@article{SedovaKalkuhlMendelsohn2020,
  author    = {Sedova, Barbora and Kalkuhl, Matthias and Mendelsohn, Robert},
  title     = {Distributional impacts of weather and climate in rural India},
  series = {Economics of disasters and climate change},
  volume    = {4},
  journal   = {Economics of disasters and climate change},
  number    = {1},
  publisher = {Springer},
  address   = {Cham},
  issn      = {2511-1280},
  doi       = {10.1007/s41885-019-00051-1},
  pages     = {5 -- 44},
  year      = {2020},
  abstract  = {Climate-related costs and benefits may not be evenly distributed across the population. We study distributional implications of seasonal weather and climate on within-country inequality in rural India. Utilizing a first difference approach, we find that the poor are more sensitive to weather variations than the non-poor. The poor respond more strongly to (seasonal) temperature changes: negatively in the (warm) spring season, more positively in the (cold) rabi season. Less precipitation is harmful to the poor in the monsoon kharif season and beneficial in the winter and spring seasons. We show that adverse weather aggravates inequality by reducing consumption of the poor farming households. Future global warming predicted under RCP8.5 is likely to exacerbate these effects, reducing consumption of poor farming households by one third until the year 2100. We also find inequality in consumption across seasons with higher consumption during the harvest and lower consumption during the sowing seasons.},
  language  = {en}
}
@article{CervantesShpritsAseevetal.2019,
  author    = {Cervantes, Sebastian and Shprits, Yuri Y. and Aseev, Nikita A. and Drozdov, Alexander Y. and Castillo Tibocha, Angelica Maria and Stolle, Claudia},
  title     = {Identifying radiation belt electron source and loss processes by assimilating spacecraft data in a three-dimensional diffusion model},
  series = {Journal of geophysical research : Space physics},
  volume    = {125},
  journal   = {Journal of geophysical research : Space physics},
  number    = {1},
  publisher = {American Geophysical Union},
  address   = {Washington},
  issn      = {2169-9380},
  doi       = {10.1029/2019JA027514},
  pages     = {16},
  year      = {2019},
  abstract  = {Data assimilation aims to blend incomplete and inaccurate data with physics-based dynamical models. In the Earth's radiation belts, it is used to reconstruct electron phase space density, and it has become an increasingly important tool in validating our current understanding of radiation belt dynamics, identifying new physical processes, and predicting the near-Earth hazardous radiation environment. In this study, we perform reanalysis of the sparse measurements from four spacecraft using the three-dimensional Versatile Electron Radiation Belt diffusion model and a split-operator Kalman filter over a 6-month period from 1 October 2012 to 1 April 2013. In comparison to previous works, our 3-D model accounts for more physical processes, namely, mixed pitch angle-energy diffusion, scattering by Electromagnetic Ion Cyclotron waves, and magnetopause shadowing. We describe how data assimilation, by means of the innovation vector, can be used to account for missing physics in the model. We use this method to identify the radial distances from the Earth and the geomagnetic conditions where our model is inconsistent with the measured phase space density for different values of the invariants mu and K. As a result, the Kalman filter adjusts the predictions in order to match the observations, and we interpret this as evidence of where and when additional source or loss processes are active. The current work demonstrates that 3-D data assimilation provides a comprehensive picture of the radiation belt electrons and is a crucial step toward performing reanalysis using measurements from ongoing and future missions.},
  language  = {en}
}
@phdthesis{Šedova2022,
  author    = {Šedov{\´a}, Barbora},
  title     = {Heterogeneous effects of weather and climate change on human migration},
  doi       = {10.25932/publishup-53673},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-536733},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xix, 284},
  year      = {2022},
