scholarly journals Evaluation of Fog and Low Stratus Cloud Microphysical Properties Derived from In Situ Sensor, Cloud Radar and SYRSOC Algorithm

Atmosphere ◽  
2018 ◽  
Vol 9 (5) ◽  
pp. 169 ◽  
Author(s):  
Jean-Charles Dupont ◽  
Martial Haeffelin ◽  
Eivind Wærsted ◽  
Julien Delanoe ◽  
Jean-Baptiste Renard ◽  
...  
Keyword(s):  
Stratus Cloud ◽  
Cloud Radar ◽  
Microphysical Properties ◽  
Sensor Cloud ◽  
In Situ Sensor

scholarly journals Observations of ice multiplication in a weakly convective cell embedded in supercooled mid-level stratus

2011 ◽  
Vol 11 (1) ◽  
pp. 257-273 ◽  
Author(s):  
J. Crosier ◽  
K. N. Bower ◽  
T. W. Choularton ◽  
C. D. Westbrook ◽  
P. J. Connolly ◽  
...  
Keyword(s):  
Carbonaceous Material ◽  
Temperature Inversion ◽  
Convective Cell ◽  
Cloud Base ◽  
Stratus Cloud ◽  
Microphysical Properties ◽  
Ice Nuclei ◽  
Immersion Freezing ◽  
Ice Multiplication

Abstract. Simultaneous observations of cloud microphysical properties were obtained by in-situ aircraft measurements and ground based Radar/Lidar. Widespread mid-level stratus cloud was present below a temperature inversion (~5 °C magnitude) at 3.6 km altitude. Localised convection (peak updraft 1.5 m s−1) was observed 20 km west of the Radar station. This was associated with convergence at 2.5 km altitude. The convection was unable to penetrate the inversion capping the mid-level stratus. The mid-level stratus cloud was vertically thin (~400 m), horizontally extensive (covering 100 s of km) and persisted for more than 24 h. The cloud consisted of supercooled water droplets and small concentrations of large (~1 mm) stellar/plate like ice which slowly precipitated out. This ice was nucleated at temperatures greater than −12.2 °C and less than −10.0 °C, (cloud top and cloud base temperatures, respectively). No ice seeding from above the cloud layer was observed. This ice was formed by primary nucleation, either through the entrainment of efficient ice nuclei from above/below cloud, or by the slow stochastic activation of immersion freezing ice nuclei contained within the supercooled drops. Above cloud top significant concentrations of sub-micron aerosol were observed and consisted of a mixture of sulphate and carbonaceous material, a potential source of ice nuclei. Particle number concentrations (in the size range 0.1

scholarly journals The Retrieval of Ice-Cloud Properties from Cloud Radar and Lidar Synergy

2005 ◽  
Vol 44 (6) ◽  
pp. 860-875 ◽  
Author(s):  
Claire Tinel ◽  
Jacques Testud ◽  
Jacques Pelon ◽  
Robin J. Hogan ◽  
Alain Protat ◽  
...  
Keyword(s):  
Extinction Coefficient ◽  
Radar Data ◽  
Mie Scattering ◽  
In Situ Measurements ◽  
Physical Parameters ◽  
Backscatter Coefficient ◽  
Blind Test ◽  
Cloud Radar ◽  
Microphysical Properties

Abstract Clouds are an important component of the earth’s climate system. A better description of their microphysical properties is needed to improve radiative transfer calculations. In the framework of the Earth, Clouds, Aerosols, and Radiation Explorer (EarthCARE) mission preparation, the radar–lidar (RALI) airborne system, developed at L’Institut Pierre Simon Laplace (France), can be used as an airborne demonstrator. This paper presents an original method that combines cloud radar (94–95 GHz) and lidar data to derive the radiative and microphysical properties of clouds. It combines the apparent backscatter reflectivity from the radar and the apparent backscatter coefficient from the lidar. The principle of this algorithm relies on the use of a relationship between the extinction coefficient and the radar specific attenuation, derived from airborne microphysical data and Mie scattering calculations. To solve radar and lidar equations in the cloud region where signals can be obtained from both instruments, the extinction coefficients at some reference range z0 must be known. Because the algorithms are stable for inversion performed from range z0 toward the emitter, z0 is chosen at the farther cloud boundary as observed by the lidar. Then, making an assumption of a relationship between extinction coefficient and backscattering coefficient, the whole extinction coefficient, the apparent reflectivity, cloud physical parameters, the effective radius, and ice water content profiles are derived. This algorithm is applied to a blind test for downward-looking instruments where the original profiles are derived from in situ measurements. It is also applied to real lidar and radar data, obtained during the 1998 Cloud Lidar and Radar Experiment (CLARE’98) field project when a prototype airborne RALI system was flown pointing at nadir. The results from the synergetic algorithm agree reasonably well with the in situ measurements.

