Detailed Analysis Of Convective Simulations From A WRF Microphysics Ensemble

This study performed a detailed analysis of convective storms across the Contiguous United States from 77 case dates using a 4-member microphysics Weather Research and Forecasting (WRF) ensemble and Stage IV gauge-adjusted radar derived precipitation. Dates included the 2016 NOAA Spring Forecast Experiment (SFE) with the remainder from 2010-2012. Quantitative attributes of precipitation objects in both simulations and observations were diagnosed using the Method of Object-Based Diagnostic Evaluation Time-Domain (MODE-TD). The microphysics schemes tested were WSM6, Thompson, Morrison, and Milbrandt. Among all simulation case dates, compared to observations, the number of precipitation objects less than 90 km in length are overpredicted, with the WSM6 scheme greatest and Morrison scheme least. All simulation members also generally initiate and dissipate precipitation objects too early. For precipitation rates, the Morrison scheme predicts them best while the Milbrandt and WSM6 schemes overpredict the strongest rates. The microphysics biases found within this study should aid in the prediction of convective events

Ethical, legal, and social implications of learning health systems

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141917/1/lrh210051-sup-0001-Supplementary_info.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/141917/2/lrh210051.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/141917/3/lrh210051_am.pd

THE COMMUNITY LEVERAGED UNIFIED ENSEMBLE (CLUE) IN THE 2016 NOAA/HAZARDOUS WEATHER TESTBED SPRING FORECASTING EXPERIMENT

One primary goal of annual Spring Forecasting Experiments (SFEs), which are coorganized by NOAA’s National Severe Storms Laboratory and Storm Prediction Center and conducted in the National Oceanic and Atmospheric Administration’s (NOAA) Hazardous Weather Testbed, is documenting performance characteristics of experimental, convection-allowing modeling systems (CAMs). Since 2007, the number of CAMs (including CAM ensembles) examined in the SFEs has increased dramatically, peaking at six different CAM ensembles in 2015. Meanwhile, major advances have been made in creating, importing, processing, verifying, and developing tools for analyzing and visualizing these large and complex datasets. However, progress toward identifying optimal CAM ensemble configurations has been inhibited because the different CAM systems have been independently designed, making it difficult to attribute differences in performance characteristics. Thus, for the 2016 SFE, a much more coordinated effort among many collaborators was made by agreeing on a set of model specifications (e.g., model version, grid spacing, domain size, and physics) so that the simulations contributed by each collaborator could be combined to form one large, carefully designed ensemble known as the Community Leveraged Unified Ensemble (CLUE). The 2016 CLUE was composed of 65 members contributed by five research institutions and represents an unprecedented effort to enable an evidence-driven decision process to help guide NOAA’s operational modeling efforts. Eight unique experiments were designed within the CLUE framework to examine issues directly relevant to the design of NOAA’s future operational CAM-based ensembles. This article will highlight the CLUE design and present results from one of the experiments examining the impact of single versus multicore CAM ensemble configurations

THE COMMUNITY LEVERAGED UNIFIED ENSEMBLE (CLUE) IN THE 2016 NOAA/HAZARDOUS WEATHER TESTBED SPRING FORECASTING EXPERIMENT

One primary goal of annual Spring Forecasting Experiments (SFEs), which are coorganized by NOAA’s National Severe Storms Laboratory and Storm Prediction Center and conducted in the National Oceanic and Atmospheric Administration’s (NOAA) Hazardous Weather Testbed, is documenting performance characteristics of experimental, convection-allowing modeling systems (CAMs). Since 2007, the number of CAMs (including CAM ensembles) examined in the SFEs has increased dramatically, peaking at six different CAM ensembles in 2015. Meanwhile, major advances have been made in creating, importing, processing, verifying, and developing tools for analyzing and visualizing these large and complex datasets. However, progress toward identifying optimal CAM ensemble configurations has been inhibited because the different CAM systems have been independently designed, making it difficult to attribute differences in performance characteristics. Thus, for the 2016 SFE, a much more coordinated effort among many collaborators was made by agreeing on a set of model specifications (e.g., model version, grid spacing, domain size, and physics) so that the simulations contributed by each collaborator could be combined to form one large, carefully designed ensemble known as the Community Leveraged Unified Ensemble (CLUE). The 2016 CLUE was composed of 65 members contributed by five research institutions and represents an unprecedented effort to enable an evidence-driven decision process to help guide NOAA’s operational modeling efforts. Eight unique experiments were designed within the CLUE framework to examine issues directly relevant to the design of NOAA’s future operational CAM-based ensembles. This article will highlight the CLUE design and present results from one of the experiments examining the impact of single versus multicore CAM ensemble configurations

