Development of an Automated Algorithm to Detect and Track Developing and Mesoscale Convective Systems

An objective algorithm is developed which is capable of identifying convective cloud clusters (CCCs) at the point of initiation, tracking any selected of all identified CCCs to maturity (MCS) and then to the point of dissipation and thereby accumulating some physical, morphological and radiative properties. The technique is able to account for periods of growth, propagating, merging, splitting and decaying, which take place during the lifetime of an individual cluster. The algorithm was tested using brightness temperature from merged infrared images of at least eight reporting geostationary satellites or channels at a spatial resolution of 4 Km and temporal resolutions of 30 minutes. The model data used is Weather Research and Forecasting (WRF) with the Advanced Research WRF (ARW) core simulated using the same spatial and temporal resolution with the satellite data for uniform comparison. This was the first attempt of developing such algorithm taking into consideration the uniqueness of West African convective systems. The algorithm loads an input (text) file where necessary inputs as preferred by the user, takes input file of satellite data or model’s output in netcdf file format. The good graphical interface for interacting with user makes it very flexible and easy to use with little computer knowledge. Good results from the comparison and validation shows a good agreement between the model’s output and satellite data shows the consistency in the tracking algorithm. The algorithm can hence be used for comparison or evaluating the performances of different Physics parameterizations/schemes in same model with the observed. It can also be used for comparison or evaluating performances of different models in simulating convective systems of the West Africa sub-region. Keywords: convective systems, tracking, properties, propagation, growth and decaying. DOI: 10.7176/JNSR/13-18-06 Publication date:October 31st 2022

Impacts of Climate Change on Rainfall Amount, Onset and Length of Rainy Season over West Africa

Onset, cessation and hence length of rainy season as well as the annual and seasonal amount of rainfall were investigated for a possible climate change impacts in the near future using ensembles of eight regional climate models (RCMs). Comparison of the model ensembles with global precipitation climatology project (GPCP) data shows a good agreement with the observation. Apart from the onset of rainfall and annual amount with error of 51%, 16.5% respectively over Guinea zone, the percentage error for all other parameters were found to be just 10% or less over the zones and in the West African sub-region. Analysis of the present (1997-2007) and its different from the near future (2027-2037) with IPCC scenario A1B shows some reductions (or early) and increment (or delay) in some of these rainfall characteristics. These changes were investigated in each of the three climatic zones and also the entire West Africa. Rainfall amount was found to be reduced to 0.29mm/day (7.9%) over the entire subregion while this reduction is more pronounced in the Sahel (with 0.37mm/day) than the other two zones. Throughout the three zones and the entire West African sub-region, there is general delay of onset, early cessation and therefore shorter length of rainy season. Over Guinea, length of rainy season was reduced by 27 days while it was 14 and 13 days respectively over Savana and Sahel, the mean over West Africa is 18 days. This threatens the agricultural practices and therefore calls for a way of getting plant species that mature and germinate early enough so at to mitigate the impacts of shorter length of rainy season in the near future over West Africa. Though, there seems to be a relief in the rainfall amount from June to September over Guinea as there is an increase of 1.63mm/day (25%), but this poses some threats in terms of damages and destruction to lives and properties that may be associated with frequent occurrences of floods and probably squall lines. Savana has little reduction of 0.3mm/day while the reduction is a bit high over Sahel with 1.0mm/day, this threatens agricultural practices and water resources management over these zones as the rainfall amount over them presently is not substantial enough for a sustainable agricultural practice. Keywords: Rainfall characteristics, West Africa, climate change, mitigation, model ensembles. DOI: 10.7176/JNSR/13-18-05 Publication date:October 31st 202

Climatology of Satellite-Derived Mesoscale Convective Systems over West Africa

This work studied the climatology of Mesoscale Convective Systems (MCSs) over West Africa. Infrared brightness temperature (BT) data was collected from Tropical Rainfall Measuring Mission (TRMM) website over the rainy season period for ten years (2006 - 2015) from 1st of June to 30th of September. The study area covers West Africa from 5ºS to 25ºN and 20ºE to 20ºW, it was divided to 5o X 5o boxes. The T-R criteria used to detect the minimum size of MCSs was a diameter of 200 km or more and a BT of 233 K (-40 ºC) or less. All the MCSs were tracked for the months of June, July, August and September and compared the wettest (2014) and the driest (2009) years as observed from the ten years annual rainfall data collected from the archives of the Nigerian Meteorological Agency (NIMET). The results indicated that for both the driest and wettest years, latitudinal row 5ºN - 10ºN is the most active zone favorable for the initiation, propagation and decay of MCSs over West Africa with 45% of MCSs found there in the month of June and row 10ºN - 15ºN as the next favorable zone (29-32% of MCSs). In the month of July, the active zone shifted to 10ºN - 15ºN while the next favorable zone also shifted to 5ºN - 10ºN, which was maintained and became more favorable through August and September. There were almost double the number of MCSs occurrence in the wettest year compared to driest year. During wettest year, MCSs were found out to be slower than their driest year counterpart. The results also gave a better insight of the behavioral patterns of West African MCSs, hence improving general forecasting method and reliability. Keywords: active zone, initiation, propagation, DETRAWACS, tracking

