Comparison of Radar-based Precipitation Mosaics
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Wednesday, 7 January 2015: 11:30 AM
129A (Phoenix Convention Center - West and North Buildings)
Radar data play a prominent role in convective weather monitoring and nowcasting, and are increasingly assimilated into high-resolution numerical weather prediction models as well. Information from a single radar may be appropriate for local-to-regional scale interests; however, radar mosaics are required for coverage of large domains, such as needed for air traffic management decisions across the United States. Here, we compare two prominent, real-time datasets of radar-based precipitation mosaics employed by the aviation community: the Corridor Integrated Weather System (CIWS) developed by MIT Lincoln Laboratory and the Multi-Radar Multi-Sensor (MRMS) products generated by the National Severe Storms Laboratory (NSSL). The focus of the comparison is on the analysis and forecasts of the vertically-integrated liquid water (VIL) and echo top (ET) fields. Preliminary results show notable differences between the two analysis products. For example, the CIWS VIL appears to be systematically larger (by about a factor of two) than the MRMS VIL. Comparison of the ETs suggests a more complicated relationship, where CIWS ETs are typically higher below ~10 km altitude, while MRMS ETs are usually higher above. The performance of the forecast products was assessed as well against their respective analysis dataset. Using a VIL threshold of 0.1 kg m-2, both CIWS and MRMS exhibit a probability of detection (POD) that exceeds 60% for forecasts one hour into the future. Overall, the mean CIWS VIL forecast POD scores are slightly higher than MRMS. The discussion will also address artifacts found in both the CIWS and MRMS products.
This research is supported by the National Weather Service (NWS) and through data sharing by the Federal Aviation Administration (FAA). The views expressed are those of the authors and do not necessarily represent the official policy or position of the NWS or the FAA.