scholarly journals The Plane-Parallel Albedo Bias of Liquid Clouds from MODIS Observations

2007 ◽  
Vol 20 (20) ◽  
pp. 5114-5125 ◽  
Author(s):  
Lazaros Oreopoulos ◽  
Robert F. Cahalan ◽  
Steven Platnick
Keyword(s):  
Optical Thickness ◽  
Radiative Forcing ◽  
Mean Value ◽  
Time Of Day ◽  
Effective Radius ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Plane Parallel ◽  
Cloud Properties ◽  
Horizontal Inhomogeneity

Abstract The authors present the global plane-parallel shortwave albedo bias of liquid clouds for two months, July 2003 and January 2004. The cloud optical properties necessary to perform the bias calculations come from the operational Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and MODIS Aqua level-3 datasets. These data, along with ancillary surface albedo and atmospheric information consistent with the MODIS retrievals, are inserted into a broadband shortwave radiative transfer model to calculate the fluxes at the atmospheric column boundaries. The plane-parallel homogeneous (PPH) calculations are based on the mean cloud properties, while independent column approximation (ICA) calculations are based either on 1D histograms of optical thickness or joint 2D histograms of optical thickness and effective radius. The (positive) PPH albedo bias is simply the difference between PPH and ICA albedo calculations. Two types of biases are therefore examined: 1) the bias due to the horizontal inhomogeneity of optical thickness alone (the effective radius is set to the grid mean value) and 2) the bias due to simultaneous variations of optical thickness and effective radius as derived from their joint histograms. The authors find that the global bias of albedo (liquid cloud portion of the grid boxes only) is ∼+0.03, which corresponds to roughly 8% of the global liquid cloud albedo and is only modestly sensitive to the inclusion of horizontal effective radius variability and time of day, but depends strongly on season and latitude. This albedo bias translates to ∼3–3.5 W m−2 of bias (stronger negative values) in the diurnally averaged global shortwave cloud radiative forcing, assuming homogeneous conditions for the fraction of the grid box not covered by liquid clouds; zonal values can be as high as 8 W m−2. Finally, the (positive) broadband atmospheric absorptance bias is about an order of magnitude smaller than the albedo bias. The substantial magnitude of the PPH bias underlines the importance of predicting subgrid variability in GCMs and accounting for its effects on cloud–radiation interactions.

scholarly journals Feasibility study of multi-pixel retrieval of optical thickness and droplet effective radius of inhomogeneous clouds using deep learning

2017 ◽  
Vol 10 (12) ◽  
pp. 4747-4759 ◽  
Author(s):  
Rintaro Okamura ◽  
Hironobu Iwabuchi ◽  
K. Sebastian Schmidt
Keyword(s):  
Deep Learning ◽  
Radiative Transfer ◽  
Optical Thickness ◽  
Cloud Droplet ◽  
Effective Radius ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Optical Remote Sensing ◽  
Transfer Effects ◽  
Cloud Properties

Abstract. Three-dimensional (3-D) radiative-transfer effects are a major source of retrieval errors in satellite-based optical remote sensing of clouds. The challenge is that 3-D effects manifest themselves across multiple satellite pixels, which traditional single-pixel approaches cannot capture. In this study, we present two multi-pixel retrieval approaches based on deep learning, a technique that is becoming increasingly successful for complex problems in engineering and other areas. Specifically, we use deep neural networks (DNNs) to obtain multi-pixel estimates of cloud optical thickness and column-mean cloud droplet effective radius from multispectral, multi-pixel radiances. The first DNN method corrects traditional bispectral retrievals based on the plane-parallel homogeneous cloud assumption using the reflectances at the same two wavelengths. The other DNN method uses so-called convolutional layers and retrieves cloud properties directly from the reflectances at four wavelengths. The DNN methods are trained and tested on cloud fields from large-eddy simulations used as input to a 3-D radiative-transfer model to simulate upward radiances. The second DNN-based retrieval, sidestepping the bispectral retrieval step through convolutional layers, is shown to be more accurate. It reduces 3-D radiative-transfer effects that would otherwise affect the radiance values and estimates cloud properties robustly even for optically thick clouds.

scholarly journals Retrieval of optical thickness and droplet effective radius of inhomogeneous clouds using deep learning

2017 ◽  
Author(s):  
Rintaro Okamura ◽  
Hironobu Iwabuchi ◽  
K. Sebastian Schmidt
Keyword(s):  
Deep Learning ◽  
Radiative Transfer ◽  
Optical Thickness ◽  
Three Dimensional ◽  
Cloud Droplet ◽  
Effective Radius ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Transfer Effects ◽  
Cloud Properties

