A real-time capability that will allow forecasters to see exactly which observations for each analysis variable go into the hourly SPoRT ADAS product has been developed. This new utility will allow WFO partners to more easily identify exactly which of the observations within their County Warning Area are being used in the analysis and will assist in determining additional stations for blacklisting, which is important to maintaining analysis fidelity. Information from the observation input files is used to determine which stations (and which variables at each station) are available for assimilation into the SPoRT ADAS analysis. Then, information about the internal SPoRT ADAS quality control is taken into account to discard some of the available observations in the observation list. The internal quality control performs a neighbor check, a check against the background field, and a check against an observation from the previous hour. Finally, the blacklisted stations that have been identified by SPoRT WFO partners are removed from the observation list. The final list is used to produce a KML file that can be downloaded from the SPoRT website under the “Real-Time Data” –> “SPoRT ADAS” tab. The figure below shows a screen capture of the temperature analysis on this page from 1900 UTC on 30 August 2010. The KML files for each analysis variable can be downloaded by clicking the appropriate link below the heading “View Observation Locations”.
Clicking on one of the links allows the user to open the file using Google Earth (or another GIS application of the users choosing). When one of the KML files is opened in Google Earth, users can view an overview of the entire domain to see the distribution of the observations. The figure below shows the distribution of temperature observations from the 1900 UTC analysis on 30 August 2010.
In the figure, red paddles denote METAR stations, green paddles denote mesonet stations, and blue paddles denote buoy/ship stations. Using the pan and zoom features in Google Earth, users can get a closer look at individual stations within a cluster of many observations such as those around larger cities. In addition, each station contains information about the station name, time, and the value of the actual observations so users can better understand exactly what is going into the analysis. Clicking on an individual station brings this information to the screen in a pop-up window as shown below.