  abstract  = {While estimated numbers of past and future climate migrants are alarming, the growing empirical evidence suggests that the association between adverse climate-related events and migration is not universally positive. This dissertation seeks to advance our understanding of when and how climate migration emerges by analyzing heterogeneous climatic influences on migration in low- and middle-income countries. To this end, it draws on established economic theories of migration, datasets from physical and social sciences, causal inference techniques and approaches from systematic literature review. In three of its five chapters, I estimate causal effects of processes of climate change on inequality and migration in India and Sub-Saharan Africa. By employing interaction terms and by analyzing sub-samples of data, I explore how these relationships differ for various segments of the population. In the remaining two chapters, I present two systematic literature reviews. First, I undertake a comprehensive meta-regression analysis of the econometric climate migration literature to summarize general climate migration patterns and explain the conflicting findings. Second, motivated by the broad range of approaches in the field, I examine the literature from a methodological perspective to provide best practice guidelines for studying climate migration empirically. Overall, the evidence from this dissertation shows that climatic influences on human migration are highly heterogeneous. Whether adverse climate-related impacts materialize in migration depends on the socio-economic characteristics of the individual households, such as wealth, level of education, agricultural dependence or access to adaptation technologies and insurance. For instance, I show that while adverse climatic shocks are generally associated with an increase in migration in rural India, they reduce migration in the agricultural context of Sub-Saharan Africa, where the average wealth levels are much lower so that households largely cannot afford the upfront costs of moving. I find that unlike local climatic shocks which primarily enhance internal migration to cities and hence accelerate urbanization, shocks transmitted via agricultural producer prices increase migration to neighboring countries, likely due to the simultaneous decrease in real income in nearby urban areas. These findings advance our current understanding by showing when and how economic agents respond to climatic events, thus providing explicit contexts and mechanisms of climate change effects on migration in the future. The resulting collection of findings can guide policy interventions to avoid or mitigate any present and future welfare losses from climate change-related migration choices.},
  language  = {en}
}
@article{Ungelenk2020,
  author    = {Ungelenk, Johannes},
  title     = {The storm is up and all is on the hazard},
  series = {Poetica},
  volume    = {51},
  journal   = {Poetica},
  number    = {1-2},
  issn      = {0303-4178},
  doi       = {10.30965/25890530-05101003},
  pages     = {119 -- 147},
  year      = {2020},
  abstract  = {The article is dedicated to the role of weather in Shakespeare's tragedies. It traces a dense net of weather instances - stage weather, narrated weather events, weather imagery - throughout his plays, and attempts to reconstruct the weather's structural implications for the tragedy genre. The way early modern humoral pathology understood the weather's influence on the humours of the human body - of which Shakespeare's plays themselves give evidence - provides the background for reconstructing the function of the weather as a source of tragic force. Its turbulence not only infects the characters in the play and thereby drives the plot, but also transgresses the boundaries of the fictional world and affects spectators in the auditorium.},
  language  = {de}
}
@article{AyzelSchefferHeistermann2020,
  author    = {Ayzel, Georgy and Scheffer, Tobias and Heistermann, Maik},
  title     = {RainNet v1.0},
  series = {Geoscientific Model Development},
  volume    = {13},
  journal   = {Geoscientific Model Development},
  number    = {6},
  publisher = {Copernicus Publ.},
  address   = {G{\"o}ttingen},
  issn      = {1991-959X},
  doi       = {10.5194/gmd-13-2631-2020},
  pages     = {2631 -- 2644},
  year      = {2020},
  abstract  = {In this study, we present RainNet, a deep convolutional neural network for radar-based precipitation nowcasting. Its design was inspired by the U-Net and SegNet families of deep learning models, which were originally designed for binary segmentation tasks. RainNet was trained to predict continuous precipitation intensities at a lead time of 5min, using several years of quality-controlled weather radar composites provided by the German Weather Service (DWD). That data set covers Germany with a spatial domain of 900km × 900km and has a resolution of 1km in space and 5min in time. Independent verification experiments were carried out on 11 summer precipitation events from 2016 to 2017. In order to achieve a lead time of 1h, a recursive approach was implemented by using RainNet predictions at 5min lead