Measuring the impact of observations on the predictability of the Kuroshio Extension in a shallow-water model

In this paper sequential importance sampling is used to assess the impact of observations on a ensemble prediction for the decadal path transitions of the Kuroshio Extension (KE). This particle filtering approach gives access to the probability density of the state vector, which allows us to determine the predictive power — an entropy based measure — of the ensemble prediction. The proposed set-up makes use of an ensemble that, at each time, samples the climatological probability distribution. Then, in a post-processing step, the impact of different sets of observations is measured by the increase in predictive power of the ensemble over the climatological signal during one-year. The method is applied in an identical-twin experiment for the Kuroshio Extension using a reduced-gravity shallow water model. We investigate the impact of assimilating velocity observations from different locations during the elongated and the contracted meandering state of the KE. Optimal observations location correspond to regions with strong potential vorticity gradients. For the elongated state the optimal location is in the first meander of the KE. During the contracted state of the KE it is located south of Japan, where the Kuroshio separates from the coast

A systematic method of parameterisation estimation using data assimilation

In numerical weather prediction, parameterisations are used to simulate missing physics in the model. These can be due to a lack of scientific understanding or a lack of computing power available to address all the known physical processes. Parameterisations are sources of large uncertainty in a model as parameter values used in these parameterisations cannot be measured directly and hence are often not well known; and the parameterisations themselves are also approximations of the processes present in the true atmosphere. Whilst there are many efficient and effective methods for combined state/parameter estimation in data assimilation (DA), such as state augmentation, these are not effective at estimating the structure of parameterisations. A new method of parameterisation estimation is proposed that uses sequential DA methods to estimate errors in the numerical models at each space-time point for each model equation. These errors are then fitted to pre-determined functional forms of missing physics or parameterisations that are based upon prior information. We applied the method to a one-dimensional advection model with additive model error, and it is shown that the method can accurately estimate parameterisations, with consistent error estimates. Furthermore, it is shown how the method depends on the quality of the DA results. The results indicate that this new method is a powerful tool in systematic model improvement

Estimating model error covariances using particle filters

A method is presented for estimating the error covariance of the errors in the model equations in observation space. Estimating model errors in this systematic way opens up the possibility to use data assimilation for systematic model improvement at the level of the model equations, which would be a huge step forward. This model error covariance is perhaps the hardest covariance matrix to estimate. It represents how the missing physics and errors in parameterisations manifest themselves at the scales the model can resolve. A new element is that we use an efficient particle filter to avoid the need to estimate the error covariance of the state as well, which most other data-assimilation methods do require. Starting from a reasonable first estimate, the method generates new estimates iteratively during the data assimilation run, and the method is shown to converge to the correct model error matrix. We also investigate the influence of the accuracy of the observation error covariance on the estimation of the model error covariance and show that when the observation errors are known the model error covariance can be estimated well, but, as expected and perhaps unavoidable, the diagonal elements are estimated too low when the observation errors are estimated too high, and vice versa

A variational approach to retrieve rain rate by combining information from rain gauges, radars, and microwave links

Accurate and reliable rain rate estimates are important for various hydrometeorological applications. Consequently, rain sensors of different types have been deployed in many regions. In this work, measurements from different instruments, namely, rain gauge, weather radar, and microwave link, are combined for the first time to estimate with greater accuracy the spatial distribution and intensity of rainfall. The objective is to retrieve the rain rate that is consistent with all these measurements while incorporating the uncertainty associated with the different sources of information. Assuming the problem is not strongly nonlinear, a variational approach is implemented and the Gauss–Newton method is used to minimize the cost function containing proper error estimates from all sensors. Furthermore, the method can be flexibly adapted to additional data sources. The proposed approach is tested using data from 14 rain gauges and 14 operational microwave links located in the Zürich area (Switzerland) to correct the prior rain rate provided by the operational radar rain product from the Swiss meteorological service (MeteoSwiss). A cross-validation approach demonstrates the improvement of rain rate estimates when assimilating rain gauge and microwave link information

