Improving Drop Size Distribution Retrieval and Rain Estimation from Polarimetric Radar Data using the Deep Neural Network

Drop size distribution (DSD) and rain rate (R) have been estimated from polarimetric radar data are now available nationwide. DSD and R are essential in understanding rain microphysics. Past studies utilized parametrized equations or empirical formulas to estimate the parameters for DSD retrieval and R estimation. The parametrized equations and empirical formulas are relatively easy to form and provide high interpretability, but often lack flexibility, nonlinearity in linear domain, and can only partially account for observational errors. The machine learning methods are often used to address these limitations. Previous machine learning approaches have been utilized, but these efforts centered solely on rain estimation rather than DSD retrievals. This study focused on estimating both DSD parameters and R using deep learning to improve understanding of precipitation microphysics and R estimation. The estimation accuracy degrades depending on errors in the radar measurements and estimation methods. Here, the deep neural network (DNN) approach has been utilized to improve the estimation of DSD and rain rate by mitigating these error effects. The performance of this approach was verified with the ground truth observed by two-dimensional video disdrometer (2DVD) in Kessler Farm, Oklahoma, and compared with the conventional estimation methods for the period 2006−2017. Physical parameters (mass-/volume-weighted diameter and liquid water content), rain rate, and polarimetric radar variables (including radar reflectivity and differential reflectivity) were obtained from the DSD data. The three methods physics-based inversion, empirical formula, and DNN were applied to two different temporal domains (instantaneous and rain-event-total) with three diverse error sources (fitting, measurement, and model errors). The DSD and rain estimations from the total 18 (= 3 × 2 × 3) cases were evaluated by calculating the bias and root mean squared errors (RMSE). DNN produced the best performance for most cases, up to 50% reduced RMSE when model errors existed. DSD parameters and rain estimated from the Oklahoma City polarimetric radar using the empirical and DNN methods were compared to the disdrometer observations; the number of outliers and errors reduced significantly (up to 5% bias and 40% RMSE) using DNN. The present results suggest that DNN would be useful for retrievals from radar observations

Microphysical characterization of microwave radar reflectivity due to volcanic ash clouds

Ground-based microwave radar systems can have a valuable role in volcanic ash cloud monitoring as evidenced by available radar imagery. Their use for ash cloud detection and quantitative retrieval has been so far not fully investigated. In order to do this, a forward electromagnetic model is set up and examined taking into account various operating frequencies such as S-, C-, X-, and Ka-bands. A dielectric and microphysical characterization of volcanic vescicular ash is carried out. Particle size-distribution (PSD) functions are derived both from the sequential fragmentation-transport (SFT) theory of pyroclastic deposits, leading to a scaled-Weibull PSD, and from more conventional scaled-Gamma PSD functions. Best fitting of these theoretical PSDs to available measured ash data at ground is performed in order to determine the value of the free PSD parameters. The radar backscattering from spherical-equivalent ash particles is simulated up to Ka-band and the accuracy of the Rayleigh scattering approximation is assessed by using an accurate ensemble particle scattering model. A classification scheme of ash average concentration and particle size is proposed and a sensitivity study of ash radar backscattering to model parameters is accomplished. A comparison with C-band radar signatures is finally illustrated and discussed. © 2006 IEEE

A multi-sensor approach to determining volcanic plume heights in the North Pacific

Thesis (M.S.) University of Alaska Fairbanks, 2012During a volcanic eruption, accurate height information is necessary to forecast a volcanic plume's trajectory with volcanic ash transport and dispersion (VATD) models. Recent events in the North Pacific (NOPAC) displayed significant discrepancies between different methods of plume height determination. This thesis describes two studies that attempted to resolve this discrepancy, and identify the most accurate method for plume height determination. The first study considered the 2009 eruption of Redoubt Volcano. This study found that the basic satellite temperature method, in which satellite thermal infrared temperatures are compared to temperature-altitude profiles, vastly underestimates volcanic plume height due to decreased optical depth of plumes soon after eruption. This study also found that the Multi-angle Imaging SpectroRadiometer (MISR) produced very accurate plume heights, even for optically thin plumes. The second study investigated the application of MISR data to multiple eruptions in the NOPAC: Augustine Volcano in 2006, Okmok, Cleveland, and Kasatochi volcanoes in 2008, and Redoubt and Sarychev Peak volcanoes in 2009. This study found that MISR data analysis retrieves accurate plume heights regardless of grain size, altitude, or water content. Exceptions include plumes of low optical depth over bright backgrounds. MISR is also capable of identifying ash clouds by aerosol type

Effect of volcanic ash to Air Transport

Tato diplomová práce komplexně zpracovává problematiku vulkanického popela a jeho vlivu na letectví včetně samotné vulkanické aktivity (podmínek pro její existenci, pro existenci erupcí a jejich základních produktů). Mimo to se práce věnuje následkům, které má vulkanický popel na letadla a letiště, možnostem jeho detekce či sledování a mechanismu jeho šíření ve vzdušném prostoru. Zvláštní důraz je pak kladen na letecké incidenty s ním související a na ohrožení, které představuje pro vzdušný prostor České republiky.This master's thesis deals with the issue of volcanic ash as a complex and its impact on aviation, including the volcanic activity itself (conditions for its existence, for existence of eruptions and their basic products). In addition, the thesis also deals with effect of volcanic ash on aircraft and airports, possibilities of its detection or monitoring as well as mechanism of its spreading in airspace. The emphasis is laid mainly on air incidents related to volcanic ash and on danger it poses to the airspace of the Czech Republic.