times as model inputs for longer lead times. In the verification experiments, trivial Eulerian persistence and a conventional model based on optical flow served as benchmarks. The latter is available in the rainymotion library and had previously been shown to outperform DWD's operational nowcasting model for the same set of verification events. RainNet significantly outperforms the benchmark models at all lead times up to 60min for the routine verification metrics mean absolute error (MAE) and the critical success index (CSI) at intensity thresholds of 0.125, 1, and 5mm h⁻¹. However, rainymotion turned out to be superior in predicting the exceedance of higher intensity thresholds (here 10 and 15mm h⁻¹). The limited ability of RainNet to predict heavy rainfall intensities is an undesirable property which we attribute to a high level of spatial smoothing introduced by the model. At a lead time of 5min, an analysis of power spectral density confirmed a significant loss of spectral power at length scales of 16km and below. Obviously, RainNet had learned an optimal level of smoothing to produce a nowcast at 5min lead time. In that sense, the loss of spectral power at small scales is informative, too, as it reflects the limits of predictability as a function of spatial scale. Beyond the lead time of 5min, however, the increasing level of smoothing is a mere artifact - an analogue to numerical diffusion - that is not a property of RainNet itself but of its recursive application. In the context of early warning, the smoothing is particularly unfavorable since pronounced features of intense precipitation tend to get lost over longer lead times. Hence, we propose several options to address this issue in prospective research, including an adjustment of the loss function for model training, model training for longer lead times, and the prediction of threshold exceedance in terms of a binary segmentation task. Furthermore, we suggest additional input data that could help to better identify situations with imminent precipitation dynamics. The model code, pretrained weights, and training data are provided in open repositories as an input for such future studies.},
  language  = {en}
}
@misc{AyzelSchefferHeistermann2020,
  author    = {Ayzel, Georgy and Scheffer, Tobias and Heistermann, Maik},
  title     = {RainNet v1.0},
  series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {964},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-47294},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-472942},
  pages     = {16},
  year      = {2020},
  abstract  = {In this study, we present RainNet, a deep convolutional neural network for radar-based precipitation nowcasting. Its design was inspired by the U-Net and SegNet families of deep learning models, which were originally designed for binary segmentation tasks. RainNet was trained to predict continuous precipitation intensities at a lead time of 5min, using several years of quality-controlled weather radar composites provided by the German Weather Service (DWD). That data set covers Germany with a spatial domain of 900km × 900km and has a resolution of 1km in space and 5min in time. Independent verification experiments were carried out on 11 summer precipitation events from 2016 to 2017. In order to achieve a lead time of 1h, a recursive approach was implemented by using RainNet predictions at 5min lead times as model inputs for longer lead times. In the verification experiments, trivial Eulerian persistence and a conventional model based on optical flow served as benchmarks. The latter is available in the rainymotion library and had previously been shown to outperform DWD's operational nowcasting model for the same set of verification events. RainNet significantly outperforms the benchmark models at all lead times up to 60min for the routine verification metrics mean absolute error (MAE) and the critical success index (CSI) at intensity thresholds of 0.125, 1, and 5mm h⁻¹. However, rainymotion turned out to be superior in predicting the exceedance of higher intensity thresholds (here 10 and 15mm h⁻¹). The limited ability of RainNet to predict heavy rainfall intensities is an undesirable property which we attribute to a high level of spatial smoothing introduced by the model. At a lead time of 5min, an analysis of power spectral density confirmed a significant loss of spectral power at length scales of 16km and below. Obviously, RainNet had learned an optimal level of smoothing to produce a nowcast at 5min lead time. In that sense, the loss of spectral power at small scales is informative, too, as it reflects the limits of predictability as a function of spatial scale. Beyond the lead time of 5min, however, the