ,is indi- wind, relative humidity (RH), and the skew-T for the second cates that the chance of extreme rainfall over Kumasi is domain. ,e atmospheric forcing data used for the initial increasing, while that of Accra is decreasing based on the and lateral boundary condition of the model was the global respective threshold values. forecasting system (GFS), which was dynamically down- Further analysis on the ADMR distribution and fre- scaled for two domains with resolutions of 27 km and 9 km, quency over Accra and Kumasi using the normal distri- respectively, as illustrated in Figure 5. ,e model’s conﬁg- bution shows that 68% of the area of the normal uration setup uses Betts–Miller–Janjic’s [38] convective distribution for Accra was between 53.03 mm and parameterization scheme for both domains with 49 vertical 136.81 mm of rainfall, which implies that most of the levels. ,e inner domain model products were used. rainfall events occurred within the 1st z-score distribution value, while 95% of the area of the normal distribution was found between 11.14 mm and 178.70 mm of the 2nd z-score 3. Results and Discussion distribution value. ,e mean ADMR over Accra is 3.1. Rainfall Time Series Analysis. ,e long-term (1981–2010) 94.92 mm with a standard deviation of 14.89, as shown in monthly mean rainfall amounts (MMR) for stations along Figure 9. ,e rainfall distribution for Kumasi shows that 68% of the area of normal distribution was between the coast and middle sectors were analyzed (Figure 6) and all showed bimodal rainfall pattern. However, the major dif- 64.37 mm and 108.87 mm, which implies that most rainfalls ferences between the two sectors are land cover and to- were within the 1st z-score distribution value. ,e 95% of pography of the area, which have an inﬂuence on the rainfall the area of normal distribution ranged from 42.12 mm to pattern [37]. Kumasi (286.3 m above MSL) in the middle 131.12 mm, which satisﬁed the 2nd z-score distribution sector where case B ﬂood occurred has more vegetation value. ,e mean ADMR is 86.62 mm with a standard de- cover than Accra (67.7 m above MSL) where case A ﬂood viation of 22.25, as shown in Figure 9(c), where N is 57, occurred. It was observed that all the stations along the coast representing the total number of years of data used. Table 1 and the middle sector recorded higher rainfall amount summarizes the 1st and 2nd z-score ADMR values over Accra and Kumasi. during the peak of the major season in June than that of the minor season in October except Akim Oda and Wenchi which recorded 200 mm and 170 mm, respectively, for the 3.2. Charts Analysis. ,is section reviews the atmospheric major season as compared to 220 mm and 200 mm for minor dynamics and associated physical mechanisms responsible peak, respectively. for the ﬂooding incidents that occurred on those fateful ,is anomaly could be attributed to seasonal to sub- days within southern Ghana. ,e 12 UTC and 18 UTC seasonal variations. Axim (37.8 m above MSL), the wettest prognostic charts used for the 18th and 28th of June 2018 rainfall station in Ghana along the western coast, recorded forecast preparation were retrieved and examined. Gen- the highest MMR of 500 mm and 230 mm for peak of the erally, the quasi-static high pressure systems and their major and minor seasons, respectively. ,is could be as- consequent eﬀect on the ITB determine the prevailing sociated with the orientation of its coastline and the vege- weather within the West Africa subregion. During the rainy tation cover. Accra and Kumasi which are the main focus of season, the country is inﬂuenced by the maritime air mass this study recorded MMR of 180 mm and 215 mm during the due to the intensiﬁcation of northward ridging of the St. peak of the major season, while the peak of the minor season Helena high-pressure system, which drives moisture into recorded 60 mm and 170 mm, respectively. the subregion, while the Azores high-pressure system ,e time series of the ADMR variability (1961–2017) weakens. over Accra has a positive slope of 0.45 and Kumasi has a negative slope of –0.07 as shown in Figure 7. ,e top three highest ADMR amounts ever recorded in Accra are 3.2.1. Mean Sea Level Pressure (MSLP). From the Meteosat 243.9 mm, 212.3 mm, and 175.3 mm which occurred on the Second Generation (MSG) Preparation for Use of MSG in 3rd of July 1995, 