scholarly journals Synergistic radar and radiometer retrievals of ice hydrometeors

2020 ◽  
Vol 13 (8) ◽  
pp. 4219-4245
Author(s):  
Simon Pfreundschuh ◽  
Patrick Eriksson ◽  
Stefan A. Buehler ◽  
Manfred Brath ◽  
David Duncan ◽  
...  
Keyword(s):  
Water Content ◽  
Information Content ◽  
Cloud Model ◽  
Cloud Microphysics ◽  
Retrieval Algorithm ◽  
Particle Model ◽  
Microphysical Properties ◽  
Millimeter Wavelengths ◽  
Wide Range ◽  
The Impact

Abstract. Remote sensing observations at sub-millimeter wavelengths provide higher sensitivity to small hydrometeors and low water content than observations at millimeter wavelengths, which are traditionally used to observe clouds and precipitation. They are employed increasingly in field campaigns to study cloud microphysics and will be integrated into the global meteorological observing system to measure the global distribution of ice in the atmosphere with the launch of the Ice Cloud Imager (ICI) radiometer on board the second generation of European operational meteorological satellites (Metop-SG). Observations at these novel wavelengths provide valuable information not only on their own but also in combination with complementary observations at other wavelengths. This study investigates the potential of combining passive sub-millimeter radiometer observations with a hypothetical W-band cloud radar for the retrieval of frozen hydrometeors. An idealized cloud model is used to investigate the information content of the combined observations and establish their capacity to constrain the microphysical properties of ice hydrometeors. A synergistic retrieval algorithm for airborne observations is proposed and applied to simulated observations from a cloud-resolving model. Results from the synergistic retrieval are compared to equivalent radar- and passive-only implementations in order to assess the benefits of the synergistic sensor configuration. The impact of the assumed ice particle shape on the retrieval results is assessed for all retrieval implementations. We find that the combined observations better constrain the microphysical properties of ice hydrometeors, which reduces uncertainties in retrieved ice water content and particle number concentrations for suitable choices of the ice particle model. Analysis of the retrieval information content shows that, although the radar contributes the largest part of the information in the combined retrieval, the radiometer observations provide complementary information over a wide range of atmospheric states. Furthermore, the combined observations yield slightly improved retrievals of liquid cloud water in mixed-phase clouds, pointing towards another potential application of combined radar–radiometer observations.

scholarly journals The response of precipitation to aerosol through riming and melting in deep convective clouds

2010 ◽  
Vol 10 (11) ◽  
pp. 29007-29050
Author(s):  
Z. Cui ◽  
S. Davies ◽  
K. S. Carslaw ◽  
A. M. Blyth
Keyword(s):  
Cloud Model ◽  
Cloud Microphysics ◽  
Mixed Phase ◽  
Cloud Base ◽  
Spatial And Temporal Variability ◽  
Convective Clouds ◽  
Ice Particles ◽  
Wide Range ◽  
Deep Convective Clouds ◽  
The Impact

Abstract. We have used a 2-D axisymmetric, non-hydrostatic, bin-resolved cloud model to examine the impact of aerosol changes on the development of mixed-phase convective clouds. We have simulated convective clouds from four different sites (three continental and one tropical marine) with a wide range of realistic aerosol loadings and initial thermodynamic conditions (a total of 93 different clouds). It is found that the accumulated precipitation responds very differently to changing aerosol in the marine and continental environments. For the continental clouds, the scaled total precipitation reaches a maximum for aerosol that produce drop numbers at cloud base between 180–430 cm−3 when other conditions are the same. In contrast, all the tropical marine clouds show an increase in accumulated precipitation and deeper convection with increasing aerosol loading. For continental clouds, drops are rapidly depleted by ice particles shortly after the onset of precipitation. The precipitation is dominantly produced by melting ice particles. The riming rate increases with aerosol when the loading is very low, and decreases when the loading is high. Peak precipitation intensities tend to increase with aerosol up to drop concentrations (at cloud base) of ~500 cm−3 then decrease with further aerosol increases. This behaviour is caused by the initial transition from warm to mixed-phase rain followed by reduced efficiency of mixed-phase rain at very high drop concentrations. The response of tropical marine clouds to increasing aerosol is different to, and larger than, that of continental clouds. In the more humid tropical marine environment with low cloud bases we find that accumulated precipitation increases with increasing aerosol. The increase is driven by the transition from warm to mixed-phase rain. Our study suggests that the response of deep convective clouds to aerosol will be an important contribution to the spatial and temporal variability in cloud microphysics and precipitation.

scholarly journals The response of precipitation to aerosol through riming and melting in deep convective clouds

