First measurement of the small-scale spatial variability of the rain drop size distribution: Results from a crucial experiment and maximum entropy modeling

TL;DR

This paper investigates the small-scale spatial variability of rain drop size distribution through experimental measurements and maximum entropy modeling, addressing measurement uncertainties and instrumental errors to improve precipitation analysis.

Contribution

It presents the first measurement of small-scale spatial variability of drop size distribution and compares maximum entropy modeling with other methods, enhancing understanding of precipitation variability.

Findings

Spatial variability affects remote sensing accuracy

MaxEnt method effectively models drop size distribution

Instrumental errors significantly impact measurements

Abstract

The main challenges of measuring precipitation are related to the spatio-temporal variability of the drop-size distribution, to the uncertainties that condition the modeling of that distribution, and to the instrumental errors present in the in situ estimations. This PhD dissertation proposes advances in all these questions. The relevance of the spatial variability of the drop-size distribution for remote sensing measurements and hydro-meteorology field studies is asserted by analyzing the measurement of a set of disdrometers deployed on a network of 5 squared kilometers. This study comprises the spatial variability of integral rainfall parameters, the ZR relationships, and the variations within the one moment scaling method. The modeling of the drop-size distribution is analyzed by applying the MaxEnt method and comparing it with the methods of moments and the maximum likelihood. The instrumental errors are analyzed with a compressive comparison of sampling and binning uncertainties that affect actual devices. These analysis are further extended in several appendices where an error analysis is developed and new studies are proposed. The relevance of the pre-processing of disdrometric measurements is also assessed. The data-sets evaluated comprise experimental measurements of the GPM (NASA-JAXA) ground validation satellite mission and synthetic distributions generated computationally.