Construction and testing of a novel host-range defective myxoma virus vaccine with the M063 gene inactivated that is non-permissive for replication in rabbit cells

Deletion of the M063 gene from myxoma virus produces a virus that is unable to replicate in rabbit cells in vitro or in live rabbits but can be propagated in non-rabbit cell lines. A targeted M063 deletion mutant was constructed in the attenuated Uriarra strain of myxoma virus and the ability of this virus to act as a safe, non-transmissible vaccine against myxomatosis was tested in outbred laboratory rabbits. Immunization with the M063 deletion vaccine provided good short-term protection against lethal challenge with virulent myxoma virus. Long-term protection was similar to reported results with heterologous live virus, with some rabbits protected but others succumbing to challenge. Replication-deficient poxvirus vaccines, like the Modified Vaccinia Virus Ankara (MVA) in man and the myxoma virus vaccine described here in rabbits, are very attractive from a safety perspective. Seasonal boosting would be predicted to provide long-term protection. Targeted host-range gene deletions could have potential for rapid development of poxvirus vaccines in general.This study was funded by the Pest Animal Control Cooperative Research Centre. Grant McFadden is an International Scholar of the Howard Hughes Medical Institute

Implicit equal-weights particle filter

Filter degeneracy is the main obstacle for the implementation of particle filter in non-linear high-dimensional models. A new scheme, the implicit equal-weights particle filter (IEWPF), is introduced. In this scheme samples are drawn implicitly from proposal densities with a different covariance for each particle, such that all particle weights are equal by construction. We test and explore the properties of the new scheme using a 1,000-dimensional simple linear model, and the 1,000-dimensional non-linear Lorenz96 model, and compare the performance of the scheme to a Local Ensemble Kalman Filter. The experiments show that the new scheme can easily be implemented in high-dimensional systems and is never degenerate, with good convergence properties in both systems

Body mass index is associated with microvascular endothelial dysfunction in patients with treated metabolic risk factors and suspected coronary artery disease

Background--Obesity is key feature of the metabolic syndrome and is associated with high cardiovascular morbidity and mortality. Obesity is associated with macrovascular endothelial dysfunction, a determinant of outcome in patients with coronary artery disease. Here, we compared the influence of obesity on microvascular endothelial function to that of established cardiovascular risk factors such as diabetes mellitus, hypertension, hypercholesterolemia, and smoking in patients with suspected coronary artery disease. Methods and Results--Endothelial function was assessed during postocclusive reactive hyperemia of the brachial artery and downstream microvascular beds in 108 patients who were scheduled for coronary angiography. In all patients, microvascular vasodilation was assessed using peripheral arterial tonometry; laser Doppler flowmetry and digital thermal monitoring were performed. Body mass index was significantly associated with decreased endothelium-dependent vasodilatation measured with peripheral arterial tonometry (r=0.23, P=0.02), laser Doppler flowmetry (r=0.30, P < 0.01), and digital thermal monitoring (r=0.30, P < 0.01). In contrast, hypertension, hypercholesterolemia, and smoking had no influence on microvascular vasodilatation. Especially in diabetic patients, endothelial function was not significantly reduced (control versus diabetes mellitus, mean±SEM or median [interquartile range], peripheral arterial tonometry: 1.90±0.20 versus 1.67±0.20, P=0.19, laser Doppler flowmetry: 728% [interquartile range, 427-1110] v

Symmetrized models of last passage percolation and non-intersecting lattice paths

It has been shown that the last passage time in certain symmetrized models of directed percolation can be written in terms of averages over random matrices from the classical groups $U(l)$, $Sp(2l)$ and $O(l)$. We present a theory of such results based on non-intersecting lattice paths, and integration techniques familiar from the theory of random matrices. Detailed derivations of probabilities relating to two further symmetrizations are also given.Comment: 21 pages, 5 figure