OSSOS. IX. Two Objects in Neptune's 9: 1 Resonance - Implications for Resonance Sticking in the Scattering Population

We discuss the detection in the Outer Solar System Origins Survey (OSSOS) of two objects in Neptune's distant 9:1 mean motion resonance at semimajor axis $a\approx~130$~au. Both objects are securely resonant on 10~Myr timescales, with one securely in the 9:1 resonance's leading asymmetric libration island and the other in either the symmetric or trailing asymmetric island. These objects are the largest semimajor axis objects with secure resonant classifications, and their detection in a carefully characterized survey allows for the first robust resonance population estimate beyond 100~au. The detection of these objects implies a 9:1 resonance population of $1.1\times10^4$ objects with $H_r<8.66$ ($D~\gtrsim~100$~km) on similar orbits (95\% confidence range of $\sim0.4-3\times10^4$). Integrations over 4~Gyr of an ensemble of clones spanning these objects' orbit fit uncertainties reveal that they both have median resonance occupation timescales of $\sim1$~Gyr. These timescales are consistent with the hypothesis that these objects originate in the scattering population but became transiently stuck to Neptune's 9:1 resonance within the last $\sim1$~Gyr of solar system evolution. Based on simulations of a model of the current scattering population, we estimate the expected resonance sticking population in the 9:1 resonance to be 1000-4500 objects with $H_r<8.66$; this is marginally consistent with the OSSOS 9:1 population estimate. We conclude that resonance sticking is a plausible explanation for the observed 9:1 population, but we also discuss the possibility of a primordial 9:1 population, which would have interesting implications for the Kuiper belt's dynamical history.Comment: accepted for publication in A

Improvements in forecasting intense rainfall: results from the FRANC (forecasting rainfall exploiting new data assimilation techniques and novel observations of convection) project

The FRANC project (Forecasting Rainfall exploiting new data Assimilation techniques and Novel observations of Convection) has researched improvements in numerical weather prediction of convective rainfall via the reduction of initial condition uncertainty. This article provides an overview of the project’s achievements. We highlight new radar techniques: correcting for attenuation of the radar return; correction for beams that are over 90% blocked by trees or towers close to the radar; and direct assimilation of radar reflectivity and refractivity. We discuss the treatment of uncertainty in data assimilation: new methods for estimation of observation uncertainties with novel applications to Doppler radar winds, Atmospheric Motion Vectors, and satellite radiances; a new algorithm for implementation of spatially-correlated observation error statistics in operational data assimilation; and innovative treatment of moist processes in the background error covariance model. We present results indicating a link between the spatial predictability of convection and convective regimes, with potential to allow improved forecast interpretation. The research was carried out as a partnership between University researchers and the Met Office (UK). We discuss the benefits of this approach and the impact of our research, which has helped to improve operational forecasts for convective rainfall event

OSSOS. VII. 800+Trans-Neptunian Objects-The Complete Data Release

The Outer Solar System Origins Survey (OSSOS), a wide-field imaging program in 2013-2017 with the Canada-France-Hawaii Telescope, surveyed 155 deg(2) of sky to depths of m(r) = 24.1-25.2. We present 838 outer solar system discoveries that are entirely free of ephemeris bias. This increases the inventory of trans-Neptunian objects (TNOs) with accurately known orbits by nearly 50%. Each minor planet has 20-60 Gaia/Pan-STARRS-calibrated astrometric measurements made over 2-5 oppositions, which allows accurate classification of their orbits within the trans-Neptunian dynamical populations. The populations orbiting in mean-motion resonance with Neptune are key to understanding Neptune's early migration. Our 313 resonant TNOs, including 132 plutinos, triple the available characterized sample and include new occupancy of distant resonances out to semimajor axis a similar to 130 au. OSSOS doubles the known population of the nonresonant Kuiper Belt, providing 436 TNOs in this region, all with exceptionally high-quality orbits of a uncertainty sigma(a)Peer reviewe

The Astropy Problem

The Astropy Project (http://astropy.org) is, in its own words, "a community effort to develop a single core package for Astronomy in Python and foster interoperability between Python astronomy packages." For five years this project has been managed, written, and operated as a grassroots, self-organized, almost entirely volunteer effort while the software is used by the majority of the astronomical community. Despite this, the project has always been and remains to this day effectively unfunded. Further, contributors receive little or no formal recognition for creating and supporting what is now critical software. This paper explores the problem in detail, outlines possible solutions to correct this, and presents a few suggestions on how to address the sustainability of general purpose astronomical software

