Climatology and dynamics of the link between dry intrusions and cold fronts during winter. Part I: global climatology

This is the final version. Available on open access from Springer via the DOI in this recordData availability: ERA-Interim data are available online (http://apps.ecmwf.int/datasets/).Cold fronts are a primary feature of the day-to-day variability of weather in the midlatitudes, and feature in conceptual extratropical cyclone models alongside the dry intrusion airstream. Here the climatological frequency and spatial distribution of the co-occurrence of these two features are quantified, and the differences in cold front characteristics (intensity, size, and precipitation) when a dry intrusion is present or not are calculated. Fronts are objectively identified in the ECMWF ERA-Interim dataset for the winter seasons in each hemisphere and split into 3 sub-types: central fronts (within a cyclone area); trailing fronts (outwith the cyclone area but connected to a central front); and isolated fronts (not connected to a cyclone). These are then associated with dry intrusions identified using Lagrangian trajectory analysis. Trailing fronts are most likely to be associated with a DI in both hemispheres, and this occurs more frequently in the western parts of the major storm track regions. Isolated fronts are linked to DIs more frequently on the eastern ends of the storm tracks, and in the subtropics. All front types, when co-occurring with a DI, are stronger in terms of their temperature gradient, are much larger in area, and typically have higher average precipitation. Therefore, climatologically the link with DIs increases the impact of cold fronts. There are some differences in the statistics of the precipitation for trailing and isolated fronts that are further investigated in Part II of this study.Australian Research CouncilSwiss National Science FoundationBenoziyo Endowment Fund for the Advancement of Scienc

Climatology and dynamics of the link between dry intrusions and cold fronts during winter, Part II: Front-centred perspective

This is the author accepted manuscript. The final version is available from Springer via the DOI in this record.The conceptual picture of an extratropical cyclone typically includes a cold front and a dry intrusion (DI) behind it. By objectively identifying fronts and DIs in ECMWF ERA-Interim data for 1979–2014, Part I quantified the climatological relationship between cold fronts and DIs. Driven by the finding that front intensity and frontal precipitation are enhanced in the presence of DIs, here we employ a front-centred perspective to focus on the dynamical and thermodynamical environment of cold fronts with and without DIs in the Northern Hemisphere winter. Distinguishing between trailing fronts (that connect to a parent cyclone) and isolated fronts, examples of DIs behind each type illustrate the baroclinic environment of the trailing front, and the lack of strong temperature gradients across the isolated front. Composite analyses of North Atlantic and North Pacific fronts outline the major differences in the presence of DIs, compared to similar fronts but without DIs in their vicinity. The magnitude and spatial structure of the modification by DIs depends on the front intensity. Yet, generally with DIs, trailing fronts occur with stronger SLP dipole, deeper upper-tropospheric trough, stronger 10-m wind gusts, enhanced ocean sensible and latent heat fluxes in the cyclone cold sector and heavier precipitation. Isolated weak fronts exhibit similar behaviour, with different spatial structure. This study highlights the central role of DIs for shaping the variability of fronts and their associated environment and impact.Australian Research Council DECR

Comparison of techniques for handling missing covariate data within prognostic modelling studies: a simulation study

Background: There is no consensus on the most appropriate approach to handle missing covariate data within prognostic modelling studies. Therefore a simulation study was performed to assess the effects of different missing data techniques on the performance of a prognostic model. Methods: Datasets were generated to resemble the skewed distributions seen in a motivating breast cancer example. Multivariate missing data were imposed on four covariates using four different mechanisms; missing completely at random (MCAR), missing at random (MAR), missing not at random (MNAR) and a combination of all three mechanisms. Five amounts of incomplete cases from 5% to 75% were considered. Complete case analysis (CC), single imputation (SI) and five multiple imputation (MI) techniques available within the R statistical software were investigated: a) data augmentation (DA) approach assuming a multivariate normal distribution, b) DA assuming a general location model, c) regression switching imputation, d) regression switching with predictive mean matching (MICE-PMM) and e) flexible additive imputation models. A Cox proportional hazards model was fitted and appropriate estimates for the regression coefficients and model performance measures were obtained. Results: Performing a CC analysis produced unbiased regression estimates, but inflated standard errors, which affected the significance of the covariates in the model with 25% or more missingness. Using SI, underestimated the variability; resulting in poor coverage even with 10% missingness. Of the MI approaches, applying MICE-PMM produced, in general, the least biased estimates and better coverage for the incomplete covariates and better model performance for all mechanisms. However, this MI approach still produced biased regression coefficient estimates for the incomplete skewed continuous covariates when 50% or more cases had missing data imposed with a MCAR, MAR or combined mechanism. When the missingness depended on the incomplete covariates, i.e. MNAR, estimates were biased with more than 10% incomplete cases for all MI approaches. Conclusion: The results from this simulation study suggest that performing MICE-PMM may be the preferred MI approach provided that less than 50% of the cases have missing data and the missing data are not MNAR

Comparison of methods for handling missing data on immunohistochemical markers in survival analysis of breast cancer

