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How do I know if my forecasts are better? Using benchmarks in Hydrological ensemble prediction
The skill of a forecast can be assessed by comparing the relative proximity of both the forecast and a benchmark to the observations. Example benchmarks include climatology or a naĂŻve forecast. Hydrological ensemble prediction systems (HEPS) are currently transforming the hydrological forecasting environment but in this new field there is little information to guide researchers and operational forecasters on how benchmarks can be best used to evaluate their probabilistic forecasts. In this study, it is identified that the forecast skill calculated can vary depending on the benchmark selected and that the selection of a benchmark for determining forecasting system skill is sensitive to a number of hydrological and system factors. A benchmark intercomparison experiment is then undertaken using the continuous ranked probability score (CRPS), a reference forecasting system and a suite of 23 different methods to derive benchmarks. The benchmarks are assessed within the operational set-up of the European Flood Awareness System (EFAS) to determine those that are âtoughest to beatâ and so give the most robust discrimination of forecast skill, particularly for the spatial average fields that EFAS relies upon.
Evaluating against an observed discharge proxy the benchmark that has most utility for EFAS and avoids the most naĂŻve skill across different hydrological situations is found to be meteorological persistency. This benchmark uses the latest meteorological observations of precipitation and temperature to drive the hydrological model. Hydrological long term average benchmarks, which are currently used in EFAS, are very easily beaten by the forecasting system and the use of these produces much naĂŻve skill. When decomposed into seasons, the advanced meteorological benchmarks, which make use of meteorological observations from the past 20 years at the same calendar date, have the most skill discrimination. They are also good at discriminating skill in low flows and for all catchment sizes. Simpler meteorological benchmarks are particularly useful for high flows. Recommendations for EFAS are to move to routine use of meteorological persistency, an advanced meteorological benchmark and a simple meteorological benchmark in order to provide a robust evaluation of forecast skill. This work provides the first comprehensive evidence on how benchmarks can be used in evaluation of skill in probabilistic hydrological forecasts and which benchmarks are most useful for skill discrimination and avoidance of naĂŻve skill in a large scale HEPS. It is recommended that all HEPS use the evidence and methodology provided here to evaluate which benchmarks to employ; so forecasters can have trust in their skill evaluation and will have confidence that their forecasts are indeed better
Synthesis of highly water-soluble fluorescent conjugated glycopoly(p-phenylene)s for lectin and Escherichia coli
Two facile, convenient, and versatile synthetic approaches are used to covalently attach carbohydrate residues to conjugated poly(p-phenylene)s (PPPs) for highly water-soluble PPPs bearing α-mannopyranosyl and ÎČ-glucopyranosyl pendants (polymers A and B), which highly fluoresce in phosphate buffer (pH 7.0). The post-polymerization functionalization approach is to treat bromo-bearing PPP (polymer 1) with 1-thiolethyl-α-D-mannose tetraacetate or 1-thiol-ÎČ-D-glucose tetraacetate in THF solution in the presence of K2CO3 at room temperature through formation of thioether bridges, affording polymer 2a or 2b. The prepolymerization functionalization approach is to polymerize a well-defined sugar-carrying monomer, affording polymer 2a. Polymers 2a and 2b were deacetylated under ZemplĂ©n conditions in methanol and methylene chloride containing sodium methoxide, affording polymers A and B, respectively. The multivalent display of carbohydrates on the fluorescent conjugated glycopolymer overcomes the characteristic low binding affinity of the individual carbohydrates to their receptor proteins. Titration of concanavalin A (Con A) to α-mannose-bearing polymer A resulted in significant fluorescent quenching of the polymer with Stern-Volmer quenching constant of 4.5 Ă 107. Incubation of polymer A with Escherichia coli (E. coli) lead to formation of fluorescently stained bacterial clusters. ÎČ-Glucose-bearing polymer B displayed no response to Con A and E. coli. © 2006 American Chemical Society