This study estimates whether surface observations of temperature, moisture, and wind at some stations in the continental United States are less critical than others for specifying weather conditions in the vicinity of those stations. Two-dimensional variational analyses of temperature, relative humidity, and wind were created for selected midday hours during summer 2008. This set of 8925 control analyses was derived from 5-km-resolution background fields and Remote Automated Weather Station (RAWS) and National Weather Service (NWS) observations within roughly 4° × 4° latitude–longitude domains. Over 570 000 cross-validation experiments were completed to assess the impact of removing each RAWS and NWS station. The presence of observational assets within relatively close proximity to one another is relatively common. The sensitivity to removing temperature, relative humidity, or wind observations varies regionally and depends on the complexity of the surrounding terrain and the representativeness of the observations. Cost savings for the national RAWS program by removing a few stations may be possible. However, nearly all regions of the country remain undersampled, especially mountainous regions of the western United States frequently affected by wildfires.

A web-based set of tools has been developed to integrate weather, fire danger and fire behaviour information for the Great Lakes region of the United States. Weather parameters obtained from selected observational networks are combined with operational high-resolution gridded analyses and forecast products from the United States National Weather Service. Fuel moisture codes and fire behaviour indices in the Fire Weather Index subsystem of the Canadian Forest Fire Danger Rating System are computed from these sources for current and forecast conditions. Applications of this Great Lakes Fire and Fuels System are demonstrated for the 2012 fire season. Fuel moisture codes and fire behaviour indices computed from gridded analyses differ from those derived from observations in a manner similar to the analysis errors typical for the underlying weather parameters. Indices that are particularly sensitive to seasonally accumulating precipitation, such as the Drought Code, exhibit the largest differences. The gridded analyses and forecasts provide considerable additional information for fire weather professionals to evaluate weather and fuel state in the region. The potential utility of these gridded analyses and forecasts throughout the continental United States is highlighted.

No abstract

Focusing on the degradation of foggy images, a restora tion approach from a single image based on spatial correlation of dark channel prior is proposed. Firstly, the transmission of each pixel is estimated by the spatial correlation of dark channel prior. Secondly, a degradation model is utilized to restore the foggy image. Thirdly, the final recovered image, with enhanced contrast, is obtained by performing a post-processing technique based on just-noticeable difference. Experimental results demonstrate that the information of a foggy image can be recovered perfectly by the proposed method, even in the case of the abrupt depth changing scene.