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- Author or Editor: Zhanqing Li x
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Abstract
This study investigated theoretically and experimentally two parameters employed in recent attempts to address cloud absorption anomaly. One is the ratio, R, of shortwave cloud radiative forcing (CRF) at the surface to that at the top of the atmosphere (TOA), and another is the slope, s, of the regressional relationship between TOA albedo and atmospheric transmittance. The physics and sensitivities of the two parameters were first examined by means of radiative transfer modeling. Neither R nor s is a direct measure of cloud absorption. However, R can indicate the effect of clouds on the atmospheric absorption of solar radiation, if clear-sky condition remains the same. A value of R > 1 implies clouds warm the atmosphere, while the converse is true for R < 1. Model simulations suggest that both R and s are sensitive to many factors, especially cloud height and surface condition. Nonetheless, modeled R rarely exceeds 1.25, and modeled s is generally less than −0.7, except for bright surfaces. The slope s can be related to R under certain conditions. Observational values of R and s were then determined using four years worth of global satellite and ground-based monthly mean solar flux data from the Earth Radiation Budget Experiment (ERBE) and the Global Surface Energy Balance Archive (GEBA). The ratio R is highly variable with both location and season and also shows strong interannual variability. Low to moderate values of R, attainable by plane-parallel radiative transfer models, tend to occur over relatively clean regions. Large values of R appear to associate with either heavy pollution in the midlatitudes or frequent occurrence of biomass burning in the Tropics. The large values of R in the Tropics are less reliable than the low and moderate R in the midlatitudes. While this study does not rule out cloud absorption anomaly, it does indicate, however, that its magnitude (if it exists) is not as large, and its occurrence not as widespread, as suggested in some recent reports.
Abstract
This study investigated theoretically and experimentally two parameters employed in recent attempts to address cloud absorption anomaly. One is the ratio, R, of shortwave cloud radiative forcing (CRF) at the surface to that at the top of the atmosphere (TOA), and another is the slope, s, of the regressional relationship between TOA albedo and atmospheric transmittance. The physics and sensitivities of the two parameters were first examined by means of radiative transfer modeling. Neither R nor s is a direct measure of cloud absorption. However, R can indicate the effect of clouds on the atmospheric absorption of solar radiation, if clear-sky condition remains the same. A value of R > 1 implies clouds warm the atmosphere, while the converse is true for R < 1. Model simulations suggest that both R and s are sensitive to many factors, especially cloud height and surface condition. Nonetheless, modeled R rarely exceeds 1.25, and modeled s is generally less than −0.7, except for bright surfaces. The slope s can be related to R under certain conditions. Observational values of R and s were then determined using four years worth of global satellite and ground-based monthly mean solar flux data from the Earth Radiation Budget Experiment (ERBE) and the Global Surface Energy Balance Archive (GEBA). The ratio R is highly variable with both location and season and also shows strong interannual variability. Low to moderate values of R, attainable by plane-parallel radiative transfer models, tend to occur over relatively clean regions. Large values of R appear to associate with either heavy pollution in the midlatitudes or frequent occurrence of biomass burning in the Tropics. The large values of R in the Tropics are less reliable than the low and moderate R in the midlatitudes. While this study does not rule out cloud absorption anomaly, it does indicate, however, that its magnitude (if it exists) is not as large, and its occurrence not as widespread, as suggested in some recent reports.
