@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} } @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} } @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{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 and Drozdov, Alexander 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} }