2011 ◽  
Vol 11 (7) ◽  
pp. 3495-3510 ◽  
Author(s):  
Z. Cui ◽  
S. Davies ◽  
K. S. Carslaw ◽  
A. M. Blyth
Keyword(s):  
Cloud Model ◽  
Cloud Microphysics ◽  
Mixed Phase ◽  
Cloud Base ◽  
Spatial And Temporal Variability ◽  
Convective Clouds ◽  
Ice Particles ◽  
Wide Range ◽  
Deep Convective Clouds ◽  
The Impact

Abstract. We have used a 2-D axisymmetric, non-hydrostatic, bin-resolved cloud model to examine the impact of aerosol changes on the development of mixed-phase convective clouds. We have simulated convective clouds from four different sites (three continental and one tropical marine) with a wide range of realistic aerosol loadings and initial thermodynamic conditions (a total of 93 different clouds). It is found that the accumulated precipitation responds very differently to changing aerosol in the marine and continental environments. For the continental clouds, the scaled total precipitation reaches a maximum for aerosol that produce drop numbers at cloud base between 180–430 cm−3 when other conditions are the same. In contrast, all the tropical marine clouds show an increase in accumulated precipitation and deeper convection with increasing aerosol loading. For continental clouds, drops are rapidly depleted by ice particles shortly after the onset of precipitation. The precipitation is dominantly produced by melting ice particles. The riming rate increases with aerosol when the loading is very low, and decreases when the loading is high. Peak precipitation intensities tend to increase with aerosol up to drop concentrations (at cloud base) of ~500 cm−3 then decrease with further aerosol increases. This behaviour is caused by the initial transition from warm to mixed-phase rain followed by reduced efficiency of mixed-phase rain at very high drop concentrations. The response of tropical marine clouds to increasing aerosol is different to, and larger than, that of continental clouds. In the more humid tropical marine environment with low cloud bases we find that accumulated precipitation increases with increasing aerosol. The increase is driven by the transition from warm to mixed-phase rain. Our study suggests that the response of deep convective clouds to aerosol will be an important contribution to the spatial and temporal variability in cloud microphysics and precipitation.

scholarly journals Sensitivity of Thermal Infrared Radiation at the Top of the Atmosphere and the Surface to Ice Cloud Microphysics

2008 ◽  
Vol 47 (10) ◽  
pp. 2545-2560 ◽  
Author(s):  
Philippe Dubuisson ◽  
Vincent Giraud ◽  
Jacques Pelon ◽  
Bertrand Cadet ◽  
Ping Yang
Keyword(s):  
Optical Thickness ◽  
Particle Shape ◽  
Cloud Model ◽  
Cloud Microphysics ◽  
Ice Clouds ◽  
Ice Cloud ◽  
Brightness Temperatures ◽  
Microphysical Properties ◽  
Effective Particle ◽  
The Impact

Abstract This paper reports on the sensitivity of the brightness temperatures associated with radiances at the surface and the top of the atmosphere, simulated for the Imaging Infrared Radiometer (IIR) 8.7-, 10.6-, and 12-μm channels under ice cloudy conditions, to the optical and microphysical properties of ice clouds. The 10.6- and 12-μm channels allow simultaneous retrieval of ice cloud optical thickness and effective particle size (Deff) less than 100 μm. It is illustrated that the particle shape and size distributions of ice crystals have noticeable effects on the brightness temperatures. Using the split window technique based on the 10.6- and 12-μm channels in conjunction with cloud properties assumed a priori, the authors show that the influence of the cloud microphysical properties can lead to differences on the order of ±10% and ±25% in retrieved effective particle sizes for small (Deff < 20 μm) and large particles (Deff > 40 μm), respectively. The impact of cloud model on retrieved optical thickness is on the order of ±10%. Different particle habits may lead to ±25% differences in ice water path (IWP). Theoretically, the use of an additional channel (i.e., 8.7 μm) can give a stronger constraint on cloud model and improve the retrieval of Deff and IWP. The present simulations have confirmed that cloud microphysics has a significant impact on the 8.7-μm brightness temperatures mainly because of particle shape. This impact is larger than the errors of the IIR measurements for cloud optical thicknesses (at 12 μm) ranging from 0.3 to 8. Furthermore, it is shown that the characterization of optical and microphysical properties of ice clouds from ground-based measurements is quite challenging. Especially, water vapor in the atmosphere has an important impact on ground-based cloud retrievals. Observation stations at higher altitudes or airborne measurements would minimize the atmospheric effect.

scholarly journals Inclusion of biomass burning in WRF-Chem: impact of wildfires on weather forecasts

