Study of the effects of cloud microphysics on cloud optical depth parameterizations using an explicit cloud microphysical model

1993 ◽  
Author(s):  
Zena N. Kogan ◽  
Douglas K. Lilly ◽  
Yefim L. Kogan
Keyword(s):  
Optical Depth ◽  
Cloud Microphysics ◽  
Cloud Optical Depth

scholarly journals Statistical Analyses of Satellite Cloud Object Data from CERES. Part III: Comparison with Cloud-Resolving Model Simulations of Tropical Convective Clouds

2007 ◽  
Vol 64 (3) ◽  
pp. 762-785 ◽  
Author(s):  
Yali Luo ◽  
Kuan-Man Xu ◽  
Bruce A. Wielicki ◽  
Takmeng Wong ◽  
Zachary A. Eitzen
Keyword(s):  
Physical Properties ◽  
Optical Depth ◽  
Weather Prediction ◽  
Cloud Microphysics ◽  
Retrieval Algorithm ◽  
Medium Size ◽  
Size Category ◽  
Cloud Optical Depth ◽  
Large Size ◽  
Cloud Resolving Model

Abstract The present study evaluates the ability of a cloud-resolving model (CRM) to simulate the physical properties of tropical deep convective cloud objects identified from a Clouds and the Earth’s Radiant Energy System (CERES) data product. The emphasis of this study is the comparisons among the small-, medium-, and large-size categories of cloud objects observed during March 1998 and between the large-size categories of cloud objects observed during March 1998 (strong El Niño) and March 2000 (weak La Niña). Results from the CRM simulations are analyzed in a way that is consistent with the CERES retrieval algorithm and they are averaged to match the scale of the CERES satellite footprints. Cloud physical properties are analyzed in terms of their summary histograms for each category. It is found that there is a general agreement in the overall shapes of all cloud physical properties between the simulated and observed distributions. Each cloud physical property produced by the CRM also exhibits different degrees of disagreement with observations over different ranges of the property. The simulated cloud tops are generally too high and cloud-top temperatures are too low except for the large-size category of March 1998. The probability densities of the simulated top-of-the-atmosphere (TOA) albedos for all four categories are underestimated for high albedos, while those of cloud optical depth are overestimated at its lowest bin. These disagreements are mainly related to uncertainties in the cloud microphysics parameterization and inputs such as cloud ice effective size to the radiation calculation. Summary histograms of cloud optical depth and TOA albedo from the CRM simulations of the large-size category of cloud objects do not differ significantly between the March 1998 and 2000 periods, consistent with the CERES observations. However, the CRM is unable to reproduce the significant differences in the observed cloud-top height while it overestimates the differences in the observed outgoing longwave radiation and cloud-top temperature between the two periods. Comparisons between the CRM results and the observations for most parameters in March 1998 consistently show that both the simulations and observations have larger differences between the large- and small-size categories than between the large- and medium-size, or between the medium- and small-size categories. However, the simulated cloud properties do not change as much with size as observed. These disagreements are likely related to the spatial averaging of the forcing data and the mismatch in time and space between the numerical weather prediction model from which the forcing data are produced and the CERES observed cloud systems.

scholarly journals A 12-year long global record of optical depth of absorbing aerosols above the clouds derived from the OMI/OMACA algorithm

2018 ◽  
Vol 11 (10) ◽  
pp. 5837-5864 ◽  
Author(s):  
Hiren Jethva ◽  
Omar Torres ◽  
Changwoo Ahn
Keyword(s):  
Atlantic Ocean ◽  
Optical Depth ◽  
Frequency Of Occurrence ◽  
Cloud Optical Depth ◽  
Low Level ◽  
Aerosol Cloud ◽  
Aerosol Absorption ◽  
The World ◽  
Absorbing Aerosols