Abstract
Lack of calibrated radiation measurements at the top of the atmosphere (TOA) between major spaceborne radiation missions entails inference of the TOA radiation budget from operational weather sensors. The inferred data are subject to uncertainties due to calibration, narrow- to broadband conversion, etc. In this study, a surrogate TOA earth radiation budget product generated from GOES-7 (Geostationary Operational Environmental Satellite) imagery data for use in the U.S. Atmospheric Radiation Measurement (ARM) program was validated using measurements from the ScaRaB radiometer flown on board the METEOR-3/7 satellite. Comparisons were made between coincident and collocated shortwave and longwave radiative quantities derived from GOES and ScaRaB sensors over an ARM experimental locale in the South Great Plains of Oklahoma, during April and July 1994. The comparisons are proven to be instrumental in validating the calibration and narrow- to broadband conversion used to obtain broadband radiative quantities from GOES digital counts. Calibrations for both visible and infrared window channels have small uncertainties, whereas narrow- to broadband conversion of shortwave measurements contains large systematic errors. The caveat stems from use of a quadratic conversion equation instead of a linear one, as was found from ScaRaB narrow- and broadband measurements. The ensuing errors in the estimates of broadband albedo depend on scene brightness, underestimation for bright scenes, and overestimation for dark scenes. As a result, the magnitude of the TOA cloud radiative forcing is underestimated by about 14 W m−2 or 7.5% on a daytime mean basis. After correcting this error, the ratio of cloud radiative forcing (a measure of the impact of clouds on atmospheric absorption) derived from ARM measurements turns out to be 1.07, which is in even closer agreement with radiative transfer models than found from previous studies using original GOES products.
Abstract
Lack of calibrated radiation measurements at the top of the atmosphere (TOA) between major spaceborne radiation missions entails inference of the TOA radiation budget from operational weather sensors. The inferred data are subject to uncertainties due to calibration, narrow- to broadband conversion, etc. In this study, a surrogate TOA earth radiation budget product generated from GOES-7 (Geostationary Operational Environmental Satellite) imagery data for use in the U.S. Atmospheric Radiation Measurement (ARM) program was validated using measurements from the ScaRaB radiometer flown on board the METEOR-3/7 satellite. Comparisons were made between coincident and collocated shortwave and longwave radiative quantities derived from GOES and ScaRaB sensors over an ARM experimental locale in the South Great Plains of Oklahoma, during April and July 1994. The comparisons are proven to be instrumental in validating the calibration and narrow- to broadband conversion used to obtain broadband radiative quantities from GOES digital counts. Calibrations for both visible and infrared window channels have small uncertainties, whereas narrow- to broadband conversion of shortwave measurements contains large systematic errors. The caveat stems from use of a quadratic conversion equation instead of a linear one, as was found from ScaRaB narrow- and broadband measurements. The ensuing errors in the estimates of broadband albedo depend on scene brightness, underestimation for bright scenes, and overestimation for dark scenes. As a result, the magnitude of the TOA cloud radiative forcing is underestimated by about 14 W m−2 or 7.5% on a daytime mean basis. After correcting this error, the ratio of cloud radiative forcing (a measure of the impact of clouds on atmospheric absorption) derived from ARM measurements turns out to be 1.07, which is in even closer agreement with radiative transfer models than found from previous studies using original GOES products.
Abstract
This paper is concerned with uncertainties in the Advanced Very High Resolution Radiometer (AVHRR)-based retrieval of optical depth for heavy smoke aerosol plumes generated from forest fires that occurred in Canada due to a lack of knowledge on their optical properties (single-scattering albedo and asymmetry parameter). Typical values of the optical properties for smoke aerosols derived from such field experiments as Smoke, Clouds, and Radiation-Brazil (SCAR-B); Transport and Atmospheric Chemistry near the Equator-Atlantic (TRACE-A); Biomass Burning Airborne and Spaceborne Experiment in the Amazonas (BASE-A); and Boreal Ecosystem–Atmosphere Study (BOREAS) were first assumed for retrieving smoke optical depths. It is found that the maximum top-of-atmosphere (TOA) reflectance values calculated by models with these aerosol parameters are less than observations whose values are considerably higher. A successful retrieval would require an aerosol model that either has a substantially smaller asymmetry parameter (g < 0.4 versus g > 0.5), or higher single-scattering albedo (ω ≫ 0.9 versus ω < 0.9), or both (e.g., g = 0.39 and ω = 0.91 versus g = 0.57 and ω = 0.87) than the existing models. Several potential causes were examined including small smoke particle size, low black carbon content, humidity effect, calibration errors, inaccurate surface albedo, mixture of cloud and aerosol layers, etc. A more sound smoke aerosol model is proposed that has a lower content of black carbon (mass ratio = 0.015) and smaller size (mean radius = 0.02 μm for dry smoke particles), together with consideration of the effect of relative humidity. Ground-based observations of smoke suggest that for τ < 2.5 there is an increasing trend in ω and a decreasing trend in g with increases in τ, which is consistent with the results of satellite retrievals. Using these relationships as constraints, more plausible values of τ can be obtained for heavy smoke aerosol. The possibility of smoke–cloud mixtures is also considered, which can also lead to high TOA reflectances. However, without measurements, the hypothesis can be neither accepted nor negated. The study demonstrates that without independent assessments of the optical properties, large uncertainties would be incurred in the retrieved values of optical depth for heavy smoke aerosol plumes.
