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.

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.

Monsoon cloud observations using a low power vertical looking Ka Band Cloud radar

2016 ◽  
Author(s):  
A. Agarwal ◽  
J. S. Pillai ◽  
K. Aurobindo ◽  
J. D. Abhyankar ◽  
G. Isola ◽  
...  
Keyword(s):  
Low Power ◽  
Ka Band ◽  
Cloud Radar

scholarly journals Identifying Insects, Clouds, and Precipitation using Vertically Pointing Polarimetric Radar Doppler Velocity Spectra

2021 ◽  
Author(s):  
Christopher R. Williams ◽  
Karen L. Johnson ◽  
Scott E. Giangrande ◽  
Joseph C. Hardin ◽  
Ruşen Öktem ◽  
...  
Keyword(s):  
Power Spectra ◽  
Department Of Energy ◽  
Cloud Base ◽  
Doppler Velocity ◽  
Polarimetric Radar ◽  
Ka Band ◽  
Ice Particles ◽  
Linear Depolarization Ratio ◽  
Velocity Spectra ◽  
Discrimination Method

Abstract. This study presents a method to identify and distinguish insects, clouds, and precipitation in 35 GHz (Ka-band) vertically pointing polarimetric radar Doppler velocity power spectra and then produce masks indicating the occurrence of hydrometeors (i.e., clouds or precipitation) and insects at each range gate. The polarimetric radar used in this study transmits a linear polarized wave and receives signals in collinear (CoPol) and cross-linear (XPol) polarized channels. The insect-hydrometeor discrimination method uses CoPol and XPol spectral information in two separate algorithms with their spectral results merged and then filtered into single value products at each range gate. The first algorithm discriminates between insects and clouds in the CoPol Doppler velocity power spectra based on the spectra texture, or spectra roughness, which varies due to the scattering characteristics of insects versus cloud particles. The second algorithm distinguishes insects from raindrops and ice particles by exploiting the larger Doppler velocity spectra linear depolarization ratio (LDR) produced by asymmetric insects. Since XPol power return is always less than CoPol power return for the same target (i.e., insect or hydrometeor), fewer insects and hydrometeors are detected in the LDR algorithm than the CoPol algorithm, which drives this need for a CoPol based algorithm. After performing both CoPol and LDR detection algorithms, regions of insect and hydrometeor scattering from both algorithms are combined in the Doppler velocity spectra domain and then filtered to produce a binary hydrometeor mask indicating the occurrence of cloud, raindrops, or ice particles at each range gate. Comparison with a collocated ceilometer indicates that hydrometeor mask column bottoms are within +/-100 meters of simultaneous ceilometer cloud base heights. Forty-seven (47) summer-time days were processed with the insect-hydrometeor discrimination method using U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program Ka-band zenith pointing radar observations in northern Oklahoma (USA). All datasets and images are available on public repositories.

scholarly journals Low-Cost Ka-Band Cloud Radar System for Distributed Measurements within the Atmospheric Boundary Layer

2020 ◽  
Vol 12 (23) ◽  
pp. 3965
Author(s):  
Roberto Aguirre ◽  
Felipe Toledo ◽  
Rafael Rodríguez ◽  
Roberto Rondanelli ◽  
Nicolas Reyes ◽  
...  
Keyword(s):  
Continuous Wave ◽  
Low Cost ◽  
Medium Size ◽  
Physical Parameters ◽  
Mountain Range ◽  
Ka Band ◽  
Range Resolution ◽  
Cloud Radar ◽  
Marine Stratus ◽  
Application Fields

Radars are used to retrieve physical parameters related to clouds and fog. With these measurements, models can be developed for several application fields such as climate, agriculture, aviation, energy, and astronomy. In Chile, coastal fog and low marine stratus intersect the coastal topography, forming a thick fog essential to sustain coastal ecosystems. This phenomenon motivates the development of cloud radars to boost scientific research. In this article, we present the design of a Ka-band cloud radar and the experiments that prove its operation. The radar uses a frequency-modulated continuous-wave with a carrier frequency of 38 GHz. By using a drone and a commercial Lidar, we were able to verify that the radar can measure reflectivities in the order of −60 dBZ at 500 m of distance, with a range resolution of 20 m. The lower needed range coverage imposed by our case of study enabled a significant reduction of the instrument cost compared to existent alternatives. The portability and low-cost of the designed instrument enable its implementation in a distributed manner along the coastal mountain range, as well as its use in medium-size aerial vehicles or balloons to study higher layers. The main features, limitations, and possible improvements to the current instrument are discussed.

scholarly journals Rain‐rate estimation algorithm using signal attenuation of Ka‐band cloud radar

