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dc.contributor.authorOuma, Gilbert O
dc.date.accessioned2013-05-09T09:20:06Z
dc.date.available2013-05-09T09:20:06Z
dc.date.issued2000
dc.identifier.citationDoctor of Philosophy in Meteorology,en
dc.identifier.urihttp://erepository.uonbi.ac.ke:8080/xmlui/handle/123456789/20694
dc.description.abstractSkillful monitoring, prediction and early warning of the extreme weather events is crucial in the planning and management of all rain dependent socio-economic activities. They are also vital for the development of effective disaster preparedness strategies. Prediction methods depend on availability of long period, high quality data with good spatial coverage. Real and near real time data are also useful as initial conditions for the integration of prediction models. Thus, high quality data with good spatial and temporal coverage is fundamental for any research and applications. Unfortunately, limitation of rainfall data is a serious problem in many parts of the world, especially in the developing countries. This study investigated the viability of satellite derived data in providing alternative rainfall information and hence enhancing rainfall monitoring, prediction and disaster preparedness in Kenya. The data sets used in the study included observed daily rainfall, dekadal Cold Cloud Duration (CCD) and pentad Total Precipitable Water (TPW) data together with the reanalysis data from ECMWF. The various methods that were used to achieve the objectives of the study included data quality analyses, S-mode Principal Component Analysis (PCA) together with correlation and regression analyses. The regression models used included simple linear, stepwise and canonical regression approaches. The skills of the developed models were further investigated during anomalous wet and dry periods. Meteorological conditions that could be associated with the decreased or increased skill of the regression models during the anomalous periods were also investigated. The results obtained from the quality control tests indicated that all data used in the study were of good quality. The S-mode PCA solutions delineated eleven regions for March- April-May (MAM), June-July-Augu§t (JJA) and December-January-February (DJF) seasons, while only nine were delineated for the September-October-November (SON) season from the rainfall data. The number of significant PCA modes was higher for the dry seasons of JJA and DJF as compared to those from the wet seasons of MAM and SON. However, these components extracted relatively low percentage of total variance in the observed rainfall and hence the development of regression models concentrated on the wet seasons of MAM and SON. Although the duration of the satellite-derived data available was shorter, the regions derived from this data set were generally consistent with those obtained using the rainfall data. The results from correlation analyses showed significant linear relationships between rain gauge rainfall and only CCD data. The results of ANOV A tests on the linear regression models indicated that the developed models had reasonable skill in estimating rainfall from CCD data. Results from stepwise regression analyses of rain gauge rainfall, CCD and the three layers of TPW data indicated that the additional variance explained by the inclusion of TPW data were generally low. It was, however, evident that, in some regions, the inclusion of TPW in the estimation models was of added value. The examinations of the skill of the models during anomalous dry and wet periods revealed better performance during the anomalous seasons. The composites of the meteorological conditions showed that for the anomalous dry seasons, a westerly component exists in the equatorial winds (surface to 800 mb layer) that interferes with the supply of moisture from the Indian Ocean and lead to a reduction in the amount of moisture available inland. During anomalous wet seasons, the southeasterly flow inland is very strong enhancing the moisture influx from the Indian Ocean. . The last part of the study examined the more complex Canonical Correlation Analysis (CCA) in the development of estimation and forecasting models. The results from the CCA indicated significant year to year variations in the skills for the estimated areal observed rainfall from satellite-derived data. !;;.. comparison of the CCA models results to those of the stepwise regression revealed that the CCA models performed considerably better during the Vll SON season in estimating the areal observed rainfall. The results from the forecasting models revealed that the canonical correlation coefficients based on CCD data were all not statistically significant in all locations and seasons. Results obtained from analyses of TPW data, however, indicated some skill in forecasting of rainfall with SON season registering higher canonical correlation coefficients. This study has, for the first time, regionalized Kenya into seasonal climatological homogeneous zones using satellite-derived data. It has also developed statistical models based on satellite-derived data that can be used to estimate areal rainfall for the derived climatological zones. The study further highlights the vital need to use distinct CCD temperature thresholds for rainfall estimation for individual climatological zones and seasons. Finally, it is the first time that a study has used CCA technique for the estimation of areal rainfall from satellite-derived data in Kenya. CCA predictors were also derived for areal rainfall forecasting. The complex CCA approach improved skills in the rainfall estimates during some seasons that had relatively poor skills with the other methods. In conclusion, the study has for the first time provided some useful information regarding the enormous potential use of satellite-derived data in the estimation of 10-day areal rainfall for specific regions in Kenya. The results from the PCA regionalization show that the satellite-derived data can be used to delineate large-scale spatial anomalies in the rainfall over the study region. Accurate delineation of these anomalies is vital in drought monitoring, flash flood forecasting and general preparedness against extreme weather eventsen
dc.description.sponsorshipUniversity of Nairobien
dc.language.isoenen
dc.titleUse of satellite data in monitoring and prediction of rainfall over Kenyaen
dc.typeThesisen
local.publisherDepartment of Meteorology Faculty of Science University of Nairobien


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