PROF. NZIOKA J. MUTHAMA

Most cities in Africa’s developing countries are evidently growing leading to significant modification on climate over the cities that affect human comfort and his environment. Proper urban atmospheric planning and management are thus key to making cities environmentally sustainable. To achieve all these, urban weather and climate needs continuous monitoring to offer accurate, reliable and timely update of any significant changes. This study examined the modification of wind speed and direction by urbanization process. There is need to understand the modification of wind since the wind spped and direction greatly affects dispersion of pollutants in the city and distribution of heat which affect human comfort. The study utilized land surface albedo, decadal population data and daily wind speeds and direction. The wind was analyzed using wind rose plot and the population and albedo analyzed by carrying out trend analysis. The urbanization is evidenced by the reducing urban land surface reflectivity and the increasing population. Wind direction does not show modification by urbanization, however, its magnitude has exhibited a reduction with time. The reduction in wind speed is harmful to human and animal comfort and the environment at large. Practical approaches such as proper planning of the cities to minimize further modification by urbanization have been made. The choice of residential and industrial places is also emphasized with regard to these findings. The findings of this work are thus important for multi-sectoral use in the urban centres in Kenya.

In investigating Kenya rainfall variability and its relationship to other climatic elements it has become imperative to analyze the irregularly distributed rainfall events in time. To meet this requirement, this study used a stepwise regression technique. The study seeks to improve existing rainfall monitoring and prediction in Nairobi. Monthly rainfall data was fitted to several mathematical functions. The best mathematical model which best simulated the March-May (MAM) and October -December (OND) seasonal rainfall over the three stations of analysis was chosen using a stepwise regression technique. The value of R-squared for the best fit was computed to show the percentage of rainfall information that is explained by the variation in the independent (time) variable. From the results obtained, the stepwise regression technique selected the fourth degree polynomial as the best fit for analyzing the March-May (MAM) and October -December (OND) seasonal rainfall data set. Solar cycle period of ten (10) years was employed to get the fourth degree polynomial variables. Hence from the study, it can be deducted that the 4th degree polynomial function can be used to predict the peak and the general pattern of seasonal rainfall over Nairobi, with acceptable error values. This information can be used in the planning and management of water resources over Nairobi. The same information can be extended to other areas.

environs. Ground based and Satellite-derived meteorological data were utilized in this study and included thunder events, total rainfall, number of rainy days, maximum rainfall in 24 hours, relative humidity, minimum and maximum temperature, wind speed and direction and Cold Cloud Duration (CCD). In the context of disaster management, the synergistic approach to risk management involves four closely related phases, one of which is the scientific analysis of specific hazard. This is the phase addressed in this study. A risk indicator based on physical and statistical characteristics of thunderstorms at the two locations was developed by examining the patterns of deviations from the mean thunderstorm events and their frequencies. Various aspects of the lightning risks at the two areas are described and discussed. Model results indicate that higher risks of lightning stroke occurred during the long rains season (March to May) as compared to the short rains (September to November) season. This was attributed to higher frequency of thunderclouds during the long rains season. The rain generating mechanisms during the long rains were observed to have higher frequencies of thunder events. The dry season (December to February and June to August) exhibits lowest lightning stroke risks. It is hoped that the results from this study may be of use to the various sectors of economy that need to take into account the dangers/risks of lightning strokes into their day to day operations so as to minimise or avert disasters from lightning strokes. Some of the sectors that may benefit from the results of this study include the Kenya Oil Refinery Depots, Kenya Power and Lighting Company, the Chemical and construction industries among others