Zależności Z-R dla różnych typów opadów jako narzędzie do radarowego szacowania wielkości opadów = The Z-R relationships for different types of precipitation as a tool for radar-based precipitation estimation

Abstract

An alternative to the use of rain gauges as sources of precipitation data is provided by laser disdrometers, which inter alia allow for high-temporal-resolution measurement of the reflectivity (Z) and intensity (R) of precipitation. In the study detailed here, an OTT Parsivel1 laser disdrometer located at the Meteorological Station of Warsaw University of Life Sciences (SGGW) generated the 95,459 Z-R data pairs recorded across 1-min time intervals that were subject to further study. Included values for the reflectivity and instantaneous intensity of precipitation were found to be in the respective ranges of -9.998‑67.898 dBZ and 0.004‑153.519 mm h−1 (given that values for precipitation intensity below 0.004 mm h−1 were excluded from further consideration). The material obtained covered the months from April to October in the years 2012‑2014 and 2019‑2020 (30 months in total), which were selected for the study due to the completeness of data. The measured reflectivity and intensity data for precipitation were used to establish the relationship pertaining between the two (by reference to descriptive parameters a and b), with such results considered to contribute to the improved calibration of meteorological radars, and hence to more-accurate radar-based estimates of amounts of precipitation. The Z-R relationship as determined for all measurement data offered a first step in the research process, whose core objective was nevertheless to determine separate Z-R relationships for datasets on rain, rain with snow (sleet), and snow (given that precipitation in the form of hail did not occur during the surveyed measurement periods). That said, it is important to note that only a few Polish studies have in any way involved disdrometer-based measurement of precipitation reflectivity and intensity, as well as the relationships between these aspects. In the event, the Z-R relationships obtained for the measurement sets were characterised by high values for coefficients of correlation (in the range 0.96‑0.97) and determination, as well as low values for the root mean-square error (ranging from 0.29 to 0.34). Statistics point to a good fit of the Z-R relationships (regression lines) to the specified datasets. Values noted for parameter a (the multiplier in the power-type Z-R relationship) were seen to differ significantly in relation to rain, rain with snow, and snow, being of 285.56, 76.07 and 914.74 respectively. In contrast, values noted for parameter b (the exponent) varied only across the narrow range of 1.47‑1.62. The obtained research results for parameter a indicate the need to consider Z-R relationships matched to specific types of precipitation in the data processing procedure of radar data. This could increase the accuracy of estimating precipitation amounts using radars belonging to the nationwide POLRAD system. The relationships Z = 285.56R1.47 for rainfall (as the dataset’s dominant type of precipitation), as well as Z = 293.76R1.46 for all data, proved highly similar to the classic relationship obtained for convective rainfall by Hunter (1996), as given by Z = 300R1.4. On the other hand, the values of the a parameter in the Z-R relationships fond for the two datasets proved to be much larger than those in the model developed by Marshall and Palmer (1948), which took the form Z = 200R1,6 and has been the relationship used in Poland as radar images are created.