scholarly journals A global analysis on the view-angle dependence of plane-parallel oceanic liquid water cloud optical thickness using data synergy from MISR and MODIS

2013 ◽  
Vol 118 (5) ◽  
pp. 2389-2403 ◽  
Author(s):  
Lusheng Liang ◽  
Larry Di Girolamo
Keyword(s):  
Optical Thickness ◽  
Liquid Water ◽  
Global Analysis ◽  
Angle Dependence ◽  
Plane Parallel ◽  
Cloud Optical Thickness ◽  
Water Cloud ◽  
Using Data ◽  
Data Synergy

scholarly journals A spectral method for discriminating thermodynamic phase and retrieving cloud optical thickness and effective radius using transmitted solar radiance spectra

2015 ◽  
Vol 8 (3) ◽  
pp. 1361-1383 ◽  
Author(s):  
S. E. LeBlanc ◽  
P. Pilewskie ◽  
K. S. Schmidt ◽  
O. Coddington
Keyword(s):  
Root Mean Square ◽  
Optical Thickness ◽  
Liquid Water ◽  
Effective Radius ◽  
Least Square ◽  
Mean Square ◽  
Ice Cloud ◽  
Cloud Optical Thickness ◽  
Water Cloud ◽  
Thermodynamic Phase

Abstract. A new retrieval scheme for cloud optical thickness, effective radius, and thermodynamic phase was developed for ground-based measurements of cloud shortwave solar spectral transmittance. Fifteen parameters were derived to quantify spectral variations in shortwave transmittance due to absorption and scattering of liquid water and ice clouds, manifested by shifts in spectral slopes, curvatures, maxima, and minima. To retrieve cloud optical thickness and effective particle radius, a weighted least square fit that matched the modeled parameters was applied. The measurements for this analysis were made with the ground-based Solar Spectral Flux Radiometer in Boulder, Colorado, between May 2012 and January 2013. We compared the cloud optical thickness and effective radius from the new retrieval to two other retrieval methods. By using multiple spectral features, we find a closer fit (with a root mean square difference over the entire spectra of 3.1% for a liquid water cloud and 5.9% for an ice cloud) between measured and modeled spectra compared to two other retrieval methods which diverge by a root mean square of up to 6.4% for a liquid water cloud and 22.5% for an ice cloud. The new retrieval introduced here has an average uncertainty in effective radius (± 1.2 μm) smaller by factor of at least 2.5 than two other methods when applied to an ice cloud.

scholarly journals Angular Variability of the Liquid Water Cloud Optical Thickness Retrieved from ADEOS–POLDER

2001 ◽  
Vol 58 (20) ◽  
pp. 3007-3018 ◽  
Author(s):  
Jean-Claude Buriez ◽  
Marie Doutriaux-Boucher ◽  
Frédéric Parol ◽  
Norman G. Loeb
Keyword(s):  
Optical Thickness ◽  
Liquid Water ◽  
Cloud Optical Thickness ◽  
Water Cloud

scholarly journals A generalized method for discriminating thermodynamic phase and retrieving cloud optical thickness and effective radius using transmitted shortwave radiance spectra

2014 ◽  
Vol 7 (6) ◽  
pp. 5293-5346 ◽  
Author(s):  
S. E. LeBlanc ◽  
P. Pilewskie ◽  
K. S. Schmidt ◽  
O. Coddington
Keyword(s):  
Root Mean Square ◽  
Optical Thickness ◽  
Liquid Water ◽  
Effective Radius ◽  
Least Square ◽  
Mean Square ◽  
Ice Cloud ◽  
Cloud Optical Thickness ◽  
Water Cloud ◽  
Thermodynamic Phase

