Cloud radar

2014 ◽  
Author(s):  
Sören Bleikertz ◽  
Carsten Vogel ◽  
Thomas Groß
Keyword(s):  
Cloud Radar

scholarly journals Cloud Radar Observations of Diurnal and Seasonal Cloudiness over Reunion Island

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 868
Author(s):  
Jonathan Durand ◽  
Edouard Lees ◽  
Olivier Bousquet ◽  
Julien Delanoë ◽  
François Bonnardot
Keyword(s):  
Sea Breeze ◽  
Satellite System ◽  
Research Infrastructure ◽  
Weather Data ◽  
Reunion Island ◽  
Wet Season ◽  
Trade Winds ◽  
Cloud Radar ◽  
Réunion Island ◽  
Global Navigation Satellite

In November 2016, a 95 GHz cloud radar was permanently deployed in Reunion Island to investigate the vertical distribution of tropical clouds and monitor the temporal variability of cloudiness in the frame of the pan-European research infrastructure Aerosol, Clouds and Trace gases Research InfraStructure (ACTRIS). In the present study, reflectivity observations collected during the two first years of operation (2016–2018) of this vertically pointing cloud radar are relied upon to investigate the diurnal and seasonal cycle of cloudiness in the northern part of this island. During the wet season (December–March), cloudiness is particularly pronounced between 1–3 km above sea level (with a frequency of cloud occurrence of 45% between 12:00–19:00 LST) and 8–12 km (with a frequency of cloud occurrence of 15% between 14:00–19:00 LST). During the dry season (June–September), this bimodal vertical mode is no longer observed and the vertical cloud extension is essentially limited to a height of 3 km due to both the drop-in humidity resulting from the northward migration of the ITCZ and the capping effect of the trade winds inversion. The frequency of cloud occurrence is at its maximum between 13:00–18:00 LST, with a probability of 35% at 15 LST near an altitude of 2 km. The analysis of global navigation satellite system (GNSS)-derived weather data also shows that the diurnal cycle of low- (1–3 km) and mid-to-high level (5–10 km) clouds is strongly correlated with the diurnal evolution of tropospheric humidity, suggesting that additional moisture is advected towards the island by the sea breeze regime. The detailed analysis of cloudiness observations collected during the four seasons sampled in 2017 and 2018 also shows substantial differences between the two years, possibly associated with a strong positive Indian Ocean Southern Dipole (IOSD) event extending throughout the year 2017.

scholarly journals Analysis of the Vertical Air Motions and Raindrop Size Distribution Retrievals of a Squall Line Based on Cloud Radar Doppler Spectral Density Data

Atmosphere ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 348
Author(s):  
Ningkun Ma ◽  
Liping Liu ◽  
Yichen Chen ◽  
Yang Zhang
Keyword(s):  
Spectral Density ◽  
Size Distribution ◽  
Squall Line ◽  
Vertical Profiles ◽  
Air Velocity ◽  
Density Data ◽  
Cloud Radar ◽  
Raindrop Size Distribution ◽  
Raindrop Size ◽  
Reflectivity Factor

A squall line is a type of strongly organized mesoscale convective system that can cause severe weather disasters. Thus, it is crucial to explore the dynamic structure and hydrometeor distributions in squall lines. This study analyzed a squall line over Guangdong Province on 6 May 2016 that was observed using a Ka-band millimeter-wave cloud radar (CR) and an S-band dual-polarization radar (PR). Doppler spectral density data obtained by the CR were used to retrieve the vertical air motions and raindrop size distribution (DSD). The results showed the following: First, the CR detected detailed vertical profiles and their evolution before and during the squall line passage. In the convection time segment (segment B), heavy rain existed with a reflectivity factor exceeding 35 dBZ and a velocity spectrum width exceeding 1.3 m s−1. In the PR detection, the differential reflectivity factor (Zdr) was 1–2 dB, and the large specific differential phase (Kdp) also represented large liquid water content. In the transition and stratiform cloud time segments (segments B and C), the rain stabilized gradually, with decreasing cloud tops, stable precipitation, and a 0 °C layer bright band. Smaller Kdp values (less than 0.9) were distributed around the 0 °C layer, which may have been caused by the melting of ice crystal particles. Second, from the CR-retrieved vertical air velocity, before squall line passage, downdrafts dominated in local convection and weak updrafts existed in higher-altitude altostratus clouds. In segment B, the updraft air velocity reached more than 8 m s−1 below the 0 °C layer. From segments C to D, the updrafts changed gradually into weak and wide-ranging downdrafts. Third, in the comparison of DSD values retrieved at 1.5 km and DSD values on the ground, the retrieved DSD line was lower than the disdrometer, the overall magnitude of the DSD retrieved was smaller, and the difference decreased from segments C to D. The standardized intercept parameter (Nw) and shape parameter (μ) of the DSD retrieved at 1.8 km showed good agreement with the disdrometer results, and the mass-weighted mean diameter (Dm) was smaller than that on the ground, but very close to the PR-retrieved Dm result at 2 km. Therefore, comparing with the DSD retrieved at around 2 km, the overall number concentration remained unchanged and Dm got larger on the ground, possibly reflecting the process of raindrop coalescence. Lastly, the average vertical profiles of several quantities in all segments showed that, first of all, the decrease of Nw and Dm with height in segments C and D was similar, reflecting the collision effect of falling raindrops. The trends were opposite in segment B, indicating that raindrops underwent intense mixing and rapid collision and growth in this segment. Then, PR-retrieved Dm profiles can verify the rationality of the CR-retrieved Dm. Finally, a vertical velocity profile peak generated a larger Dm especially in segments C and D.