Abstract. Aerosol–cloud interaction continues to be one of the leading uncertain components of climate models, primarily due to the lack of adequate knowledge of the complex microphysical and radiative processes of the aerosol–cloud system. Situations when light-absorbing aerosols such as carbonaceous particles and windblown dust overlay low-level cloud decks are commonly found in several regions of the world. Contrary to the known cooling effects of these aerosols in cloud-free scenario over darker surfaces, an overlapping situation of the absorbing aerosols over the cloud can lead to a significant level of atmospheric absorption exerting a positive radiative forcing (warming) at the top of the atmosphere. We contribute to this topic by introducing a new global product of above-cloud aerosol optical depth (ACAOD) of absorbing aerosols retrieved from the near-UV observations made by the Ozone Monitoring Instrument (OMI) onboard NASA's Aura platform. Physically based on an unambiguous “color ratio” effect in the near-UV caused by the aerosol absorption above the cloud, the OMACA (OMI above-cloud aerosols) algorithm simultaneously retrieves the optical depths of aerosols and clouds under a prescribed state of the atmosphere. The OMACA algorithm shares many similarities with the two-channel cloud-free OMAERUV algorithm, including the use of AIRS carbon monoxide for aerosol type identification, CALIOP-based aerosol layer height dataset, and an OMI-based surface albedo database. We present the algorithm architecture, inversion procedure, retrieval quality flags, initial validation results, and results from a 12-year long OMI record (2005–2016) including global climatology of the frequency of occurrence, ACAOD, and aerosol-corrected cloud optical depth. A comparative analysis of the OMACA-retrieved ACAOD, collocated with equivalent accurate measurements from the HSRL-2 lidar for the ORACLES Phase I operation (August–September 2016), revealed a good agreement (R = 0.77, RMSE = 0.10). The long-term OMACA record reveals several important regions of the world, where the carbonaceous aerosols from the seasonal biomass burning and mineral dust originated over the continents are found to overlie low-level cloud decks with moderate (0.3 < ACAOD < 0.5, away from the sources) to higher levels of ACAOD (> 0.8 in the proximity to the sources), including the southeastern Atlantic Ocean, southern Indian Ocean, Southeast Asia, the tropical Atlantic Ocean off the coast of western Africa, and northern Arabian sea. No significant long-term trend in the frequency of occurrence of aerosols above the clouds and ACAOD is noticed when OMI observations that are free from the “row anomaly” throughout the operation are considered. If not accounted for, the effects of aerosol absorption above the clouds introduce low bias in the retrieval of cloud optical depth with a profound impact on increasing ACAOD and cloud brightness. The OMACA aerosol product from OMI presented in this paper offers a crucial missing piece of information from the aerosol loading above cloud that will help us to quantify the radiative effects of clouds when overlaid with aerosols and their resultant impact on cloud properties and climate.

Retrieval of Cloud Optical Depth: Synergies between Whole Sky Imagers and Radiative Transfer Modeling.

2021 ◽  
Author(s):  
Caterina Peris-Ferrús ◽  
José Luís Gómez-Amo ◽  
Francesco Scarlatti ◽  
Roberto Román ◽  
Claudia Emde ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Optical Depth ◽  
Cloud Optical Depth ◽  
Radiative Transfer Modeling

scholarly journals The interdependence of continental warm cloud properties derived from unexploited solar background signals in ground-based lidar measurements

2014 ◽  
Vol 14 (16) ◽  
pp. 8389-8401 ◽  
Author(s):  
J. C. Chiu ◽  
J. A. Holmes ◽  
R. J. Hogan ◽  
E. J. O'Connor
Keyword(s):  
Optical Depth ◽  
Effective Radius ◽  
Liquid Water Path ◽  
Cloud Optical Depth ◽  
Cloud Properties ◽  
Warm Cloud ◽  
Lidar Measurements ◽  
Atmospheric Radiation Measurement ◽  
Ground Based Lidar ◽  
Geometric Thickness

Abstract. We have extensively analysed the interdependence between cloud optical depth, droplet effective radius, liquid water path (LWP) and geometric thickness for stratiform warm clouds using ground-based observations. In particular, this analysis uses cloud optical depths retrieved from untapped solar background signals that are previously unwanted and need to be removed in most lidar applications. Combining these new optical depth retrievals with radar and microwave observations at the Atmospheric Radiation Measurement (ARM) Climate Research Facility in Oklahoma during 2005–2007, we have found that LWP and geometric thickness increase and follow a power-law relationship with cloud optical depth regardless of the presence of drizzle; LWP and geometric thickness in drizzling clouds can be generally 20–40% and at least 10% higher than those in non-drizzling clouds, respectively. In contrast, droplet effective radius shows a negative correlation with optical depth in drizzling clouds and a positive correlation in non-drizzling clouds, where, for large optical depths, it asymptotes to 10 μm. This asymptotic behaviour in non-drizzling clouds is found in both the droplet effective radius and optical depth, making it possible to use simple thresholds of optical depth, droplet size, or a combination of these two variables for drizzle delineation. This paper demonstrates a new way to enhance ground-based cloud observations and drizzle delineations using existing lidar networks.