3rd of June 2015, and 22nd of June 1973, Africa (PUMA) station, MSLP charts at 12 UTC for case A respectively, while Kumasi recorded 167.9 mm, 145.8 mm, showed that St. Helena had a center value of 1027 hPa, while st Azores was 1030 hPa with the equatorial trough (ET) ex- and 125.2 mm on the 21 of July 1966, 13th of June 1986, and 5th of May 1997, respectively. It is interesting to note that tended zonally to the east of the continent as shown in 6 Advances in Meteorology 18 June 2018 (mm) 29 June 2018 (mm) 12°N 12°N 300 300 200 200 150 150 10°N 10°N 130 130 70 70 8°N 8°N 30 30 6°N 6°N 4°N 4°N 4°W 3°W 2°W 1°W 0° 1°E 2°E 4°W 3°W 2°W 1°W 0° 1°E 2°E (a) (b) Figure 4: Distribution of rainfall (mm). (a) Case A and (b) case B. 20°N 15°N d02 10°N 5°N 0° 5°S 10°S 15°W 10°W 5°W 0°W 5°E 10°E Figure 5: Two domain conﬁgurations for GMET’s WRF model. Resolutions of 27 km and 9 km (white cell), respectively. Figure 10(a). ,e country was between the pressures of ,e Azores and St. Helena high-pressure centers for case B were 1022 hPa and 1027 hPa, respectively, as at 12 UTC. ,e 1012 hPa to the north and 1016 hPa to the south. By 18 UTC, the St. Helena and Azores center pressures dropped to ET extended covering most parts of Niger (excluding the 1025 hPa and 1029 hPa, respectively, with zonal and me- southern fringes and whole of Burkina Faso), northern half ridional expansion of the ET where the country’s pressure of Mali, and eastern Mauritania through to southern half of was between 1015 hPa and 1010 hPa. Algeria. ,e isobar over Ghana ranged between 1012 hPa to Low pressure centers of 1005 hPa and 999 hPa were the north and 1014 hPa to the south (Figure 11(a)). At 18 UTC, observed, respectively, between the borders of Mauritania there was a 2 hPa drop in the core values of the Saint Helena and Mali as well as central Chad as shown from Figure 10(b). high-pressure system, while that of Azores was maintained. ,is Advances in Meteorology 7 550 300 Month Month Axim Accra Akim Oda Ho Takoradi Tema Koforidua Abetiﬁ Saltpond Ada Akuse Kumasi Akatsi Sunyani Sefwi Bekwai Wenchi (a) (b) Figure 6: MMR (1981–2010). Bimodal rainfall pattern within Southern Ghana: six and eleven stations along the coast (a) and over the middle sector (b), respectively. Accra Slope = 0.45 (1995, 243.9 mm) (2015, 212.8 mm) (1973, 175.3 mm) 1960 1970 1980 1990 2000 2010 2020 Years Kumasi Slope = –0.07 (1986, 145.8 mm) (1966, 167.9 mm) (1997, 125.4 mm) 1960 1970 1980 1990 2000 2010 2020 Years Figure 7: ADMR (1961–2017). Rainfall variability over Accra and Kumasi. condition triggered both zonal and meridional expansions of the sector. ,e Africa Easterly Jet (AEJ) at 700 hPa level ET with the lower portions stretching and covering north (steering level) which had southerly components was weak –1 (Nigeria, Benin, Togo, Ghana, Ivory Coast, Guinea, and Sene- (i.e., speed between 5knots (2.5 ms ) and 15 knots –1 gal). ,e isobar over Ghana ranged between 1010 hPa to the (7.5 ms )); hence, it inﬂuenced the slow propagation of north and 1012 hPa to the south (Figure 11(b)). the storm as marked by the broken red oval shape in Figures 12(b) and 12(e). In case B, signiﬁcant amount of moisture (RH> 80%) 3.2.2. Wind and Moisture. ,e GMet WRF model was able was also observed at the 850 hPa and the 700 hPa levels, to forecast the moisture laden winds at the 850 hPa level which reﬂects the extent of moisture depth within the at- (Figures 12(a) and 12(d)) with speed ranging between 10 –1 –1 mosphere. ,e south westerly winds ﬁlled a cyclonic vortex knots (5 ms ) and 20 knots (10 ms ) at 12 UTC and then –1 –1 with speed between 5 and 10 kts (3–5 ms ) located north of it increased to 25 knots (13 ms ) by 18 UTC for the coastal Rainfall (mm) Jan Feb Mar Apr May Jun Rainfall (mm) Rainfall (mm) Jul Aug Sep Oct Nov Dec Rainfall (mm) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 8 Advances in Meteorology Accra Slope = –0.02 1960 1970 1980 1990 2000 2010 2020 Years Kumasi Slope = 0.02 1960 1970 1980 1990 2000 2010 2020 Years Figure 8: Annual rainfall frequency (ARF). Accra and Kumasi ARF based on the 95th percentile thresholds of 45.10 and 42.16 mm, respectively. Probability distribution of rainfall in Accra 18 0.8 0.7 Mean = 94.92 mm 0.6 Std. Dev. = 41.89 N = 57 0.5 0.4 0.3 0.2 0.1 0 50 100 150 200 250 –3 –2 –1 012345 Rainfall (mm) Standard deviation Rainfall