scholarly journals Application of the shipborne remote sensing supersite OCEANET for profiling of Arctic aerosols and clouds during <i>Polarstern</i> cruise PS106

2020 ◽  
Vol 13 (10) ◽  
pp. 5335-5358
Author(s):  
Hannes J. Griesche ◽  
Patric Seifert ◽  
Albert Ansmann ◽  
Holger Baars ◽  
Carola Barrientos Velasco ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Sea Level ◽  
Microwave Radiometer ◽  
High Arctic ◽  
Observation Time ◽  
Doppler Velocity ◽  
Data Set ◽  
Detection Range ◽  
Low Level ◽  
Cloud Radar

Abstract. From 25 May to 21 July 2017, the research vessel Polarstern performed the cruise PS106 to the high Arctic in the region north and northeast of Svalbard. The mobile remote-sensing platform OCEANET was deployed aboard Polarstern. Within a single container, OCEANET houses state-of-the-art remote-sensing equipment, including a multiwavelength Raman polarization lidar PollyXT and a 14-channel microwave radiometer HATPRO (Humidity And Temperature PROfiler). For the cruise PS106, the measurements were supplemented by a motion-stabilized 35 GHz cloud radar Mira-35. This paper describes the treatment of technical challenges which were immanent during the deployment of OCEANET in the high Arctic. This includes the description of the motion stabilization of the cloud radar Mira-35 to ensure vertical-pointing observations aboard the moving Polarstern as well as the applied correction of the vessels heave rate to provide valid Doppler velocities. The correction ensured a leveling accuracy of ±0.5∘ during transits through the ice and an ice floe camp. The applied heave correction reduced the signal induced by the vertical movement of the cloud radar in the PSD of the Doppler velocity by a factor of 15. Low-level clouds, in addition, frequently prevented a continuous analysis of cloud conditions from synergies of lidar and radar within Cloudnet, because the technically determined lowest detection height of Mira-35 was 165 m above sea level. To overcome this obstacle, an approach for identification of the cloud presence solely based on data from the near-field receiver of PollyXT at heights from 50 m and 165 m above sea level is presented. We found low-level stratus clouds, which were below the lowest detection range of most automatic ground-based remote-sensing instruments during 25 % of the observation time. We present case studies of aerosol and cloud studies to introduce the capabilities of the data set. In addition, new approaches for ice crystal effective radius and eddy dissipation rates from cloud radar measurements and the retrieval of aerosol optical and microphysical properties from the observations of PollyXT are introduced.

scholarly journals Application of the shipborne remote sensing supersite OCEANET for profiling of Arctic aerosols and clouds during Polarstern cruise PS106

2019 ◽  
Author(s):  
Hannes Jascha Griesche ◽  
Patric Seifert ◽  
Albert Ansmann ◽  
Holger Baars ◽  
Carola Barrientos Velasco ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Sea Level ◽  
Microwave Radiometer ◽  
Value Added ◽  
High Arctic ◽  
Surface Processes ◽  
The Arctic ◽  
Target Classification ◽  
Cloud Radar ◽  
Microphysical Properties

Abstract. From 25 May to 21 July 2017, the research vessel Polarstern performed the cruise PS106 to the high Arctic in the region north and northeast of Svalbard. PS106 contributed observations for the initiative "Arctic Amplification: Climate Relevant Atmospheric and Surface Processes and Feedback Mechanisms (AC)3" which involves numerous projects aiming on understanding the role of atmospheric and surface processes in the ongoing rapid changes in the Arctic climate. As one of the central facilities of (AC)3, the mobile remote sensing platform OCEANET was deployed aboard Polarstern. Within a single container, OCEANET houses state-of-the-art remote sensing equipment, including a multi-wavelength Raman polarization lidar PollyXT and a 14-channel microwave radiometer HATPRO. For the cruise PS106 the measurements were supplemented by a motion-stabilized 35-GHz cloud radar Mira-35. This paper describes the treatment of technical challenges which were immanent during the deployment of OCEANET in the high Arctic. This includes the description of the motion stabilization of the cloud radar Mira-35 to ensure vertical-stare observations aboard the moving Polarstern. Also, low-level clouds and the presence of fog frequently prevented a continuous analysis of cloud conditions from synergies of lidar and radar within Cloudnet, because the technically determined lowest detection height of Mira-35 was 165m above sea level. To overcome this obstacle, an approach for identification of the cloud presence solely based on data from the near-field receiver of PollyXT at heights from 50m and 165m above sea level is presented. In addition, we provide an overview of the data processing chain of the OCEANET observations and demonstrate case studies of aerosol and cloud studies to introduce the capabilities of the dataset. The retrieval of aerosol optical and microphysical properties from the observations of PollyXT is presented by means of observations performed during the ice floe camp. Synergies between the remote sensing instruments and auxiliary observations from aboard Polarstern were analyzed by means of Cloudnet which provides as primary output a target classification mask. This target classification is the basis for value-added products such as liquid- and ice-cloud microphysical properties, cloud dynamics which can in subsequent steps be used as input for the investigation of cloud microphysical processes, radiative transfer calculations, or model evaluation. To this end, new approaches for ice crystal effective radius and eddy dissipation rates have been implemented into Cloudnet.

