In at the Deep End. Impressions of farming in a year gaining experience: 1952-3; 1978-85

The book describes a wide variety of students’ experiences in their practical year prior to entering University to study BSc Agriculture. Until comparatively recently it was the normal requirement for all such students, whether or not they already had home farming experience, to gain a full year’s experience of practical agriculture – and to write a report thereon. This record of 41 students’ reports of the pre-entry year begins with Paul’s own experience in the early 1950s before 41 reports from 30 or more years ago. The essays provide compelling and fascinating stories, well-articulated with clear acknowledgement for most part of the humanity and the warmth with which each student was treated by farmers and farm workers alike, despite the difference in both age and experience (considerable!). [This summary is an extract from the full overview which is archived here together with the book.]

Portmanteau model diagnostics and tests for nonlinearity: a comparative Monte Carlo study of two alternative methods

This paper employs an extensive Monte Carlo study to test the size and power of the BDS and close return methods of testing for departures from independent and identical distribution. It is found that the finite sample properties of the BDS test are far superior and that the close return method cannot be recommended as a model diagnostic. Neither test can be reliably used for very small samples, while the close return test has low power even at large sample sizes

Noisy Monte Carlo: convergence of Markov chains with approximate transition kernels

Monte Carlo algorithms often aim to draw from a distribution π by simulating a Markov chain with transition kernel P such that π is invariant under P. However, there are many situations for which it is impractical or impossible to draw from the transition kernel P. For instance, this is the case with massive datasets, where is it prohibitively expensive to calculate the likelihood and is also the case for intractable likelihood models arising from, for example, Gibbs random fields, such as those found in spatial statistics and network analysis. A natural approach in these cases is to replace P by an approximation Pˆ. Using theory from the stability of Markov chains we explore a variety of situations where it is possible to quantify how ’close’ the chain given by the transition kernel Pˆ is to the chain given by P . We apply these results to several examples from spatial statistics and network analysis.

Reducing noise associated with the Monte Carlo Independent Column Approximation for weather forecasting models

The Monte Carlo Independent Column Approximation (McICA) is a flexible method for representing subgrid-scale cloud inhomogeneity in radiative transfer schemes. It does, however, introduce conditional random errors but these have been shown to have little effect on climate simulations, where spatial and temporal scales of interest are large enough for effects of noise to be averaged out. This article considers the effect of McICA noise on a numerical weather prediction (NWP) model, where the time and spatial scales of interest are much closer to those at which the errors manifest themselves; this, as we show, means that noise is more significant. We suggest methods for efficiently reducing the magnitude of McICA noise and test these methods in a global NWP version of the UK Met Office Unified Model (MetUM). The resultant errors are put into context by comparison with errors due to the widely used assumption of maximum-random-overlap of plane-parallel homogeneous cloud. For a simple implementation of the McICA scheme, forecasts of near-surface temperature are found to be worse than those obtained using the plane-parallel, maximum-random-overlap representation of clouds. However, by applying the methods suggested in this article, we can reduce noise enough to give forecasts of near-surface temperature that are an improvement on the plane-parallel maximum-random-overlap forecasts. We conclude that the McICA scheme can be used to improve the representation of clouds in NWP models, with the provision that the associated noise is sufficiently small.

Aretusa: continuity, rupture, and space for intervention, 1944-1946

This article focuses on the cultural activity of Aretusa (1944-1946), a journal that was deeply connected to the inner circle of philosopher and politician Benedetto Croce (1866-1952). The article analyses the role played by periodical editors Francesco Flora (1891-1962) and Carlo Muscetta (1912-2004) in shaping the mission and direction of this journal. By drawing on Pierre Bourdieu’s theory of habitus, and the notion of hysteresis in particular, this study details the factors influencing the aesthetic dispositions, political positioning, and the wider impact of historical circumstances on the cultural practice of each editor while at the helm of the review.