2011 ◽  
Vol 11 (11) ◽  
pp. 5289-5303 ◽  
Author(s):  
G. Grell ◽  
S. R. Freitas ◽  
M. Stuefer ◽  
J. Fast
Keyword(s):  
Biomass Burning ◽  
Cloud Model ◽  
Cloud Condensation Nuclei ◽  
Cloud Microphysics ◽  
Fire Season ◽  
Strong Impact ◽  
Emission Rates ◽  
Weather Forecasts ◽  
High Concentrations ◽  
The Impact

Abstract. A plume rise algorithm for wildfires was included in WRF-Chem, and applied to look at the impact of intense wildfires during the 2004 Alaska wildfire season on weather simulations using model resolutions of 10 km and 2 km. Biomass burning emissions were estimated using a biomass burning emissions model. In addition, a 1-D, time-dependent cloud model was used online in WRF-Chem to estimate injection heights as well as the vertical distribution of the emission rates. It was shown that with the inclusion of the intense wildfires of the 2004 fire season in the model simulations, the interaction of the aerosols with the atmospheric radiation led to significant modifications of vertical profiles of temperature and moisture in cloud-free areas. On the other hand, when clouds were present, the high concentrations of fine aerosol (PM2.5) and the resulting large numbers of Cloud Condensation Nuclei (CCN) had a strong impact on clouds and cloud microphysics, with decreased precipitation coverage and precipitation amounts during the first 12 h of the integration. During the afternoon, storms were of convective nature and appeared significantly stronger, probably as a result of both the interaction of aerosols with radiation (through an increase in CAPE) as well as the interaction with cloud microphysics.

scholarly journals Resource allocation and task admission control in cloud systems

2021 ◽  
Author(s):  
Haleh Khojasteh
Keyword(s):  
Resource Allocation ◽  
Performance Metrics ◽  
Cloud Model ◽  
Blocking Probability ◽  
Arrival Rate ◽  
Cloud System ◽  
Wide Range ◽  
Priority Scheme ◽  
The Impact ◽  
And Task

The focus of this thesis is solving the problem of resource allocation in cloud datacenter using an Infrastructure-as-a-Service (IaaS) cloud model. We have investigated the behavior of IaaS cloud datacenters through detailed analytical and simulation models that model linear, transitional and saturated operation regimes. We have obtained accurate performance metrics such as task blocking probability, total delay, utilization and energy consumption. Our results show that the offered load does not offer complete characterization of datacenter operation; therefore, in our evaluations, we have considered the impact of task arrival rate and task service time separately. To keep the cloud system in the linear operation regime, we have proposed several dynamic algorithms to control the admission of incoming tasks. In our first solution, task admission is based on task blocking probability and predefined thresholds for task arrival rate. The algorithms in our second solution are based on full rate task acceptance threshold and filtering coefficient. Our results confirm that the proposed task admission mechanisms are capable of maintaining the stability of cloud system under a wide range of input parameter values. Finally, we have developed resource allocation solutions for mobile clouds in which offloading requests from a mobile device can lead to forking of new tasks in on-demand manner. To address this problem, we have proposed two flexible resource allocation mechanisms with different prioritization: one in which forked tasks are given full priority over newly arrived ones, and another in which a threshold is established to control the priority. Our results demonstrate that threshold-based priority scheme presents better system performance than the full priority scheme. Our proposed solution for clouds with mobile users can be also applied in other clouds which their users’ applications fork new tasks.

scholarly journals Resource allocation and task admission control in cloud systems

2021 ◽  
Author(s):  
Haleh Khojasteh
Keyword(s):  
Resource Allocation ◽  
Performance Metrics ◽  
Cloud Model ◽  
Blocking Probability ◽  
Arrival Rate ◽  
Cloud System ◽  
Wide Range ◽  
Priority Scheme ◽  
The Impact ◽  
And Task

The focus of this thesis is solving the problem of resource allocation in cloud datacenter using an Infrastructure-as-a-Service (IaaS) cloud model. We have investigated the behavior of IaaS cloud datacenters through detailed analytical and simulation models that model linear, transitional and saturated operation regimes. We have obtained accurate performance metrics such as task blocking probability, total delay, utilization and energy consumption. Our results show that the offered load does not offer complete characterization of datacenter operation; therefore, in our evaluations, we have considered the impact of task arrival rate and task service time separately. To keep the cloud system in the linear operation regime, we have proposed several dynamic algorithms to control the admission of incoming tasks. In our first solution, task admission is based on task blocking probability and predefined thresholds for task arrival rate. The algorithms in our second solution are based on full rate task acceptance threshold and filtering coefficient. Our results confirm that the proposed task admission mechanisms are capable of maintaining the stability of cloud system under a wide range of input parameter values. Finally, we have developed resource allocation solutions for mobile clouds in which offloading requests from a mobile device can lead to forking of new tasks in on-demand manner. To address this problem, we have proposed two flexible resource allocation mechanisms with different prioritization: one in which forked tasks are given full priority over newly arrived ones, and another in which a threshold is established to control the priority. Our results demonstrate that threshold-based priority scheme presents better system performance than the full priority scheme. Our proposed solution for clouds with mobile users can be also applied in other clouds which their users’ applications fork new tasks.