Abstract. Three-dimensional (3D) radiative transfer effects are a major source of retrieval errors in satellite-based optical re- mote sensing of clouds. In this study, we present two retrieval methods based on deep learning. We use deep neural networks (DNNs) to retrieve multipixel estimates of cloud optical thickness and column-mean cloud droplet effective radius simultane- ously from multispectral, multipixel radiances. Cloud field data are obtained from large-eddy simulations, and a 3D radiative transfer model is employed to simulate upward radiances from clouds. The cloud and radiance data are used to train and test the DNNs. The proposed DNN-based retrieval is shown to be more accurate than the existing look-up table approach that assumes plane-parallel, homogeneous clouds. By using convolutional layers, the DNN method estimates cloud properties robustly, even for optically thick clouds, and can correct the 3D radiative transfer effects that would otherwise affect the radiance values.

scholarly journals Vertical profiles of droplet effective radius in shallow convective clouds

2011 ◽  
Vol 11 (10) ◽  
pp. 4633-4644 ◽  
Author(s):  
S. Zhang ◽  
H. Xue ◽  
G. Feingold
Keyword(s):  
Radiative Forcing ◽  
Effective Radius ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Liquid Water Path ◽  
Cumulus Clouds ◽  
Convective Clouds ◽  
Additional Influence ◽  
The Individual ◽  
Vertical Inhomogeneity

Abstract. Conventional satellite retrievals can only provide information on cloud-top droplet effective radius (re). Given the fact that cloud ensembles in a satellite snapshot have different cloud-top heights, Rosenfeld and Lensky (1998) used the cloud-top height and the corresponding cloud-top re from the cloud ensembles in the snapshot to construct a profile of re representative of that in the individual clouds. This study investigates the robustness of this approach in shallow convective clouds based on results from large-eddy simulations (LES) for clean (aerosol mixing ratio Na = 25 mg−1), intermediate (Na = 100 mg−1), and polluted (Na = 2000 mg−1) conditions. The cloud-top height and the cloud-top re from the modeled cloud ensembles are used to form a constructed re profile, which is then compared to the in-cloud re profiles. For the polluted and intermediate cases where precipitation is negligible, the constructed re profiles represent the in-cloud re profiles fairly well with a low bias (about 10 %). The method used in Rosenfeld and Lensky (1998) is therefore validated for nonprecipitating shallow cumulus clouds. For the clean, drizzling case, the in-cloud re can be very large and highly variable, and quantitative profiling based on cloud-top re is less useful. The differences in re profiles between clean and polluted conditions derived in this manner are however, distinct. This study also investigates the subadiabatic characteristics of the simulated cumulus clouds to reveal the effect of mixing on re and its evolution. Results indicate that as polluted and moderately polluted clouds develop into their decaying stage, the subadiabatic fraction fad becomes smaller, representing a higher degree of mixing, and re becomes smaller (~10 %) and more variable. However, for the clean case, smaller fad corresponds to larger re (and larger re variability), reflecting the additional influence of droplet collision-coalescence and sedimentation on re. Finally, profiles of the vertically inhomogeneous clouds as simulated by the LES and those of the vertically homogeneous clouds are used as input to a radiative transfer model to study the effect of cloud vertical inhomogeneity on shortwave radiative forcing. For clouds that have the same liquid water path, re of a vertically homogeneous cloud must be about 76–90 % of the cloud-top re of the vertically inhomogeneous cloud in order for the two clouds to have the same shortwave radiative forcing.

Estimation of Surface and Top-of-Atmosphere Shortwave Irradiance in Biomass-Burning Regions during SCAR-B

2000 ◽  
Vol 39 (10) ◽  
pp. 1742-1753 ◽  
Author(s):  
Sundar A. Christopher ◽  
Xiang Li ◽  
Ronald M. Welch ◽  
Jeffrey S. Reid ◽  
Peter V. Hobbs ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Biomass Burning ◽  
Optical Thickness ◽  
Radiative Forcing ◽  
Wavelength Dependence ◽  
Single Scattering ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Aerosol Radiative Forcing ◽  
Mean Square Errors