Abstract. A new retrieval scheme for cloud optical thickness, effective radius, and thermodynamic phase was developed for ground-based measurements of cloud shortwave spectral transmittance. 15 parameters were derived to quantify spectral variations in shortwave transmittance due to absorption and scattering of liquid water and ice clouds, manifested by shifts in spectral slopes, curvatures, maxima, and minima. To retrieve cloud optical thickness and effective particle radius a weighted least square fit that matched the modeled parameters was applied. The measurements for this analysis were made with a ground-based Solar Spectral Flux Radiometer (SSFR) in Boulder, Colorado, between May 2012 and January 2013. We compared the cloud optical thickness and effective radius from the new retrieval to two other retrieval methods. By using multiple spectral features, we find a closer fit (with a root mean square difference over the entire spectra of 3.1% for a liquid water cloud and 5.9% for an ice cloud) between measured and modeled spectra compared to two other retrieval methods, which diverge by a root-mean-square of up to 6.4% for a liquid water cloud and 22.5% for an ice cloud. The new retrieval introduced here has an average uncertainty in effective radius (±1.2 μm) smaller by factor of at least 2.5 than two other methods when applied to an ice cloud.

scholarly journals Combined retrieval of Arctic liquid water cloud and surface snow properties using airborne spectral solar remote sensing

2017 ◽  
Vol 10 (9) ◽  
pp. 3215-3230 ◽  
Author(s):  
André Ehrlich ◽  
Eike Bierwirth ◽  
Larysa Istomina ◽  
Manfred Wendisch
Keyword(s):  
Remote Sensing ◽  
Grain Size ◽  
Sea Ice ◽  
Optical Thickness ◽  
Liquid Water ◽  
Retrieval Algorithm ◽  
Continuous Transition ◽  
Cloud Properties ◽  
Cloud Optical Thickness ◽  
The Impact

Abstract. The passive solar remote sensing of cloud properties over highly reflecting ground is challenging, mostly due to the low contrast between the cloud reflectivity and that of the underlying surfaces (sea ice and snow). Uncertainties in the retrieved cloud optical thickness τ and cloud droplet effective radius reff, C may arise from uncertainties in the assumed spectral surface albedo, which is mainly determined by the generally unknown effective snow grain size reff, S. Therefore, in a first step the effects of the assumed snow grain size are systematically quantified for the conventional bispectral retrieval technique of τ and reff, C for liquid water clouds. In general, the impact of uncertainties of reff, S is largest for small snow grain sizes. While the uncertainties of retrieved τ are independent of the cloud optical thickness and solar zenith angle, the bias of retrieved reff, C increases for optically thin clouds and high Sun. The largest deviations between the retrieved and true original values are found with 83 % for τ and 62 % for reff, C. In the second part of the paper a retrieval method is presented that simultaneously derives all three parameters (τ, reff, C, reff, S) and therefore accounts for changes in the snow grain size. Ratios of spectral cloud reflectivity measurements at the three wavelengths λ1 = 1040 nm (sensitive to reff, S), λ2 = 1650 nm (sensitive to τ), and λ3 = 2100 nm (sensitive to reff, C) are combined in a trispectral retrieval algorithm. In a feasibility study, spectral cloud reflectivity measurements collected by the Spectral Modular Airborne Radiation measurement sysTem (SMART) during the research campaign Vertical Distribution of Ice in Arctic Mixed-Phase Clouds (VERDI, April/May 2012) were used to test the retrieval procedure. Two cases of observations above the Canadian Beaufort Sea, one with dense snow-covered sea ice and another with a distinct snow-covered sea ice edge are analysed. The retrieved values of τ, reff, C, and reff, S show a continuous transition of cloud properties across snow-covered sea ice and open water and are consistent with estimates based on satellite data. It is shown that the uncertainties of the trispectral retrieval increase for high values of τ, and low reff, S but nevertheless allow the effective snow grain size in cloud-covered areas to be estimated.

scholarly journals Cloud-type dependencies of MODIS and AMSR-E liquid water path differences

2009 ◽  
Vol 9 (1) ◽  
pp. 3367-3399 ◽  
Author(s):  
M. de la Torre Juárez ◽  
B. H. Kahn ◽  
E. J. Fetzer
Keyword(s):  
Optical Thickness ◽  
Liquid Water ◽  
Near Infrared ◽  
Cloud Type ◽  
Liquid Water Path ◽  
Cloud Liquid Water ◽  
Water Path ◽  
Cloud Water Content ◽  
Cloud Optical Thickness ◽  
Cloud Top Temperature