scholarly journals Investigation of the adiabatic assumption for estimating cloud micro- and macrophysical properties from satellite and ground observations

2016 ◽  
Vol 16 (2) ◽  
pp. 933-952 ◽  
Author(s):  
D. Merk ◽  
H. Deneke ◽  
B. Pospichal ◽  
P. Seifert
Keyword(s):  
Optical Depth ◽  
Microwave Radiometer ◽  
Cloud Droplet ◽  
Satellite Observations ◽  
Data Set ◽  
Cloud Optical Depth ◽  
Cloud Radar ◽  
Aerosol Cloud ◽  
Droplet Concentration ◽  
Aerosol Cloud Interactions

Abstract. Cloud properties from both ground-based as well as from geostationary passive satellite observations have been used previously for diagnosing aerosol–cloud interactions. In this investigation, a 2-year data set together with four selected case studies are analyzed with the aim of evaluating the consistency and limitations of current ground-based and satellite-retrieved cloud property data sets. The typically applied adiabatic cloud profile is modified using a sub-adiabatic factor to account for entrainment within the cloud. Based on the adiabatic factor obtained from the combination of ground-based cloud radar, ceilometer and microwave radiometer, we demonstrate that neither the assumption of a completely adiabatic cloud nor the assumption of a constant sub-adiabatic factor is fulfilled (mean adiabatic factor 0.63 ± 0.22). As cloud adiabaticity is required to estimate the cloud droplet number concentration but is not available from passive satellite observations, an independent method to estimate the adiabatic factor, and thus the influence of mixing, would be highly desirable for global-scale analyses. Considering the radiative effect of a cloud described by the sub-adiabatic model, we focus on cloud optical depth and its sensitivities. Ground-based estimates are here compared vs. cloud optical depth retrieved from the Meteosat SEVIRI satellite instrument resulting in a bias of −4 and a root mean square difference of 16. While a synergistic approach based on the combination of ceilometer, cloud radar and microwave radiometer enables an estimate of the cloud droplet concentration, it is highly sensitive to radar calibration and to assumptions about the moments of the droplet size distribution. Similarly, satellite-based estimates of cloud droplet concentration are uncertain. We conclude that neither the ground-based nor satellite-based cloud retrievals applied here allow a robust estimate of cloud droplet concentration, which complicates its use for the study of aerosol–cloud interactions.

scholarly journals A unified data set of airborne cloud remote sensing using the HALO Microwave Package (HAMP)

2018 ◽  
Author(s):  
Heike Konow ◽  
Marek Jacob ◽  
Felix Ament ◽  
Susanne Crewell ◽  
Florian Ewald ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Microwave Remote Sensing ◽  
Joint Analysis ◽  
Wet Season ◽  
Data Set ◽  
Cloud Radar ◽  
Cloud Properties ◽  
Wide Range ◽  
Research Aircraft ◽  
Microwave Radiometers