scholarly journals Fog Analysis during SOFOG3D Experiment

2021 ◽  
Author(s):  
Pragya Vishwakarma ◽  
Julien Delanoë ◽  
Christophe Le Gac ◽  
Fabrice Bertrand ◽  
Jean-Charles Dupont ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Microwave Radiometer ◽  
3D Structure ◽  
In Situ Measurements ◽  
Radiation Budget ◽  
Radar Signal ◽  
Cloud Radar ◽  
Microphysical Properties ◽  
South West

<p>Transportation especially aviation sector all around the world is severely hindered due to Fog and hence observations and specific research for fog is necessary. The SOFOG3D (SOuth west FOGs 3D) experiment took place in South-West of France which is particularly prone to fog occurrence, during the period between November 2019 to March 2020 with primary objective to advance our understanding of fog processes and to improve fog forecast. Simultaneous measurements from various remote sensing instruments like BASTA: a 95 GHz cloud radar with scanning capability, HATPRO Microwave radiometer (MWR), doppler lidar, and balloon-borne in-situ measurements were collected to characterize the spatio-temporal evolution of Fog. On the supersite, detailed measurements of meteorological conditions, aerosol properties, fog microphysics, water deposition, radiation budget, heat, and momentum fluxes are collected to provide 3D structure of the boundary layer during fog events. The improvement in the retrieval of fog parameters and understanding of fog dynamics based on cloud radar and microwave (MWR) synergy will be addressed. We will present our work on the retrieval of key fog parameters like dynamics and microphysics using a combination of cloud radar and MWR observations. The retrievals will be validated with the tethered-balloon and radio-sounding observations. In-situ measurements and remote-sensing retrievals of fog microphysical properties will be compared. We will show a detailed analysis of retrieved LWP derived from BASTA radar only with LWP derived from HATPRO microwave radiometer, considering instrumental uncertainty and sensitivity. A closer analysis of the in-situ data (measured by granulometer) will be presented in order to assess and improve the retrieval derived with cloud radar in vertically pointing mode. Radar attenuation will be quantified by measuring the backscattered radar signal on well-known calibrated reflectivity metallic targets installed at the top of 20 m mast. The integrated attenuation along the radar beam path will be measured by the cloud radar and used as a new constraint to improve the microphysical properties.</p>

In Situ Sensor Monitoring of the Processing and Cure Properties of Solid Propellants

1993 ◽  
Author(s):  
D. E. Kranbuehl ◽  
S. Hart ◽  
Y. Wang ◽  
D. F. Schwartz
Keyword(s):  
Solid Propellants ◽  
In Situ Sensor ◽  
Sensor Monitoring

In situ optical and microphysical properties of tropospheric aerosols in the Canadian High Arctic from 2016 to 2019

2021 ◽  
Vol 250 ◽  
pp. 118254
Author(s):  
Andy Vicente-Luis ◽  
Samantha Tremblay ◽  
Joelle Dionne ◽  
Rachel Y.-W. Chang ◽  
Pierre F. Fogal ◽  
...  
Keyword(s):  
High Arctic ◽  
Canadian High Arctic ◽  
Microphysical Properties ◽  
Tropospheric Aerosols

scholarly journals Aerosol optical and microphysical retrievals from a hybrid multiwavelength lidar data set – DISCOVER-AQ 2011