Standardized data Normal distribution Normal distribution GEV (a) (b) Probability distribution of rainfall in Kumasi 18 0.6 Mean = 86.92 mm 0.5 Std. Dev. = 22.25 N = 57 12 0.4 0.3 6 0.2 0.1 0 0 –3 –2 –1 01 2 3 4 5 0 20 40 60 80 100 120 140 160 180 Standard deviation Rainfall (mm) Rainfall Standardized data Normal distribution Normal distribution GEV (c) (d) Figure 9: Normal and standardized rainfall distribution. Plots (a) and (b) and (c) and (d) for cases A and B, respectively. Frequency Frequency Frequency Frequency Density Density Advances in Meteorology 9 MON 18/06/2018 MSILP 18UTC MON 18/06/2018 MSILP 12UTC 1010hPa isobar 1010hPa isobar (a) (b) Figure 10: Meteo France Arpege 0.5 MSLP charts. (a) 12 UTC and (b) 18 UTC for case A. Table 1: ,e 68% and 95% of the area of normal distribution for cases A and B from annual daily maximum rainfall (mm), respectively. 68% area of normal distribution 95% area of normal distribution Event μ – ε μ + σ μ – 2σ Μ + 2σ Case A 53.03 136.81 11.14 178.7 Case B 64.37 108.87 42.12 131.12 THUR 28/06/2018 MSILP 18UTC THUR 28/06/2018 MSILP 12UTC 1010hPa isobar 1010hPa isobar (a) (b) Figure 11: Meteo France Arpege 0.5 MSLP charts. (a) 12 UTC and (b) 18 UTC for case B. Benin at 12 UTC (Figure 13(a)). At 18 UTC (Figure 14(b)), 25 kts (Figures 12(b), 12(e), and 13(b)). ,e upper level jet –1 the speed increased to15 kts (8 ms ), especially for the coast. (200 hPa) was direct easterlies with speed around 35 kts. ,ere were direct AEJ at 700 hPa with speeds between 15 and ,e convective activities observed in Figure 3 were a result 10 Advances in Meteorology 20°N 20°N 20°N 90 90 15°N 15°N 15°N 80 80 80 70 70 10°N 10°N 10°N 60 60 5°N 5°N 5°N 50 50 40 40 40 0° 0° 0° 30 30 20 20 5°S 5°S 5°S 10 10 10°S 10°S 10°S 15°W 10°W 5°W 0° 5°E 10°E 15°W 10°W 5°W 0° 5°E 10°E 15°W 10°W 5°W 0° 5°E 10°E (a) (b) (c) 20°N 20°N 20°N 90 90 90 15°N 15°N 15°N 80 80 80 70 70 70 10°N 10°N 10°N 60 60 60 5°N 5°N 5°N 50 50 50 40 40 40 0° 0° 0° 30 30 30 20 20 20 5°S 5°S 5°S 10 10 10 10°S 10°S 10°S 15°W 10°W 5°W 0° 5°E 10°E 15°W 10°W 5°W 0° 5°E 10°E 15°W 10°W 5°W 0° 5°E 10°E (d) (e) (f) Figure 12: WRF wind speed and direction. For 850 hPa, 700 hPa, and 200 hPa at 12 UTC ((a), (b), and (c)) and 18 UTC ((d), (e), and (f)) for case A. 20°N 20°N 20°N 90 90 15°N 15°N 15°N 80 80 70 70 10°N 10°N 10°N 60 60 5°N 5°N 5°N 50 50 40 40 0° 0° 0° 30 30 30 20 20 20 5°S 5°S 5°S 10 10 10°S 10°S 10°S 15°W 10°W 5°W 0° 5°E 10°E 15°W 10°W 5°W 0° 5°E 10°E 15°W 10°W 5°W 0° 5°E 10°E (a) (b) (c) 20°N 20°N 20°N 90 90 90 15°N 15°N 15°N 80 80 80 70 70 70 10°N 10°N 10°N 60 60 60 5°N 5°N 5°N 50 50 50 40 40 40 0° 0° 0° 30 30 30 20 20 20 5°S 5°S 5°S 10 10 10 10°S 10°S 10°S 15°W 10°W 5°W 0° 5°E 10°E 15°W 10°W 5°W 0° 5°E 10°E 15°W 10°W 5°W 0° 5°E 10°E (d) (e) (f) Figure 13: WRF wind speed and direction for 850 hPa, 700 hPa, and 200 hPa at 12 UTC ((a) (b), and (c)) and 18 UTC ((d), (e), and (f)) for case B. Relative humidity (%) Relative humidity (%) Relative humidity (%) Relative humidity (%) Relative humidity (%) Relative humidity (%) Relative humidity (%) Relative humidity (%) Relative humidity (%) Relative humidity (%) Relative humidity (%) Relative humidity (%) Advances in Meteorology 11 Accra (KIA) at 2018-06-18_12:00:00 Accra (KIA) at 2018-06-18_18:00:00 Picl = 911 Ticl[C] = 19 Shox = 0 Pwat[cm] = 6 Cape[j] = 2282 Picl = 029 Ticl[C] = 20 Shox = 0 Pwat[cm] = 5 Cape[j] = 1030 100 100 16 16 15 15 14 14 700 3 850 1 –30 –20 –10 0 10 20 30 40 –30 –20 –10 0 10 20 30 40 Temperature (°C) Temperature (°C) (a) (b) Figure 14: WRF skew-T diagram showing atmospheric dynamics for case A (a) at 12 UTC and (b) at 18 UTC. of local meteorological features where orographic forcing water-saturated cloud layer from which deep moist con- and vegetation cover as in Kumasi (above 286.3 m) have vection subsequently evolves [42]. been established for many years by both research and local ,e dew point (blue lines) and the temperature (red forecasters experience [33] to have signiﬁcant inﬂuence on lines) proﬁles showed high levels of humidity (>70%) and convective initiations. ,e main cause of this triggering is temperature proﬁles of the atmosphere for more cloud the diurnal heating over higher elevations coupled with the formations especially between 925 hPa and 850 hPa where moisture within the atmosphere. ,ese characteristics the convective condensation level (CCL) can be estimated. contributed to the development and spread of the con- ,is is in agreement with the relative humidity estimates seen on the WRF output in Figures 12 and 13. ,e con- vective cells, resulting in the heavy rainfall experienced in Kumasi. vective available potential energy (CAPE) at 12 UTC for cases A and B was 2282 J/kg and 2439 J/kg, respectively, Meanwhile, the African Easterly Wave (AEW) troughed along Togo-Benin coast and then extended to the coast of while at 18 UTC it dropped to 1039 J/kg and 1997 J/kg, Ghana and deepened by 18 UTC (Figures 12(b) and 12(e)). respectively. Tajbakhsh et al. [43] used the Miller checklist to From Figures 12(c) and 12(f), an anticyclonic vortex ob- categorize the severity of thunderstorms. From this served at 200 hPa level, located close to the western border of checklist, it is observed that most of the parameters available Mali, ridged over the subregion with weak Tropical Easterlies to determine the strength in both cases implied that the Jets (TEJ). ,ese conditions ahead of the system supported storms were weak (Table 2). the westward propagation of the storm, thereby playing Comparing the daily rainfall amounts recorded for cases signiﬁcant role in its kinetics. It was observed that an ap- A and B, they were not exceptional and not even close to the 3rd highest of the ADMR amounts which caused the ﬂoods preciable amount of moisture (RH) above 80% at 850 hPa and the 700 hPa was within the atmosphere indicat- and resulted in casualties and damage of properties. As ing saturation for storm formation at the 850 hPa levels climate varies geographically, the deﬁnition of extreme (Figure 12). Moreover, tidal waves on that day were high, weather thresholds will also vary. ,is was observed for the which were aided by low surface pressure over the coastal 95th percentile threshold values over Accra and Kumasi. For area for the entire period of the event. ,e convective best practices globally, [44] established a tasked team on the available potential energy (CAPE) in the WRF skew-T deﬁnition of extreme weather and climate events which (Figures 14 and 15) showed enhanced instability in the classiﬁed heavy rainfall threshold greater than 50 mm atmosphere for convective processes. It has been established (>50 mm/24 hr.) per day. It was observed that most rainfalls that low level convergence (i.e., at 925 hPa pressure level) of for the ﬂood events fell below the threshold over Accra and moisture and upper level divergence are good indicators for Kumasi (Figure 16). Possible reasons for these ﬂooding cases can be related to large scale precipitation [40, 41]. However, localized mois- ture convergence and mesoscale lifting could also produce a nonmeteorological factors which included poor drainage P (hPa) Height (km) P (hPa) Height (km) 12 Advances in Meteorology Kumasi (KSI) at 2018-06-18_12:00:00 Kumasi (KSI) at 2018-06-18_18:00:00 Picl = 892 Ticl[C] = 19 Shox = 0 Pwat[cm] = 5 Cape[j] = 2439 Picl = 945 Ticl[C] = 22 Shox = 0 Pwat[cm] = 5 Cape[j] = 1997 100 100 16 16 15 15 14 14 150 150 13 13 12 12 200 200 11 11 250 250 10 10 300 300 9 9 8 8 400 400 7 7 500 500 5 5 4 4 700 3 700 3 2 2 850 850 1 1 1000 1000 0 0 –30 –20 –10 0 10 20 30 40 –30 –20 –10 0 10 20 30 40 Temperature (°C) Temperature (°C) (a) (b) Figure 15: WRF skew-T diagram showing atmospheric dynamics for case B (a) at 12 UTC and (b) at 18 UTC. Table 2: Miller checklist of some selected parameters for severe weather adopted from Tajbakhsh et al. [43]. Case A Case B Miller checklist Parameter 12 UTC 18 UTC 12 UTC 18 UTC Weak Moderate Strong Surface pressure 1015 hPa 1014 hPa 1014 hPa 1012 hPa >1010 hPa 1010–1005 hPa <1005 hPa ° ° ° ° ° ° ° Dew point 22 C 23 C 21 C 22 C <2.8 C 12.8–17.8 C 18.3 C CAPE 2282 J/kg 1039 J/kg 2439 J/kg 1997J/kg 800–1500 J/kg 1500–2500 J/kg >2500 J/kg Mean RH 90% 70% 90% 80% 70–80% or 40–50% 50–70% 50–70% 850–500 hPa wind shear 5–35 kts 5–35 kts 5–25 kts 5–20 kts 15–25 kts 26–35 kts >35 kts ultimately end up in the sea. City dwellers occupy riparian systems, building on water ways, improper disposal of re- fuse, limited roof-top rain harvesting in urban area, land zones and wet-lands leading to ﬂood initiation when cover change, less lawn, and limited tree planting. In their moderate rains occur. It is a great challenge for city work also conﬁrmed that intensive and unplanned human planners to control and regulate infrastructural activities in settlements in ﬂood-prone areas played a major role in the area. ,ese play a greater role in causing ﬂood events increasing ﬂood risk in Africa as observed in most parts of with moderate rainfall. Accra (e.g., Avenor and Kaneshie) and Kumasi (e.g., Buokrom and Anloga junction) [6]. 