Using the local ensemble transform Kalman filter for upper atmospheric modelling

The Advanced Ensemble electron density (Ne) Assimilation System (AENeAS) is a new data assimilation model of the ionosphere/thermosphere. The background model is provided by the Thermosphere Ionosphere Electrodynamics General Circulation Model (TIE-GCM) and the assimilation uses the local ensemble transform Kalman filter (LETKF). An outline derivation of the LETKF is provided and the equations are presented in a form analogous to the classic Kalman filter. An enhancement to the efficient LETKF implementation to reduce computational cost is also described. In a 3 day test in June 2017, AENeAS exhibits a total electron content (TEC) RMS error of 2.1 TECU compared with 5.5 TECU for NeQuick and 6.8 for TIE-GCM (with an NeQuick topside)

On the use of multi-model ensemble techniques for ionospheric and thermospheric characterisation

Space weather can have a negative impact on a number of radio frequency (RF) systems, with mitigation by ionospheric and thermospheric modelling one approach to improving system performance. However, before a model can be adopted operationally its performance must be quantified. Taylor diagrams, which show a model’s standard deviation and correlation, have been extended to further illustrate the model’s bias, standard deviation of error and mean square error in comparison to observational data. By normalising the statistics, multiple parameters can be shown simultaneously for a number of models. Using these modified Taylor diagrams, the first known long term (one month) comparison of three model types – empirical, physics and data assimilation - has been performed. The data assimilation models performed best, offering a statistically significant improvement in performance. One physics model performed sufficiently well that it is a viable background model option in future data assimilation schemes. Finally, multi-model thermospheric ensembles (MMEs) have been constructed from which the thermospheric forecasts exhibited a reduced root mean square error compared to non-ensemble approaches. Using an equally weighted MME the reduction was 55% and using a mean square error weighted approach the reduction was 48%

Improved forecasting of thermospheric densities using multi-model ensembles

This paper presents the first known application of multi-model ensembles to the forecasting of the thermosphere. A multi-model ensemble (MME) is a method for combining different, independent models. The main advantage of using an MME is to reduce the effect of model errors and bias, since it is expected that the model errors will, at least partly, cancel. The MME, with its reduced uncertainties, can then be used as the initial conditions in a physics-based thermosphere model for forecasting. This should increase the forecast skill since a reduction in the errors of the initial conditions of a model generally increases model skill. In this paper the Thermosphere–Ionosphere Electrodynamic General Circulation Model (TIE-GCM), the US Naval Research Laboratory Mass Spectrometer and Incoherent Scatter radar Exosphere 2000 (NRLMSISE-00), and Global Ionosphere–Thermosphere Model (GITM) have been used to construct the MME. As well as comparisons between the MMEs and the “standard” runs of the model, the MME densities have been propagated forward in time using the TIE-GCM. It is shown that thermospheric forecasts of up to 6 h, using the MME, have a reduction in the root mean square error of greater than 60 %. The paper also highlights differences in model performance between times of solar minimum and maximum

Lensing from small-scale travelling ionospheric disturbances observed using LOFAR

Observations made using the LOw-Frequency ARray (LOFAR) between 10:15 and 11:48 UT on the 15th of September 2018 over a bandwidth of approximately 25-65 MHz contain discrete pseudo-periodic features of ionospheric origin. These features occur within a period of approximately 10 min and collectively last roughly an hour. They are strongly frequency dependent, broadening significantly in time towards the lower frequencies, and show an overlaid pattern of diffraction fringes. By modelling the ionosphere as a thin phase screen containing a wave-like disturbance, we are able to replicate the observations, suggesting that they are associated with small-scale travelling ionospheric disturbances (TIDs). This modelling indicates that the features observed here require a compact radio source at a low elevation and that the TID or TIDs in question have a wavelength <~30 km. Several features suggest the presence of deviations from an idealised sinusoidal wave form. These results demonstrate LOFAR-s capability to identify and characterise small-scale ionospheric structures

The interaction of Escherichia coli O157 :H7 and Salmonella Typhimurium flagella with host cell membranes and cytoskeletal components

Bacterial flagella have many established roles beyond swimming motility. Despite clear evidence of flagella-dependent adherence, the specificity of the ligands and mechanisms of binding are still debated. In this study, the molecular basis of Escherichia coli O157:H7 and Salmonella enterica serovar Typhimurium flagella binding to epithelial cell cultures was investigated. Flagella interactions with host cell surfaces were intimate and crossed cellular boundaries as demarcated by actin and membrane labelling. Scanning electron microscopy revealed flagella disappearing into cellular surfaces and transmission electron microscopy of S. Typhiumurium indicated host membrane deformation and disruption in proximity to flagella. Motor mutants of E. coli O157:H7 and S. Typhimurium caused reduced haemolysis compared to wild-type, indicating that membrane disruption was in part due to flagella rotation. Flagella from E. coli O157 (H7), EPEC O127 (H6) and S. Typhimurium (P1 and P2 flagella) were shown to bind to purified intracellular components of the actin cytoskeleton and directly increase in vitro actin polymerization rates. We propose that flagella interactions with host cell membranes and cytoskeletal components may help prime intimate attachment and invasion for E. coli O157:H7 and S. Typhimurium, respectively