increasing level of smoothing is a mere artifact - an analogue to numerical diffusion - that is not a property of RainNet itself but of its recursive application. In the context of early warning, the smoothing is particularly unfavorable since pronounced features of intense precipitation tend to get lost over longer lead times. Hence, we propose several options to address this issue in prospective research, including an adjustment of the loss function for model training, model training for longer lead times, and the prediction of threshold exceedance in terms of a binary segmentation task. Furthermore, we suggest additional input data that could help to better identify situations with imminent precipitation dynamics. The model code, pretrained weights, and training data are provided in open repositories as an input for such future studies.},
  language  = {en}
}
@article{Ungelenk2020,
  author    = {Ungelenk, Johannes},
  title     = {Satyrs, Spirits and Dionysian Intemperance in Shakespeare's 'Tempest'},
  series = {Cahiers {\´E}lisab{\´e}thains},
  volume    = {101},
  journal   = {Cahiers {\´E}lisab{\´e}thains},
  number    = {1},
  publisher = {Sage Publications},
  address   = {London},
  issn      = {0184-7678},
  doi       = {10.1177/0184767819897082},
  pages     = {45 -- 64},
  year      = {2020},
  abstract  = {The article focuses on the rebellious subplot of William Shakespeare's The Tempest that forms around Caliban, Stephano, and Trinculo, and reads it as a satyr play. Demonstrated is how the Dionysian subplot stands in close analogical connection with the play's main action. It is also argued that the storyline emphasises a dimension of the play that is of high relevance to the analysis of its metatheatrical implications. The correspondences between the main action and the satyr play elements highlight the important role that intemperance, excess and the suspension of control play in the Shakespearean theatrical setting.},
  language  = {en}
}
@misc{MolnosMamdouhPetrietal.2017,
  author    = {Molnos, Sonja and Mamdouh, Tarek and Petri, Stefan and Nocke, Thomas and Weinkauf, Tino and Coumou, Dim},
  title     = {A network-based detection scheme for the jet stream core},
  series = {Postprints der Universit{\"a}t Potsdam : Mathematisch Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Mathematisch Naturwissenschaftliche Reihe},
  number    = {625},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-41909},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-419099},
  pages     = {75 -- 89},
  year      = {2017},
  abstract  = {The polar and subtropical jet streams are strong upper-level winds with a crucial influence on weather throughout the Northern Hemisphere midlatitudes. In particular, the polar jet is located between cold arctic air to the north and warmer subtropical air to the south. Strongly meandering states therefore often lead to extreme surface weather. Some algorithms exist which can detect the 2-D (latitude and longitude) jets' core around the hemisphere, but all of them use a minimal threshold to determine the subtropical and polar jet stream. This is particularly problematic for the polar jet stream, whose wind velocities can change rapidly from very weak to very high values and vice versa. We develop a network-based scheme using Dijkstra's shortest-path algorithm to detect the polar and subtropical jet stream core. This algorithm not only considers the commonly used wind strength for core detection but also takes wind direction and climatological latitudinal position into account. Furthermore, it distinguishes between polar and subtropical jet, and between separate and merged jet states. The parameter values of the detection scheme are optimized using simulated annealing and a skill function that accounts for the zonal-mean jet stream position (Rikus, 2015). After the successful optimization process, we apply our scheme to reanalysis data covering 1979-2015 and calculate seasonal-mean probabilistic maps and trends in wind strength and position of jet streams. We present longitudinally defined probability distributions of the positions for both jets for all on the Northern Hemisphere seasons. This shows that winter is characterized by two well-separated jets over Europe and Asia (ca. 20 degrees W to 140 degrees E). In contrast, summer normally has a single merged jet over the western hemisphere but can have both merged and separated jet states in the eastern hemisphere. With this algorithm it is possible to investigate the position of the jets' cores around the hemisphere and it is therefore very suitable to analyze jet stream patterns in observations and models, enabling more advanced model-validation.},