Seasonal variability of cloud optical depth over northwestern China derived from CERES/MODIS satellite measurements

2008 ◽  
Vol 6 (6) ◽  
pp. 454-457 ◽  
Author(s):  
陈勇航 陈勇航 ◽  
Yonghang Chen Yonghang Chen ◽  
白鸿涛 白鸿涛 ◽  
Hongtao Bai Hongtao Bai ◽  
黄建平 黄建平 ◽  
...  
Keyword(s):  
Optical Depth ◽  
Seasonal Variability ◽  
Northwestern China ◽  
Satellite Measurements ◽  
Cloud Optical Depth

scholarly journals Combining visible and infrared radiometry and lidar data to test ice clouds optical properties

2010 ◽  
Vol 10 (3) ◽  
pp. 7215-7264
Author(s):  
A. Bozzo ◽  
T. Maestri ◽  
R. Rizzi
Keyword(s):  
High Resolution ◽  
Optical Depth ◽  
Near Infrared ◽  
Natural Variability ◽  
Ice Clouds ◽  
Cloud Optical Depth ◽  
Cloud Parameters ◽  
Modeling Methodology ◽  
Clear Cases ◽  
Spectral Data Analysis

Abstract. Measurements taken during the 2003 Pacific THORPEX Observing System Test (P-TOST) by the MODIS Airborne Simulator (MAS), the Scanning High-resolution Interferometer Sounder (S-HIS) and the Cloud Physics Lidar (CPL) are compared to simulations performed with a line-by-line and multiple scattering modeling methodology (LBLMS). Formerly used for infrared hyper-spectral data analysis, LBLMS has been extended to the visible and near infrared with the inclusion of surface bi-directional reflectance properties. A number of scenes are evaluated: two clear scenes, one with nadir geometry and one cross-track encompassing sun glint, and three cloudy scenes, all with nadir geometry. CPL data is used to estimate the particulate optical depth at 532 nm for the clear and cloudy scenes. Cloud optical depth is also retrieved from S-HIS infrared window radiances, and it agrees with CPL values, to within natural variability. MAS data are simulated convolving high resolution radiances. The paper discusses the results of the comparisons for the clear cases and for the three cloudy cases. LBLMS clear simulations agree with MAS data to within 20% in the shortwave (SW) and near infrared (NIR) spectrum and within 2 K in the infrared (IR) range. It is shown that cloudy sky simulations using cloud parameters retrieved from IR radiances systematically underestimate the measured radiance in the SW and NIR by nearly 50%, although the IR retrieved optical thickness agree with same measured by CPL. MODIS radiances measured from Terra are also compared to LBLMS simulations in cloudy conditions using retrieved cloud optical depth and effective radius from MODIS, to understand the origin for the observed discrepancies. It is shown that the simulations agree, to within natural variability, with measurements in selected MODIS SW bands. The paper dwells on a possible explanation of these contraddictory results, involving the phase function of ice particles in the shortwave.

scholarly journals The interdependence of continental warm cloud properties derived from unexploited solar background signal in ground-based lidar measurements

2014 ◽  
Vol 14 (7) ◽  
pp. 8963-8996
Author(s):  
J. C. Chiu ◽  
J. A. Holmes ◽  
R. J. Hogan ◽  
E. J. O'Connor
Keyword(s):  
Optical Depth ◽  
Effective Radius ◽  
Liquid Water Path ◽  
Background Signal ◽  
Cloud Optical Depth ◽  
Cloud Properties ◽  
Lidar Measurements ◽  
Atmospheric Radiation Measurement ◽  
Ground Based Lidar ◽  
Geometric Thickness

Abstract. We have extensively analysed the interdependence between cloud optical depth, droplet effective radius, liquid water path (LWP) and geometric thickness for stratiform warm clouds using ground-based observations. In particular, this analysis uses cloud optical depths retrieved from untapped solar background signal that is previously unwanted and needs to be removed in most lidar applications. Combining these new optical depth retrievals with radar and microwave observations at the Atmospheric Radiation Measurement (ARM) Climate Research Facility in Oklahoma during 2005–2007, we have found that LWP and geometric thickness increase and follow a power-law relationship with cloud optical depth regardless of the presence of drizzle; LWP and geometric thickness in drizzling clouds can be generally 20–40% and at least 10% higher than those in non-drizzling clouds, respectively. In contrast, droplet effective radius shows a negative correlation with optical depth in drizzling clouds, while it increases with optical depth and reaches an asymptote of 10 μm in non-drizzling clouds. This asymptotic behaviour in non-drizzling clouds is found in both droplet effective radius and optical depth, making it possible to use simple thresholds of optical depth, droplet size, or a combination of these two variables for drizzle delineation. This paper demonstrates a new way to enhance ground-based cloud observations and drizzle delineations using existing lidar networks.

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