Details:

Journal Title:
IEEE Transactions on Geoscience and Remote Sensing
Personal Author:
Gou, Yabin ; Chen, Haonan ; Chandrasekar, V.
Gou, Yabin ; Chen, Haonan ; Chandrasekar, V. Less -
NOAA Program & Office:
OAR (Oceanic and Atmospheric Research) ; PSL (Physical Sciences Laboratory) ; CIRA (Cooperative Institute for Research in the Atmosphere)
OAR (Oceanic and Atmospheric Research) ; PSL (Physical Sciences Laboratory) ; CIRA (Cooperative Institute for Research in the Atmosphere) Less -
Description:
Effective utilization of the changing precipitation microphysics in real-time radar quantitative precipitation estimation (QPE) is challenging, which requires dynamic adjustment of the radar reflectivity (Z) and rain rate (R) relations. This article develops and demonstrates two dynamic radar rainfall approaches using 16 Doppler weather radars and 4579 surface rain gauges deployed over the Eastern Jiang Huai River Basin (EJRB) in China. Both approaches are derived based on the radar-gauge feedback mechanism. Although the Z-R relations in both approaches are dynamically adjusted within a precipitation system, one is using a single global optimum (SGO) Z-R relation, while the other is using different Z-R relations for different storm cells identified by a storm cell identification and tracking (SCIT) algorithm. Four precipitation events featured by different rainfall characteristics are investigated to evaluate the performances of various QPE methodologies. In addition, the shortterm vertical profile of reflectivity (VPR) clusters is extensively analyzed to resolve the storm-scale characteristics of different storm cells. The evaluation results based on independent gauge observations show that both rainfall approaches with dynamic Z-R relations perform much better than the fixed Z-R relations. The adaptive approach incorporating the SCIT algorithm and real-time gauge measurements has the best performance since it can better capture the spatial variability and evolution of precipitation.
Keywords:
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Precipitations
Quantitative Precipitation Estimation (QPE)
Radar
Storms

Source:
IEEE Transactions on Geoscience and Remote Sensing ( Volume: 58, Issue: 9, Sept. 2020)
DOI:
https://doi.org/10.1109/TGRS.2020.2976724
Document Type:
Journal Article
Rights Information:
Other
Compliance:
Submitted
Main Document Checksum:
[+]
urn:sha256:c25436a07df7729dcc3fd53b94e42ca7b4dd92e5ebce6d4bbe2e3527ac45c381
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