4. Conclusion Generally, the month of June is the peak of the major rainy season over southern Ghana. Consecutive days of ,is study investigated the meteorological dynamics for the rainfall over the high grounds and the inland areas were heavy rainfall that resulted in ﬂood over Kumasi and Accra. continually drained oﬀ to the sea. Prior to these ﬂood events, Qualitative analysis was done using graphical representation signiﬁcant amount of rainfall was experienced. For case B, of the thunderstorm propagation from GSMaP and global the city being a coastal town and at comparatively low al- and regional model outputs results. In order to establish the titude receives run-oﬀs from the high grounds and dis- uniqueness of the event as a result of its damage, a com- charges oﬀ to the sea. parative analysis was done with historical ﬂood occurrence. Accra is one of the most densely populated cities in the ,e results from meteorological analysis indicate that MSLP country, with high rural-urban migration resulting in was low with signiﬁcant moisture inﬂux for thunderstorm unplanned settlements. ,e waste generated mostly ends system growth and development. It was observed that the up in the drains and is washed into water bodies which thunderstorm thrived under weak synoptic features, even P (hPa) Height (km) P (hPa) Height (km) Advances in Meteorology 13 Accra Data Availability ,e in situ rainfall and WRF data used to support the ﬁndings of this study are available from the corresponding author upon request. Conflicts of Interest ,e authors declare that there are no conﬂicts of interest Flood date regarding the publication of this paper. 95th % of 45.1 mm Rainfall (mm) References Kumasi [1] R. Few, “Flooding, vulnerability and coping strategies: local responses to a global threat,” Progress in Development Studies, vol. 3, no. 1, pp. 43–58, 2003. [2] P. Tschakert, R. Sagoe, G. Ofori-Darko, and S. N. Codjoe, “Floods in the sahel: an analysis of anomalies, memory, and anticipatory learning,” Climatic Change, vol. 103, no. 3-4, pp. 471–502, 2010. [3] D. J. Parker, Floods, Routledge, Abingdon, UK, 2000. Flood date [4] I. Douglas, K. Alam, M. Maghenda, Y. Mcdonnell, L. McLean, 95th % of 42.16 mm and J. Campbell, “Unjust waters: climate change, ﬂooding and Rainfall (mm) the urban poor in Africa,” Environment and Urbanization, vol. 20, no. 1, pp. 187–205, 2008. Figure 16: Accra and Kumasi 95th percentile threshold. ,is shows [5] BBC, 2007, http://news.bbc.co.uk/1/hi/world/africa/6998651.stm. most ﬂood events below the threshold. [6] G. D. Baldassarre, A. Montanari, H. Lins, D. Koutsoyiannis, and L. Brandimarte, “Flood fatalities in Africa: from diagnosis though the moisture was enough for its survival. ,is led to to mitigation,” Journal of Geophysical Research Letters, vol. 37, no. 22, 2010. its slower movement and subsequent stagnation. Moreover, [7] T. G. Amangabara and M. Obenade, “Flood vulnerability it was also observed that the weak AEJ (at 12 UTC and 18 assessment of Niger delta states relative to 2012 ﬂood disaster UTC) contributed to its weak propagation. To quantify the in Nigeria,” American Journal of Environmental Protection, frequency of anomalous rainfall events and to determine the vol. 3, no. 3, pp. 76–83, 2015. strength of the thunderstorm, we conducted trend analysis [8] N. K. Karley, “Flooding and physical planning in urban areas that showed an increasing trend for Accra with slope of 0.045 in west Africa: situational analysis of Accra, Ghana,” 2eo- and a decreasing trend for Kumasi with slope of –0.07. ,is retical and Empirical Researches in Urban Management, vol. 4, also coincides with the less days with heavy rainfall for Accra no. 13, pp. 25–41, 2009. using the 95th percentile threshold of 45.10 mm and more [9] Y. A. Twumasi and R. Asomani-Boateng, “Mapping days with less heavy rainfall for Kumasi using the 95th seasonal hazards for ﬂood management in Accra, Ghana using GIS,” in Proceedings of the Geoscience and Remote percentile threshold