Abstract. Cloud properties and their environmental conditions were observed during four aircraft campaigns over the North Atlantic on 37 flights. The Halo Microwave Package (HAMP) was deployed on the German research aircraft HALO (High Altitude LOng range research aircraft) during these four campaigns. HAMP comprises microwave radiometers with 26 channels in the frequency range between 20 and 183 GHz and a 35 GHz cloud radar. The four campaigns took place between December 2013 and October 2016 out of Barbados and Iceland. Measured situations cover a wide range of conditions including the dry and wet season over the tropical Atlantic and the cold and warm sectors of mid-latitude cyclones. The data set we present here contains measurements of the radar reflectivity factor and linear depolarization ratio from cloud radar, brightness temperatures from microwave radiometers, and atmospheric profiles from dropsondes. It represents a unique combination of active and passive microwave remote sensing measurements and 525 in-situ measured dropsonde profiles. The data from these different instruments are quality controlled and unified into one common format for easy combination of data and joint analysis. The data are available from the CERA database for the four campaigns individually (https://doi.org/10.1594/WDCC/HALO_measurements_1, https://doi.org/10.1594/WDCC/HALO_measurements_2, https://doi.org/10.1594/WDCC/HALO_measurements_3, https://doi.org/10.1594/WDCC/HALO_measurements_4). This data set allows for analyses to get insight into cloud properties and atmospheric state in remote regions over the tropical and mid-latitude Atlantic. In this paper, we describe the four campaigns, the data, and the quality control applied to the data.

scholarly journals Monitoring aerosol–cloud interactions at the CESAR Observatory in the Netherlands

2017 ◽  
Vol 10 (5) ◽  
pp. 1987-1997 ◽  
Author(s):  
Karolina Sarna ◽  
Herman W. J. Russchenberg
Keyword(s):  
Remote Sensing ◽  
The Netherlands ◽  
Climate Models ◽  
Cloud Droplet ◽  
Doppler Velocity ◽  
Backscatter Coefficient ◽  
Liquid Water Path ◽  
Data Set ◽  
Aerosol Cloud ◽  
The Impact

Abstract. The representation of aerosol–cloud interaction (ACI) processes in climate models, although long studied, still remains the source of high uncertainty. Very often there is a mismatch between the scale of observations used for ACI quantification and the ACI process itself. This can be mitigated by using the observations from ground-based remote sensing instruments. In this paper we presented a direct application of the aerosol–cloud interaction monitoring technique (ACI monitoring). ACI monitoring is based on the standardised Cloudnet data stream, which provides measurements from ground-based remote sensing instruments working in synergy. For the data set collected at the CESAR Observatory in the Netherlands we calculate ACI metrics. We specifically use attenuated backscatter coefficient (ATB) for the characterisation of the aerosol properties and cloud droplet effective radius (re) and number concentration (Nd) for the characterisation of the cloud properties. We calculate two metrics: ACIr  =  ln(re)/ln(ATB) and ACIN  =  ln(Nd)/ln(ATB). The calculated values of ACIr range from 0.001 to 0.085, which correspond to the values reported in previous studies. We also evaluated the impact of the vertical Doppler velocity and liquid water path (LWP) on ACI metrics. The values of ACIr were highest for LWP values between 60 and 105 g m−2. For higher LWP other processes, such as collision and coalescence, seem to be dominant and obscure the ACI processes. We also saw that the values of ACIr are higher when only data points located in the updraught regime are considered. The method presented in this study allow for monitoring ACI daily and further aggregating daily data into bigger data sets.

scholarly journals A unified data set of airborne cloud remote sensing using the HALO Microwave Package (HAMP)

2019 ◽  
Vol 11 (2) ◽  
pp. 921-934 ◽  
Author(s):  
Heike Konow ◽  
Marek Jacob ◽  
Felix Ament ◽  
Susanne Crewell ◽  
Florian Ewald ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Microwave Remote Sensing ◽  
Joint Analysis ◽  
Wet Season ◽  
Data Set ◽  
Cloud Radar ◽  
Cloud Properties ◽  
Wide Range ◽  
Research Aircraft ◽  
Microwave Radiometers

