Session 11R.6 Precipitation physics: the structure of precipitation and its modeling in space and time

Friday, 28 October 2005: 4:45 PM
Alvarado GH (Hotel Albuquerque at Old Town)
GyuWon Lee, McGill Univ., Ste-Anne-de-Bellevue, QC, Canada; and A. W. Seed and I. Zawadzki

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Remote sensing measurements are outcome of the interaction between instrumental characteristics and observed fields. Thus, the quantitative definitions of the measurements are affected by the structure of precipitation fields as well as by the spatial and temporal resolutions. The uncertainty in rain estimates from radar measurements is greatly affected by the spatial and temporal variability of drop size distributions that is responsible for the large stochastic components in an R-Z scatterplot as compared to the deterministic component. We investigate the correlation structure of the stochastic component and its connection to precipitation physics. We have modeled the space-time variability of two moments of drop size distributions (DSDs), to allow the study of the characteristics of remote sensing measurements and various correction techniques (raingage adjustment, attenuation corrections). Briefly, this model generates a vector field whose two components are moments of DSDs, (for example, radar reflectivity Z and rainfall rate R or N0' and Dm'). A multiplicative cascade is used to generate stochastic fluctuations around a deterministic Z-R relationship. These fluctuations vary in space and time and preserve the correlation structure and the variance of the observed variability in the R-Z relationship. The model is validated using concurrent radar and disdrometer data. The modeling can serve as an experimental tool for studying the errors in gage adjustments, the undersampling problem in disdrometric measurements, for testing attenuation correction methods, etc.
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