Abstract
This paper is concerned with uncertainties in the Advanced Very High Resolution Radiometer (AVHRR)-based retrieval of optical depth for heavy smoke aerosol plumes generated from forest fires that occurred in Canada due to a lack of knowledge on their optical properties (single-scattering albedo and asymmetry parameter). Typical values of the optical properties for smoke aerosols derived from such field experiments as Smoke, Clouds, and Radiation-Brazil (SCAR-B); Transport and Atmospheric Chemistry near the Equator-Atlantic (TRACE-A); Biomass Burning Airborne and Spaceborne Experiment in the Amazonas (BASE-A); and Boreal Ecosystem–Atmosphere Study (BOREAS) were first assumed for retrieving smoke optical depths. It is found that the maximum top-of-atmosphere (TOA) reflectance values calculated by models with these aerosol parameters are less than observations whose values are considerably higher. A successful retrieval would require an aerosol model that either has a substantially smaller asymmetry parameter (g < 0.4 versus g > 0.5), or higher single-scattering albedo (ω ≫ 0.9 versus ω < 0.9), or both (e.g., g = 0.39 and ω = 0.91 versus g = 0.57 and ω = 0.87) than the existing models. Several potential causes were examined including small smoke particle size, low black carbon content, humidity effect, calibration errors, inaccurate surface albedo, mixture of cloud and aerosol layers, etc. A more sound smoke aerosol model is proposed that has a lower content of black carbon (mass ratio = 0.015) and smaller size (mean radius = 0.02 μm for dry smoke particles), together with consideration of the effect of relative humidity. Ground-based observations of smoke suggest that for τ < 2.5 there is an increasing trend in ω and a decreasing trend in g with increases in τ, which is consistent with the results of satellite retrievals. Using these relationships as constraints, more plausible values of τ can be obtained for heavy smoke aerosol. The possibility of smoke–cloud mixtures is also considered, which can also lead to high TOA reflectances. However, without measurements, the hypothesis can be neither accepted nor negated. The study demonstrates that without independent assessments of the optical properties, large uncertainties would be incurred in the retrieved values of optical depth for heavy smoke aerosol plumes.
Abstract
Deep convective clouds (DCCs) are an important player in the climate system. In this paper the authors use remote sensing data mainly from the Moderate Resolution Imaging Spectroradiometer (MODIS) cloud product to investigate a few general cloud macro- and microphysical properties of DCCs. This investigation concentrates on the tallest convective clouds and associated thick anvils that are labeled “deep convective clouds.” General geographical patterns of DCCs from MODIS data are consistent with previous studies. By examining statistics of optical properties of DCCs over different locations of the globe, it is found that cloud optical depth distribution for DCCs shows little interannual variability for individual regions. These distributions, however, change with geographical regions. DCC ice particle size varies with surface elevation and cloud brightness temperature. DCCs that develop over elevated areas tend to have smaller ice particles at cloud top. There is a positive correlation between ice particle size and brightness temperature. The slope of this correlation has significant regional variations, which can be explained either with a simple thermodynamic consideration or with homogeneous freezing of aerosols. The findings have important implications in studying radiation budget, ice cloud microphysics parameterization, and troposphere–stratosphere water vapor exchange.