Abstract Using in situ measurements of aerosol optical properties and ground-based measurements of aerosol optical thickness (τs) during the Smoke, Clouds and Radiation—Brazil (SCAR-B) experiment, a four-stream broadband radiative transfer model is used to estimate the downward shortwave irradiance (DSWI) and top-of-atmosphere (TOA) shortwave aerosol radiative forcing (SWARF) in cloud-free regions dominated by smoke from biomass burning in Brazil. The calculated DSWI values are compared with broadband pyranometer measurements made at the surface. The results show that, for two days when near-coincident measurements of single-scattering albedo ω0 and τs are available, the root-mean-square errors between the measured and calculated DSWI for daytime data are within 30 W m−2. For five days during SCAR-B, however, when assumptions about ω0 have to be made and also when τs was significantly higher, the differences can be as large as 100 W m−2. At TOA, the SWARF per unit optical thickness ranges from −20 to −60 W m−2 over four major ecosystems in South America. The results show that τs and ω0 are the two most important parameters that affect DSWI calculations. For SWARF values, surface albedos also play an important role. It is shown that ω0 must be known within 0.05 and τs at 0.55 μm must be known to within 0.1 to estimate DSWI to within 20 W m−2. The methodology described in this paper could serve as a potential strategy for determining DSWI values in the presence of aerosols. The wavelength dependence of τs and ω0 over the entire shortwave spectrum is needed to improve radiative transfer calculations. If global retrievals of DSWI and SWARF from satellite measurements are to be performed in the presence of biomass-burning aerosols on a routine basis, a concerted effort should be made to develop methodologies for estimating ω0 and τs from satellite and ground-based measurements.

scholarly journals The shortwave radiative forcing bias of liquid and ice clouds from MODIS observations

2009 ◽  
Vol 9 (16) ◽  
pp. 5865-5875 ◽  
Author(s):  
L. Oreopoulos ◽  
S. Platnick ◽  
G. Hong ◽  
P. Yang ◽  
R. F. Cahalan
Keyword(s):  
Radiative Transfer ◽  
Optical Thickness ◽  
Radiative Forcing ◽  
Global Climate ◽  
Effective Radius ◽  
Negative Bias ◽  
Ice Clouds ◽  
Plane Parallel ◽  
The Absolute ◽  
The Difference

Abstract. We present an assessment of the plane-parallel bias of the shortwave cloud radiative forcing (SWCRF) of liquid and ice clouds at 1 deg scales using global MODIS (Terra and Aqua) cloud optical property retrievals for four months of the year 2005 representative of the meteorological seasons. The (negative) bias is estimated as the difference of SWCRF calculated using the Plane-Parallel Homogeneous (PPH) approximation and the Independent Column Approximation (ICA). PPH calculations use MODIS-derived gridpoint means while ICA calculations use distributions of cloud optical thickness and effective radius. Assisted by a broadband solar radiative transfer algorithm, we find that the absolute value of global SWCRF bias of liquid clouds at the top of the atmosphere is about 6 W m−2 for MODIS overpass times while the SWCRF bias for ice clouds is smaller in absolute terms by about 0.7 W m−2, but with stronger spatial variability. If effective radius variability is neglected and only optical thickness horizontal variations are accounted for, the absolute SWCRF biases increase by about 0.3–0.4 W m−2 on average. Marine clouds of both phases exhibit greater (more negative) SWCRF biases than continental clouds. Finally, morning (Terra)–afternoon (Aqua) differences in SWCRF bias are much more pronounced for ice clouds, up to about 15% (Aqua producing stronger negative bias) on global scales, with virtually all contribution to the difference coming from land areas. The substantial magnitude of the global SWCRF bias, which for clouds of both phases is collectively about 4 W m−2 for diurnal averages, should be considered a strong motivation for global climate modelers to accelerate efforts linking cloud schemes capable of subgrid condensate variability with appropriate radiative transfer schemes.

scholarly journals Vertical profiles of droplet effective radius in shallow convective clouds

2010 ◽  
Vol 10 (12) ◽  
pp. 30971-30998
Author(s):  
S. Zhang ◽  
H. Xue ◽  
G. Feingold
Keyword(s):  
Radiative Forcing ◽  
Effective Radius ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Vertical Profiles ◽  
Liquid Water Path ◽  
Cumulus Clouds ◽  
Convective Clouds ◽  
Additional Influence ◽  
Vertical Inhomogeneity