2019 ◽  
Vol 27 (1) ◽  
Author(s):  
Su‐Bin Oh ◽  
Pavlos Kollias ◽  
Jeong‐Soon Lee ◽  
Seung‐Woo Lee ◽  
Yong Hee Lee ◽  
...  
Keyword(s):  
Rain Rate ◽  
Estimation Algorithm ◽  
Signal Attenuation ◽  
Ka Band ◽  
Rate Estimation ◽  
Cloud Radar

scholarly journals Thunderstorms in Corsica Island measured during the EXAEDRE aircraft campaign

2021 ◽  
Author(s):  
Keunok Lee ◽  
Eric Defer ◽  
Pauline Combarnous ◽  
Jean-Pierre Pinty ◽  
Magalie Buguet ◽  
...  
Keyword(s):  
Atmospheric Electricity ◽  
Convective Cloud ◽  
Cloud Microphysics ◽  
Convective Systems ◽  
Cloud Radar ◽  
Weather Radars ◽  
Cloud Resolving Model ◽  
Mapping Array ◽  
Locating System ◽  
Horizontal Grid

<p>The aim of this study is to enhance our understanding about the microphysical structure of convective cloud systems and its relationships to the ambient electrical field, and to assess the capability of a model to capture the cloud electrical properties. This study relies on the EXAEDRE (EXploiting new Atmospheric Electricity Data for Research and the Environment) aircraft campaign that took place from 13 September to 8 October 2018 in Corsica Island. Eight electrified convective systems were successfully sampled during the campaign by the French Falcon 20 aircraft (e.g. RASTA Doppler cloud radar, microphysics probes, electric field mills) and ground-based platforms (Lightning Mapping Array network, Météorage operational lightning locating system and Météo-France weather radars). In this study, a multi-cell thunderstorm which developed over the complex topography of Corsica Island on 17 September 2018 was selected to investigate and to understand the physical processes linking lightning occurrence, electrification efficiency, cloud microphysics and dynamics. The detailed analysis results using the unprecedented airborne and ground-based dataset and their comparison to the numerical simulation results with a horizontal grid spacing of 1 km comprising the explicit electrical scheme CELLS (Cloud Electrification and Lightning Scheme) implemented in the cloud resolving model Meso-NH has been conducted. The key results will be presented at the conference.</p>

scholarly journals An Extended Radar Relative Calibration Adjustment (eRCA) Technique for Higher Frequency Radars and RHI Scans

2020 ◽  
Author(s):  
Alexis Hunzinger ◽  
Joseph C. Hardin ◽  
Nitin Bharadwaj ◽  
Adam Varble ◽  
Alyssa Matthews
Keyword(s):  
Temporal Stability ◽  
Selection Procedure ◽  
Absolute Calibration ◽  
Ka Band ◽  
Extended Technique ◽  
Weather Radars ◽  
Ground Clutter ◽  
Precipitation Estimation ◽  
The Stability ◽  
Calibration Techniques

Abstract. This study extends the relative calibration adjustment technique for calibration of weather radars to higher frequency radars, as well as range-height indicator scans. The calibration of weather radars represents one of the most dominant sources of error for their use in a variety of fields including quantitative precipitation estimation and model comparisons. While most weather radars are routinely calibrated, the frequency of calibration is often less than required resulting in miscalibrated time periods. While full absolute calibration techniques often require the radar to be taken offline for a period of time, there have been online calibration techniques discussed in the literature. The relative calibration adjustment (RCA) technique uses the statistics of the ground clutter surrounding the radar as a monitoring source for the stability of calibration but has only been demonstrated to work at S- and C-Band for plan-position indicator scans at a constant elevation. In this work the RCA technique is modified to work with higher frequency radars, including Ka-band cloud radars. At higher frequencies the properties of clutter can be much more variable. This work introduces an extended clutter selection procedure that incorporates the temporal stability of clutter and helps to improve the operational stability of RCA for relatively higher frequency radars. The technique is also extended to utilize range-height scans from radars where the elevation is varied rather than the azimuth. These types of scans are often utilized with research radars to examine the vertical structure of clouds. The newly extended technique (eRCA) is applied to four DOE-ARM weather radars ranging in frequency from C- to Ka- band. Cross comparisons of three co-located radars with frequencies C, X, and Ka at the ARM CACTI site show the technique can determine changes in calibration. Using an X-band radar at the ARM Eastern North Atlantic site, we show how the technique can be modified to be more resilient to clutter fields that show an increased variability, in this case due to sea clutter. The results show that this technique is promising for a-posteriori data calibration and monitoring.

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