@phdthesis{Banerjee2022,
  author    = {Banerjee, Abhirup},
  title     = {Characterizing the spatio-temporal patterns of extreme events},
  doi       = {10.25932/publishup-55983},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-559839},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xiv, 91},
  year      = {2022},
  abstract  = {Over the past decades, there has been a growing interest in 'extreme events' owing to the increasing threats that climate-related extremes such as floods, heatwaves, droughts, etc., pose to society. While extreme events have diverse definitions across various disciplines, ranging from earth science to neuroscience, they are characterized mainly as dynamic occurrences within a limited time frame that impedes the normal functioning of a system. Although extreme events are rare in occurrence, it has been found in various hydro-meteorological and physiological time series (e.g., river flows, temperatures, heartbeat intervals) that they may exhibit recurrent behavior, i.e., do not end the lifetime of the system. The aim of this thesis to develop some sophisticated methods to study various properties of extreme events. One of the main challenges in analyzing such extreme event-like time series is that they have large temporal gaps due to the paucity of the number of observations of extreme events. As a result, existing time series analysis tools are usually not helpful to decode the underlying information. I use the edit distance (ED) method to analyze extreme event-like time series in their unaltered form. ED is a specific distance metric, mainly designed to measure the similarity/dissimilarity between point process-like data. I combine ED with recurrence plot techniques to identify the recurrence property of flood events in the Mississippi River in the United States. I also use recurrence quantification analysis to show the deterministic properties and serial dependency in flood events. After that, I use this non-linear similarity measure (ED) to compute the pairwise dependency in extreme precipitation event series. I incorporate the similarity measure within the framework of complex network theory to study the collective behavior of climate extremes. Under this architecture, the nodes are defined by the spatial grid points of the given spatio-temporal climate dataset. Each node is associated with a time series corresponding to the temporal evolution of the climate observation at that grid point. Finally, the network links are functions of the pairwise statistical interdependence between the nodes. Various network measures, such as degree, betweenness centrality, clustering coefficient, etc., can be used to quantify the network's topology. We apply the methodology mentioned above to study the spatio-temporal coherence pattern of extreme rainfall events in the United States and the Ganga River basin, which reveals its relation to various climate processes and the orography of the region. The identification of precursors associated with the occurrence of extreme events in the near future is extremely important to prepare the masses for an upcoming disaster and mitigate the potential risks associated with such events. Under this motivation, I propose an in-data prediction recipe for predicting the data structures that typically occur prior to extreme events using the Echo state network, a type of Recurrent Neural Network which is a part of the reservoir computing framework. However, unlike previous works that identify precursory structures in the same variable in which extreme events are manifested (active variable), I try to predict these structures by using data from another dynamic variable (passive variable) which does not show large excursions from the nominal condition but carries imprints of these extreme events. Furthermore, my results demonstrate that the quality of prediction depends on the magnitude of events, i.e., the higher the magnitude of the extreme, the better is its predictability skill. I show quantitatively that this is because the input signals collectively form a more coherent pattern for an extreme event of higher magnitude, which enhances the efficiency of the machine to predict the forthcoming extreme events.},
  language  = {en}
}
@phdthesis{RamezaniZiarani2020,
  author    = {Ramezani Ziarani, Maryam},
  title     = {Characterization of atmospheric processes related to hydro-meteorological extreme events over the south-central Andes},
  doi       = {10.25932/publishup-47175},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-471755},
  school      = {Universit{\"a}t Potsdam},
  pages     = {i, 88},
  year      = {2020},
  abstract  = {The significant environmental and socioeconomic consequences of hydrometeorological extreme events, such as extreme rainfall, are constituted as a most important motivation for analyzing these events in the south-central Andes of NW Argentina. The steep topographic and climatic gradients and their interactions frequently lead to the formation of deep convective storms and consequently trigger extreme rainfall generation. In this dissertation, I focus on identifying the dominant climatic variables and atmospheric conditions and their spatiotemporal variability leading to deep convection and extreme rainfall in the south-central Andes. This dissertation first examines the significant contribution of temperature on atmospheric humidity (dew-point temperature, Td) and on convection (convective available potential energy, CAPE) for deep convective storms and hence, extreme rainfall along the topographic and climatic gradients. It was found that both climatic variables play an important role in extreme rainfall generation. However, their contributions differ depending on topographic and climatic sub-regions, as well as rainfall percentiles. Second, this dissertation explores if (near real-time) the measurements conducted by the Global Navigation Satellite System (GNSS) on integrated water vapor (IWV) provide reliable data for explaining atmospheric humidity. I argue that GNSS-IWV, in conjunction with other atmospheric stability parameters such as CAPE, is able to decipher the extreme rainfall in the eastern central Andes. In my work, I rely on a multivariable regression analysis described by a theoretical relationship and fitting function analysis. Third, this dissertation identifies the local impact of convection on extreme rainfall in the eastern Andes. Relying on a Principal Component Analysis (PCA) it was found that during the existence of moist and warm air, extreme rainfall is observed more often during local night hours. The analysis includes the mechanisms for this observation. Exploring the atmospheric conditions and climatic variables leading to extreme rainfall is one of the main findings of this dissertation. The conditions and variables are a prerequisite for understanding the dynamics of extreme rainfall and predicting these events in the eastern Andes.},
  language  = {en}
}