Abstract. Vertical profiles of droplet effective radius (re) in shallow convective clouds are investigated using results from large-eddy simulations (LES) for clean (aerosol mixing ratio Na=25 mg−1), intermediate (Na=100 mg−1), and polluted (Na=2000 mg−1) conditions. Cloud-top re for cloud populations comprising clouds with different heights and at different stages of their development are used to construct a vertical profile of re. For the polluted and intermediate cases where precipitation is negligible, the constructed re profiles represent the in-cloud re profiles fairly well with a low bias (about 10%). For the clean, drizzling case the in-cloud re can be very large and highly variable and profiling based on cloud-top re is less useful. The differences in re profiles between clean and polluted conditions derived in this manner are however, distinct. The subadiabatic characteristics of the simulated cumulus clouds are investigated to reveal the effect of mixing on re and its evolution. Results indicate that as polluted and moderately polluted clouds develop into their decaying stage, the subadiabatic fraction fad becomes smaller, representing a higher degree of mixing, and re becomes smaller (~10%) and more variable. However, for the clean case, smaller fad corresponds to larger re (and larger re variability), reflecting the additional influence of droplet collision-coalescence and sedimentation on re. Profiles of the vertically inhomogeneous clouds as simulated from the LES and those of the vertically homogeneous clouds are used as input to a radiative transfer model to study the effect of cloud vertical inhomogeneity on shortwave radiative forcing. For clouds that have the same liquid water path (LWP), re of a vertically homogeneous cloud must be about 76–90% of the cloud-top re of the vertically inhomogeneous cloud in order for the two clouds to have the same shortwave radiative forcing.

scholarly journals The shortwave radiative forcing bias of liquid and ice clouds from MODIS observations

2009 ◽  
Vol 9 (2) ◽  
pp. 10337-10366 ◽  
Author(s):  
L. Oreopoulos ◽  
S. E. Platnick ◽  
G. Hong ◽  
P. Yang ◽  
R. F. Cahalan
Keyword(s):  
Radiative Transfer ◽  
Optical Thickness ◽  
Radiative Forcing ◽  
Global Climate ◽  
Effective Radius ◽  
Negative Bias ◽  
Ice Clouds ◽  
Plane Parallel ◽  
The Absolute ◽  
The Difference

Abstract. We present an assessment of the plane-parallel bias of the shortwave cloud radiative forcing SWCRF of liquid and ice clouds at 1 deg scales using global MODIS (Terra and Aqua) cloud optical property retrievals for four months of 2005 representative of the meteorological seasons. The (negative) bias is estimated as the difference of SWCRF calculated using the Plane-Parallel Homogeneous (PPH) approximation and the Independent Column Approximation (ICA). PPH calculations require MODIS-derived gridpoint means while ICA calculations require distributions of cloud optical thickness and effective radius as well as ancillary surface albedo and atmospheric information consistent with the MODIS retrievals. With the aid of broadband solar radiative transfer algorithm we find that the absolute value of global SWCRF bias of liquid clouds at the top of the atmosphere is about 6 W m−2 for MODIS overpass times while the SWCRF bias for ice clouds is smaller in absolute terms by about 0.7 W m−2, but with stronger spatial variability. If effective radius variability is neglected and only optical thickness horizontal variations are accounted for, the absolute SWCRF biases increase by about 0.3–0.4 W m−2 on average. Marine clouds of both phases exhibit greater (more negative) SWCRF biases than continental clouds. Finally, morning (Terra)–afternoon (Aqua) differences in SWCRF bias are much more pronounced for ice than liquid clouds, up to about 15% (Aqua producing stronger negative bias) on global scales, with virtually all contribution to the difference coming from land areas. The substantial magnitude of the SWCRF bias, which for clouds of both phases is collectively about 4 W m−2 for diurnal averages, should be a strong motivation for global climate modelers to accelerate efforts linking cloud schemes capable of subgrid condensate variability with appropriate radiative transfer schemes.

scholarly journals Observation-Based Longwave Cloud Radiative Kernels Derived from the A-Train

2016 ◽  
Vol 29 (6) ◽  
pp. 2023-2040 ◽  
Author(s):  
Qing Yue ◽  
Brian H. Kahn ◽  
Eric J. Fetzer ◽  
Mathias Schreier ◽  
Sun Wong ◽  
...  
Keyword(s):  
Radiative Forcing ◽  
Single Layer ◽  
Cloud Fraction ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Clear Sky ◽  
Cloud Properties ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract The authors present a new method to derive both the broadband and spectral longwave observation-based cloud radiative kernels (CRKs) using cloud radiative forcing (CRF) and cloud fraction (CF) for different cloud types using multisensor A-Train observations and MERRA data collocated on the pixel scale. Both observation-based CRKs and model-based CRKs derived from the Fu–Liou radiative transfer model are shown. Good agreement between observation- and model-derived CRKs is found for optically thick clouds. For optically thin clouds, the observation-based CRKs show a larger radiative sensitivity at TOA to cloud-cover change than model-derived CRKs. Four types of possible uncertainties in the observed CRKs are investigated: 1) uncertainties in Moderate Resolution Imaging Spectroradiometer cloud properties, 2) the contributions of clear-sky changes to the CRF, 3) the assumptions regarding clear-sky thresholds in the observations, and 4) the assumption of a single-layer cloud. The observation-based CRKs show the TOA radiative sensitivity of cloud types to unit cloud fraction change as observed by the A-Train. Therefore, a combination of observation-based CRKs with cloud changes observed by these instruments over time will provide an estimate of the short-term cloud feedback by maintaining consistency between CRKs and cloud responses to climate variability.

