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.

Analysis of the serological variability of Lettuce mosaic virus using monoclonal antibodies and surface plasmon resonance technology

A panel of 19 monoclonal antibodies (mAbs) was used to study the immunological variability of Lettuce mosaic virus (LMV), a member of the genus Potyvirus, and to perform a first epitope characterization of this virus. Based on their specificity of recognition against a panel of 15 LMV isolates, the mAbs could be clustered in seven reactivity groups. Surface plasmon resonance analysis indicated the presence, on the LMV particles, of at least five independent recognition/binding regions, correlating with the seven mAbs reactivity groups. The results demonstrate that LMV shows significant serological variability and shed light on the LMV epitope structure. The various mAbs should prove a new and efficient tool for LMV diagnostic and field epidemiology studies.

Development of diagnotic capacity for Cotton Leaf Roll Virus from Thailand

Viral diseases of cotton are of economic significance in many parts of the world and several of these remain biosecurity threats to the Australian cotton industry, including Cotton Leaf Roll Virus (CLRV) from South East Asia. The proposed project will result in a greater understanding of the field symptoms of CLRV in Thailand and diagnostic assays used for its detection. I will also determine if the diagnostic assay being developed for Brazilian CLRDV as part of the CRDC project (11-12FRP00062) may also detect Thailand CLRV. It will provide educational opportunities to increase the knowledge base of staff currently working on cotton virus research and in doing so help to protect the Australian cotton industry from incursions of exotic viruses.