Data assimilation with correlated observation errors: experiments with a 1-D shallow water model

Remote sensing observations often have correlated errors, but the correlations are typically ignored in data assimilation for numerical weather prediction. The assumption of zero correlations is often used with data thinning methods, resulting in a loss of information. As operational centres move towards higher-resolution forecasting, there is a requirement to retain data providing detail on appropriate scales. Thus an alternative approach to dealing with observation error correlations is needed. In this article, we consider several approaches to approximating observation error correlation matrices: diagonal approximations, eigendecomposition approximations and Markov matrices. These approximations are applied in incremental variational assimilation experiments with a 1-D shallow water model using synthetic observations. Our experiments quantify analysis accuracy in comparison with a reference or ‘truth’ trajectory, as well as with analyses using the ‘true’ observation error covariance matrix. We show that it is often better to include an approximate correlation structure in the observation error covariance matrix than to incorrectly assume error independence. Furthermore, by choosing a suitable matrix approximation, it is feasible and computationally cheap to include error correlation structure in a variational data assimilation algorithm

The JmjC domain protein Epe1 prevents unregulated assembly and disassembly of heterochromatin

Heterochromatin normally has prescribed chromosomal positions and must not encroach on adjacent regions. We demonstrate that the fission yeast protein Epe1 stabilises silent chromatin, preventing the oscillation of heterochromatin domains. Epe1 loss leads to two contrasting phenotypes: alleviation of silencing within heterochromatin and expansion of silent chromatin into neighbouring euchromatin. Thus, we propose that Epe1 regulates heterochromatin assembly and disassembly, thereby affecting heterochromatin integrity, centromere function and chromosome segregation fidelity. Epe1 regulates the extent of heterochromatin domains at the level of chromatin, not via the RNAi pathway. Analysis of an ectopically silenced site suggests that heterochromatin oscillation occurs in the absence of heterochromatin boundaries. Epe1 requires predicted iron- and 2-oxyglutarate (2-OG)-binding residues for in vivo function, indicating that it is probably a 2-OG/Fe(II)-dependent dioxygenase. We suggest that, rather than being a histone demethylase, Epe1 may be a protein hydroxylase that affects the stability of a heterochromatin protein, or protein–protein interaction, to regulate the extent of heterochromatin domains. Thus, Epe1 ensures that heterochromatin is restricted to the domains to which it is targeted by RNAi

Real-world evidence from the first online healthcare analytics platform—Livingstone. Validation of its descriptive epidemiology module

Incidence and prevalence are key epidemiological determinants characterizing the quantum of a disease. We compared incidence and prevalence estimates derived automatically from the first ever online, essentially real-time, healthcare analytics platform—Livingstone—against findings from comparable peer-reviewed studies in order to validate the descriptive epidemiology module. The source of routine NHS data for Livingstone was the Clinical Practice Research Datalink (CPRD). After applying a general search strategy looking for any disease or condition, 76 relevant studies were first retrieved, of which 10 met pre-specified inclusion and exclusion criteria. Findings reported in these studies were compared with estimates produced automatically by Livingstone. The published reports described elements of the epidemiology of 14 diseases or conditions. Lin’s concordance correlation coefficient (CCC) was used to evaluate the concordance between findings from Livingstone and those detailed in the published studies. The concordance of incidence values in the final year reported by each study versus Livingstone was 0.96 (95% CI: 0.89–0.98), whilst for all annual incidence values the concordance was 0.93 (0.91–0.94). For prevalence, concordance for the final annual prevalence reported in each study versus Livingstone was 1.00 (0.99–1.00) and for all reported annual prevalence values, the concordance was 0.93 (0.90–0.95). The concordance between Livingstone and the latest published findings was near perfect for prevalence and substantial for incidence. For the first time, it is now possible to automatically generate reliable descriptive epidemiology from routine health records, and in near-real time. Livingstone provides the first mechanism to rapidly generate standardised, descriptive epidemiology for all clinical events from real world data