By using 3-year global positioning system (GPS)measurements from December 2013 to November 2016, we provide in this study a detailed survey on the climatology of the GPS signal loss of Swarm onboard receivers. Our results show that the GPS signal losses prefer to occur at both low latitudes between ±5 and ±20 ◦ magnetic latitude (MLAT) and high latitudes above 60 ◦ MLAT in both hemispheres. These events at all latitudes are observed mainly during equinoxes and December solstice months, while totally absent during June solstice months. At low latitudes the GPS signal losses are caused by the equatorial plasma irregularities shortly after sunset, and at high latitude they are also highly related to the large density gradients associated with ionospheric irregularities. Additionally, the high-latitude events are more often observed in the Southern Hemisphere, occurring mainly at the cusp region and along nightside auroral latitudes. The signal losses mainly happen for those GPS rays with elevation angles less than 20 ◦ , and more commonly occur when the line of sight between GPS and Swarm satellites is aligned with the shell structure of plasma irregularities. Our results also confirm that the capability of the Swarm receiver has been improved after the bandwidth of the phase-locked loop (PLL) widened, but the updates cannot radically avoid the interruption in tracking GPS satellites caused by the ionospheric plasma irregularities. Additionally, after the PLL bandwidth increased larger than 0.5 Hz, some unexpected signal losses are observed even at middle latitudes, which are not related to the ionospheric plasma irregularities. Our results suggest that rather than 1.0 Hz, a PLL bandwidth of 0.5 Hz is a more suitable value for the Swarm receiver.

The purpose of the project was to develop the Discounting Inventory (DI), a measure of individual differences in delay, probability, effort, and social discounting, all related to behavioral impulsivity. Over 400 items relating to four types of discounting were generated. Next, a study followed by a series of psychometric analyses of data obtained from a group of 2843 individuals was conducted. Principal Component Analysis yielded a four-factor structure of data, reflecting the four types of discounting. The results of Confirmatory Factor Analysis showed good fit of the four-factor model to data. Through several iterations of retaining and deleting items on the basis of their component loadings, item intercorrelations, and contribution to coefficient alphas, the total number of items was reduced to 48. The final 48-item version of the inventory has satisfactory psychometric characteristics, including Cronbach’s alpha and test–retest stability. In addition, significant correlations were observed between the DI and traditional discounting instruments, suggesting that the DI measures a construct similar to the behavioral discounting process. The development of the tool was based on the assumption that discounting is a personality trait. However, the present data suggest that discounting may reflect more a state than trait function.

Quantitative precipitation nowcasting (QPN) has become an essential technique in various application contexts, such as early warning or urban sewage control. A common heuristic prediction approach is to track the motion of precipitation features from a sequence of weather radar images and then to displace the precipitation field to the imminent future (minutes to hours) based on that motion, assuming that the intensity of the features remains constant (“Lagrangian persistence”). In that context, “optical flow” has become one of the most popular tracking techniques. Yet the present landscape of computational QPN models still struggles with producing open software implementations. Focusing on this gap, we have developed and extensively benchmarked a stack of models based on different optical flow algorithms for the tracking step and a set of parsimonious extrapolation procedures based on image warping and advection. We demonstrate that these models provide skillful predictions comparable with or even superior to state-of-the-art operational software. Our software library (“rainymotion”) for precipitation nowcasting is written in the Python programming language and openly available at GitHub (https://github.com/hydrogo/rainymotion, Ayzel et al., 2019). That way, the library may serve as a tool for providing fast, free, and transparent solutions that could serve as a benchmark for further model development and hypothesis testing – a benchmark that is far more advanced than the conventional benchmark of Eulerian persistence commonly used in QPN verification experiments.

In Europe, almost half of the terrestrial landscape is used for agriculture. Thus, semi-natural habitats such as field margins are substantial for maintaining diversity in intensively managed farmlands. However, plants located at field margins are threatened by agricultural practices such as the application of pesticides within the fields. Pesticides are chemicals developed to control for undesired species within agricultural fields to enhance yields. The use of pesticides implies, however, effects on non-target organisms within and outside of the agricultural fields. Non-target organisms are organisms not intended to be sprayed or controlled for. For example, plants occurring in field margins are not intended to be sprayed, however, can be impaired due to herbicide drift exposure. The authorization of plant protection products such as herbicides requires risk assessments to ensure that the application of the product has no unacceptable effects on the environment. For non-target terrestrial plants (NTTPs), the risk assessment is based on standardized greenhouse studies on plant individual level. To account for the protection of plant populations and communities under realistic field conditions, i.e. extrapolating from greenhouse studies to field conditions and from individual-level to community-level, assessment factors are applied. However, recent studies question the current risk assessment scheme to meet the specific protection goals for non-target terrestrial plants as suggested by the European Food Safety Authority (EFSA). There is a need to clarify the gaps of the current risk assessment and to include suitable higher tier options in the upcoming guidance document for non-target terrestrial plants. In my thesis, I studied the impact of herbicide drift exposure on NTTP communities using a mechanistic modelling approach. I addressed main gaps and uncertainties of the current risk assessment and finally suggested this modelling approach as a novel higher tier option in future risk assessments. Specifically, I extended the plant community model IBC-grass (Individual-based community model for grasslands) to reflect herbicide impacts on plant individuals. In the first study, I compared model predictions of short-term herbicide impacts on artificial plant communities with empirical data. I demonstrated the capability of the model to realistically reflect herbicide impacts. In the second study, I addressed the research question whether or not reproductive endpoints need to be included in future risk assessments to protect plant populations and communities. I compared the consequences of theoretical herbicide impacts on different plant attributes for long-term plant population dynamics in the community context. I concluded that reproductive endpoints only need to be considered if the herbicide effect is assumed to be very high. The endpoints measured in the current vegetative vigour and seedling emergence studies had high impacts for the dynamic of plant populations and communities already at lower effect intensities. Finally, the third study analysed long-term impacts of herbicide application for three different plant communities. This study highlighted the suitability of the modelling approach to simulate different communities and thus detecting sensitive environmental conditions. Overall, my thesis demonstrates the suitability of mechanistic modelling approaches to be used as higher tier options for risk assessments. Specifically, IBC-grass can incorporate available individual-level effect data of standardized greenhouse experiments to extrapolate to community-level under various environmental conditions. Thus, future risk assessments can be improved by detecting sensitive scenarios and including worst-case impacts on non-target plant communities.