Misdiagnosing Melioidosis

Melioidosis is endemic in southern and Southeast Asia and northern Australia. Although relatively few indigenous cases are recognized in the Indian subcontinent, a substantial proportion of cases imported into the United Kingdom originate there, probably reflecting patterns of immigration and travel, and underdiagnosis within the Indian subcontinent

Noise reduction in commercial refrigerators - a practical approach

An Adande refrigeration unit originally designed for use in the commercial catering industry was redesigned for use in households. This sector is more sensitive to refrigeration noise, following the introduction of the EU noise labelling directive. A practical noise control ap-proach was taken consisting of benchmarking the existing commercial unit, diagnosing the primary noise sources, redesigning the system components without affecting the refrigera-tion performance and assessing improvements. The aim was to reduce noise emissions and improve sound quality to those of frost free household refrigerators. Value engineering was used to optimise the performance gains such that the new unit suitable for the domestic mar-ket would be also used in the commercial sector. The sound power reduction achieved was greater than 4 dB. The sound quality of both the existing standard refrigerator and the opti-mised prototype unit were evaluated by a jury in a real living environment. The subjective exercise showed that the optimised prototype was perceived as being quieter and of im-proved sound quality compared to the standard refrigerator

Estimating correlated observation error statistics using an ensemble transform Kalman filter

For certain observing types, such as those that are remotely sensed, the observation errors are correlated and these correlations are state- and time-dependent. In this work, we develop a method for diagnosing and incorporating spatially correlated and time-dependent observation error in an ensemble data assimilation system. The method combines an ensemble transform Kalman filter with a method that uses statistical averages of background and analysis innovations to provide an estimate of the observation error covariance matrix. To evaluate the performance of the method, we perform identical twin experiments using the Lorenz ’96 and Kuramoto-Sivashinsky models. Using our approach, a good approximation to the true observation error covariance can be recovered in cases where the initial estimate of the error covariance is incorrect. Spatial observation error covariances where the length scale of the true covariance changes slowly in time can also be captured. We find that using the estimated correlated observation error in the assimilation improves the analysis

Tetra­phenyl­phospho­nium hydrogen oxalate

In the title compound, C24H20P+·C2HO4 −, two symmetry-independent ion pairs are present. The cations aggregate into puckered sheets via zigzag infinite chains of sixfold phenyl embraces and parallel fourfold phenyl embraces, while the anions form hydrogen-bonded chains between the sheets of cations. In the two independent oxalate anions, the angles between the normals to the two least-squares carboxyl­ate COO planes are unusually large, viz. 72.5 (1) and 82.1 (1)°

Diagnosing observation error correlations for Doppler radar radial winds in the Met Office UKV model using observation-minus-background and observation-minus-analysis statistics

With the development of convection-permitting numerical weather prediction the efficient use of high-resolution observations in data assimilation is becoming increasingly important. The operational assimilation of these observations, such as Doppler radar radial winds (DRWs), is now common, though to avoid violating the assumption of uncorrelated observation errors the observation density is severely reduced. To improve the quantity of observations used and the impact that they have on the forecast requires the introduction of the full, potentially correlated, error statistics. In this work, observation error statistics are calculated for the DRWs that are assimilated into the Met Office high-resolution UK model using a diagnostic that makes use of statistical averages of observation-minus-background and observation-minus-analysis residuals. This is the first in-depth study using the diagnostic to estimate both horizontal and along-beam observation error statistics. The new results obtained show that the DRW error standard deviations are similar to those used operationally and increase as the observation height increases. Surprisingly the estimated observation error correlation length-scales are longer than the operational thinning distance. They are dependent both on the height of the observation and on the distance of the observation away from the radar. Further tests show that the long correlations cannot be attributed to the background error covariance matrix used in the assimilation, although they are, in part, a result of using superobservations and a simplified observation operator. The inclusion of correlated error statistics in the assimilation allows less thinning of the data and hence better use of the high-resolution observations

Observation error statistics for Doppler radar radial wind superobservations assimilated into the DWD COSMO-KENDA system

Currently in operational numerical weather prediction (NWP) the density of high-resolution observations, such as Doppler radar radial winds (DRWs), is severely reduced in part to avoid violating the assumption of uncorrelated observation errors. To improve the quantity of observations used and the impact that they have on the forecast requires an accurate specification of the observation uncertainties. Observation uncertainties can be estimated using a simple diagnostic that utilises the statistical averages of observation-minus-background and observation-minus-analysis residuals. We are the first to use a modified form of the diagnostic to estimate spatial correlations for observations used in an operational ensemble data assimilation system. The uncertainties for DRW superobservations assimilated into the Deutscher Wetterdienst convection-permitting NWP model are estimated and compared to previous uncertainty estimates for DRWs. The new results show that most diagnosed standard deviations are smaller than those used in the assimilation, hence it may be feasible assimilate DRWs using reduced error standard deviations. However, some of the estimated standard deviations are considerably larger than those used in the assimilation; these large errors highlight areas where the observation processing system may be improved. The error correlation length scales are larger than the observation separation distance and influenced by both the superobbing procedure and observation operator. This is supported by comparing these results to our previous study using Met Office data. Our results suggest that DRW error correlations may be reduced by improving the superobbing procedure and observation operator; however, any remaining correlations should be accounted for in the assimilation

On discretization in time in simulations of particulate flows

We propose a time discretization scheme for a class of ordinary differential equations arising in simulations of fluid/particle flows. The scheme is intended to work robustly in the lubrication regime when the distance between two particles immersed in the fluid or between a particle and the wall tends to zero. The idea consists in introducing a small threshold for the particle-wall distance below which the real trajectory of the particle is replaced by an approximated one where the distance is kept equal to the threshold value. The error of this approximation is estimated both theoretically and by numerical experiments. Our time marching scheme can be easily incorporated into a full simulation method where the velocity of the fluid is obtained by a numerical solution to Stokes or Navier-Stokes equations. We also provide a derivation of the asymptotic expansion for the lubrication force (used in our numerical experiments) acting on a disk immersed in a Newtonian fluid and approaching the wall. The method of this derivation is new and can be easily adapted to other cases

Accounting for observation uncertainty and bias due to unresolved scales with the Schmidt-Kalman filter

Data assimilation combines observations with numerical model data, to provide a best estimate of a real system. Errors due to unresolved scales arise when there is a spatiotemporal scale mismatch between the processes resolved by the observations and model. We present theory on error, uncertainty and bias due to unresolved scales for situations where observations contain information on smaller scales than can be represented by the numerical model. The Schmidt-Kalman filter, which accounts for the uncertainties in the unrepresented processes, is investigated and compared with an optimal Kalman filter that treats all scales, and a suboptimal Kalman filter that accounts for the largescales only. The equation governing true analysis uncertainty is reformulated to include representation uncertainty for each filter. We apply the filters to a random walk model with one variable for large-scale processes and one variable for small-scale processes. Our new results show that the Schmidt-Kalman filter has the largest benefit over a suboptimal filter in regimes of high representation uncertainty and low instrument uncertainty but performs worse than the optimal filter. Furthermore, we review existing theory showing that errors due to unresolved scales often result in representation error bias. We derive a novel bias-correcting form of the Schmidt-Kalman filter and apply it to the random walk model with biased observations. We show that the bias-correcting Schmidt-Kalman filter successfully compensates for representation error biases. Indeed, it is more important to treat an observation bias than an unbiased error due to unresolved scales