Spatial and Temporal Extrapolation of Disdrometer Size Distributions Based on a Lagrangian Trajectory Model of Falling Rain

TL;DR

This paper presents a Lagrangian trajectory model incorporating wind field estimates to improve the spatial and temporal extrapolation of disdrometer size distributions, enhancing rain measurement accuracy.

Contribution

It introduces a novel methodology combining radar data analysis with hydrometeor trajectory modeling to better estimate rain drop distributions over space and time.

Findings

Improved rain drop distribution estimates using wind-informed Lagrangian models

Significant enhancement in disdrometer data processing accuracy

Effective extraction of vertical velocity from radar reflectivity and velocity data

Abstract

Methodologies to improve disdrometer processing, loosely based on mathematical techniques common to the field of particle flow and fluid mechanics, are examined and tested. The inclusion of advection and vertical wind field estimates appears to produce significantly improved results in a Lagrangian hydrometeor trajectory model, in spite of very strict assumptions of noninteracting hydrometeors, constant vertical air velocity, and time independent advection during a radar scan time interval. Wind field data can be extracted from each radar elevation scan by plotting and analyzing reflectivity contours over the disdrometer site and by collecting the radar radial velocity data to obtain estimates of advection. Specific regions of disdrometer spectra (drop size versus time) often exhibit strong gravitational sorting signatures, from which estimates of vertical velocity can be extracted. These independent wind field estimates can be used as initial conditions to the Lagrangian trajectory simulation of falling hydrometeors.