Extended Abstract (1.2M)14.6
Empirical radar rainfall data quality description for usage in the latent heat nudging scheme
Andrea Rossa, Meteorological Centre of Teolo, Teolo, Italy; and F. Del Guerra and D. Leuenberger
Radar-derived quantitative precipitation estimates (QPE) are becoming an increasingly important element in storm-scale numerical weather prediction (NWP). As such they complement conventional data like surface or upper-air observations. Unlike the latter, radar data exhibit a highly variable quality, in that they are affected by a number of factors that limit their accuracy in estimating precipitation at the surface. In the context of the latent heat nudging, for instance, radar-derived QPE are assimilated without explicitly taking the observation quality into account in terms of an optimal combination evaluating observation and model error covariances. Observation errors are, therefore, directly fed into the assimilation process and can give rise to significant error amplification, especially for non-rain echoes and convectively unstable environments. Alternatively, in a region where the radar visibility leads to strong underestimation or blocking of the QPE the assimilation scheme would try to eliminate rainfall correctly present in the model and possibly cause spurious circulations.
In order to take the high spatial variability of radar QPE quality into account, an empirical data quality function is proposed, which is based on the frequency of occurrence of a particular radar pixel. For a sufficiently long period the frequency of occurrence of a radar echo is related to how regularly a radar ‘sees' rain for a specific pixel. For very high frequencies non-rain echoes need to be separated from real rain, while for very low frequencies serious visibility problems might indeed exist. This analysis is synthesized into a quality function w(x, y, t) which ranges from 0 to 1, varies in space and slowly in time, depending on the period over which the analysis is performed. The tuning parameters inherent in this approach are discussed, the quality function is presented, and an impact on the latent heat nudging scheme is illustrated with a case study.
Session 14, Data Assimilation II
Friday, 9 October 2009, 10:30 AM-12:00 PM, Auditorium
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