Inferential, non-parametric statistics to assess quality of probabilistic forecast systems

Many statistical forecast systems are available to interested users. In order to be useful for decision-making, these systems must be based on evidence of underlying mechanisms. Once causal connections between the mechanism and their statistical manifestation have been firmly established, the forecasts must also provide some quantitative evidence of `quality’. However, the quality of statistical climate forecast systems (forecast quality) is an ill-defined and frequently misunderstood property. Often, providers and users of such forecast systems are unclear about what ‘quality’ entails and how to measure it, leading to confusion and misinformation. Here we present a generic framework to quantify aspects of forecast quality using an inferential approach to calculate nominal significance levels (p-values) that can be obtained either by directly applying non-parametric statistical tests such as Kruskal-Wallis (KW) or Kolmogorov-Smirnov (KS) or by using Monte-Carlo methods (in the case of forecast skill scores). Once converted to p-values, these forecast quality measures provide a means to objectively evaluate and compare temporal and spatial patterns of forecast quality across datasets and forecast systems. Our analysis demonstrates the importance of providing p-values rather than adopting some arbitrarily chosen significance levels such as p < 0.05 or p < 0.01, which is still common practice. This is illustrated by applying non-parametric tests (such as KW and KS) and skill scoring methods (LEPS and RPSS) to the 5-phase Southern Oscillation Index classification system using historical rainfall data from Australia, The Republic of South Africa and India. The selection of quality measures is solely based on their common use and does not constitute endorsement. We found that non-parametric statistical tests can be adequate proxies for skill measures such as LEPS or RPSS. The framework can be implemented anywhere, regardless of dataset, forecast system or quality measure. Eventually such inferential evidence should be complimented by descriptive statistical methods in order to fully assist in operational risk management.

Improved Approach for Analyzing Bromophenols in Seafood Using Stable Isotope Dilution Analysis in Combination with SPME

An analytical method for the measurement of five naturally occurring bromophenols of sensory relevance in seafood (barramundi and prawns) is presented. The method combines simultaneous distillation−extraction followed by alkaline back extraction of a hexane extract and subsequent acetylation of the bromophenols. Analysis of the bromophenol acetates was accomplished by headspace solid phase microextraction and gas chromatography−mass spectrometry using selected ion monitoring. The addition of 13C6 bromophenol stable isotope internal standards for each of the five congeners studied permitted the accurate quantitation of 2-bromophenol, 4-bromophenol, 2,6-dibromophenol, 2,4-dibromophenol, and 2,4,6-tribromophenol down to a limit of quantification of 0.05 ng/g of fish flesh. The method indicated acceptable precision and repeatability and excellent linearity over the typical concentration range of these compounds in seafood (0.5−50 ng/g). The analytical method was applied to determine the concentration of bromophenols in a range of farmed and wild barramundi and prawns and was also used to monitor bromophenol uptake in a pilot feeding trial.

An unusual combination in papaya ( Carica papaya): the good (glucosinolates) and the bad (cyanogenic glycosides)

Glucosinolates are a group of sulphur-containing glycosides found in the plant order Brassicales which includes the Brassica vegetables such as broccoli, cabbage and cauliflower. When brought into contact with the plant enzymes, myrosinases, the glucosinolates break down releasing glucose and other products which serve principally in plant defence against herbivores. The most important of the products from a human nutritional viewpoint, are the isothiocyanates. These potent inducers of detoxifying enzymes bestow the distinct anti-cancer properties on these plants. Unique among tropical fruits, papaya is known to contain an abundance of one particular glucosinolate, glucotropaeolin. Other compounds that play a pivotal role in the chemical defence system of many plants are the cyanogenic glycosides. Cyanogenic glycosides are activated by plant enzymes in the event of pest attack, releasing the deterrent: toxic hydrogen cyanide. Papaya, in addition to glucosinolates, also contains low levels of cyanogenic glycosides, an unusual occurrence because it was assumed that the two classes of metabolites were mutually exclusive. Studies measuring the levels of both in the edible parts of the papaya fruit and other utilised tissues are discussed and considered in the context of potential human health ramifications. All rights reserved, Elsevier.