of 42.16 mm. Sensing Symposium: IGARSS Apos; 02 IEEE Interna- Miller checklist for severe weather categorization was tional, vol. 5, pp. 2874–2876, Toronto, Canada, February used to assess the strength of the thunderstorms for some selected meteorological parameters. From the results as [10] D. Rain, R. Engstrom, C. Ludlow, and S. Antos, “Accra Ghana: summarized in Table 2, the thunderstorms that led to the a city vulnerable to ﬂooding and drought-induced migration: heavy rainfalls were weak. ,is study concludes that the case study prepared for cities and climate change,” Global meteorological conditions that aided the formation and Report on Human Settlements 2011, http://www.unhabitat. propagation of the weather systems that produced the heavy org/grhs/2011, 2011. rainfall events resulting in the ﬂoods that occurred over the [11] C. Amoako and E. Frimpong Boamah, “,e three-dimen- study areas were nonmeteorologically induced. Anthropo- sional causes of ﬂooding in Accra, Ghana,” International genic activities such as buildings on water ways and choked Journal of Urban Sustainable Development, vol. 7, no. 1, pp. 109–129, 2015. drainage systems were responsible for the ﬂoods. Accurate [12] K. Appeaning Addo and M. Adeyemi, “Assessing the impact and timely weather forecast is needed in planning the day- of sea-level rise on a vulnerable coastal community in Accra, to-day activities and to serve as early warning system. ,is Ghana,” Jamb ` a: ´ Journal of Disaster Risk Studies, vol. 5, no. 1, study has shown some of the main synoptic features that p. 8, 2013. produced the heavy rainfall events in Ghana and could serve [13] O. Cred, 2e Ofda/cred International Disaster Database, Em- as a reference for forecasters in Ghana and the West African dat, Brussels, Belgium, 2009. subregion as far as forecasting heavy rainfall event is con- [14] S. Asumadu-Sarkodie, P. A. Owusu, and M. P. C. Jayaweera, cerned. Further studies must be carried out over other areas “Flood risk management in Ghana: a case study in Accra,” within the country to determine the 95th threshold for Advances in Applied Science Research, vol. 6, no. 4, pp. 196– extreme rainfall, especially the ﬂood-prone zones. 201, 2015. Rainfall (mm) Rainfall (mm) 4-Feb 27-Feb 3-Mar 24-Mar 12-Mar 14-Mar 16-Mar 20-Apr 17-Mar 20-Mar 24-Mar 2-May 26-Mar 4-Apr 15-Apr 8-May 18-Apr 22-Apr 25-Apr 17-May 27-Apr 29-Apr 3-May 5-May 25-May 10-May 11-May 12-May 6-Jun 13-May 17-May 21-May 14-Jun 25-May 26-May 28-May 18-Jun 29-May 3-Jun 28-Jun 20-Jun 29-Jun 3-Jul 4-Jul 14 Advances in Meteorology [15] S. Janicot, G. Caniaux, F. Chauvin et al., “Intraseasonal [32] H. Paeth, K. Born, R. Podzun, and D. Jacob, “Regional dy- variability of the west african monsoon,” Atmospheric Science namical downscaling over west Africa: model evaluation and Letters, vol. 12, no. 1, pp. 58–66, 2011. comparison of wet and dry years,” Meteorologische Zeitschrift, vol. 14, no. 3, pp. 349–367, 2005. [16] L. Amekudzi, E. Yamba, K. Preko et al., “Variabilities in rainfall onset, cessation and length of rainy season for the [33] D. J. Parker, Meteorology of Tropical West Africa: 2e Fore- various agro-ecological zones of Ghana,” Climate, vol. 3, no. 2, casters’ Handbook, p. 33, John Wiley & Sons, Hoboken, New pp. 416–434, 2015. Jersey, USA, 2017. [17] J. B. Omotosho, “Long-range prediction of the onset and end [34] M. I. Lele and P. J. Lamb, “Variability of the intertropical front of the rainy season in the west African Sahel,” International (ITF) and rainfall over the west African Sudan-sahel zone,” Journal of Climatology, vol. 12, no. 4, pp. 369–382, 1992. Journal of Climate, vol. 23, no. 14, pp. 3984–4004, 2010. [18] J. B. Omotosho, A. A. Balogun, and K. Ogunjobi, “Predicting [35] C. Flamant, P. Knippertz, D. J. Parker et al., “,e impact of a monthly and seasonal rainfall, onset and cessation of the rainy mesoscale convective system cold pool on the northward season in west Africa using only surface data,” International propagation of the intertropical discontinuity over west Journal of Climatology, vol. 20, no. 8, pp. 865–880, 2000. Africa,” Quarterly Journal of the Royal Meteorological Society, [19] B. J. Abiodun, Z. D. Adeyewa, P. G. Oguntunde, A. T. Salami, vol. 135, no. 638, pp. 139–159, 2009. and V. O. Ajayi, “Modeling the impacts of reforestation on [36] T. O. Odekunle, “An assessment