Abstract
Deep convective clouds (DCCs) are an important player in the climate system. In this paper the authors use remote sensing data mainly from the Moderate Resolution Imaging Spectroradiometer (MODIS) cloud product to investigate a few general cloud macro- and microphysical properties of DCCs. This investigation concentrates on the tallest convective clouds and associated thick anvils that are labeled “deep convective clouds.” General geographical patterns of DCCs from MODIS data are consistent with previous studies. By examining statistics of optical properties of DCCs over different locations of the globe, it is found that cloud optical depth distribution for DCCs shows little interannual variability for individual regions. These distributions, however, change with geographical regions. DCC ice particle size varies with surface elevation and cloud brightness temperature. DCCs that develop over elevated areas tend to have smaller ice particles at cloud top. There is a positive correlation between ice particle size and brightness temperature. The slope of this correlation has significant regional variations, which can be explained either with a simple thermodynamic consideration or with homogeneous freezing of aerosols. The findings have important implications in studying radiation budget, ice cloud microphysics parameterization, and troposphere–stratosphere water vapor exchange.
Abstract
Narrowband (NB) to broadband (BB) conversion is a common practice to acquire radiation budget data from operational imagery data. This study attempts to gain further insights into the relationship between NB visible (VIS) albedo and BB shortwave (SW) albedo by means of observational analysis and radiative transfer modeling. Multiple observation datasets were employed including Scanner for Radiation Budget (ScaRaB) satellite measurements, National Centers for Environmental Prediction–National Center for Atmospheric Research reanalysis of precipitable water and temperature profiles, Total Ozone Mapping Spectrometer ozone amount, etc. Radiative transfer modeling was done with an adding–doubling model of high spectral resolution for a range of surface, atmospheric, and cloud conditions. ScaRaB provided calibrated synergistic measurements of VIS and SW albedos. The two types of albedos were found to be linearly correlated with much higher correlation coefficients than previously obtained from other instruments. In combination with other datasets, the impact of various parameters on the VIS–SW relation was investigated and compared with the results of modeling. The most significant parameter influencing the relation is the solar zenith angle, followed by cloud-top height, precipitable water amount, ozone amount, aerosol, and cloud microphysics. Narrow- to broadband conversion models with a varying number of input parameters were developed and validated.
Abstract
Narrowband (NB) to broadband (BB) conversion is a common practice to acquire radiation budget data from operational imagery data. This study attempts to gain further insights into the relationship between NB visible (VIS) albedo and BB shortwave (SW) albedo by means of observational analysis and radiative transfer modeling. Multiple observation datasets were employed including Scanner for Radiation Budget (ScaRaB) satellite measurements, National Centers for Environmental Prediction–National Center for Atmospheric Research reanalysis of precipitable water and temperature profiles, Total Ozone Mapping Spectrometer ozone amount, etc. Radiative transfer modeling was done with an adding–doubling model of high spectral resolution for a range of surface, atmospheric, and cloud conditions. ScaRaB provided calibrated synergistic measurements of VIS and SW albedos. The two types of albedos were found to be linearly correlated with much higher correlation coefficients than previously obtained from other instruments. In combination with other datasets, the impact of various parameters on the VIS–SW relation was investigated and compared with the results of modeling. The most significant parameter influencing the relation is the solar zenith angle, followed by cloud-top height, precipitable water amount, ozone amount, aerosol, and cloud microphysics. Narrow- to broadband conversion models with a varying number of input parameters were developed and validated.