scholarly journals Monte Carlo-based subgrid parameterization of vertical velocity and stratiform cloud microphysics in ECHAM5.5-HAM2

2013 ◽  
Vol 13 (2) ◽  
pp. 5477-5507
Author(s):  
J. Tonttila ◽  
P. Räisänen ◽  
H. Järvinen
Keyword(s):  
Monte Carlo ◽  
Vertical Velocity ◽  
Cloud Droplet ◽  
Cloud Microphysics ◽  
Radiative Effects ◽  
Cloud Droplets ◽  
Microphysical Properties ◽  
Cloud Radiative Effects ◽  
Cloud Activation ◽  
The Impact

Abstract. A new method for parameterizing the subgrid variations of vertical velocity and cloud droplet number concentration (CDNC) is presented for GCMs. These parameterizations build on top of existing parameterizations that create stochastic subgrid cloud columns inside the GCM grid-cells, which can be employed by the Monte Carlo independent column approximation approach for radiative transfer. The new model version adds a description for vertical velocity in individual subgrid columns, which can be used to compute cloud activation and the subgrid distribution of the number of cloud droplets explicitly. This provides a consistent way for simulating the cloud radiative effects with two-moment cloud microphysical properties defined in subgrid-scale. The primary impact of the new parameterizations is to decrease the CDNC over polluted continents, while over the oceans the impact is smaller. This promotes changes in the global distribution of the cloud radiative effects and might thus have implications on model estimation of the indirect radiative effect of aerosols.

scholarly journals Synergy of stereo cloud top height and ORAC optimal estimation cloud retrieval: evaluation and application to AATSR

2015 ◽  
Vol 8 (5) ◽  
pp. 5283-5327
Author(s):  
D. Fisher ◽  
C. A. Poulsen ◽  
G. E. Thomas ◽  
J.-P. Muller
Keyword(s):  
Cloud Model ◽  
A Priori ◽  
Single Layer ◽  
Optimal Estimation ◽  
A Priori Information ◽  
Cloud Parameters ◽  
Microphysical Properties ◽  
Priori Information ◽  
The Impact ◽  
High Clouds

Abstract. In this paper we evaluate the retrievals of cloud top height when stereo derived heights are combined with the radiometric cloud top heights retrieved from the ORAC (Optimal Retrieval of Aerosol and Cloud) algorithm. This is performed in a mathematically rigorous way using the ORAC optimal estimation retrieval framework, which includes the facility to use independent a priori information. Key to the use of a priori information is a characterisation of their associated uncertainty. This paper demonstrates the improvements that are possible using this approach and also considers their impact on the microphysical cloud parameters retrieved. The AATSR instrument has two views and three thermal channels so is well placed to demonstrate the synergy of the two techniques. The stereo retrieval is able to improve the accuracy of the retrieved cloud top height when compared to collocated Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), particularly in the presence of boundary layer inversions and high clouds. The impact on the microphysical properties of the cloud such as optical depth and effective radius was evaluated and found to be very small with the biggest differences occurring over bright land surfaces and for high clouds. Overall the cost of the retrievals increased indicating a poorer radiative fit of radiances to the cloud model, which currently uses a single layer cloud model. Best results and improved fit to the radiances may be obtained in the future if a multi-layer model is used.

scholarly journals Monitoring Surface Soil Moisture Content over the Vegetated Area by Integrating Optical and SAR Satellite Observations in the Permafrost Region of Tibetan Plateau

2020 ◽  
Vol 12 (1) ◽  
pp. 183 ◽  
Author(s):  
Chenyang Xu ◽  
John J. Qu ◽  
Xianjun Hao ◽  
Di Wu
Keyword(s):  
Soil Moisture ◽  
Tibetan Plateau ◽  
Water Content ◽  
Vegetation Index ◽  
Surface Soil ◽  
Cloud Model ◽  
Incidence Angle ◽  
Permafrost Region ◽  
Surface Soil Moisture ◽  
The Impact

