Tropical Cyclone Boundary-Layer Models

Hurricanes are some of the most spectacular yet deadly natural disasters. Especially in times of the widely discussed anthropogenic climate change, public interest focusses on such extreme weather events. Nowadays, highly sophisticated numerical models are used for example for track prediction, but still there are many fundamental open questions. Among these, the question how intense a tropical cyclone may become is of major interest. In this work a study of the two most common types of models for the hurricane boundary layer is carried out. This study reveals major deficiencies of boundary layer models and finally leads to a reassessment of the established theory of potential intensity of hurricanes. In chapter (2), a linear model for the hurricane boundary layer is derived from a detailed scale analysis of the full equations of motions. It is shown how analytic solutions for the model may be calculated and how these solutions may be used to appraise the integrity of the linear approximation. Some of the results of this chapter are published in Vogl and Smith (2009). In chapter (3), a slab model is examined, which yields results for the main thermodynamic quantities. Depending on the chosen boundary layer depth and the imposed wind profile, two different types of solution behaviour found and interpreted. Other aspects of the dynamics and thermodynamics of the boundary layer are studied as for example the influence of shallow convection. The limitations and strengths of the slab model are discussed at the end of chapter (3). The results are published in Smith and Vogl (2008). The results of the detailed investigation of the linear and the slab model both point out an important deficiency of hurricane boundary layer models, namely the assumption of gradient wind balance. In chapter (4) it is shown that indeed the major deficiency of the established hurricane (P)otential (I)ntensity theory is the tacit assumption of gradient wind balance in the boundary layer. The results of chapter (4) show a fundamental problem of the established PI theory and then point to an improved conceptual model of the hurricane inner core region. Thus this work suggests a way forward to an urgently needed more consistent theory for the hurricane potential intensity. It is published in Smith, Montgomery and Vogl (2008)

Modelling Precipitation Intensities from X-Band Radar Measurements Using Artificial Neural Networks—A Feasibility Study for the Bavarian Oberland Region

Radar data may potentially provide valuable information for precipitation quantification, especially in regions with a sparse network of in situ observations or in regions with complex topography. Therefore, our aim is to conduct a feasibility study to quantify precipitation intensities based on radar measurements and additional meteorological variables. Beyond the well-established Z–R relationship for the quantification, this study employs Artificial Neural Networks (ANNs) in different settings and analyses their performance. For this purpose, the radar data of a station in Upper Bavaria (Germany) is used and analysed for its performance in quantifying in situ observations. More specifically, the effects of time resolution, time offsets in the input data, and meteorological factors on the performance of the ANNs are investigated. It is found that ANNs that use actual reflectivity as only input are outperforming the standard Z–R relationship in reproducing ground precipitation. This is reflected by an increase in correlation between modelled and observed data from 0.67 (Z–R) to 0.78 (ANN) for hourly and 0.61 to 0.86, respectively, for 10 min time resolution. However, the focus of this study was to investigate if model accuracy benefits from additional input features. It is shown that an expansion of the input feature space by using time-lagged reflectivity with lags up to two and additional meteorological variables such as temperature, relative humidity, and sunshine duration significantly increases model performance. Thus, overall, it is shown that a systematic predictor screening and the correspondent extension of the input feature space substantially improves the performance of a simple Neural Network model. For instance, air temperature and relative humidity provide valuable additional input information. It is concluded that model performance is dependent on all three ingredients: time resolution, time lagged information, and additional meteorological input features. Taking all of these into account, the model performance can be optimized to a correlation of 0.9 and minimum model bias of 0.002 between observed and modelled precipitation data even with a simple ANN architecture

Modelling precipitation intensities from x-band radar measurements using Artificial Neural Networks — a feasibility study for the Bavarian Oberland region

Radar data may potentially provide valuable information for precipitation quantification, especially in regions with a sparse network of in situ observations or in regions with complex topography. Therefore, our aim is to conduct a feasibility study to quantify precipitation intensities based on radar measurements and additional meteorological variables. Beyond the well-established Z–R relationship for the quantification, this study employs Artificial Neural Networks (ANNs) in different settings and analyses their performance. For this purpose, the radar data of a station in Upper Bavaria (Germany) is used and analysed for its performance in quantifying in situ observations. More specifically, the effects of time resolution, time offsets in the input data, and meteorological factors on the performance of the ANNs are investigated. It is found that ANNs that use actual reflectivity as only input are outperforming the standard Z–R relationship in reproducing ground precipitation. This is reflected by an increase in correlation between modelled and observed data from 0.67 (Z–R) to 0.78 (ANN) for hourly and 0.61 to 0.86, respectively, for 10 min time resolution. However, the focus of this study was to investigate if model accuracy benefits from additional input features. It is shown that an expansion of the input feature space by using time-lagged reflectivity with lags up to two and additional meteorological variables such as temperature, relative humidity, and sunshine duration significantly increases model performance. Thus, overall, it is shown that a systematic predictor screening and the correspondent extension of the input feature space substantially improves the performance of a simple Neural Network model. For instance, air temperature and relative humidity provide valuable additional input information. It is concluded that model performance is dependent on all three ingredients: time resolution, time lagged information, and additional meteorological input features. Taking all of these into account, the model performance can be optimized to a correlation of 0.9 and minimum model bias of 0.002 between observed and modelled precipitation data even with a simple ANN architecture