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 Retrieval of Atmospheric Attenuation Using Combined Ground-Based and Airborne 95-GHz Cloud Radar Measurements

2001 ◽  
Vol 18 (8) ◽  
pp. 1345-1353 ◽  
Author(s):  
Lihua Li ◽  
Stephen M. Sekelsky ◽  
Steven C. Reising ◽  
Calvin T. Swift ◽  
Stephen L. Durden ◽  
...  
Keyword(s):  
Atmospheric Attenuation ◽  
Cloud Radar ◽  
Radar Measurements

Advanced open-source tools for detecting and analyzing peaks in cloud radar Doppler spectra

2021 ◽  
Author(s):  
Teresa Vogl ◽  
Martin Radenz ◽  
Heike Kalesse-Los
Keyword(s):  
Supercooled Liquid ◽  
Detection Algorithm ◽  
Supervised Machine Learning ◽  
Doppler Spectrum ◽  
The Past ◽  
Cloud Radar ◽  
Depolarisation Ratio ◽  
Instrument Type ◽  
Peak Detection Algorithm ◽  
Binary Tree Structure

<p>Cloud radar Doppler spectra contain vertically highly resolved valuable information about the hydrometeors present in the cloud. A mixture of different hydrometeor types can lead to several peaks in the Doppler spectrum due to their different fall speeds, giving a hint about the size/ density/ number of the respective particles. Tools to separate and interpret peaks in cloud radar Doppler spectra have been developed in the past, but their application is often limited to certain radar settings, or the code not freely available to other users.</p> <p>We here present the effort of joining two methods, which have been developed and published (Radenz et al., 2019; Kalesse et al., 2019) with the aim to make them insensitive to instrument type and settings, and available on GitHub, and applicable to all cloud radars which are part of the ACTRIS CloudNet network.</p> <p>A supervised machine learning peak detection algorithm (PEAKO, Kalesse et al., 2019) is used to derive the optimal parameters to detect peaks in cloud radar Doppler spectra for each set of instrument settings. In the next step, these parameters are used by peakTree (Radenz et al., 2019), which is a tool for converting multi-peaked (cloud) radar Doppler spectra into a binary tree structure. PeakTree yields the (polarimetric) radar moments of each detected peak and can thus be used to classify the hydrometeor types. This allows us to analyze Doppler spectra of different cloud radars with respect to, e.g. the occurrence of supercooled liquid water or ice needles/columns with high linear depolarisation ratio (LDR).</p>

First Doppler lidar and cloud radar measurements of orographic convection initiation from a mountain observatory in the Al Hajar Mountains of the United Arab Emirates.

2021 ◽  
Author(s):  
Oliver Branch ◽  
Andreas Behrendt ◽  
Osama Alnayef ◽  
Florian Späth ◽  
Thomas Schwitalla ◽  
...  
Keyword(s):  
United Arab Emirates ◽  
Sea Breeze ◽  
Doppler Lidar ◽  
Cloud Seeding ◽  
Low Level ◽  
Cloud Radar ◽  
Orographic Convection ◽  
Radar Measurements ◽  
Convection Initiation ◽  
Convective Cells

<p>We present exciting Doppler lidar and cloud radar measurements from a high-vantage mountain observatory in the hyper-arid United Arab Emirates (UAE) - initiated as part of the UAE Research Program for Rain Enhancement Science (UAEREP). The observatory was designed to study the clear-air pre-convective environment and subsequent convective events in the arid Al Hajar Mountains, with the overarching goal of improving understanding and nowcasting of seedable orographic clouds. During summer in the Al Hajar Mountains (June to September), weather processes are often complex, with summer convection being initiated by several phenomena acting in concert, e.g., interaction between sea breeze and horizontal convective rolls. These interactions can combine to initiate sporadic convective storms and these can be intense enough to cause flash floods and erosion. Such events here are influenced by mesoscale phenomena like the low-level jet and local sea breeze, and are constrained by larger-scale synoptic conditions.</p><p>The Doppler lidar and cloud radar were employed for approximately two years at a high vantage-point to capture valley wind flows and observe convective cells. The instruments were configured to run synchronized polar (PPI) scans at 0°, 5°, and 45° elevation angles and vertical cross-section (RHI) scans at 0°, 30°, 60, 90°, 120°, and 150° azimuth angles. Using this imagery, along with local C-band radar and satellite data, we were able to identify and analyze several convective cases. To illustrate our results, we have selected two cases under unstable conditions - the 5 and 6 September 2018. In both cases, we observed areas of low-level convergence/divergence, particularly associated with wind flow around a peak 2 km to the south-west of the observatory. The extension of these deformations are visible in the atmosphere to a height of 3 km above sea level. Subsequently, we observed convective cells developing at those approximate locations – apparently initiated because of these phenomena. The cloud radar images provided detailed observations of cloud structure, evolution, and precipitation. In both convective cases, pre-convective signatures were apparent before CI, in the form of convergence, wind shear structures, and updrafts.</p><p>These results have demonstrated the value of synergetic observations for understanding orographic convection initiation, improvement of forecast models, and cloud seeding guidance. The manuscript based on these results is now the subject of a peer review (Branch et al., 2021).</p><p> </p><p>Branch, O., Behrendt, Andreas Alnayef, O., Späth, F., Schwitalla, Thomas, Temimi, M., Weston, M., Farrah, S., Al Yazeedi, O., Tampi, S., Waal, K. de and Wulfmeyer, V.: The new Mountain Observatory of the Project “Optimizing Cloud Seeding by Advanced Remote Sensing and Land Cover Modification (OCAL)” in the United Arab Emirates: First results on Convection Initiation, J. Geophys. Res.  Atmos., 2021. In review (submitted 23.11.2020).</p>

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