Surface soil moisture (SSM), the average water content of surface soil (up to 5 cm depth), plays a key role in the energy exchange within the ecosystem. We estimated SSM in areas with vegetation cover (grassland) by combining microwave and optical satellite measurements in the central Tibetan Plateau (TP) in 2015. We exploited TERRA moderate resolution imaging spectroradiometer (MODIS) and Sentinel-1A synthetic aperture radar (SAR) observations to estimate SSM through a simplified water-cloud model (sWCM). This model considers the impact of vegetation water content (VWC) to SSM retrieval by integrating the vegetation index (VI), the normalized difference water index (NDWI), or the normalized difference infrared index (NDII). Sentinel-1 SAR C-band backscattering coefficients, incidence angle, and NDWI/NDII were assimilated in the sWCM to monitor SSM. The soil moisture and temperature monitoring network on the central TP (CTP-SMTMN) measures SSM within the study area, and ground measurements were applied to train and validate the model. Via the proposed methods, we estimated the SSM in vegetated area with an R2 of 0.43 and a ubRMSE of 0.06 m3/m3 when integrating the NDWI and with an R2 of 0.45 and a ubRMSE of 0.06 m3/m3 when integrating the NDII.

scholarly journals Impact of aerosols on the OMI tropospheric NO<sub>2</sub> retrievals over industrialized regions: how accurate is the aerosol correction of cloud-free scenes via a simple cloud model?

2016 ◽  
Vol 9 (2) ◽  
pp. 359-382 ◽  
Author(s):  
J. Chimot ◽  
T. Vlemmix ◽  
J. P. Veefkind ◽  
J. F. de Haan ◽  
P. F. Levelt
Keyword(s):  
Fine Particles ◽  
Cloud Model ◽  
Satellite Observation ◽  
Retrieval Algorithm ◽  
Ozone Monitoring Instrument ◽  
Complex Behaviour ◽  
Cloud Parameters ◽  
Aerosol Pollution ◽  
Tropospheric No2 ◽  
The Impact

Abstract. The Ozone Monitoring Instrument (OMI) has provided daily global measurements of tropospheric NO2 for more than a decade. Numerous studies have drawn attention to the complexities related to measurements of tropospheric NO2 in the presence of aerosols. Fine particles affect the OMI spectral measurements and the length of the average light path followed by the photons. However, they are not explicitly taken into account in the current operational OMI tropospheric NO2 retrieval chain (DOMINO – Derivation of OMI tropospheric NO2) product. Instead, the operational OMI O2 − O2 cloud retrieval algorithm is applied both to cloudy and to cloud-free scenes (i.e. clear sky) dominated by the presence of aerosols. This paper describes in detail the complex interplay between the spectral effects of aerosols in the satellite observation and the associated response of the OMI O2 − O2 cloud retrieval algorithm. Then, it evaluates the impact on the accuracy of the tropospheric NO2 retrievals through the computed Air Mass Factor (AMF) with a focus on cloud-free scenes. For that purpose, collocated OMI NO2 and MODIS (Moderate Resolution Imaging Spectroradiometer) Aqua aerosol products are analysed over the strongly industrialized East China area. In addition, aerosol effects on the tropospheric NO2 AMF and the retrieval of OMI cloud parameters are simulated. Both the observation-based and the simulation-based approach demonstrate that the retrieved cloud fraction increases with increasing Aerosol Optical Thickness (AOT), but the magnitude of this increase depends on the aerosol properties and surface albedo. This increase is induced by the additional scattering effects of aerosols which enhance the scene brightness. The decreasing effective cloud pressure with increasing AOT primarily represents the shielding effects of the O2 − O2 column located below the aerosol layers. The study cases show that the aerosol correction based on the implemented OMI cloud model results in biases between −20 and −40 % for the DOMINO tropospheric NO2 product in cases of high aerosol pollution (AOT  ≥ 0.6) at elevated altitude. These biases result from a combination of the cloud model error, used in the presence of aerosols, and the limitations of the current OMI cloud Look-Up-Table (LUT). A new LUT with a higher sampling must be designed to remove the complex behaviour between these biases and AOT. In contrast, when aerosols are relatively close to the surface or mixed with NO2, aerosol correction based on the cloud model results in an overestimation of the DOMINO tropospheric NO2 column, between 10 and 20 %. These numbers are in line with comparison studies between ground-based and OMI tropospheric NO2 measurements in the presence of high aerosol pollution and particles located at higher altitudes. This highlights the need to implement an improved aerosol correction in the computation of tropospheric NO2 AMFs.

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