Abstract. Cloud properties and their environmental conditions were observed during four aircraft campaigns over the North Atlantic on 37 flights. The Halo Microwave Package (HAMP) was deployed on the German research aircraft HALO (High Altitude Long Range Research Aircraft) during these four campaigns. HAMP comprises microwave radiometers with 26 channels in the frequency range between 20 and 183 GHz and a 35 GHz cloud radar. The four campaigns took place between December 2013 and October 2016 out of Barbados and Iceland. Measured situations cover a wide range of conditions including the dry and wet season over the tropical Atlantic and the cold and warm sectors of midlatitude cyclones. The data set we present here contains measurements of the radar reflectivity factor and linear depolarization ratio from cloud radar, brightness temperatures from microwave radiometers and atmospheric profiles from dropsondes. It represents a unique combination of active and passive microwave remote sensing measurements and 525 in situ-measured dropsonde profiles. The data from these different instruments are quality controlled and unified into one common format for easy combination of data and joint analysis. The data are available from the CERA database for the four campaigns individually (https://doi.org/10.1594/WDCC/HALO_measurements_1, https://doi.org/10.1594/WDCC/HALO_measurements_2, https://doi.org/10.1594/WDCC/HALO_measurements_3, https://doi.org/10.1594/WDCC/HALO_measurements_4). This data set allows for analyses to gain insight into cloud properties and the atmospheric state in remote regions over the tropical and midlatitude Atlantic. In this paper, we describe the four campaigns, the data and the quality control applied to the data.

Arctic low-level clouds and their importance for radiative transfer simulations

2021 ◽  
Author(s):  
Hannes Griesche ◽  
Carola Barrientos Velasco ◽  
Patric Seifert
Keyword(s):  
Remote Sensing ◽  
Radiative Transfer ◽  
Liquid Water ◽  
Remote Sensing Data ◽  
The Arctic ◽  
Liquid Water Path ◽  
Low Level ◽  
Cloud Radar ◽  
Microphysical Properties ◽  
Water Detection

&lt;p&gt;The observation of low-level stratocumulus cloud decks in the Arctic poses challenges to ground-based remote sensing. These clouds frequently occur during summer below the lowest range gate of common zenith-pointing cloud radar instruments, like the KAZR and the Mira-35. In addition, the optical thickness of these low-level clouds often do cause a complete attenuation of the lidar beam. For remote-sensing instrument synergy retrievals, as Cloudnet (Illingworth, 2007) or ARSCL (Active Remote Sensing of Clouds, Shupe, 2007), liquid-water detection in clouds is usually based on lidar backscatter. Thus, a complete attenuation can cause misclassification of mixed-phase clouds as pure-ice clouds. Moreover, the missing cloud radar information makes it difficult to derive the cloud microphysical properties, as most common retrievals are based on cloud radar reflectivity.&lt;/p&gt; &lt;p&gt;A new low-level stratus detection mask (Griesche, 2020) was used to detect these clouds. The liquid-water cloud microphysical properties were derived by a simple but effective analysis of the liquid-water path. This approach was applied to remote-sensing data from a shipborne expedition performed in the Arctic summer 2017. The values calculated by applying the adjusted method improve the results of radiative transfer simulations yielding the determination of radiative closure.&lt;/p&gt; &lt;p&gt;&amp;#160;&lt;/p&gt; &lt;p&gt;&amp;#160;&lt;/p&gt; &lt;p&gt;Illingworth et al. (2007). &amp;#8220;Cloudnet&amp;#8221;. BAMS.&lt;/p&gt; &lt;p&gt;Shupe (2007). &amp;#8220;A ground-based multisensor cloud phase classifier&amp;#8221;. GRL.&lt;/p&gt; &lt;p&gt;Griesche et al. (2020). &amp;#8220;Application of the shipborne remote sensing supersite OCEANET for profiling of Arctic aerosols and clouds during Polarstern cruise PS106&amp;#8221;. AMT.&lt;/p&gt;

Can we observe a correlation between vertical Doppler velocity and upwelling solar radiance for shallow marine boundary clouds?

2020 ◽  
Author(s):  
Florian Ewald ◽  
Silke Groß ◽  
Martin Hagen ◽  
Tobias Kölling ◽  
Bernhard Mayer
Keyword(s):  
Spatial Resolution ◽  
Climate Models ◽  
Cloud Microphysics ◽  
Radiative Properties ◽  
Radiation Budget ◽  
Doppler Velocity ◽  
Data Set ◽  
Shallow Marine ◽  
Cloud Radar ◽  
Cloud Dynamics