of the inﬂuence of the inter- tropical discontinuity on inter-annual rainfall characteristics future climate in west Africa,” 2eoretical and Applied Cli- matology, vol. 110, no. 1-2, pp. 77–96, 2012. in Nigeria,” Geographical Research, vol. 48, no. 3, pp. 314–326, [20] I. Diallo, M. B. Sylla, F. Giorgi, A. T. Gaye, and M. Camara, 2009. “Multimodel GCM-RCM ensemble-based projections of [37] C. Mensah, L. K. Amekudzi, N. A. B. Klutse, J. N. A. Aryee, temperature and precipitation over west Africa for the early and K. Asare, “Comparison of rainy season onset, cessation 21st century,” International Journal of Geophysics, vol. 2012, and duration for Ghana from RegCM4 and GMet datasets,” Article ID 972896, 19 pages, 2012. Atmospheric and Climate Sciences, vol. 6, no. 2, pp. 300–309, [21] M. B. Sylla, F. Giorgi, J. S. Pal, P. Gibba, I. Kebe, and 2016. M. Nikiema, “Projected changes in the annual cycle of high- [38] S. S. Vaidya and S. S. Singh, “Applying the betts-miller-janjic intensity precipitation events over west Africa for the late scheme of convection in prediction of the Indian monsoon,” twenty-ﬁrst century,” Journal of Climate, vol. 28, no. 16, Weather and Forecasting, vol. 15, no. 3, pp. 349–356, 2000. pp. 6475–6488, 2015. [39] F. Mounier, S. Janicot, and G. N. Kiladis, “,e west African [22] A. Dosio and H. J. Panitz, “Climate change projections for monsoon dynamics. Part III: the quasi-biweekly zonal dipole,” Journal of Climate, vol. 21, no. 9, pp. 1911–1928, 2008. CORDEX-Africa with COSMO-CLM regional climate mode land diﬀerences with the driving global climate models,” [40] W. Ma, W. Huang, Z. Yang, B. Wang, D. Lin, and X. He, Climate Dynamics, vol. 46, no. 5-6, pp. 1599–1625, 2016. “Dynamic and thermodynamic factors associated with dif- [23] N. Kumi and B. J. Abiodun, “Potential impacts of 1.5 C and ferent precipitation regimes over south China during pre- 2 C global warming on rainfall onset, cessation and length of monsoon season,” Atmosphere, vol. 9, no. 6, 2018. rainy season in west Africa,” Environmental Research Letters, [41] S. Minobe, A. Kuwano-Yoshida, N. Komori, S.-P. Xie, and vol. 13, no. 5, Article ID 055009, 2018. R. J. Small, “Inﬂuence of the Gulf stream on the troposphere,” [24] A. Sossa, B. Liebmann, I. Blade´ et al., “Statistical connection Nature, vol. 452, no. 7184, pp. 206–209, 2008. between the madden-julian oscillation and large daily pre- [42] C. L. Ziegler, T. J. Lee, and R. A. Pielke, “Convective initiation cipitation events in west Africa,” Journal of Climate, vol. 30, at the dryline: a modeling study,” Monthly Weather Review, no. 6, pp. 1999–2010, 2017. vol. 125, no. 6, pp. 1001–1026, 1997. [25] E. D. Maloney and J. Shaman, “Intraseasonal variability of the [43] S. Tajbakhsh, P. Ghafarian, and F. Sahraian, “Instability in- west african monsoon and atlantic ITCZ,” Journal of Climate, dices and forecasting thunderstorms: the case of 30 April vol. 21, no. 12, pp. 2898–2918, 2008. 2009,” Natural Hazards and Earth System Sciences, vol. 12, [26] S. E. Nicholson, “A revised picture of the structure of the no. 2, pp. 403–413, 2012. “monsoon” and land ITCZ over west Africa,” Climate Dy- [44] TT-DEWCE WMO, Guidelines on the Monitoring of Extreme namics, vol. 32, no. 7-8, pp. 1155–1171, 2009. Weather and Climate Events, Draft Version-First Review by [27] M. Diop and D. I. F. Grimes, “Satellite-based rainfall esti- TT-DEWCE, TT-DEWCE WMO, Geneva, Switzerland, 2015. mation for river ﬂow forecasting in Africa. II: African easterly waves, convection and rainfall,” Hydrological Sciences Journal, vol. 48, no. 4, pp. 585–599, 2003. [28] J. Cretat, ´ E. K. Vizy, and K. H. Cook, “,e relationship be- tween African easterly waves and daily rainfall over west Africa: observations and regional climate simulations,” Cli- mate Dynamics, vol. 44, no. 1-2, pp. 385–404, 2015. [29] S. Janicot, V. Moron, and B. Fontaine, “Sahel droughts and ENSO dynamics,” Geophysical Research Letters, vol. 23, no. 5, pp. 515–518, 1996. [30] S. Janicot, “Impact of warm ENSO events on atmospheric circulation and convection over the tropical Atlantic and west Africa,” Annales Geophysicae, vol. 15, no. 4, pp. 471–475, 1997. [31] G. Panthou, T. Vischel, and T. Lebel, “Recent trends in the regime of extreme rainfall in the central Sahel,” International Journal of Climatology, vol. 34, no. 15, pp. 3998–4006, 2014.

