A method for determining a quality controlled sensor set from a redundant sensor set comprising calculating a first time correlation coefficient and a first autocorrelation coefficient based on a first sensor time series data, calculating a second time correlation coefficient and a second autocorrelation coefficient based on a second sensor time series data, calculating a first and second sensor correlation coefficient based on the first sensor time series data and the second sensor time series data, and determining the quality controlled sensor set with a highest confidence level.
While the weather forecast enterprise has dramatically increased its capabilities to provide accurate forecasts of weather hazards at greater lead times and finer scales, a growing body of research demonstrates that people have a difficult time understanding what the impacts of those hazards will be. Even worse, people find it difficult to impossible to conceive of what the impact means for their unique situation. Very few people have the technical background to assess their vulnerability and interpret probabilistic forecasts of hazards to calculate the risks of specific consequences. The HurricaneRiskCalculator® web app is being created to fill these gaps.
The HurricaneRiskCalculator® web app is a public-facing decision support tool based on a probabilistic risk framework that intersects real-time tropical cyclone wind hazard predictions with information from a structural vulnerability assessment. Through this intersection of hazard, vulnerability, and exposure, the tool calculates the risks of various consequences, such as different degrees of structural damage and whether the structure will be habitable following the tropical cyclone.
Since it is a forecast product, the GTG is most useful for route planning, i.e., strategic avoidance of turbulence. However, given the rapidly evolving character of turbulence, for tactical avoidance it is more useful to have a rapidly updated nowcast system. This system, called GTGN™ (N for nowcast), has been developed at RAL and is primarily driven by the most recent available turbulence observations (in situ EDR measurements, turbulence pilot reports, NTDA output, and satellite–based turbulence inferences) merged together with a GTG short–term forecast. The product updates every 15 min.
Example of a GTG turbulence forecast as it appears on the Operational Aviation Weather Center web site.
Example GTGN™ output (lower right) showing modifications from the GTG input (upper left).
The GTGN™ procedure uses GTG short–term forecast grids which are modified on a point–by–point basis to provide better agreement with the latest observations. The observations used include PIREPs, in situ EDR data, lightning, Metar and NTDA EDR measurements. The gridded output is EDR. An example of the adaptive procedure is shown in the bottom right figure.
The operational GTGN™ system is now available from the National Weather Service.".