&lt;p&gt;Clouds play an important role in the climate system since they have a profound influence on Earth&amp;#8217;s radiation budget and the water cycle. Uncertainties in current climate models arise from &lt;span&gt;a limited understanding&lt;/span&gt; of the coupling between cloud dynamics, cloud microphysics and, in turn, cloud radiative properties. Over decades, radiative properties of cloud tops were extensively studied using passive observations from multiple satellite missions. In recent years, our understanding of the inner workings of clouds&lt;span&gt; has been greatly advanced by the deployment of cloud profiling microwave radars from low-earth orbit like CloudSat or the upcoming EarthCARE satellite mission.&lt;/span&gt; In order to exploit the future synergy between the cloud radar and the passive imager on EarthCARE, the scientific community is in dire need of collocated and spatially highly resolved measurements in advance of future spaceborne missions. &amp;#160;&lt;/p&gt;&lt;p&gt;&lt;span&gt;In this context, the &lt;/span&gt;German research aircraft HALO is equipped with the high-power (30kW) cloud radar HAMP MIRA operating at 35 GHz and the hyperspectral imager specMACS (400 nm &amp;#8211; 2500 nm). &lt;span&gt;During the EUREC4A campaign, &lt;/span&gt;&lt;span&gt;a number of flights were conducted over shallow marine boundary clouds in the vicinity of Barbados to collect simultaneous measurements with both instruments. For the first time, the spatial resolution of the Doppler velocity measurements from HALO now better match (&lt;100 meter) the spatial resolution of the radiance imager, allowing for a more detailed separation of small up- und down-drafts.&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;In this presentation, we will give first impressions of these collocated, highly resolved radar-imager measurements of shallow marine boundary clouds during EUREC4A. On the basis of this data set we will try to answer the question if &lt;/span&gt;&lt;span&gt;a correlation between the vertical Doppler velocity and the upwelling solar radiance for this kind of clouds can be observed. Such a relationship could prove valuable to assist synergistic retrievals (e.g. radar-lidar) in narrowing down the microphysical assumptions on which these retrievals rely upon. Furthermore, this data set could serve as a benchmark for cloud resolving modeling by constraining the relationship between cloud dynamics and radiation.&lt;/span&gt;&lt;/p&gt;

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

&lt;p&gt;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.&lt;/p&gt;

scholarly journals The COTUR project: remote sensing of offshore turbulence for wind energy application

2021 ◽  
Vol 14 (9) ◽  
pp. 6137-6157
Author(s):  
Etienne Cheynet ◽  
Martin Flügge ◽  
Joachim Reuder ◽  
Jasna B. Jakobsen ◽  
Yngve Heggelund ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Boundary Layer ◽  
Surface Layer ◽  
Atmospheric Boundary Layer ◽  
Wind Turbines ◽  
Microwave Radiometer ◽  
Wind Load ◽  
Offshore Wind ◽  
Data Set ◽  
Offshore Wind Turbines

Abstract. The paper presents the measurement strategy and data set collected during the COTUR (COherence of TURbulence with lidars) campaign. This field experiment took place from February 2019 to April 2020 on the southwestern coast of Norway. The coherence quantifies the spatial correlation of eddies and is little known in the marine atmospheric boundary layer. The study was motivated by the need to better characterize the lateral coherence, which partly governs the dynamic wind load on multi-megawatt offshore wind turbines. During the COTUR campaign, the coherence was studied using land-based remote sensing technology. The instrument setup consisted of three long-range scanning Doppler wind lidars, one Doppler wind lidar profiler and one passive microwave radiometer. Both the WindScanner software and LidarPlanner software were used jointly to simultaneously orient the three scanner heads into the mean wind direction, which was provided by the lidar wind profiler. The radiometer instrument complemented these measurements by providing temperature and humidity profiles in the atmospheric boundary layer. The scanning beams were pointed slightly upwards to record turbulence characteristics both within and above the surface layer, providing further insight on the applicability of surface-layer scaling to model the turbulent wind load on offshore wind turbines. The preliminary results show limited variations of the lateral coherence with the scanning distance. A slight increase in the identified Davenport decay coefficient with the height is partly due to the limited pointing accuracy of the instruments. These results underline the importance of achieving pointing errors under 0.1∘ to study properly the lateral coherence of turbulence at scanning distances of several kilometres.

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

2020 ◽  
Vol 38 (4A) ◽  
pp. 510-514
Author(s):  
Tay H. Shihab ◽  
Amjed N. Al-Hameedawi ◽  
Ammar M. Hamza
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Satellite Image ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019.  They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

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