2014 ◽  
Vol 7 (9) ◽  
pp. 3095-3112 ◽  
Author(s):  
P. Sawamura ◽  
D. Müller ◽  
R. M. Hoff ◽  
C. A. Hostetler ◽  
R. A. Ferrare ◽  
...  
Keyword(s):  
Remote Sensing ◽  
In Situ Measurements ◽  
Index Of Refraction ◽  
High Spectral Resolution ◽  
Lidar Data ◽  
Data Set ◽  
Microphysical Properties ◽  
Lidar Measurements ◽  
Multiwavelength Lidar

Abstract. Retrievals of aerosol microphysical properties (effective radius, volume and surface-area concentrations) and aerosol optical properties (complex index of refraction and single-scattering albedo) were obtained from a hybrid multiwavelength lidar data set for the first time. In July 2011, in the Baltimore–Washington DC region, synergistic profiling of optical and microphysical properties of aerosols with both airborne (in situ and remote sensing) and ground-based remote sensing systems was performed during the first deployment of DISCOVER-AQ. The hybrid multiwavelength lidar data set combines ground-based elastic backscatter lidar measurements at 355 nm with airborne High-Spectral-Resolution Lidar (HSRL) measurements at 532 nm and elastic backscatter lidar measurements at 1064 nm that were obtained less than 5 km apart from each other. This was the first study in which optical and microphysical retrievals from lidar were obtained during the day and directly compared to AERONET and in situ measurements for 11 cases. Good agreement was observed between lidar and AERONET retrievals. Larger discrepancies were observed between lidar retrievals and in situ measurements obtained by the aircraft and aerosol hygroscopic effects are believed to be the main factor in such discrepancies.

Temperature and Viscosity in-Situ Sensor for Hostile Processes

1999 ◽  
pp. 1163-1170 ◽  
Author(s):  
Krishnan Balasubramaniam ◽  
V. Vimal ◽  
Gary Boudreaux ◽  
R. Daniel Costley ◽  
Clinton Menezes ◽  
...  
Keyword(s):  
In Situ Sensor

Dynamic Processes in Cirrus Clouds: Concepts and Models

Cirrus ◽  
2002 ◽  
Author(s):  
David O’C. Starr ◽  
Markus Quante
Keyword(s):  
Cirrus Clouds ◽  
Radiative Properties ◽  
Cirrus Cloud ◽  
Early Model ◽  
Minimum Requirement ◽  
Microphysical Properties ◽  
Cloud Properties ◽  
Cloud Models ◽  
New Concepts

Advancement in the understanding of cirrus clouds and their life cycle comes through symbiotic use of models, observations, and related concepts (fig. 18.1). Models of cirrus clouds represent an integration of our knowledge of cirrus cloud properties and processes. They provide a capacity to extend knowledge and enhance understanding in ways that complement existing observational capabilities. Models can be used to develop new theories, such as parameterizations, and focus science issues and observational requirements and developments. For example, early model results of Starr and Cox (1985a) and Starr (1987b) predicted that fine cellular structure (~lkm or less) would be found in the upper part of extended stratiform cirrus clouds. This prediction was confirmed when high-frequency sensors were deployed both for active remote sensing (Sassen et al. 1990a, 1995) and later for in-situ measurements (Quante and Brown 1992; Gultepe et al. 1995; Quante et al. 1996). Sampling rates of 10Hz, or better, are now accepted as a minimum requirement for resolving cirrus cloud internal structure and circulation where 1-Hz or coarser measurements were previously used. Similarly, discrepancies between observed cloud radiative properties and calculations (theory) based on corresponding in-situ observations of cloud microphysical properties (Sassen et al. 1990b) led to the development of improved observing capabilities for small ice crystals (Arnott et al. 1994; Miloshevich and Heymsfield 1997; Lawson et al. 1998). Such sensors are now regarded as part of the standard complement when doing in-situ microphysical measurements in cirrus. At the same time, observations are absolutely essential in developing and evaluating cloud models. No cloud modeler wants to apply a model or theory too far beyond the limits of what can be observationally confirmed, at least in gross terms. The third aspect of this triad is concepts. Although models and observations can lead to predictions or diagnosis of unexpected relationships, they are each limited by the concepts that were used in their design and/or implementation. In the end, new concepts arising from analogy to other phenomena and/or from synergistic integration of existing knowledge can lead to new understanding, new models, new instruments, and new sampling strategies (fig. 18.1). Chapter 17 focuses on observations of internal cloud circulation and structure.

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