Hydrometeor classification in convective clouds using cloud profiler data

2020 ◽  
Author(s):  
Jana Minářová ◽  
Zbyněk Sokol
Keyword(s):  
Central Europe ◽  
Terminal Velocity ◽  
Weather Station ◽  
Depolarization Ratio ◽  
Air Velocity ◽  
Convective Clouds ◽  
Linear Depolarization Ratio ◽  
Using Data ◽  
Profiler Data

<p>In this contribution, we investigate hydrometeors and their distribution in thunderclouds. We classify 5 kinds of hydrometeors using data of a Ka-band cloud profiler (35 GHz) situated at the weather station Milešovka in Central Europe. The classification of hydrometeors is based on calculated vertical air velocity, terminal velocity of a target, minimum and maximum terminal velocity of hydrometeor classes, and Linear Depolarization Ratio within three temperature intervals. We performed the classification for convective events that were observed at the station in 2018 and 2019 and were related to lightning in the vicinity of the station.</p><p>Results suggest that there is a link between lightning flashes observed close to the weather station (based on EUCLID data) and the presence of graupel, ice, snow, and hail. These are the hydrometeors (graupel and ice in particular) that are considered to play major role in thundercloud electrification by the collision of hydrometeors.</p>

scholarly journals Hydrometeor Distribution and Linear Depolarization Ratio in Thunderstorms

2020 ◽  
Vol 12 (13) ◽  
pp. 2144 ◽  
Author(s):  
Zbyněk Sokol ◽  
Jana Minářová ◽  
Ondřej Fišer
Keyword(s):  
Central Europe ◽  
Radar Data ◽  
Ice Crystals ◽  
Vertical Profiles ◽  
Depolarization Ratio ◽  
Air Velocity ◽  
Cloud Radar ◽  
Lightning Discharges ◽  
Linear Depolarization Ratio

The distribution of hydrometeors in thunderstorms is still under investigation as well as the process of electrification in thunderclouds leading to lightning discharges. One indicator of cloud electrification might be high values of the Linear Depolarization Ratio (LDR) at higher vertical levels. This study focuses on LDR values derived from vertically pointing cloud radars and the distribution of five hydrometeor species during 38 days with thunderstorms which occurred in 2018 and 2019 in Central Europe, close to our radar site. The study shows improved algorithms for de-aliasing, the derivation of vertical air velocity and the classification of hydrometeors in clouds using radar data. The comparison of vertical profiles with observed lightning discharges in the vicinity of the radar site (≤1 km) suggested that cloud radar data can indirectly identify “lightning” areas by high LDR values observed at higher gates due to the alignment of ice crystals, likely because of an intensified electric field in thunderclouds. Simultaneously, the results indicated that at higher gates, there is a mixture of several hydrometeor species, which suggests a well-known electrification process by collisions of hydrometeors.

scholarly journals Classification of Hydrometeors Using Measurements of the Ka-Band Cloud Radar Installed at the Milešovka Mountain (Central Europe)

2018 ◽  
Vol 10 (11) ◽  
pp. 1674 ◽  
Author(s):  
Zbyněk Sokol ◽  
Jana Minářová ◽  
Petr Novák
Keyword(s):  
Central Europe ◽  
Terminal Velocity ◽  
Small Particles ◽  
Cloud Droplets ◽  
Vertical Temperature Profile ◽  
Ka Band ◽  
Cloud Radar ◽  
Weather Radars ◽  
Cloud Particles ◽  
Linear Depolarization Ratio

In radar meteorology, greater interest is dedicated to weather radars and precipitation analyses. However, cloud radars provide us with detailed information on cloud particles from which the precipitation consists of. Motivated by research on the cloud particles, a vertical Ka-band cloud radar (35 GHz) was installed at the Milešovka observatory in Central Europe and was operationally measuring since June 2018. This study presents algorithms that we use to retrieve vertical air velocity (Vair) and hydrometeors. The algorithm calculating Vair is based on small-particle tracers, which considers the terminal velocity of small particles negligible and, thereby, Vair corresponds to the velocity of the small particles. The algorithm classifying hydrometeors consists of calculating the terminal velocity of hydrometeors and the vertical temperature profile. It identifies six hydrometeor types (cloud droplets, ice, and four precipitating particles: rain, graupel, snow, and hail) based on the calculated terminal velocity of hydrometeors, temperature, Vair, and Linear Depolarization Ratio. The results of both the Vair and the distribution of hydrometeors were found to be realistic for a thunderstorm associated with significant lightning activity on 1 June 2018.