Abstract. Comparisons of cloud liquid water path (LWP) retrievals are presented from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Microwave Scanning Radiometer (AMSR-E) located aboard the Aqua spacecraft. LWP differences as a function of cloud top height, cloud fraction, cloud top temperature, LWP, cloud effective radius and cloud optical thickness are quantified in most geophysical conditions. The assumption of vertically homogeneous distributions of cloud water content in the MODIS LWP retrieval yields a slightly poorer agreement than the assumption of stratified cloud liquid water. Furthermore, for a fixed cloud top pressure, the cloud top temperature can lead to sign changes in the LWP difference. In general, AMSR-E LWP is larger than MODIS for small cloud fractions, low values of LWP, and warmer cloud top temperatures. On the other hand, clouds with optical thicknesses above 20 lead to larger MODIS LWP. Using cloud optical thickness as a proxy for cloud type, deep convective clouds and stratus are shown to have the poorest agreement between AMSR-E and MODIS LWP. Particularly large differences are also found at latitudes poleward of 50°. The results of this work help characterize the scene- and cloud-dependent performance of microwave and visible/near infrared retrievals of LWP.

scholarly journals Bounded cascade clouds: albedo and effective thickness

1994 ◽  
Vol 1 (2/3) ◽  
pp. 156-167 ◽  
Author(s):  
R. F. Cahalan
Keyword(s):  
Optical Thickness ◽  
Liquid Water ◽  
Climate Models ◽  
Simple Expression ◽  
Effective Thickness ◽  
Liquid Water Path ◽  
Scaling Properties ◽  
Fractal Parameter ◽  
Plane Parallel ◽  
The Mean

Abstract. If climate models produced clouds having liquid water amounts close to those observed, they would compute a mean albedo that is often much too large, due to the treatment of clouds as plane-parallel. An approximate lower-bound for this "plane-parallel albedo bias" may be obtained from a fractal model having a range of optical thicknesses similar to those observed in marine stratocumulus, since they are more nearly plane-parallel than most other cloud types. We review and extend results from a model which produces a distribution of liquid water path having a lognormal-like probability density and a power-law wavenumber spectrum, with parameters determined by stratocumulus observations. As the spectral exponent approaches -1, the simulated cloud approaches a well-known multifractal, referred to as the "singular model", but when the exponent is -5/3, similar to what is observed, the cloud exhibits qualitatively different scaling properties, the socalled "bounded model". The mean albedo for bounded cascade clouds is a function of a fractal parameter, 0 << 1, as well as the usual plane-parallel parameters such as single scattering albedo, asymmetry, solar zenith angle, and mean vertical optical thickness. A simple expression is derived to determine from the variance of the logarithm of the vertically-integrated liquid water. The albedo is shown to be approximated well by the plane-parallel albedo of a cloud having an "effective" vertical optical thickness, smaller than the mean thickness by a factor χ(f), which is given as an analytic function of f. California stratocumulus have a mean fractal parameter (f) ≈ 0.5, relative albedo bias of 15%, and an effective thickness 30% smaller than the mean thickness (χ ≈ 0.7). For typical observed values of mean liquid water and (f), the effective thickness approximation gives a plane-parallel albedo within 3% of the mean albedo.

scholarly journals Long-Term Changes in Solar Shortwave Irradiance Due to Different Atmospheric Factors According to Measurements and Reconstruction Model in Northern Eurasia

2020 ◽  
Vol 4 (1) ◽  
pp. 5
Author(s):  
Elena Volpert ◽  
Natalia Chubarova
Keyword(s):  
Optical Thickness ◽  
Cloud Amount ◽  
Shortwave Radiation ◽  
Northern Eurasia ◽  
Factors Affecting ◽  
Meteorological Observatory ◽  
Cloud Optical Thickness ◽  
Reconstruction Model ◽  
Good Agreement

