Application of Meso-net Observations Towards High Resolution

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Application of Meso-net Observations Towards High Resolution National
Weather Service Graphical Forecasting
"The National Weather Service has been actively pushing the frontiers of graphic based
forecasting since before the year 2000. During the past few years, the Eastern Kentucky forecast
office in Jackson has helped lead this venture through the development of innovative and cutting
edge techniques. This approach stemmed from an office-wide goal to provide the most accurate
and highly detailed forecasts that the state of the science would allow. As a result, the Weather
Forecast Office (WFO) in Jackson was the first National Weather Service WFO east of the
Mississippi to increase the areal resolution of its graphical forecasts from 5 km to 2.5 km. To
take full advantage of this higher resolution, background topography was edited on a point by
point basis, more accurately representing elevations at nearly one hundred key locations in our
area of responsibility. This effort has enabled meteorologists to create precise temperature
forecasts, to the point that significant differences now show up for valley versus ridge locations.
In addition, this approach to graphical forecasting is well suited for the numerous web
applications that allow users to “point and click" on a map and obtain a highly detailed forecast
for a specific location. The key to maintaining and improving the accuracy of forecasts at this
level of resolution hinges on our ability to verify these forecasts, on a point by point basis, via the
ground truth derived from local observations.
This presentation will demonstrate the current state of graphical forecast production at
WFO Jackson, Kentucky and the crucial role that the current local observing network plays in
the process. It will also highlight key areas of improvement that can be expected from the
forecasts through future enhancements to the observing network and more specifically through a
concerted effort to establish a meso-net of reliable automated observations."
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