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Advances in Meteorology – Hindawi Publishing Corporation

Published: Mar 24, 2020

References

Flooding, vulnerability and coping strategies: local responses to a global threat,

R. Few
Floods in the sahel: an analysis of anomalies, memory, and anticipatory learning,

P. Tschakert, R. Sagoe, G. Ofori-Darko, and S. N. Codjoe
Unjust waters: climate change, flooding and the urban poor in Africa,

I. Douglas, K. Alam, M. Maghenda, Y. Mcdonnell, L. McLean, and J. Campbell
Flood fatalities in Africa: from diagnosis to mitigation,

G. D. Baldassarre, A. Montanari, H. Lins, D. Koutsoyiannis, and L. Brandimarte
Flood vulnerability assessment of Niger delta states relative to 2012 flood disaster in Nigeria,

T. G. Amangabara and M. Obenade
Flooding and physical planning in urban areas in west Africa: situational analysis of Accra, Ghana,

N. K. Karley
Mapping seasonal hazards for flood management in Accra, Ghana using GIS,

Y. A. Twumasi and R. Asomani-Boateng
Accra Ghana: a city vulnerable to flooding and drought-induced migration: case study prepared for cities and climate change,

D. Rain, R. Engstrom, C. Ludlow, and S. Antos
The three-dimensional causes of flooding in Accra, Ghana,

C. Amoako and E. Frimpong Boamah
Assessing the impact of sea-level rise on a vulnerable coastal community in Accra, Ghana,

K. Appeaning Addo and M. Adeyemi
Flood risk management in Ghana: a case study in Accra,

S. Asumadu-Sarkodie, P. A. Owusu, and M. P. C. Jayaweera
Intraseasonal variability of the west african monsoon,

S. Janicot, G. Caniaux, F. Chauvin et al.
Variabilities in rainfall onset, cessation and length of rainy season for the various agro-ecological zones of Ghana,

L. Amekudzi, E. Yamba, K. Preko et al.
Long-range prediction of the onset and end of the rainy season in the west African Sahel,

J. B. Omotosho
Predicting monthly and seasonal rainfall, onset and cessation of the rainy season in west Africa using only surface data,

J. B. Omotosho, A. A. Balogun, and K. Ogunjobi
Modeling the impacts of reforestation on future climate in west Africa,

B. J. Abiodun, Z. D. Adeyewa, P. G. Oguntunde, A. T. Salami, and V. O. Ajayi
Multimodel GCM-RCM ensemble-based projections of temperature and precipitation over west Africa for the early 21st century,

I. Diallo, M. B. Sylla, F. Giorgi, A. T. Gaye, and M. Camara
Projected changes in the annual cycle of high-intensity precipitation events over west Africa for the late twenty-first century,

M. B. Sylla, F. Giorgi, J. S. Pal, P. Gibba, I. Kebe, and M. Nikiema
Climate change projections for CORDEX-Africa with COSMO-CLM regional climate mode land differences with the driving global climate models,

A. Dosio and H. J. Panitz
Potential impacts of 1.5°C and 2°C global warming on rainfall onset, cessation and length of rainy season in west Africa,

N. Kumi and B. J. Abiodun
Statistical connection between the madden-julian oscillation and large daily precipitation events in west Africa,

A. Sossa, B. Liebmann, I. Bladé et al.
Intraseasonal variability of the west african monsoon and atlantic ITCZ,

E. D. Maloney and J. Shaman
A revised picture of the structure of the “monsoon

S. E. Nicholson
Satellite-based rainfall estimation for river flow forecasting in Africa. II: African easterly waves, convection and rainfall,

M. Diop and D. I. F. Grimes
The relationship between African easterly waves and daily rainfall over west Africa: observations and regional climate simulations,

J. Crétat, E. K. Vizy, and K. H. Cook
Sahel droughts and ENSO dynamics,

S. Janicot, V. Moron, and B. Fontaine
Impact of warm ENSO events on atmospheric circulation and convection over the tropical Atlantic and west Africa,

S. Janicot
Recent trends in the regime of extreme rainfall in the central Sahel,

G. Panthou, T. Vischel, and T. Lebel
Regional dynamical downscaling over west Africa: model evaluation and comparison of wet and dry years,

H. Paeth, K. Born, R. Podzun, and D. Jacob
Variability of the intertropical front (ITF) and rainfall over the west African Sudan-sahel zone,

M. I. Lele and P. J. Lamb
The impact of a mesoscale convective system cold pool on the northward propagation of the intertropical discontinuity over west Africa,

C. Flamant, P. Knippertz, D. J. Parker et al.
An assessment of the influence of the inter-tropical discontinuity on inter-annual rainfall characteristics in Nigeria,

T. O. Odekunle
Comparison of rainy season onset, cessation and duration for Ghana from RegCM4 and GMet datasets,

C. Mensah, L. K. Amekudzi, N. A. B. Klutse, J. N. A. Aryee, and K. Asare
Applying the betts-miller-janjic scheme of convection in prediction of the Indian monsoon,

S. S. Vaidya and S. S. Singh
The west African monsoon dynamics. Part III: the quasi-biweekly zonal dipole,

F. Mounier, S. Janicot, and G. N. Kiladis
Dynamic and thermodynamic factors associated with different precipitation regimes over south China during pre-monsoon season,

W. Ma, W. Huang, Z. Yang, B. Wang, D. Lin, and X. He
Influence of the Gulf stream on the troposphere,

S. Minobe, A. Kuwano-Yoshida, N. Komori, S.-P. Xie, and R. J. Small
Convective initiation at the dryline: a modeling study,

C. L. Ziegler, T. J. Lee, and R. A. Pielke
Instability indices and forecasting thunderstorms: the case of 30 April 2009,

S. Tajbakhsh, P. Ghafarian, and F. Sahraian

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Meteorological Analysis of Floods in Ghana

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Meteorological Analysis of Floods in Ghana

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