The temporal variability of solar shortwave radiation (SSR) has been assessed over northern Eurasia (40°–80° N; 10° W–180° E) by using an SSR reconstruction model since the middle of the 20th century. The reconstruction model estimates the year-to-year SSR variability as a sum of variations in SSR due to changes in aerosol, effective cloud amount and cloud optical thickness, which are the most effective factors affecting SSR. The retrievals of year-to-year SSR variations according to different factors were tested against long-term measurements in the Moscow State University Meteorological Observatory from 1968–2016. The reconstructed changes show a good agreement with measurements with determination factor R2 = 0.8. The analysis of SSR trends since 1979 has detected a significant growth of 2.5% per decade, which may be explained by its increase due to the change in cloud amount (+2.4% per decade) and aerosol optical thickness (+0.4% per decade). The trend due to cloud optical thickness was statistically insignificant. Using the SSR reconstruction model, we obtained the long-term SSR variability due to different factors for the territory of northern Eurasia. The increasing SSR trends have been detected on most sites since 1979. The long-term SSR variability over northern Eurasia is effectively explained by changes in cloud amount and, in addition, by changes in aerosol loading over the polluted regions. The retrievals of the SSR variations showed a good agreement with the changes in global radiance measurements from the World Radiation Data Center (WRDC) archive. The work was supported by RFBR grant number 18-05-00700.

scholarly journals Resolving ice cloud optical thickness biases between CALIOP and MODIS using infrared retrievals

2016 ◽  
Vol 16 (8) ◽  
pp. 5075-5090 ◽  
Author(s):  
Robert E. Holz ◽  
Steven Platnick ◽  
Kerry Meyer ◽  
Mark Vaughan ◽  
Andrew Heidinger ◽  
...  
Keyword(s):  
Optical Thickness ◽  
Spatial Scales ◽  
Radiative Properties ◽  
Radiation Balance ◽  
Data Set ◽  
Mean Values ◽  
Ice Cloud ◽  
Lidar Ratio ◽  
Cloud Optical Thickness ◽  
The Impact

Abstract. Despite its importance as one of the key radiative properties that determines the impact of upper tropospheric clouds on the radiation balance, ice cloud optical thickness (IOT) has proven to be one of the more challenging properties to retrieve from space-based remote sensing measurements. In particular, optically thin upper tropospheric ice clouds (cirrus) have been especially challenging due to their tenuous nature, extensive spatial scales, and complex particle shapes and light-scattering characteristics. The lack of independent validation motivates the investigation presented in this paper, wherein systematic biases between MODIS Collection 5 (C5) and CALIOP Version 3 (V3) unconstrained retrievals of tenuous IOT (< 3) are examined using a month of collocated A-Train observations. An initial comparison revealed a factor of 2 bias between the MODIS and CALIOP IOT retrievals. This bias is investigated using an infrared (IR) radiative closure approach that compares both products with MODIS IR cirrus retrievals developed for this assessment. The analysis finds that both the MODIS C5 and the unconstrained CALIOP V3 retrievals are biased (high and low, respectively) relative to the IR IOT retrievals. Based on this finding, the MODIS and CALIOP algorithms are investigated with the goal of explaining and minimizing the biases relative to the IR. For MODIS we find that the assumed ice single-scattering properties used for the C5 retrievals are not consistent with the mean IR COT distribution. The C5 ice scattering database results in the asymmetry parameter (g) varying as a function of effective radius with mean values that are too large. The MODIS retrievals have been brought into agreement with the IR by adopting a new ice scattering model for Collection 6 (C6) consisting of a modified gamma distribution comprised of a single habit (severely roughened aggregated columns); the C6 ice cloud optical property models have a constant g ≈ 0.75 in the mid-visible spectrum, 5–15 % smaller than C5. For CALIOP, the assumed lidar ratio for unconstrained retrievals is fixed at 25 sr for the V3 data products. This value is found to be inconsistent with the constrained (predominantly nighttime) CALIOP retrievals. An experimental data set was produced using a modified lidar ratio of 32 sr for the unconstrained retrievals (an increase of 28 %), selected to provide consistency with the constrained V3 results. These modifications greatly improve the agreement with the IR and provide consistency between the MODIS and CALIOP products. Based on these results the recently released MODIS C6 optical products use the single-habit distribution given above, while the upcoming CALIOP V4 unconstrained algorithm will use higher lidar ratios for unconstrained retrievals.

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