Improving Satellite Quantitative Precipitation Estimates By Incorporating Deep Convective Cloud Optical Depth

As Deep Convective Systems (DCSs) are responsible for most severe weather events, increased understanding of these systems along with more accurate satellite precipitation estimates will improve NWS (National Weather Service) warnings and monitoring of hazardous weather conditions. A DCS can be classified into convective core (CC) regions (heavy rain), stratiform (SR) regions (moderate-light rain), and anvil (AC) regions (no rain). These regions share similar infrared (IR) brightness temperatures (BT), which can create large errors for many existing rain detection algorithms. This study assesses the performance of the National Mosaic and Multi-sensor Quantitative Precipitation Estimation System (NMQ) Q2, and a simplified version of the GOES-R Rainfall Rate algorithm (also known as the Self-Calibrating Multivariate Precipitation Retrieval, or SCaMPR), over the state of Oklahoma (OK) using OK MESONET observations as ground truth. While the average annual Q2 precipitation estimates were about 35% higher than MESONET observations , there were very strong correlations between these two data sets for multiple temporal and spatial scales. Additionally, the Q2 estimated precipitation distributions over the CC, SR, and AC regions of DCSs strongly resembled the MESONET observed ones, indicating that Q2 can accurately capture the precipitation characteristics of DCSs although it has a wet bias . SCaMPR retrievals were typically three to four times higher than the collocated MESONET observations, with relatively weak correlations during a year of comparisons in 2012. Overestimates from SCaMPR retrievals that produced a high false alarm rate were primarily caused by precipitation retrievals from the anvil regions of DCSs when collocated MESONET stations recorded no precipitation. A modified SCaMPR retrieval algorithm, employing both cloud optical depth and IR temperature, has the potential to make significant improvements to reduce the SCaMPR false alarm rate of retrieved precipitation especially over non-precipitating (anvil) regions of a DCS. Preliminary testing of this new algorithm to identify precipitating area has produced significant improvements over the current SCaMPR algorithm. This modified version of SCaMPR can be used to provide precipitation estimates in gaps of radar and rain gauge coverage to aid in hydrological and flood forecasting

Longitudinal Data Models with Nonparametric Random Effect Distributions

There is the saying which says you cannot see the woods for the trees. This thesis aims to circumvent this unfortunate situation: Longitudinal data on tree growth, as an example of multiple observations of similar individuals pooled together in one data set, are modeled simultaneously rather than each individual separately. This is done under the assumption that one model is suitable for all individuals but its parameters vary following un- known nonparametric random effect distributions. The goal is a maximum likelihood estimation of these distributions considering all provided data and using basis-spline-approximations for the densities of each distribution func- tion over the same spline-base. The implementation of all procedures is carried out in R and attached to this thesis

Bad Agent, Good Citizen?

Analyses of agents’ behavior normally focus on whether an agent is a good agent or a bad agent— whether or not an agent is faithfully pursuing the interests of her principal. But we should also consider whether a lawyer acting as a good agent is also promoting the public interest (i.e., a good citizen) or not (i.e., a bad citizen). Similarly, we should ask whether lawyers acting as bad agents are also harming society, or whether they may actually be promoting the public interest even though they are not promoting their clients’ interests

Draft Genome Sequences of Sanguibacteroides justesenii, gen. nov., sp. nov., Strains OUH 308042T (= ATCC BAA-2681T) and OUH 334697 (= ATCC BAA-2682), Isolated from Blood Cultures from Two Different Patients

We announce here the draft genome sequences of Sanguibacteroides justesenii, gen. nov., sp. nov., strains OUH 308042(T) (= DSM 28342(T) = ATCC BAA-2681(T)) and OUH 334697 (= DSM 28341 = ATCC BAA-2682), isolated from blood cultures from two different patients and composed of 51 and 39 contigs for totals of 3,385,516 and 3,410,672 bp, respectively

Ten years of tuberculosis intervention in Greenland – has it prevented cases of childhood tuberculosis?

Background: The incidence of tuberculosis (TB) disease in Greenland doubled in the 1990s. To combat the increase, national TB interventions were initiated in 2000 and strengthened in 2007. Objective: To determine whether the effect of interventions could be detected, we estimated the TB disease risk among children≤15 years before and after interventions were implemented. Design: For a study cohort, we recruited all children ≤15 years of age included in the Greenlandic Civil Registration System (CRS) from 1990 to 2010. The CRS identifier was used to link cohort participants with TB cases identified based on the Greenlandic National TB registry. Bacille Calmette Guerin (BCG) vaccination status was identified through year of birth, as BCG was offered to newborns born either before 1991 or after 1996. Years with interventions were defined as 2000–2006 (primary interventions) and 2007–2010 (intensified interventions). Risk of TB was estimated using Poisson regression. Results: The study included 35,858 children, of whom 209 had TB disease. The TB disease incidence decreased after interventions were implemented (2007–2010: IRR [incidence rate ratios] 0.62, 95% CI: 0.39–0.95, p=0.03, compared with the 1995–1999 period). The TB disease risk was inversely associated with BCG vaccination (IRR: 0.54, 95% CI: 0.41–0.72, p<0.001). Conclusions: Years with national TB interventions in Greenland, including neonate BCG vaccination, are associated with a lower TB disease incidence among children ≤15 years of age

High-precision 3D object capturing with static and kinematic terrestrial laser scanning in industrial applications-approaches of quality assessment

Abstract Terrestrial laser scanning is used in many disciplines of engineering. Examples include mobile mapping, architecture surveying, archaeology, as well as monitoring and surveillance measurements. For most of the mentioned applications, 3D object capturing in an accuracy range of several millimeters up to a few centimeters is sufficient. However, in engineering geodesy, particularly in industrial surveying or monitoring measurements, accuracies in a range of a few millimeters are required. Additional increased quality requirements apply to these applications. This paper focuses on the quality investigation of data captured with static and kinematic terrestrial laser scanning. For this purpose, suitable sensors, which are typically used in the approach of a multi-sensor-system, as well as the corresponding data capturing/acquisition strategies, are presented. The aim of such systems is a geometry- and surface-based analysis in an industrial environment with an accuracy of +/- 1-2 mm or better