Summary. Probabilistic forecasts of continuous variables take the form of predictive densities or predictive cumulative distribution functions. We propose a diagnostic approach to the evaluation of predictive performance that is based on the paradigm of maximizing the sharpness of the predictive distributions subject to calibration. Calibration refers to the statistical consistency between the distributional forecasts and the observations and is a joint property of the predictions and the events that materialize. Sharpness refers to the concentration of the predictive distributions and is a property of the forecasts only. A simple theoretical framework allows us to distinguish between probabilistic calibration, exceedance calibration and marginal calibration. We propose and study tools for checking calibration and sharpness, among them the probability integral transform histogram, marginal calibration plots, the sharpness diagram and proper scoring rules. The diagnostic approach is illustrated by an assessment and ranking of probabilistic forecasts of wind speed at the Stateline wind energy centre in the US Pacific Northwest. In combination with cross-validation or in the time series context, our proposal provides very general, nonparametric alternatives to the use of information criteria for model diagnostics and model selection.

Results revealed that no single metric is sufficient but rather a suite of measures is required to fully characterize a model’s performance. Additionally, these measures need to be examined spatially, temporally, and over varying concentration ranges to adequately characterize a model’s performance. For discrete-type evaluations, mean and normalized measures of bias and error were chosen. These revealed the following: (1) two of the three models overpredicted ozone (O 3) concentrations (mean bias ranged from �1.41 to �9.51 ppb for maximum 1 hr and from �1.16 to �8.31 ppb for maximum 8 hr), and (2) the root mean square errors produced by the models ranged from 14.63 to 21.25 ppb for maximum 1 hr and from 13.04 to 18.18 ppb for maximum 8 hr. Metrics associated with the categorical-type evaluation revealed that each model was able to achieve an accuracy �90 % for the maximum 1-hr O 3 forecast, a minimum goal for the initial implementation of the new National Air Quality Forecast capability. However, this metric is heavily influenced by the very large number of correctly forecast nonexceedances. To circumvent this influence, two more stringent measures of categorical performance, the critical success index and the hit rate, were also calculated. These revealed that only a small percentage (between 6 and 36 % depending on model and metric) of exceedances can be expected to be forecasted correctly. There is also a large false alarm ratio associated with each of the three models, which ranged from 64 to 87%. Evaluation results of the three prototype models have shown promise, but they have also shown that considerable work needs to be done as National Oceanic and Atmospheric Administration develops a National Air Quality Forecasting

This study addresses three issues: spatial downscaling, calibration, and combination of seasonal predictions produced by different coupled ocean-atmosphere climate models. It examines the feasibility of using a Bayesian procedure for producing combined, well-calibrated downscaled seasonal rainfall forecasts for two regions in South America and river flow forecasts for the Paraná river in the south of Brazil and the Tocantins river in the north of Brazil. These forecasts are important for national electricity generation management and planning. A Bayesian procedure, referred to here as forecast assimilation, is used to combine and calibrate the rainfall predictions produced by three climate models. Forecast assimilation is able to improve the skill of 3-month lead November-December-January multi-model rainfall predictions over the two South American regions. Improvements are noted in forecast seasonal mean values and uncertainty estimates. River flow forecasts are less skilful than rainfall forecasts. This is partially because natural river flow is a derived quantity that is sensitive to hydrological as well as meteorological processes, and to human intervention in the form of reservoir management.

The real-time National Air Quality Forecast Capability (NAQFC), developed through a collaborative effort between the National

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Weather plays an important role in agricultural production. It has a profound influence on the growth, development and yields of a crop, incidence of pests and diseases, water needs and fertilizer requirements in terms of differences in nutrient mobilization due to water stresses and timeliness and effectiveness of prophylactic and cultural operations on crops. Weather aberrations may cause (i) physical damage to crops and (ii) soil erosion. The quality of crop produce during movement from field to storage and transport to market depends on weather. Bad weather may affect the quality of produce during transport and viability and vigor of seeds and planting material during storage. Thus, there is no aspect of crop culture that is devoid of the impact of weather. However, (a) the weather requirements for optimal growth, development and yield of crops, incidence, multiplication and spread of pests and diseases and susceptibility to weather-induced stresses and affliction by pests and diseases vary amongst crops, with the same crop with the varieties and with the same crop variety with its growth stages. Even on a climatological basis weather factors show spatial variations in an area at a given time, temporal variations at a given place and year to year variations for a given place and time. For cropping purposes weather over short time periods and

recombination events between nonallelic copies. The finding of a recombinagenic motif within the repeats may therefore help to explain the observation that the breakpoints of nonallelic recombination events are often clustered (12). The overall influence of mobile DNA elements on recombination remains unclear, however, with some over- and some underrepresented within hotspots. The seven-nucleotide motif is not among those previously associated with recombination in other species. However, its role in influencing recombination is supported by spermtyping experiments, as is the role of another

(HMT) program

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