  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.