Statistical Analysis of Molecular Signal Recording

A molecular device that records time-varying signals would enable new approaches in neuroscience. We have recently proposed such a device, termed a “molecular ticker tape”, in which an engineered DNA polymerase (DNAP) writes time-varying signals into DNA in the form of nucleotide misincorporation patterns. Here, we define a theoretical framework quantifying the expected capabilities of molecular ticker tapes as a function of experimental parameters. We present a decoding algorithm for estimating time-dependent input signals, and DNAP kinetic parameters, directly from misincorporation rates as determined by sequencing. We explore the requirements for accurate signal decoding, particularly the constraints on (1) the polymerase biochemical parameters, and (2) the amplitude, temporal resolution, and duration of the time-varying input signals. Our results suggest that molecular recording devices with kinetic properties similar to natural polymerases could be used to perform experiments in which neural activity is compared across several experimental conditions, and that devices engineered by combining favorable biochemical properties from multiple known polymerases could potentially measure faster phenomena such as slow synchronization of neuronal oscillations. Sophisticated engineering of DNAPs is likely required to achieve molecular recording of neuronal activity with single-spike temporal resolution over experimentally relevant timescales.United States. Defense Advanced Research Projects Agency. Living Foundries ProgramGoogle (Firm)New York Stem Cell Foundation. Robertson Neuroscience Investigator AwardNational Institutes of Health (U.S.) (EUREKA Award 1R01NS075421)National Institutes of Health (U.S.) (Transformative R01 1R01GM104948)National Institutes of Health (U.S.) (Single Cell Grant 1 R01 EY023173)National Institutes of Health (U.S.) (Grant 1R01DA029639)National Institutes of Health (U.S.) (Grant 1R01NS067199)National Science Foundation (U.S.) (CAREER Award CBET 1053233)National Science Foundation (U.S.) (Grant EFRI0835878)National Science Foundation (U.S.) (Grant DMS1042134)Paul G. Allen Family Foundation (Distinguished Investigator in Neuroscience Award

A Survey of Alumni of the Entrepreneurship Program Of Case Western Reserve University: Final Report

As originally conceived the goal of this project was to assess the impacts of entrepreneurship programs at six Universities across the United States to learn about entrepreneurial experiences, income and other key post graduation outcomes. Kauffman Foundation staff assumed responsibility for recruiting participation from these programs, while University of Kansas researchers developed the survey instrument. In the end, only Case Western Reserve University’s Science & Technology Entrepreneurship Program (STEP) agreed to participate in the study. The limitation of data to a single program and the small size of the sample of respondents, especially among the control group, limits the strength of the conclusions that can be drawn from this study. Nonetheless, the survey results show that STEP program alumni were much more likely to engage in a broad range of entrepreneurial activities during enrollment in, and after completing, their education, than they had been before entering the STEP program. In contrast there is no parallel increase in these activities among members of the control group. STEP program alumni also experienced more positive economic rewards from completion of their program. They earned higher salaries than the control group members and they were more likely to increase their incomes. We cannot of course rule out the possibility that some of these differences are due to sample selection. That is that students enrolling in the STEP program are different from students enrolling in the parallel masters degree programs from which the control group was drawn. Indeed the survey documents some important differences, such as undergraduate major. But there is considerable evidence that suggest that the STEP program is successful in preparing its students for careers in entrepreneurship