Afterword: Concluding thoughts on the politics of memory in Asia

Anthropological research on memory and memorial practices has proliferated in the discipline in recent years. This unprecedented ‘memory boom’ has not been without its critics, however. David Berliner (2005), for example, has argued that the study of memory in its multiple discursive forms and settings (social, national, material, cultural...) has resulted in categorical and terminological confusion. On the other hand, as the papers in this collection so aptly demonstrate, the ethnographic study of memory remains a fertile terrain for examining the high stakes involved in struggles to attain voice, presence and representation in history (Litzinger 2000: 69). For studies of memory, Michel-Rolph Trouillot once argued, must firstly attend to competing claims to history, truth, power and subjectivity. ‘What matters most,’ he wrote, ‘are the process and conditions of the production of [historical] narratives (...) [and] the differential exercise of power that makes some narratives possible and silences others’ (1995: 25)

Large-Eddy simulation of nocturnal radiation fog: Advances in microphysical representation and process-level evaluation

Fog is characterized by the presence of liquid or solid water particles in the vicinity of Earth’s surface, that leads to a reduction in visibility to less than 1 km. This reduced visibility poses a significant threat to humans, especially in transportation. However, numerical weather prediction (NWP) models still frequently fail to predict fog correctly. This can be attributed to small-scale processes, which interact with one another on different scales. The research presented in this thesis consists of four research articles and aims to represent, understand, and quantify the significant processes during the life cycle of fog using highly resolved large- eddy simulation (LES). The first study investigates the effect of different microphysical parametrization on simu- lating fog. As found by other research, the number of cloud droplets is a crucial parameter determining the fog depth and the time of fog dissipation, which is, however, a fixed para- meter in many numerical models. After major model development to include a prognostic equation of the cloud droplet number concentration and schemes for activation and diffusio- nal growth, the error made by commonly used microphysical parameterizations (cloud bulk models) for simulating fog was evaluated. It was found that simulated fog reacts sensitive- ly to the method of calculating supersaturation, which determines the number of activated droplets. However, bulk cloud models like the one used in the first study are not suitable to remedy their immanent limitations, such as prescribing the shape of the cloud droplet size distribution (DSD) rather than simulating it. In the second study, an advanced method in cloud modeling (a so-called particle-based method) was applied for the first time to simulate fog. It was found that the shape of DSD in fog undergoes a temporal development. Moreover, compared to the particle-based microphysics, the bulk cloud model tends to overestimate the droplet number concentration but underrate droplet sedimentation. The subject of the third study was a model intercomparison of LES and single-column models (SCMs) for a radiation fog event. The study revealed significant differences between the SCMs (which are based on NWP models), but the LES models also showed a non-uniform picture. The representation of microphysics has been identified as the primary source of uncertainty in the simulation of fog, but with surface-layer fluxes also contributing to the uncertainty. The final study in this thesis discusses the influence of nocturnal fog on the evolution of the daytime boundary layer. The simulation results indicate that failing to resolve nocturnal fog leads to a faster boundary layer development, i.e., a higher temperature within the boundary layer and a higher inversion height during daytime

On the effect of nocturnal radiation fog on the development of the daytime convective boundary layer: A large-eddy simulation study

The potential effect of failing to predict nocturnal deep radiation fog on the development of the daytime convective boundary layer (CBL) is studied using large-eddy simulations. Typical spring and autumn conditions for the mid-latitudes are used to perform simulations in pairs. Fog formation is allowed in one simulation of each pair (nocturnal fog [NF]) and is suppressed in the other (clear sky [CS]). This allows for the identification of properties (temperature, humidity, boundary-layer depth), conditions, and processes in CBL development that are affected by fog. Mixing-layer temperatures and boundary-layer depths immediately after fog dissipation in CSs are shown to be up to 2.5 K warmer and 200 m higher, respectively, than the NF counterparts. Additionally, greater water vapor mixing ratios are found in the CSs. However, owing to greater temperatures, relative humidities at the CBL top are found to be less in CSs than in the corresponding NFs. This relative humidity difference might be an indication that cloud formation is suppressed to some extent. The magnitude of the differences between CSs and NFs during the day is mainly correlated to the fog depth (in terms of duration and liquid water path), whereas the key processes responsible for differences are the atmospheric long-wave cooling of the fog layer (for temperature development) and droplet deposition (for water vapor mixing ratio development).publishedVersio

