AbstractIn this study, the capability of using a C-band polarimetric Doppler radar and a two-dimensional video disdrometer (2DVD) to estimate monsoon-influenced summer rainfall during the Observation, Prediction and Analysis of Severe Convection of China (OPACC) field campaign in 2014 and 2015 in Eastern China is investigated. Three different rainfall estimators,R(Zh), R (Zh, Zdr) and R (KDP), are derived from two-year 2DVD observations of summer precipitation systems. The radar estimated rainfall is compared to gauge observations from 8 rainfall episodes. Results show that the two polarimetric estimators, R (Zh, Zdr) and R (KDP), perform better than the traditional Zh-R relation R(Zh). The KDP-based estimator R(KDP) produces the best rainfall accumulations.The radar rainfall estimators perform differently across the three organized convective systems (Meiyu rainband, typhoon rainband, and squall line). R(Zh) overestimates rainfall in the Meiyu rainband and squall line, R (Zh, Zdr) mitigates the overestimation in the Meiyu rainband but has a large bias in the squall line. QPE from R(KDP) is the most accurate among the three estimators, but possesses a relatively large bias for the squall line than in the Meiyu case. The high variability of drop size distribution (DSD) related to the precipitation microphysics in different types of rain is largely responsible for the case-dependent QPE performance using any single radar rainfall estimator. The squall line has a distinct ice-phase process with a large mean size of raindrops, while the Meiyu rainband and typhoon rainband are composed of smaller raindrops. Based on the statistical QPE error in the ZH-ZDR space, a new composite rainfall estimator by combining R(Zh), R(Zh, Zdr) and R(KDP) is constructed and proven to outperform any single rainfall estimator.
Journal of Hydrometeorology – American Meteorological Society
Published: Feb 28, 2017
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 12 million articles from more than
10,000 peer-reviewed journals.
All for just $49/month
Read as many articles as you need. Full articles with original layout, charts and figures. Read online, from anywhere.
Keep up with your field with Personalized Recommendations and Follow Journals to get automatic updates.
It’s easy to organize your research with our built-in tools.
Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera