Estimating forecast error covariances for strongly coupled atmosphere-ocean 4D-Var data assimilation

Strongly coupled data assimilation emulates the real world pairing of the atmosphere and ocean by solving the assimilation problem in terms of a single combined atmosphere-ocean state. A significant challenge in strongly coupled variational atmosphere-ocean data assimilation is a priori specification of the cross-covariances between the errors in the atmosphere and ocean model forecasts. These covariances must capture the correct physical structure of interactions across the air-sea interface as well as the different scales of evolution in the atmosphere and ocean; if prescribed correctly, they will allow observations in one medium to improve the analysis in the other. Here we investigate the nature and structure of atmosphere-ocean forecast error cross-correlations using an idealised strongly coupled single-column atmosphere-ocean 4D-Var assimilation system. We present results from a set of identical twin experiments that use an ensemble of coupled 4D-Var assimilations to derive estimates of the atmosphere-ocean error cross-correlations. Our results show significant variation in the strength and structure of cross-correlations in the atmosphere-ocean boundary layer between summer and winter and between day and night. These differences provide a valuable insight into the nature of coupled atmosphere-ocean correlations for different seasons and points in the diurnal cycle

The role of cross-domain error correlations in strongly coupled 4D-Var atmosphere-ocean data assimilation

Strongly coupled atmosphere-ocean data assimilation offers the ability to improve information exchange across the modelled air-sea interface by enabling observations in one domain to have a direct influence on the analysis in the other. For incremental 4D-Var assimilation a strongly coupled approach enables both domains to be updated at the beginning of the assimilation window, whether they are observed or not, and is hence more likely to produce consistent initial model states. This is made possible by the explicit inclusion of cross-domain forecast error covariance information in the coupled forecast error covariance matrix. In this study we use an idealised 1D single column coupled atmosphere-ocean model to examine the extent to which explicit cross-domain forecast error covariances play a role in shaping the coupled analysis increments compared to those implicitly generated in the inner-loop of the incremental formulation of the 4D-Var algorithm. This is done via a set of single observation experiments with and without initial cross-domain forecast error covariances prescribed. Using single observations allows us to obtain explicit expressions for the atmosphere and ocean analysis updates, separating out the individual effects of the explicitly prescribed and implicitly generated cross-domain covariances. Our experiments show that when only one domain is observed,including explicit cross-domain error covariances allows more consistent adjustment of the unobserved domain. Neglecting the cross-domain terms and relying solely on the covariances implicitly generated by the coupled tangent linear and adjoint models restricts the ability of the covariance matrix to impose balance between the two domains. In this case the coupling is essentially one-way; the update to the observed domain is independent of the unobserved domain and so is likely to produce atmosphere and ocean updates that are inconsistent with one another. As we show, this has important consequences for the balance of the coupled analysis state

Observation impact, domain length and parameter estimation in data assimilation for flood forecasting

Accurate inundation forecasting provides vital information about the behaviour of fluvial flood water. Using data assimilation with an Ensemble Transform Kalman Filter we combine forecasts from a numerical hydrodynamic model with synthetic observations of water levels. We show that reinitialising the model with corrected water levels can cause an initialisation shock and demonstrate a simple novel solution. In agreement with others, we find that although assimilation can accurately correct water levels at observation times, the corrected forecast quickly relaxes to the open loop forecast. Our new work shows that the time taken for the forecast to relax to the open loop case depends on domain length; observation impact is longer-lived in a longer domain. We demonstrate that jointly correcting the channel friction parameter as well as water levels greatly improves the forecast. We also show that updating the value of the channel friction parameter can compensate for bias in inflow

Treating sample covariances for use in strongly coupled atmosphere-ocean data assimilation

Strongly coupled data assimilation requires cross-domain forecast error covariances; information from ensembles can be used, but limited sampling means that ensemble derived error covariances are routinely rank deficient and/or ill-conditioned and marred by noise. Thus they require modification before they can be incorporated into a standard assimilation framework. Here, we compare methods for improving the rank and conditioning of multivariate sample error covariance matrices for coupled atmosphere-ocean data assimilation. The first method, reconditioning, alters the matrix eigenvalues directly; this preserves the correlation structures but does not remove sampling noise. We show it is better to recondition the correlation matrix rather than the covariance matrix as this prevents small but dynamically important modes from being lost. The second method, model state-space localisation via the Schur product, effectively removes sample noise but can dampen small cross-correlation signals. A combination that exploits the merits of each is found to offer an effective alternative

Patterns of intracolumnar size variation inform the heterochronic mechanisms underlying extreme body shape divergence in microcephalic sea snakes

Sea snakes (Hydrophiinae) that specialise on burrowing eel prey have repeatedly evolved tiny heads and reduced forebody relative to hindbody girths. Previous research has found that these ‘microcephalic’ forms have higher counts of precaudal vertebrae, and postnatal ontogenetic changes cause their hindbodies to reach greater girths relative to their forebodies. We examine variation in vertebral size along the precaudal axis of neonates and adults of two species. In the non-microcephalic Hydrophis curtus, intracolumnar patterns take the form of symmetrical curved profiles, with longer vertebrae in the midbody relative to distal regions. In contrast, intracolumnar profiles in the microcephalic H. macdowelli are strongly left-skewed curves due to the presence of numerous, smaller-sized vertebrate in the forebody (above the heart). Neonate and adult H. macdowelli specimens both exhibit this pattern, implying an onset of fore- versus hindbody decoupling in the embryo stage. Based on this, we suggest plausible developmental mechanisms involving the presence and positioning of Hox boundaries and heterochronic changes in segmentation. Tests of our hypotheses would give new insights into the drivers of rapid convergent shifts in evolution, but will ultimately require studies of gene expression in the embryos of relevant taxa.Emma Sherratt, Kate L. Sander