scholarly journals UAV Image Multi-Labeling with Data-Efficient Transformers

2021 ◽  
Vol 11 (9) ◽  
pp. 3974
Author(s):  
Laila Bashmal ◽  
Yakoub Bazi ◽  
Mohamad Mahmoud Al Rahhal ◽  
Haikel Alhichri ◽  
Naif Al Ajlan
Keyword(s):  
Data Augmentation ◽  
Feature Representation ◽  
Aerial Image ◽  
Remote Sensing Images ◽  
Training Set ◽  
Proposed Model ◽  
Class Labels ◽  
Using Data ◽  
Uav Image

In this paper, we present an approach for the multi-label classification of remote sensing images based on data-efficient transformers. During the training phase, we generated a second view for each image from the training set using data augmentation. Then, both the image and its augmented version were reshaped into a sequence of flattened patches and then fed to the transformer encoder. The latter extracts a compact feature representation from each image with the help of a self-attention mechanism, which can handle the global dependencies between different regions of the high-resolution aerial image. On the top of the encoder, we mounted two classifiers, a token and a distiller classifier. During training, we minimized a global loss consisting of two terms, each corresponding to one of the two classifiers. In the test phase, we considered the average of the two classifiers as the final class labels. Experiments on two datasets acquired over the cities of Trento and Civezzano with a ground resolution of two-centimeter demonstrated the effectiveness of the proposed model.

scholarly journals Studying of Cultural Properties of Pyricularia oryzae Cav. Strains in the South of Russia

2021 ◽  
Vol 12 (1) ◽  
pp. 21-28
Author(s):  
Dmitry Nartymov ◽  
Evgeny Kharitonov ◽  
Elena Dubina ◽  
Sergey Garkusha ◽  
Margarita Ruban ◽  
...  
Keyword(s):  
Pyricularia Oryzae ◽  
Blast Disease ◽  
The South ◽  
Directional Growth ◽  
Rice Plants ◽  
Affected Plant ◽  
Cultural Traits ◽  
Plant Form ◽  
Using Data

This article presents the results of the development of a methodology for describing the main morphological and cultural traits of the Pyricularia oryzae Cav. strains widespread in the south of Russia. At the same time, the types of traits are identified and listed, which make it possible to unambiguously determine the uniqueness and variety of the pathogen. The relationships and patterns established using cluster and statistical analysis make it possible to identify the conditions for the development of a pathogen that determine its predominant forms. Thus, research shows that leaf forms of P. oryzae strains isolated from rice plants with leaf form of blast disease have an equally directional growth pattern of a colony with a felt structure, and strains isolated from neck-affected plant form often produce a zone of a colony with a clumpy structure. The classification of cultural traits will make it possible to obtain scientifically grounded and comparable data that can be used in the analysis of the interaction of P. oryzae strains with rice plants on various varieties and in various agro-technological conditions in order to improve and rationalize agricultural activities. The study opens up the possibility of using data in breeding, making it possible to identify forms of a pathogen that infect certain varieties.

DEVELOPMENT OF AN ALGORITHM FOR FINDING ANOMALIES IN WEATHER REPORTS TO IMPROVE THE QUALITY OF FORECASTS

2021 ◽  
pp. 45-52
Author(s):  
K. G. Yashchenkov ◽  
K. S. Dymko ◽  
N. O. Ukhanov ◽  
A. V. Khnykin
Keyword(s):  
Optimal Method ◽  
Implementation Processes ◽  
Advantages And Disadvantages ◽  
Message Exchange ◽  
Text Information ◽  
Preliminary Preparation ◽  
Using Data ◽  
The Moment

The issues of using data analysis methods to find and correct errors in the reports issued by meteorologists are considered. The features of processing various types of meteorological messages are studied. The advantages and disadvantages of existing methods of classification of text information are considered. The classification methods are compared in order to identify the optimal method that will be used in the developed algorithm for analyzing meteorological messages. The prospects of using each of the methods in the developed algorithm are described. An algorithm for processing the source data is proposed, which consists in using syntactic and logical analysis to preclean the data from various kinds of noise and determine format errors for each type of message. After preliminary preparation the classification method correlates the received set of message characteristics with the previously trained model to determine the error of the current weather report and output the corresponding message to the operator in real time. The software tools used in the algorithm development and implementation processes are described. A complete description of the process of processing a meteorological message is presented from the moment when the message is entered in a text editor until the message is sent to the international weather message exchange service. The developed software is demonstrated, in which the proposed algorithm is implemented, which allows to improve the quality of messages and, as a result, the quality of meteorological forecasts. The results of the implementation of the new algorithm are described by comparing the number of messages containing various types of errors before the implementation of the algorithm and after the implementation.