Assessing large-scale weekly cycles in meteorological variables

Several studies have claimed to have found significant weekly cycles of meteorological variables appearing over large domains, which can hardly be related to urban effects exclusively. Nevertheless, there is still an ongoing scientific debate whether these large-scale weekly cycles exist or not, and some other studies fail to reproduce them with statistical significance. In addition to the lack of the positive proof for the existence of these cycles, their possible physical explanations have been controversially discussed during the last years. In this work we review the main results about this topic published during the recent two decades, including a summary of the existence or non-existence of significant weekly weather cycles across different regions of the world, mainly over the US, Europe and Asia. In addition, some shortcomings of common statistical methods for analyzing weekly cycles are listed. Finally, a brief summary of supposed causes of the weekly cycles, focusing on the aerosol-cloud-radiation interactions and their impact on meteorological variables as a result of the weekly cycles of anthropogenic activities, and possible directions for future research, is presented

Transplantation of progenitor cells and regeneration enhancement in acute myocardial infarction Final one-year results of the TOPCARE-AMI Trial

ObjectivesThe Transplantation of Progenitor Cells And Regeneration Enhancement in Acute Myocardial Infarction (TOPCARE-AMI) trial investigates both safety, feasibility, and potential effects on parameters of myocardial function of intracoronary infusion of either circulating progenitor cells (CPC) or bone marrow-derived progenitor cells (BMC) in patients with acute myocardial infarction (AMI).BackgroundIn animal experiments, therapy with adult progenitor cells was shown to improve vascularization, left ventricular (LV) remodeling, and contractility after AMI.MethodsA total of 59 patients with AMI were randomly assigned to receive either CPC (n = 30) or BMC (n = 29) into the infarct artery at 4.9 ± 1.5 days after AMI.ResultsIntracoronary progenitor cell application did not incur any measurable ischemic myocardial damage, but one patient experienced distal embolization before cell therapy. During hospital follow-up, one patient in each cell group developed myocardial infarction; one of these patients died of cardiogenic shock. No further cardiovascular events, including ventricular arrhythmias or syncope, occurred during one-year follow-up. By quantitative LV angiography at four months, LV ejection fraction (EF) significantly increased (50 ± 10% to 58 ± 10%; p < 0.001), and end-systolic volumes significantly decreased (54 ± 19 ml to 44 ± 20 ml; p < 0.001), without differences between the two cell groups. Contrast-enhanced magnetic resonance imaging after one year revealed an increased EF (p < 0.001), reduced infarct size (p < 0.001), and absence of reactive hypertrophy, suggesting functional regeneration of the infarcted ventricles.ConclusionsIntracoronary infusion of progenitor cells (either BMC or CPC) is safe and feasible in patients after AMI successfully revascularized by stent implantation. Both the excellent safety profile and the observed favorable effects on LV remodeling, provide the rationale for larger randomized double-blind trials

Alarmins MRP8 and MRP14 Induce Stress Tolerance in Phagocytes under Sterile Inflammatory Conditions

Hyporesponsiveness by phagocytes is a well-known phenomenon in sepsis that is frequently induced by low-dose endotoxin stimulation of Toll-like receptor 4 (TLR4) but can also be found under sterile inflammatory conditions. We now demonstrate that the endogenous alarmins MRP8 and MRP14 induce phagocyte hyporesponsiveness via chromatin modifications in a TLR4-dependent manner that results in enhanced survival to septic shock in mice. During sterile inflammation, polytrauma and burn trauma patients initially present with high serum concentrations of myeloid-related proteins (MRPs). Human neonatal phagocytes are primed for hyporesponsiveness by increased peripartal MRP concentrations, which was confirmed in murine neonatal endotoxinemia in wild-type and MRP14(-/-) mice. Our data therefore indicate that alarmin-triggered phagocyte tolerance represents a regulatory mechanism for the susceptibility of neonates during systemic infections and sterile inflammation

Intraligand Charge Transfer Enables Visible‐Light‐Mediated Nickel‐Catalyzed Cross‐Coupling Reactions

We demonstrate that several visible‐light‐mediated carbon−heteroatom cross‐coupling reactions can be carried out using a photoactive NiII precatalyst that forms in situ from a nickel salt and a bipyridine ligand decorated with two carbazole groups (Ni(Czbpy)Cl2). The activation of this precatalyst towards cross‐coupling reactions follows a hitherto undisclosed mechanism that is different from previously reported light‐responsive nickel complexes that undergo metal‐to‐ligand charge transfer. Theoretical and spectroscopic investigations revealed that irradiation of Ni(Czbpy)Cl2 with visible light causes an initial intraligand charge transfer event that triggers productive catalysis. Ligand polymerization affords a porous, recyclable organic polymer for heterogeneous nickel catalysis of cross‐coupling reactions. The heterogeneous catalyst shows stable performance in a packed‐bed flow reactor during a week of continuous operation