The antiviral treatment of chronic C hepatitis has improved significantly over the past decade with the introduction of interferons (IFNs), and more recently, pegylated IFNs. Up to two-thirds of all patients treated with pegylated IFN combined with ribavirin can now achieve viral eradication if treated according to current guidelines. Despite this success rate, hematological, immunological, rheumatological and dermatological side effects have been reported in chronic hepatitis C patients treated with IFN-alpha. The subjects of this report are two young females with chronic hepatitis C, who developed rheumatoid syndrome and/or erythema nodosum during antiviral treatment with IFN-alpha or pegylated IFN combined with ribavirin.

Motivated by the promising results of global-scale ensemble forecasting, a number of groups have attempted mesoscale, short-range ensemble forecasting (SREF), focusing mainly over the eastern half of the United States. To evaluate the performance of mesoscale SREF over the Pacific Northwest and to test the value of using different initial analyses as a means of ensemble forecast generation, a five-member mesoscale SREF system was constructed in which the Pennsylvania State University-National Center for Atmospheric Research fifthgeneration Mesoscale Model (MM5) was run with initializations and forecast boundary conditions from major operational centers. The ensemble system was evaluated over the Pacific Northwest from January to June 2000. The model verification presented in this study considers only near-surface weather variables, especially the observed 10-m wind direction. The ensemble mean forecast displays lower mean absolute wind direction errors than the component ensemble members when averaged over all cases. The frequency with which the ensemble mean forecast verifies best is no better than the frequency of any individual member forecast. The wind direction forecast errors for the 12-km ensemble mean forecasts are comparable to 4-km deterministic forecast errors. Ensemble mean forecasts are observed to retain much of the orographically forced mesoscale structure in the component forecasts while smoothing out phase differences for propagating features. The correlation between forecast spread and forecast error for wind direction is approximately 0.6 for most lead times. Spread-error correlations rise to roughly 0.8 when only cases with high or low spread are considered. Such high correlations suggest that the ensemble system possesses the ability to predict forecast skill for high-and low-spread cases. The tendency toward higher spread-error correlation when cases with medium spread are filtered out is also found for each component member of the ensemble.

The Western Wind and Solar Integration Study (WWSIS) is one of the world's largest regional integration studies to date. This paper discusses the creation of the wind dataset that will be the basis for assessing the operating impacts and mitigation options due to the variability and uncertainty of wind power on the utility grids. The dataset is based on output from a mesoscale numerical weather prediction (NWP) model, covering over 4 million square kilometers with a spatial resolution of approximately two-kilometers over a period of three years with a temporal resolution of 10 minutes. The mesoscale model dataset includes all the meteorological variables necessary to calculate wind energy production. Individual time series were produced for over 30 thousand locations representing more than 900 GW of potential wind power generation.

SUMMARYAn analogue of the linear continuous ranked probability score is introduced that applies to probabilistic forecasts of circular quantities, such as wind direction. This scoring rule is proper and thereby discourages hedging. The circular continuous ranked probability score reduces to angular distance when the forecast is deterministic, just as the linear continuous ranked probability score generalizes the absolute error. Furthermore, the circular continuous ranked probability score provides a direct way of comparing deterministic forecasts, discrete forecast ensembles, and post-processed forecast ensembles that can take the form of circular probability density functions.The circular continuous ranked probability score is used in this study to compare predictions of 10 m wind direction for 361 cases of mesoscale, short-range ensemble forecasts over the North American Pacific Northwest. Simple, calibrated probability forecasts based on the ensemble mean and its forecast error history over the period outperform probability forecasts constructed directly from the ensemble sample statistics. These results suggest that short-term forecast uncertainty is not yet well predicted at mesoscale resolutions near the surface, despite the inclusion of multi-scheme physics diversity and surface boundary parameter perturbations in the mesoscale ensemble design.

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