Rule based classification of neurodegenerative diseases using data driven gait features

2018 ◽  
Vol 9 (4) ◽  
pp. 547-560 ◽  
Author(s):  
Kartikay Gupta ◽  
Aayushi Khajuria ◽  
Niladri Chatterjee ◽  
Pradeep Joshi ◽  
Deepak Joshi
Keyword(s):  
Neurodegenerative Diseases ◽  
Data Driven ◽  
Rule Based ◽  
Gait Features ◽  
Using Data

scholarly journals Benefit of depolarization ratio at λ = 1064 nm for the retrieval of the aerosol microphysics from lidar measurements

2014 ◽  
Vol 7 (11) ◽  
pp. 3773-3781 ◽  
Author(s):  
J. Gasteiger ◽  
V. Freudenthaler
Keyword(s):  
Optical Properties ◽  
Mass Concentration ◽  
Volcanic Ash ◽  
Single Scattering ◽  
Depolarization Ratio ◽  
Complex Relation ◽  
Microphysical Properties ◽  
Lidar Measurements ◽  
Increased Sensitivity ◽  
Linear Depolarization Ratio

Abstract. A better quantification of aerosol properties is required for improving the modelling of aerosol effects on weather and climate. This task is methodologically demanding due to the diversity of the microphysical properties of aerosols and the complex relation between their microphysical and optical properties. Advanced lidar systems provide spatially and temporally resolved information on the aerosol optical properties that is sufficient for the retrieval of important aerosol microphysical properties. Recently, the mass concentration of transported volcanic ash, which is relevant for the flight safety of aeroplanes, was retrieved from measurements of such lidar systems in southern Germany. The relative uncertainty of the retrieved mass concentration was on the order of ±50%. The present study investigates improvements of the retrieval accuracy when the capability of measuring the linear depolarization ratio at 1064 nm is added to the lidar setup. The lidar setups under investigation are based on those of MULIS and POLIS of the Ludwig-Maximilians-Universität in Munich (Germany) which measure the linear depolarization ratio at 355 and 532 nm with high accuracy. The improvements are determined by comparing uncertainties from retrievals applied to simulated measurements of this lidar setup with uncertainties obtained when the depolarization at 1064 nm is added to this setup. The simulated measurements are based on real lidar measurements of transported Eyjafjallajökull volcano ash. It is found that additional 1064 nm depolarization measurements significantly reduce the uncertainty of the retrieved mass concentration and effective particle size. This significant improvement in accuracy is the result of the increased sensitivity of the lidar setup to larger particles. The size dependence of the depolarization does not vary strongly with refractive index, thus we expect similar benefits for the retrieval in case of measurements of other volcanic ash compositions and also for transported desert dust. For the retrieval of the single scattering albedo, which is relevant to the radiative transfer in aerosol layers, no significant improvements were found.

scholarly journals A simple model for the estimation of the number of fatalities due to floods in central Europe

2014 ◽  
Vol 14 (7) ◽  
pp. 1663-1676 ◽  
Author(s):  
M. Brazdova ◽  
J. Riha
Keyword(s):  
Central Europe ◽  
Pairwise Comparison ◽  
The Czech Republic ◽  
Factors Affecting ◽  
Hazard Exposure ◽  
Proposed Model ◽  
River Floods ◽  
Using Data ◽  
Human Losses ◽  
Significant Factors

Abstract. In this paper a model for the estimation of the number of potential fatalities is proposed based on data from 19 past floods in central Europe. First, the factors contributing to human losses during river floods are listed and assigned to the main risk factors: hazard – exposure – vulnerability. The order of significance of individual factors has been compiled by pairwise comparison based on experience with real flood events. A comparison with factors used in existing models for the estimation of fatalities during floods shows good agreement with the significant factors identified in this study. The most significant factors affecting the number of human losses in floods have been aggregated into three groups and subjected to correlation analysis. A close-fitting regression dependence is proposed for the estimation of loss of life and calibrated using data from selected real floods in central Europe. The application of the proposed model for the estimation of fatalities due to river floods is shown via a flood risk assessment for the locality of Krnov in the Czech Republic.

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