On the effect of nocturnal radiation fog on the development of the daytime convective boundary layer: A large-eddy simulation study

The potential effect of failing to predict nocturnal deep radiation fog on the development of the daytime convective boundary layer (CBL) is studied using large-eddy simulations. Typical spring and autumn conditions for the mid-latitudes are used to perform simulations in pairs. Fog formation is allowed in one simulation of each pair (nocturnal fog [NF]) and is suppressed in the other (clear sky [CS]). This allows for the identification of properties (temperature, humidity, boundary-layer depth), conditions, and processes in CBL development that are affected by fog. Mixing-layer temperatures and boundary-layer depths immediately after fog dissipation in CSs are shown to be up to 2.5 K warmer and 200 m higher, respectively, than the NF counterparts. Additionally, greater water vapor mixing ratios are found in the CSs. However, owing to greater temperatures, relative humidities at the CBL top are found to be less in CSs than in the corresponding NFs. This relative humidity difference might be an indication that cloud formation is suppressed to some extent. The magnitude of the differences between CSs and NFs during the day is mainly correlated to the fog depth (in terms of duration and liquid water path), whereas the key processes responsible for differences are the atmospheric long-wave cooling of the fog layer (for temperature development) and droplet deposition (for water vapor mixing ratio development)

Towards a better representation of fog microphysics in large-eddy simulations based on an embedded Lagrangian cloud model

The development of radiation fog is influenced by multiple physical processes such as radiative cooling and heating, turbulent mixing, and microphysics, which interact on different spatial and temporal scales with one another. Once a fog layer has formed, the number of fog droplets and their size distribution have a particularly large impact on the development of the fog layer due to their feedback on gravitational settling and radiative cooling at the fog top, which are key processes for fog. However, most models do not represent microphysical processes explicitly, or parameterize them rather crudely. In this study we simulate a deep radiation fog case with a coupled large-eddy simulation (LES)–Lagrangian cloud model (LCM) approach for the first time. By simulating several hundred million fog droplets as Lagrangian particles explicitly (using the so-called superdroplet approach), we include a size-resolved diffusional growth including Köhler theory and gravitational sedimentation representation. The results are compared against simulations using a state of the art bulk microphysics model (BCM). We simulate two different aerosol backgrounds (pristine and polluted) with each microphysics scheme. The simulations show that both schemes generally capture the key features of the deep fog event, but also that there are significant differences: the drop size distribution produced by the LCM is broader during the formation and dissipation phase than in the BCM. The LCM simulations suggest that its spectral shape, which is fixed in BCMs, exhibits distinct changes during the fog life cycle, which cannot be taken into account in BCMs. The picture of the overall fog droplet number concentration is twofold: For both aerosol environments, the LCM shows lower concentrations of larger fog droplets, while we observe a higher number of small droplets and swollen aerosols reducing the visibility earlier than in the BCM. As a result of the different model formulation we observe higher sedimentation rates and lower liquid water paths for the LCM. The present work demonstrates that it is possible to simulate fog with the computational demanding approach of LCMs to assess the advantages of high-resolution cloud models and further to estimate errors of traditional parameterizations.publishedVersio

Online learning with stability guarantees: A memory-based real-time model predictive controller

We propose and analyze a real-time model predictive control (MPC) scheme that utilizes stored data to improve its performance by learning the value function online with stability guarantees. For linear and nonlinear systems, a learning method is presented that makes use of basic analytic properties of the cost function and is proven to learn the MPC control law and the value function on the limit set of the closed-loop state trajectory. The main idea is to generate a smart warm start based on historical data that improves future data points and thus future warm starts. We show that these warm starts are asymptotically exact and converge to the solution of the MPC optimization problem. Thereby, the suboptimality of the applied control input resulting from the real-time requirements vanishes over time. Simulative examples show that existing real-time MPC schemes can be improved by storing data and the proposed learning scheme.Comment: This article is an extended version of the paper "Online learning with stability guarantees: A memory-based warm starting for real-time MPC" published in Automatica, Volume 122, 109247, 2020, including all proofs, an application example, and a detailed description of the used algorith