Raindrop size distribution retrieval model from polarization radar observations using neural network techniques

<p>Raindrop size distributions (DSD) information plays a significant role in many scientific fields, especially in radar meteorology. DSD has spatial and temporal variation across different storm types and climatic regimes. Since the development of polarimetric weather radar, the large-scale DSD estimation has been a long-standing goal in radar meteorology. Traditional polynomial regression algorithms for ground polarimetric radars are widely used to estimate DSD parameters due to their simple methodology and acceptable accuracy. However, a simple polynomial regression may not be able to deeply explore the intrinsic relationship using available observations. This study therefore proposes a DSD retrieval model that uses dual-polarization radar observations based on long short-term memory (LSTM) network techniques. Three schemes of a normalized gamma DSD parameters (LSTM- D<sub>0</sub>, LSTM- N<sub>w</sub>, and LSTM-µ) are designed with different combinations of polarimetric radar measurement inputs. Results show that all LSTM estimators exhibit better performance than the polynomial regression method. The proposed retrieval model using neural network techniques helps to improve quantitative precipitation estimation of weather radar and make sense of a better understanding of precipitation microphysics.</p>

Influence of Disdrometer Type on Weather Radar Algorithms from Measured DSD: Application to Italian Climatology

Relations for retrieving precipitation and attenuation information from radar measurements play a key role in radar meteorology. The uncertainty in such relations highly affects the precipitation and attenuation estimates. Weather radar algorithms are often derived by applying regression methods to precipitation measurements and radar observables simulated from datasets of drop size distributions (DSD) using microphysical and electromagnetic assumptions. DSD datasets can be derived from theoretical considerations or obtained from experimental measurements collected throughout the years by disdrometers. Although the relations obtained from experimental disdrometer datasets can be generally considered more representative of a specific climatology, the measuring errors, which depend on the specific type of disdrometer used, introduce an element of uncertainty to the final retrieval algorithms. Eventually, data quality checks and filtering procedures applied to disdrometer measurements play an important role. In this study, we pursue two main goals: (i) evaluate two different techniques for establishing weather radar algorithms from measured DSD, and (ii) investigate to what extent dual-polarization radar algorithms derived from experimental DSD datasets are influenced by the different error structures introduced by the various disdrometer types (namely 2D video disdrometer, first and second generation of OTT Parsivel disdrometer, and Thies Clima disdrometer) used to collect the data. Furthermore, weather radar algorithms optimized for Italian climatology are presented and discussed.

Another Look at the Refractive Index Structure Function

This paper discusses the derivation of the refractive index structure function. It shows that the traditional formulation, which is based on the hydrostatic assumption, leads to increasing errors with height when compared with a formulation that is based on the potential temperature. The paper corrects a long-standing problem of extrapolating the traditional boundary layer approximation beyond its region of validity (i.e., to the upper troposphere and lower stratosphere). The new derivation may have applications in observational work to measure and seeing and in numerical modeling efforts. A preliminary analysis of the influence of the new formulation in numerical modeling of seeing suggests that impact on seeing will be small in general, because the largest contribution to seeing generally comes from the lower troposphere. However, an accurate profile is needed because other astroclimatic parameters, such as the isoplanatic angle, can suffer from the lack of accuracy at high altitude. This work may also have application in radar meteorology, since clear-air radar sensitivity depends on accurate estimation of .