Skills Assessment of Selected Supervised Machine Learning Algorithms in Predicting Seasonal Rainfall over Bauchi in Nigeria

An attempt is made to use four selected machine learning algorithms (MLAs) to predict the seasonal and monthly amount of rainfall over a Savana station in Nigeria. The four MLAs are the artificial neural network (ANN), Random Forest model (RFM), K-Nearest Neighbor (KNN), and kernel basis Support Vector Machine (SVM). Monthly mean rainfall and monthly mean air temperature data from June to October over a period of 34 years (1986-2019) were used and seventeen atmospheric variables are used to develop the model during training period. The period is divided into two, the training (1986 - 2013) and testing (2014 - 2019) periods. The results show that SVM and ANN better reproduce both monthly and annual rainfall amount over the study area by accessing their skills during training period and also having lowest RMSE and MAE during testing period. SVM is the most suitable among the four MLAs. Though, some show better results for specific month(s), the SVM and ANN summary yield 84% and 82% respectively of good forecasts for seasonal rainfall amount over Bauchi. The web interface was developed using R (ShinyR Package) programming has a very interactive and good graphical user interface (GUI) for user with little or no computer knowledge. It is recommended that the two MLAs can be used to predict monthly and seasonal rainfall over Savana climatic zone of West Africa using the seventeen input variables and hence other variables can be selected for forecasting other rainfall properties like onset, cessation and length of rainy season over West Africa sub-region. The results also show the importance and weight of each of the seventeen input variables has in reproducing the dependent variable and hence be useful in choosing which input variable can be used in further studying the dynamics of West African rain producing systems. Keywords: Machine Learning, rainfall amount, training period, error analysis. DOI: 10.7176/JEES/12-10-06 Publication date:October 31st 2022

Application of Geo-Spatial Technology in Identifying Areas Vulnerable to Flooding in Ibadan Metropolis

This study makes use of the integrated approach of Remote Sensing and GIS techniques in flood management with the goal of identifying areas vulnerable to flood hazard in Ibadan Metropolis. Ibadan is the largest indigenous city in the continent of Africa and had experienced a lot of various severities of flood occurrences in the last fifty years. Topographic Map and Landsat TM image of 1993 and 2000 respectively were processed, scanned, digitized, interpolated, classified and overlaid using ILWIS 3.3 academic and ARC GIS 9.2 software modules to generate classified land cover map, Digital Terrain Map (DTM), Triangulated Irregular Network (TIN) and flood vulnerability map of the study area respectively. The results obtained shows that, areas lying along the banks of River Ona and River Ogunpa are most vulnerable to flood hazards with the vulnerability decreasing towards the northern part of the city, much of the area is built up with improper planning and this gives rise to high vulnerability to flash flood hazards. The Odo Ona, Idi Isin, Eleyele, Olopometa and Molete areas are the most vulnerable to flood threat. The incessant violation of land use plan, unchecked population growth, old nature of the structures and poor materials used in the construction of the houses make the areas vulnerable to flood hazard. In reducing the vulnerability of these areas from flood there is need for improved land use planning, removal of structures from River Ona and Ogunpa flood plains around the city, intensify environmental education to the residents and enhance the active participation of government agencies in the continual generation of flood vulnerability maps of urban centres. Keywords: Flood, Vulnerability, DEM, GIS, Remote Sensing, TIN

Verification of satellite and model products against a dense rain gauge network for a severe flooding event in Kumasi, Ghana

Floods as a result of severe storms cause significant impacts on lives and properties. Therefore, timely and accurate forecasts of the storms will reduce the associated risks. In this study, we look at the characteristics of a storm on 28 June, 2018 in Kumasi from a rain gauge network and satellite data, and reanalysis data. The storm claimed at least 8 lives and displaced 293 people in Kumasi, Ghana. The ability of satellite and reanalysis data to capture the temporal variations of the storm was assessed using a high temporal resolution (accumulation per minute) rain gauge data. We employed the observation data from the Dynamics–Aerosol–Chemistry–Cloud Interactions in West Africa (DACCIWA) rain gauges to assess the storm's onset, duration, and cessation. Subsequently, the performance of the ERA5 reanalysis and Global Precipitation Measurement (GPM) satellite precipitation estimates in capturing the rainfall is assessed. Both GPM and the ERA5 had difficulty reproducing the hourly pattern of the rain. However, the GPM produced variability that is similar to the observed. Generally, the region of maximum rainfall was located in the southern parts of the study domain in ERA5, while GPM placed it in the northern parts. The study contributes a verification measure to improve weather forecasting in Ghana as part of the objectives of the GCRF African Science for Weather Information and Forecasting Techniques (SWIFT) project

Challenges and ways forward for sustainable weather and climate services in Africa

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