Ensemble prediction for nowcasting with a convection-permitting model - II: forecast error statistics

A 24-member ensemble of 1-h high-resolution forecasts over the Southern United Kingdom is used to study short-range forecast error statistics. The initial conditions are found from perturbations from an ensemble transform Kalman filter. Forecasts from this system are assumed to lie within the bounds of forecast error of an operational forecast system. Although noisy, this system is capable of producing physically reasonable statistics which are analysed and compared to statistics implied from a variational assimilation system. The variances for temperature errors for instance show structures that reflect convective activity. Some variables, notably potential temperature and specific humidity perturbations, have autocorrelation functions that deviate from 3-D isotropy at the convective-scale (horizontal scales less than 10 km). Other variables, notably the velocity potential for horizontal divergence perturbations, maintain 3-D isotropy at all scales. Geostrophic and hydrostatic balances are studied by examining correlations between terms in the divergence and vertical momentum equations respectively. Both balances are found to decay as the horizontal scale decreases. It is estimated that geostrophic balance becomes less important at scales smaller than 75 km, and hydrostatic balance becomes less important at scales smaller than 35 km, although more work is required to validate these findings. The implications of these results for high-resolution data assimilation are discussed.

A method for merging flow-dependent forecast error statistics from an ensemble with static statistics for use in high resolution variational data assimilation

The background error covariance matrix, B, is often used in variational data assimilation for numerical weather prediction as a static and hence poor approximation to the fully dynamic forecast error covariance matrix, Pf. In this paper the concept of an Ensemble Reduced Rank Kalman Filter (EnRRKF) is outlined. In the EnRRKF the forecast error statistics in a subspace defined by an ensemble of states forecast by the dynamic model are found. These statistics are merged in a formal way with the static statistics, which apply in the remainder of the space. The combined statistics may then be used in a variational data assimilation setting. It is hoped that the nonlinear error growth of small-scale weather systems will be accurately captured by the EnRRKF, to produce accurate analyses and ultimately improved forecasts of extreme events.

A study of the antioxidant capacity of oak wood used in wine ageing and the correlation with polyphenol composition

The antioxidant capacity of oak wood used in the ageing of wine was studied by four different methods: measurement of scavenging capacity against a given radical (ABTS, DPPH), oxygen radical absorbance capacity (ORAC) and the ferric reducing antioxidant power (FRAP). Although, the four methods tested gave comparable results for the antioxidant capacity measured in oak wood extracts, the ORAC method gave results with some differences from the other methods. Non-toasted oak wood samples displayed more antioxidant power than toasted ones due to differences in the polyphenol compositon. A correlation analysis revealed that ellagitannins were the compounds mainly responsible for the antioxidant capacity of oak wood. Some phenolic acids, mainly gallic acid, also showed a significant correlation with antioxidant capacity.

The inflation hedging characteristics of US and UK investments: a multi-factor error correction approach

Historic analysis of the inflation hedging properties of stocks produced anomalous results, with equities often appearing to offer a perverse hedge against inflation. This has been attributed to the impact of real and monetary shocks to the economy, which influence both inflation and asset returns. It has been argued that real estate should provide a better hedge: however, empirical results have been mixed. This paper explores the relationship between commercial real estate returns (from both private and public markets) and economic, fiscal and monetary factors and inflation for US and UK markets. Comparative analysis of general equity and small capitalisation stock returns in both markets is carried out. Inflation is subdivided into expected and unexpected components using different estimation techniques. The analyses are undertaken using long-run error correction techniques. In the long-run, once real and monetary variables are included, asset returns are positively linked to anticipated inflation but not to inflation shocks. Adjustment processes are, however, gradual and not within period. Real estate returns, particularly direct market returns, exhibit characteristics that differ from equities.

Serial persistence in individual real estate returns in the UK

Persistence of property returns is a topic of perennial interest to fund managers as it suggests that choosing those properties that will perform well in the future is as simple as looking at those that performed well in the past. Consequently, much effort has been expended to determine if such a rule exists in the real estate market. This paper extends earlier studies in US, Australian, and UK markets in two ways. First, this study applies the same methodology originally used in Young and Graff (1996) making the results directly comparable with those in the US and Australian property markets. Second, this study uses a much longer and larger database covering all commercial property data available from the Investment Property Databank (IPD), for the years 1981 to 2002 for as many as 216,758 individual property returns. While the performance results of this study mimic the US and Australian results of greater persistence in the extreme first and fourth quartiles, they also evidence persistence in the moderate second and third quartiles, a notable departure from previous studies. Likewise patterns across property type, location, time, and holding period are remarkably similar leading to the conjecture that behaviors in the practice of commercial real estate investment management are themselves deeply rooted and persistent and perhaps influenced for good or ill by agency effects

I and I: immunity to error through misidentification of the subject

Abstract I argue for the following claims: [1] all uses of I (the word ‘I’ or thought-element I) are absolutely immune to error through misidentification relative to I. [2] no genuine use of I can fail to refer. Nevertheless [3] I isn’t univocal: it doesn’t always refer to the same thing, or kind of thing, even in the thought or speech of a single person. This is so even though [4] I always refers to its user, the subject of experience who speaks or thinks, and although [5] if I’m thinking about something specifically as myself, I can’t fail to be thinking of myself, and although [6] a genuine understanding use of I always involves the subject thinking of itself as itself, whatever else it does or doesn’t involve, and although [7] if I take myself to be thinking about myself, then I am thinking about myself.

Correlation between process cheese meltability determined by sensory analysis, computer vision method and Olson and Price test

The meltabilities of 14 process cheese samples were determined at 2 and 4 weeks after manufacture using sensory analysis, a computer vision method, and the Olson and Price test. Sensory analysis meltability correlated with both computer vision meltability (R-2 = 0.71, P < 0.001) and Olson and Price meltability (R-2 = 0.69, P < 0.001). There was a marked lack of correlation between the computer vision method and the Olson and Price test. This study showed that the Olson and Price test gave greater repeatability than the computer vision method. Results showed process cheese meltability decreased with increasing inorganic salt content and with lower moisture/fat ratios. There was very little evidence in this study to show that process cheese meltability changed between 2 and 4 weeks after manufacture..

The correlation structure of spatial autoregressions

This paper investigates how the correlations implied by a first-order simultaneous autoregressive (SAR(1)) process are affected by the weights matrix and the autocorrelation parameter. A graph theoretic representation of the covariances in terms of walks connecting the spatial units helps to clarify a number of correlation properties of the processes. In particular, we study some implications of row-standardizing the weights matrix, the dependence of the correlations on graph distance, and the behavior of the correlations at the extremes of the parameter space. Throughout the analysis differences between directed and undirected networks are emphasized. The graph theoretic representation also clarifies why it is difficult to relate properties ofW to correlation properties of SAR(1) models defined on irregular lattices.