Abstract
Using echo-top height and hourly rainfall datasets, a new reflectivity-rainfall (Z-R) relationship is established in the present study for the radar-based quantitative precipitation estimation (RQPE), taking into account both the temporal evolution (dynamical) and the types of echoes (i.e., based on echo-top height classification). The new Z-R relationship is then applied to derive the RQPE over the middle and lower reaches of Yangtze River for two short-time intense rainfall cases in summer (2200 UTC 1 June 2016 and 2200 UTC 18 June 2016) and one stratiform rainfall case in winter (0000 UTC 15 December 2017), and then the comparative analyses between the RQPE and the RQPEs derived by the other two methods (the fixed Z-R relationship and the dynamical Z-R relationship based on radar reflectivity classification) are accomplished. The results show that the RQPE from the new Z-R relationship is much closer to the observation than those from the other two methods because the new method simultaneously considers the echo intensity (reflecting the size and concentration of hydrometers to a certain extent) and the echo-top height (reflecting the updraft to a certain extent). Two statistics of 720 rainfall events in summer (April to June 2017) and 50 rainfall events in winter (December 2017) over the same region show that the correlation coefficient (root-mean-squared error and relative error) between RQPE derived by the new Z-R relationship and observation is significantly increased (decreased) compared to the other two Z-R relationships. Besides, the new Z-R relationship is also good at estimating rainfall with different intensities as compared to the other two methods, especially for the intense rainfall.
Highlights
Radar quantitative precipitation estimation (RQPE) has finer temporal and spatial resolutions than those of traditional gauge-based station rainfall observations and can accurately reflect the nonuniformness of the precipitation over a large area [1,2,3]. erefore, RQPE is of great importance to severe weather monitoring, industrial and agricultural production, natural disasters prediction and preventing, and even weather modification [4,5,6]
It has been shown in the above three cases that the ETDM proposed in the present study results in a more accurate RQPE than the traditional SM and EIDM do. Is this viewpoint universality? To address this problem, 720 rainfall cases in the summer season (April to June 2017) and 50 rainfall cases in the winter season (December 2017) over the middle and lower reaches of Yangtze River were collected to further compare SM, EIDM, and ETDM methods. e precipitations over the region in the summer time comprise convective rainfall and stratiform precipitation, while the precipitations occurring in the winter time system are mainly induced by the stratiform system. ree statistics, correlation coe cient (R), rootmean-squared error (RMSE), and relative error (RE), between RQPEs derived by the three methods and observed precipitation are used to evaluate the performance of each method: R
A new dynamical reflectivity-rainfall (Z-R) relationship is established for the operational RQPE, based on the echo-top (ET) height which can preferably reflect the development of the rainfall storm. en, it is applied to derive the RQPE over the middle and lower reaches of Yangtze River for three cases. e results show that the RQPEs derived from two summer cases are more accurate compared to that from the winter case
Summary
Radar quantitative precipitation estimation (RQPE) has finer temporal and spatial resolutions than those of traditional gauge-based station rainfall observations and can accurately reflect the nonuniformness of the precipitation over a large area [1,2,3]. erefore, RQPE is of great importance to severe weather monitoring, industrial and agricultural production, natural disasters prediction and preventing, and even weather modification [4,5,6]. Erefore, using ET height instead of radar reflectivity to categorize observational precipitation and reflectivity into different groups is expected to further improve the accuracy of RQPE because the new method simultaneously considers the content of hydrometers (radar reflectivity) and the updraft (ET height) of a storm to a certain extent. Liu et al [20] and Chumchean et al [19] take into account the spatial variation (i.e., classification of precipitation) and Alfieri et al [21] consider the temporal variation (i.e., dynamical fitting of a and b using observational rainfall and reflectivity in a given time) to construct the Z-R relationship. Repeatedly performing the above 1–5 steps in different times (i.e., dynamical) can induce the coefficients a and b in the Z-R relationship to vary with the reflectivity intensity and time, forming the dynamical Z-R relationship based on echo intensity classification
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