A Novel Approach For Identifying Cloud Clusters Developing Into Tropical Cyclones

Providing advance notice of rare events, such as a cloud cluster (CC) developing into a tropical cyclone (TC), is of great importance. Having advance warning of such rare events possibly can help avoid or reduce the risk of damages and allow emergency responders and the affected community enough time to respond appropriately. Considering this, forecasters need better data mining and data driven techniques to identify developing CCs. Prior studies have attempted to predict the formation of TCs using numerical weather prediction models as well as satellite and radar data. However, refined observational data and forecasting techniques are not always available or accurate in areas such as the North Atlantic Ocean where data are sparse. Consequently, this research provides the predictive features that contribute to a CC developing into a TC using only global gridded satellite data that are readily available. This was accomplished by identifying and tracking CCs objectively where no expert knowledge is required to investigate the predictive features of developing CCs. We have applied the proposed oversampling technique named the Selective Clustering based Oversampling Technique (SCOT) to reduce the bias of the non-developing CCs when using standard classifiers. Our approach identifies twelve predictive features for developing CCs and demonstrates predictive skill for 0 - 48 hours prior to development. The results confirm that the proposed technique can satisfactorily identify developing CCs for each of the nine forecasts using standard classifiers such as Classification and Regression Trees (CART), neural networks, and support vector machines (SVM) and ten-fold cross validation. These results are based on the geometric mean values and are further verified using seven case studies such as Hurricane Katrina (2005). These results demonstrate that our proposed approach could potentially improve weather prediction and provide advance notice of a developing CC by using solely gridded satellite data

Tracing The Origins And Propagation Of African Easterly Waves And Mesoscale Convective Systems Using Pattern Recognition And Data Fusion

This thesis is focus on developing pattern recognition techniques to trace the origins and propagation of the Pre-Tropical Storm Debby (2006) African easterly waves (AEWs) and mesoscale convective systems (MCSs) using satellite imagery. The results are used to verify a numerical weather prediction (NWP) model. The pre-Debby MCSs’ movement and formation needs to be precisely and objectively tracked. These MCSs could be generated over mountains in North Africa, such as Ethiopian Highlands (EH), Darfur Mountains (DF), and Asir Mountains (AS), going through complicated splitting and merging processes. Thus, an objectively analyzed MCS movement is essential not only to help provide data to verify numerical modeling results, but also to help understand the formation and propagation of the African easterly waves and MCSs. The technique used could be applied to other AEWs and MCSs leading to tropical cyclogenesis. This, in turn, will improve the NWP over the data sparse areas, such as over eastern and central North Africa. The accuracy of numerical simulations of pre-tropical cyclone (TC) AEWs and MCSs is improved by fusing data with different data fusion techniques. In order to provide enhanced information to help predict the weather, this thesis investigates various techniques of data fusion. In many cases, fusing data provides more accurate and complete initial data for the NWP models which will then reduce the errors in weather prediction