Exploring strategies for coupled 4D-Var data assimilation using an idealised atmosphere-ocean model

Operational forecasting centres are currently developing data assimilation systems for coupled atmosphere-ocean models. Strongly coupled assimilation, in which a single assimilation system is applied to a coupled model, presents significant technical and scientific challenges. Hence weakly coupled assimilation systems are being developed as a first step, in which the coupled model is used to compare the current state estimate with observations, but corrections to the atmosphere and ocean initial conditions are then calculated independently. In this paper we provide a comprehensive description of the different coupled assimilation methodologies in the context of four dimensional variational assimilation (4D-Var) and use an idealised framework to assess the expected benefits of moving towards coupled data assimilation. We implement an incremental 4D-Var system within an idealised single column atmosphere-ocean model. The system has the capability to run both strongly and weakly coupled assimilations as well as uncoupled atmosphere or ocean only assimilations, thus allowing a systematic comparison of the different strategies for treating the coupled data assimilation problem. We present results from a series of identical twin experiments devised to investigate the behaviour and sensitivities of the different approaches. Overall, our study demonstrates the potential benefits that may be expected from coupled data assimilation. When compared to uncoupled initialisation, coupled assimilation is able to produce more balanced initial analysis fields, thus reducing initialisation shock and its impact on the subsequent forecast. Single observation experiments demonstrate how coupled assimilation systems are able to pass information between the atmosphere and ocean and therefore use near-surface data to greater effect. We show that much of this benefit may also be gained from a weakly coupled assimilation system, but that this can be sensitive to the parameters used in the assimilation

What Do We Feed to Food-Production Animals? A Review of Animal Feed Ingredients and Their Potential Impacts on Human Health

OBJECTIVE: Animal feeding practices in the United States have changed considerably over the past century. As large-scale, concentrated production methods have become the predominant model for animal husbandry, animal feeds have been modified to include ingredients ranging from rendered animals and animal waste to antibiotics and organoarsenicals. In this article we review current U.S. animal feeding practices and etiologic agents that have been detected in animal feed. Evidence that current feeding practices may lead to adverse human health impacts is also evaluated. DATA SOURCES: We reviewed published veterinary and human-health literature regarding animal feeding practices, etiologic agents present in feed, and human health effects along with proceedings from animal feed workshops. DATA EXTRACTION: Data were extracted from peer-reviewed articles and books identified using PubMed, Agricola, U.S. Department of Agriculture, Food and Drug Administration, and Centers for Disease Control and Prevention databases. DATA SYNTHESIS: Findings emphasize that current animal feeding practices can result in the presence of bacteria, antibiotic-resistant bacteria, prions, arsenicals, and dioxins in feed and animal-based food products. Despite a range of potential human health impacts that could ensue, there are significant data gaps that prevent comprehensive assessments of human health risks associated with animal feed. Limited data are collected at the federal or state level concerning the amounts of specific ingredients used in animal feed, and there are insufficient surveillance systems to monitor etiologic agents “from farm to fork.” CONCLUSIONS: Increased funding for integrated veterinary and human health surveillance systems and increased collaboration among feed professionals, animal producers, and veterinary and public health officials is necessary to effectively address these issues

Measurement of the neutrino component of an anti-neutrino beam observed by a non-magnetized detector

Two independent methods are employed to measure the neutrino flux of the anti-neutrino-mode beam observed by the MiniBooNE detector. The first method compares data to simulated event rates in a high purity \numu induced charged-current single \pip (CC1\pip) sample while the second exploits the difference between the angular distributions of muons created in \numu and \numub charged-current quasi-elastic (CCQE) interactions. The results from both analyses indicate the prediction of the neutrino flux component of the pre-dominately anti-neutrino beam is over-estimated - the CC1\pip analysis indicates the predicted \numu flux should be scaled by $0.76 \pm 0.11$, while the CCQE angular fit yields $0.65 \pm 0.23$. The energy spectrum of the flux prediction is checked by repeating the analyses in bins of reconstructed neutrino energy, and the results show that the spectral shape is well modeled. These analyses are a demonstration of techniques for measuring the neutrino contamination of anti-neutrino beams observed by future non-magnetized detectors.Comment: 15 pages, 7 figures, published in Physical Review D, latest version reflects changes from referee comment

Measurement of the \nu_\mu charged current \pi^+ to quasi-elastic cross section ratio on mineral oil in a 0.8 GeV neutrino beam

Using high statistics samples of charged current $\nu_\mu$ interactions, MiniBooNE reports a measurement of the single charged pion production to quasi-elastic cross section ratio on mineral oil (CH$_2$), both with and without corrections for hadron re-interactions in the target nucleus. The result is provided as a function of neutrino energy in the range 0.4 GeV $< E_\nu <$ 2.4 GeV with 11% precision in the region of highest statistics. The results are consistent with previous measurements and the prediction from historical neutrino calculations.Comment: 4 pages, 2 figure