Consistent retrieval of cloud/aerosol single scattering properties and surface reflectance

2021 ◽  
Author(s):  
Marta Luffarelli ◽  
Yves Govaerts
Keyword(s):  
Radiative Transfer ◽  
Optical Thickness ◽  
Solution Space ◽  
Single Scattering ◽  
Aerosol Optical Thickness ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Surface Reflectance ◽  
Earth System ◽  
System Components

<p>The CISAR (Combined Inversion of Surface and AeRosols) algorithm is exploited in the framework of the ESA Aerosol Climate Change Initiatiave (CCI) project, aiming at providing a set of atmospheric (cloud and aerosol) and surface reflectance products derived from S3A/SLSTR observations using the same radiative transfer physics and assumptions. CISAR is an advance algorithm developed by Rayference originally designed for the retrieval of aerosol single scattering properties and surface reflectance from both geostationary and polar orbiting satellite observations.  It is based on the inversion of a fast radiative transfer model (FASTRE). The retrieval mechanism allows a continuous variation of the aerosol and cloud single scattering properties in the solution space.</p><p> </p><p>Traditionally, different approaches are exploited to retrieve the different Earth system components, which could lead to inconsistent data sets. The simultaneous retrieval of different atmospheric and surface variables over any type of surface (including bright surfaces and water bodies) with the same forward model and inversion scheme ensures the consistency among the retrieved Earth system components. Additionally, pixels located in the transition zone between pure clouds and pure aerosols are often discarded from both cloud and aerosol algorithms. This “twilight zone” can cover up to 30% of the globe. A consistent retrieval of both cloud and aerosol single scattering properties with the same algorithm could help filling this gap.</p><p> </p><p>The CISAR algorithm aims at overcoming the need of an external cloud mask, discriminating internally between aerosol and cloud properties. This approach helps reducing the overestimation of aerosol optical thickness in cloud contaminated pixels. The surface reflectance product is delivered both for cloud-free and cloudy observations.  </p><p> </p><p>Global maps obtained from the processing of S3A/SLSTR observations will be shown. The SLSTR/CISAR products over events such as, for instance, the Australian fire in the last months of 2019, will be discussed in terms of aerosol optical thickness, aerosol-cloud discrimination and fine/coarse mode fraction.</p>

scholarly journals Quantifying the Dependence of Satellite Cloud Retrievals on Instrument Uncertainty

2017 ◽  
Vol 30 (17) ◽  
pp. 6959-6976 ◽  
Author(s):  
Yolanda L. Shea ◽  
Bruce A. Wielicki ◽  
Sunny Sun-Mack ◽  
Patrick Minnis
Keyword(s):  
Climate Change ◽  
Optical Thickness ◽  
Indirect Effect ◽  
Effective Temperature ◽  
Radiative Forcing ◽  
Cloud Fraction ◽  
Effective Radius ◽  
Aerosol Indirect Effect ◽  
Sources Of Uncertainty ◽  
Water Cloud

Cloud response to Earth’s changing climate is one of the largest sources of uncertainty among global climate model (GCM) projections. Two of the largest sources of uncertainty are the spread in equilibrium climate sensitivity (ECS) and uncertainty in radiative forcing due to uncertainty in the aerosol indirect effect. Satellite instruments with sufficient accuracy and on-orbit stability to detect climate change–scale trends in cloud properties will improve confidence in the understanding of the relationship between observed climate change and cloud property trends, thus providing information to better constrain ECS and radiative forcing. This study applies a climate change uncertainty framework to quantify the impact of measurement uncertainty on trend detection times for cloud fraction, effective temperature, optical thickness, and water cloud effective radius. Although GCMs generally agree that the total cloud feedback is positive, disagreement remains on its magnitude. With the climate uncertainty framework, it is demonstrated how stringent measurement uncertainty requirements for reflected solar and infrared satellite measurements enable improved constraint of SW and LW cloud feedbacks and the ECS by significantly reducing trend uncertainties for cloud fraction, optical thickness, and effective temperature. The authors also demonstrate improved constraint on uncertainty in the aerosol indirect effect by reducing water cloud effective radius trend uncertainty.

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