  language  = {en}
}
@phdthesis{Barsch2003,
  author    = {Barsch, Andreas},
  title     = {Zum Einfluss von Witterung und Klima auf den Landschaftszustand und die Landschaftsentwicklung im Uvs-Nuur-Becken (NW-Mongolei)},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-0001184},
  school      = {Universit{\"a}t Potsdam},
  year      = {2003},
  abstract  = {Im Landschaftszustand und in der Landschaftsentwicklung kommen funktionale Beziehungen zwischen dem naturbedingten Energie-, Wasser- und Stoffhaushalt einerseits und den Auswirkungen der Landnutzung andererseits zum Ausdruck. Gegenw{\"a}rtig ver{\"a}ndert der globale Anstieg der bodennahen Temperaturen vielerorts den landschaftlichen Energie-, Wasser- und Stoffhaushalt, wobei besonders in Trockengebieten zu erwarten ist, dass dieser Trend in Verbindung mit einer unangepassten Landnutzung das Regenerationsverm{\"o}gen der Vegetation einschr{\"a}nkt und zur Zerst{\"o}rung der Bodendecke f{\"u}hrt. F{\"u}r die Mongolei und f{\"u}r benachbarte Gebiete Asiens sind in Szenarien zur globalen Erw{\"a}rmung hohe Werte des Temperaturanstiegs prognostiziert worden. Eine globale Einsch{\"a}tzung der anthropogen induzierten Bodendegradation hat diese Region als stark oder extrem stark betroffen eingestuft. Vor diesem Hintergrund wurde im Uvs-Nuur-Becken, das im Nordwesten der Mongolei und damit in einer der trockensten Regionen des Landes gelegen ist, untersucht, wie sich der globale Temperaturanstieg auf der lokalen und regionalen Ebene widerspiegelt und wie der Landschaftshaushalt dabei ver{\"a}ndert wird. Die Auswirkungen des sommerlichen Witterungsverlaufes auf den Landschaftszustand sind 1997 bis 1999 an einem Transsekt erfasst worden, das sich zwischen dem Kharkhiraa-Gebirge am Westrand des Beckens und dem See Uvs Nuur im Beckeninneren von den Polsterfluren und Matten der alpinen Stufe {\"u}ber die Gebirgswaldsteppe, die Trockensteppe bis zur Halbw{\"u}ste erstreckt. An neun Messpunkten wurden witterungsklimatische Daten in Verbindung mit Merkmalen der Vegetation, des Bodens und der Bodenfeuchte aufgenommen. Die im Sommer 1998 gewonnenen Messwerte wurden mit Hilfe einer Clusteranalyse geb{\"u}ndelt und verdichtet. Auf dieser Grundlage konnten landschaftliche Zustandsformen inhaltlich gekennzeichnet, zeitlich eingeordnet und durch Zeit-Verhaltens-Modelle (Stacks) abgebildet werden. Aus den Zeit-Verhaltens-Modellen wird ersichtlich, dass man Zustandsformen, in denen die Hitze und die Trockenheit des Sommers 1998 besonders stark zum Ausdruck kommen, an allen Messpunkten beobachten kann, nimmt man die Station auf dem fast 3.000 m hohen Gipfel des Khukh Uul sowie die grundwasserbeeinflusste Station in unmittelbarer Seen{\"a}he aus. In ihrer extremen Form sind Trockenperioden jedoch nur im Beckeninneren und am Fuß der Randgebirge, also in der Halbw{\"u}ste, in der Trockensteppe und in der Wiesensteppe aufgetreten. Im Bergwald sowie im Bereich der alpinen Matten und Polsterfluren fehlen sie. Am st{\"a}rksten sind die grundwasserfreien Bereiche der Halbw{\"u}ste von der Hitze und Niederschlagsarmut des Sommers 1998 betroffen. An vier F{\"u}nfteln der Tage des Beobachtungszeitraumes herrscht an diesem Messpunkt extreme Trockenheit. Es f{\"a}llt entweder gar kein Niederschlag oder nur so wenig, dass der seit dem Fr{\"u}hjahr ersch{\"o}pfte Bodenwasservorrat nicht aufgef{\"u}llt wird. Das Verh{\"a}ltnis zwischen Niederschlag und potenzieller Verdunstung liegt hier bei 1:12. In der Halbw{\"u}ste zeichnet sich eine fortschreitende Desertifikation ab, zumal hier eine nichtangepasste Weidenutzung dominiert, in der Ziegen eine immer gr{\"o}ßere Rolle spielen. Dies gilt insbesondere f{\"u}r Bereiche in Siedlungsn{\"a}he. {\"O}rtlich ist auch der Bestand der Trockensteppe gef{\"a}hrdet, die sich an die Halbw{\"u}ste zum Beckenrand hin anschließt. Hier ist nicht nur die Viehdichte am h{\"o}chsten, sondern hier werden auch die meisten unbefestigten Fahrwege wild angelegt und die Bodendecke damit zerst{\"o}rt. Dies kann im Endeffekt zu einem {\"U}bergreifen von Prozessen der Desertifikation f{\"u}hren. Aus methodischer Sicht zeigt sich, dass die Kennzeichnung landschaftlicher Zustandsformen durch Zeit-Verhaltens-Modelle die Ermittlung der Auswirkungen von Witterung und Klima auf den Landschaftszustand erleichtert, da sie deren Aussage konzentriert. Zur Interpretation der Ergebnisse ist jedoch ein R{\"u}ckgriff auf die beschreibende Darstellung der Messwerte notwendig. Die im westlichen Uvs-Nuur-Becken und seinen Randgebirgen angewandte Verfahrensweise erm{\"o}glicht es, globale Aussagen zur globalen Erw{\"a}rmung der Kontinente regional oder lokal zu {\"u}berpr{\"u}fen und zu untersetzen."},
  language  = {de}
}