TY - GEN A1 - Ayzel, Georgy A1 - Scheffer, Tobias A1 - Heistermann, Maik T1 - RainNet v1.0 BT - a convolutional neural network for radar-based precipitation nowcasting T2 - Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - 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. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 964 KW - weather KW - models KW - skill Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-472942 SN - 1866-8372 IS - 964 ER - TY - JOUR A1 - Ayzel, Georgy A1 - Scheffer, Tobias A1 - Heistermann, Maik T1 - RainNet v1.0 BT - a convolutional neural network for radar-based precipitation nowcasting JF - Geoscientific Model Development N2 - 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. KW - weather KW - models KW - skill Y1 - 2020 U6 - https://doi.org/10.5194/gmd-13-2631-2020 SN - 1991-959X SN - 1991-9603 VL - 13 IS - 6 SP - 2631 EP - 2644 PB - Copernicus Publ. CY - Göttingen ER - TY - GEN A1 - Hortobágyi, Tibor A1 - Lesinski, Melanie A1 - Fernandez‐del‐Olmo, Miguel A1 - Granacher, Urs T1 - Small and inconsistent effects of whole body vibration on athletic performance BT - a systematic review and meta-analysis T2 - Postprints der Universität Potsdam : Humanwissenschaftliche Reihe N2 - Purpose We quantified the acute and chronic effects of whole body vibration on athletic performance or its proxy measures in competitive and/or elite athletes. Methods Systematic literature review and meta-analysis. Results Whole body vibration combined with exercise had an overall 0.3 % acute effect on maximal voluntary leg force (−6.4 %, effect size = −0.43, 1 study), leg power (4.7 %, weighted mean effect size = 0.30, 6 studies), flexibility (4.6 %, effect size = −0.12 to 0.22, 2 studies), and athletic performance (−1.9 %, weighted mean effect size = 0.26, 6 studies) in 191 (103 male, 88 female) athletes representing eight sports (overall effect size = 0.28). Whole body vibration combined with exercise had an overall 10.2 % chronic effect on maximal voluntary leg force (14.6 %, weighted mean effect size = 0.44, 5 studies), leg power (10.7 %, weighted mean effect size = 0.42, 9 studies), flexibility (16.5 %, effect size = 0.57 to 0.61, 2 studies), and athletic performance (−1.2 %, weighted mean effect size = 0.45, 5 studies) in 437 (169 male, 268 female) athletes (overall effect size = 0.44). Conclusions Whole body vibration has small and inconsistent acute and chronic effects on athletic performance in competitive and/or elite athletes. These findings lead to the hypothesis that neuromuscular adaptive processes following whole body vibration are not specific enough to enhance athletic performance. Thus, other types of exercise programs (e.g., resistance training) are recommended if the goal is to improve athletic performance. T3 - Zweitveröffentlichungen der Universität Potsdam : Humanwissenschaftliche Reihe - 627 KW - exercise KW - muscle KW - force KW - power KW - skill KW - reflex KW - endocrine KW - metabolism Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-431993 SN - 1866-8364 IS - 627 ER - TY - JOUR A1 - Kühne, Franziska A1 - Lacki, Fiona Janina A1 - Muse, Kate A1 - Weck, Florian T1 - Strengthening competence of therapists-in-training in the treatment of health anxiety (hypochondriasis) BT - validation of the assessment of Core CBT Skills (ACCS) JF - Clinical psychology & psychotherapy : an international journal of theory and practice N2 - Although the observation and assessment of psychotherapeutic competences are central to training, supervision, patient care, quality control, and life-long practice, structured instruments are used only occasionally. In the current study, an observation-based tool for the Assessment of Core CBT Skills (ACCS) was translated into German and adapted, and its psychometric properties were pilot evaluated. Competence of therapists-in-training was assessed in a random sample of n = 30 videos on cognitive behavioural therapy including patients diagnosed with hypochondriasis. Two of three raters independently assessed the competences demonstrated in the entire, active treatment sessions (n = 60). In our sample, internal consistency was excellent, and interrater reliability was good. Convergent validity (Cognitive Therapy Scale) and discriminant validity (Helping Alliance Questionnaire) were within the expected ranges. The ACCS total score did not significantly predict the reduction of symptoms of hypochondriasis, and a one-factorial structure of the instrument was found. By providing multiple opportunities for feedback, self-reflection, and supervision, the ACCS may complement current tools for the assessment of psychotherapeutic competences and importantly support competence-based training and supervision. KW - adherence KW - assessment KW - process research KW - psychotherapy KW - skill Y1 - 2019 U6 - https://doi.org/10.1002/cpp.2353 SN - 1063-3995 SN - 1099-0879 VL - 26 IS - 3 SP - 319 EP - 327 PB - Wiley CY - Hoboken ER -