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Figure 3: False returns due to anomalous propagation not included in mosaic.
Radar Data Quality Editing
The Corridor Integrated Weather System (CIWS) makes use of High Resolution Vertically Integrated Liquid water (HRVIL) and High Resolution Enhanced Echo Top (HREET) radar data to provide information about precipitation location and intensity. The CIWS system also produces short-term forecasts of these products which are used for air traffic control planning.
Figure 1: Bull's eye in NEXRAD (top) and after data quality editing (bottom).Problems in the data quality of these radar products adversely affect all of these outputs. Data quality editing (DQE) algorithms to improve the radar data quality take place at several different stages within the system.
NEXRAD Radar Data Quality Editing
Inside the NEXRAD Open Radar Product Generator (ORPG) the data quality assurance algorithm is used to detect and remove such artifacts as constant power returns which can result in bull’s eye patterns and sun strobes. It also removes some clutter due to anomalous propagation returns using low velocity and spectrum width signatures.
Efforts to further improve the data quality within the NEXRAD ORPG include a spike and speckle remover and a check within the echo top generator to suppress high echo top peaks not supported by what is shown on lower elevation scans. Figure 1 shows an example of a bull’s eye pattern removed within the NEXRAD ORPG by the Data Quality Assurance algorithm (DQA).
Figure 2: Lint in VIL image before removal (top) and after lint removal (bottom)."Lint" Removal
Data quality efforts outside of the initial radar processors include an image processing editor to remove “lint” returns. Lint returns are small, thin, isolated cells unlikely to be true weather. Figure 2 (right) shows an example of lint removal.
Cloud Mask Editor
Another data quality editing process detects and removes false radar returns from individual radar VIL and echo top products by comparing the data to a satellite cloud mask. This cloud mask editor is currently running in the national CIWS system. At this time, cloud mask editing is only used during daytime conditions where the satellite cloud mask data quality is at its best, and it only edits in areas away from cloud. The cloud mask data quality editor is the first step in an effort to use data from additional sensors for data quality editing.
CIWS VIL Mosaic DQE Algorithm
Finally, the CIWS VIL mosaic has logic that allows it to sometimes throw out high VIL values from one radar if those values are not confirmed by values from surrounding radars. This algorithm takes into account the distance of the location to the various radars that were able to observe it, and their update times. Figure 3 shows an example of this situation.
Ongoing Efforts in CIWS Radar Data Quality Improvements
Taken together, these DQE algorithms improve the CIWS radar data quality. However, there is room for improvement, and efforts are underway to further improve the quality of CIWS radar data.
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