Background:Tissue micro-arrays (TMAs) are increasingly used to generate data of the molecular phenotype of tumours in clinical epidemiology studies, such as studies of disease prognosis. However, TMA data are particularly prone to missingness. A variety of methods to deal with missing data are available. However, the validity of the various approaches is dependent on the structure of the missing data and there are few empirical studies dealing with missing data from molecular pathology. The purpose of this study was to investigate the results of four commonly used approaches to handling missing data from a large, multi-centre study of the molecular pathological determinants of prognosis in breast cancer.Patients and Methods:We pooled data from over 11 000 cases of invasive breast cancer from five studies that collected information on seven prognostic indicators together with survival time data. We compared the results of a multi-variate Cox regression using four approaches to handling missing data-complete case analysis (CCA), mean substitution (MS) and multiple imputation without inclusion of the outcome (MI) and multiple imputation with inclusion of the outcome (MI). We also performed an analysis in which missing data were simulated under different assumptions and the results of the four methods were compared.Results:Over half the cases had missing data on at least one of the seven variables and 11 percent had missing data on 4 or more. The multi-variate hazard ratio estimates based on multiple imputation models were very similar to those derived after using MS, with similar standard errors. Hazard ratio estimates based on the CCA were only slightly different, but the estimates were less precise as the standard errors were large. However, in data simulated to be missing completely at random (MCAR) or missing at random (MAR), estimates for MI were least biased and most accurate, whereas estimates for CCA were most biased and least accurate.Conclusion:In this study, empirical results from analyses using CCA, MS, MI and MI were similar, although results from CCA were less precise. The results from simulations suggest that in general MI is likely to be the best. Given the ease of implementing MI in standard statistical software, the results of MI and CCA should be compared in any multi-variate analysis where missing data are a problem. © 2011 Cancer Research UK. All rights reserved

On the variational cohomology of g-invariant foliations

Let S be an integrable Pfaffian system. If it is invariant under a transversally free infinitesimal action of a finite dimensional real Lie algebra g, we show that the 'vertical' variational cohomology of S is equal to the Lie algebra cohomology of g with values in the space of the 'horizontal' cohomology in a maximum dimension. This result, besides giving an effective algorithm for the computation of the variational cohomology of an invariant Pfaffian system, provides a method for detecting obstructions to the existence of infinitesimal actions leaving a given system invariant. (C) 2003 American Institute of Physics.44104702471

Stereotyping and the treatment of missing data for drug and alcohol clinical trials

Stigma and stereotyping of marginalized groups often is insidious and shows up in unlikely places, for instance in how clinical trials consider dropouts in treatment research. A surprising number of studies presume that people who do not complete the study protocol relapse and code their data as if they had been observed. There is no good statistical rationale for this treatment of missing data and numerous and more defensible alternative methods are available. We need to be mindful about our attitudes and preconceptions about the people we are intending to help. There is no good reason to continue to support science built on this scientifically indefensible stereotyping, however unintentional

Combining estimates of interest in prognostic modelling studies after multiple imputation: current practice and guidelines

Background: Multiple imputation (MI) provides an effective approach to handle missing covariate data within prognostic modelling studies, as it can properly account for the missing data uncertainty. The multiply imputed datasets are each analysed using standard prognostic modelling techniques to obtain the estimates of interest. The estimates from each imputed dataset are then combined into one overall estimate and variance, incorporating both the within and between imputation variability. Rubin's rules for combining these multiply imputed estimates are based on asymptotic theory. The resulting combined estimates may be more accurate if the posterior distribution of the population parameter of interest is better approximated by the normal distribution. However, the normality assumption may not be appropriate for all the parameters of interest when analysing prognostic modelling studies, such as predicted survival probabilities and model performance measures. Methods: Guidelines for combining the estimates of interest when analysing prognostic modelling studies are provided. A literature review is performed to identify current practice for combining such estimates in prognostic modelling studies. Results: Methods for combining all reported estimates after MI were not well reported in the current literature. Rubin's rules without applying any transformations were the standard approach used, when any method was stated. Conclusion: The proposed simple guidelines for combining estimates after MI may lead to a wider and more appropriate use of MI in future prognostic modelling studies

Multiple Imputation Ensembles (MIE) for dealing with missing data

Missing data is a significant issue in many real-world datasets, yet there are no robust methods for dealing with it appropriately. In this paper, we propose a robust approach to dealing with missing data in classification problems: Multiple Imputation Ensembles (MIE). Our method integrates two approaches: multiple imputation and ensemble methods and compares two types of ensembles: bagging and stacking. We also propose a robust experimental set-up using 20 benchmark datasets from the UCI machine learning repository. For each dataset, we introduce increasing amounts of data Missing Completely at Random. Firstly, we use a number of single/multiple imputation methods to recover the missing values and then ensemble a number of different classifiers built on the imputed data. We assess the quality of the imputation by using dissimilarity measures. We also evaluate the MIE performance by comparing classification accuracy on the complete and imputed data. Furthermore, we use the accuracy of simple imputation as a benchmark for comparison. We find that our proposed approach combining multiple imputation with ensemble techniques outperform others, particularly as missing data increases

Methane storms as a driver of Titan's dune orientation

Titan's equatorial regions are covered by eastward propagating linear dunes. This direction is opposite to mean surface winds simulated by Global Climate Models (GCMs), which are oriented westward at these latitudes, similar to trade winds on Earth. Different hypotheses have been proposed to address this apparent contradiction, involving Saturn's gravitational tides, large scale topography or wind statistics, but none of them can explain a global eastward dune propagation in the equatorial band. Here we analyse the impact of equinoctial tropical methane storms developing in the superrotating atmosphere (i.e. the eastward winds at high altitude) on Titan's dune orientation. Using mesoscale simulations of convective methane clouds with a GCM wind profile featuring superrotation, we show that Titan's storms should produce fast eastward gust fronts above the surface. Such gusts dominate the aeolian transport, allowing dunes to extend eastward. This analysis therefore suggests a coupling between superrotation, tropical methane storms and dune formation on Titan. Furthermore, together with GCM predictions and analogies to some terrestrial dune fields, this work provides a general framework explaining several major features of Titan's dunes: linear shape, eastward propagation and poleward divergence, and implies an equatorial origin of Titan's dune sand.Comment: Published online on Nature Geoscience on 13 April 201