Response of Marine Boundary Layer Cloud Properties to Aerosol Perturbations Associated with Meteorological Conditions from the 19-Month AMF-Azores CampaignAbstract
This study investigates the response of marine boundary layer (MBL) cloud properties to aerosol loading by accounting for the contributions of large-scale dynamic and thermodynamic conditions and quantifies the first indirect effect (FIE). It makes use of 19-month measurements of aerosols, clouds, and meteorology acquired during the Atmospheric Radiation Measurement Mobile Facility field campaign over the Azores. Cloud droplet number concentrations 
Abstract
This study investigates the response of marine boundary layer (MBL) cloud properties to aerosol loading by accounting for the contributions of large-scale dynamic and thermodynamic conditions and quantifies the first indirect effect (FIE). It makes use of 19-month measurements of aerosols, clouds, and meteorology acquired during the Atmospheric Radiation Measurement Mobile Facility field campaign over the Azores. Cloud droplet number concentrations
Abstract
A new technique for estimating broadband reflectance from Advanced Very High-Resolution Radiometer (AVHRR) narrowband reflectances in channel 1 and 2 is developed. The data used are simultaneous and coincident narrowband and broadband measurements made by the AVHRR and Earth Radiation Budget Experiment (ERBE) radiometers aboard NOAA-9 during four days in July 1985 in the region north of 60°N. The limitations and inefficiency of classical regressional methods when applied to datasets with high spatial auto-correlation, which is often the case for remotely sensed data, are discussed. A statistical variable, Moran's I, is introduced, which is specifically designed for testing against a null hypothesis of spatial independence. On the basis of Moran's I and a correlogram analysis of the spatial autocorrelation of measured reflectances, the data are sampled to provide a spatially independent dataset. In addition to sampling, the data are also screened with respect to spatial homogeneity. Both scene-dependent and scene-independent regressional models are developed that are based on these spatially independent datasets. The rms errors of the predicted broadband reflectance are found to be 1.0, 1.8, 2.0, and 3.1 for the ocean, land, ice-snow, and cloud data, respectively. The effects of scene discrimination and solar and viewing geometry on the regressions are investigated, and comparisons are made between two-channel and single-channel models. The use of two solar channels is found to give a significant improvement in the predicted broadband reflectance for datasets in which there is no scene discrimination, a small improvement for measurements over land, and no improvement for the other homogeneous scene types. Geometric factors are found to have no significant effect on the regressions.
Abstract
A new technique for estimating broadband reflectance from Advanced Very High-Resolution Radiometer (AVHRR) narrowband reflectances in channel 1 and 2 is developed. The data used are simultaneous and coincident narrowband and broadband measurements made by the AVHRR and Earth Radiation Budget Experiment (ERBE) radiometers aboard NOAA-9 during four days in July 1985 in the region north of 60°N. The limitations and inefficiency of classical regressional methods when applied to datasets with high spatial auto-correlation, which is often the case for remotely sensed data, are discussed. A statistical variable, Moran's I, is introduced, which is specifically designed for testing against a null hypothesis of spatial independence. On the basis of Moran's I and a correlogram analysis of the spatial autocorrelation of measured reflectances, the data are sampled to provide a spatially independent dataset. In addition to sampling, the data are also screened with respect to spatial homogeneity. Both scene-dependent and scene-independent regressional models are developed that are based on these spatially independent datasets. The rms errors of the predicted broadband reflectance are found to be 1.0, 1.8, 2.0, and 3.1 for the ocean, land, ice-snow, and cloud data, respectively. The effects of scene discrimination and solar and viewing geometry on the regressions are investigated, and comparisons are made between two-channel and single-channel models. The use of two solar channels is found to give a significant improvement in the predicted broadband reflectance for datasets in which there is no scene discrimination, a small improvement for measurements over land, and no improvement for the other homogeneous scene types. Geometric factors are found to have no significant effect on the regressions.
Abstract
The concept of cloud radiative forcing (CRF) has been widely employed in studying the effects of clouds on the earth’s radiation budget and climate. CRF denotes, in principle, the net influence of cloud alone on the radiation budget of a system. In practice, however, observational determination of CRF is fraught with uncertainties due to factors other than cloud that induce changes in atmospheric background conditions. The most notable variables include aerosol, water vapor, and the data sampling scheme. The impact of these factors on the derivation of CRF and cloud absorption is investigated here by means of modeling and analysis of multiple datasets. Improved estimation of CRF is attempted at the top of the atmosphere (TOA) and at the surface from spatially and temporally collocated ground and satellite measurements for broadband shortwave fluxes. Satellite data employed include pixel measurements from ERBE (1988–90), ScaRaB (1994–95), and CERES (1998), as well as surface data acquired across the Canadian radiation network, the ARM Central Facility site in Oklahoma, the US/NOAA SURFRAD networks, and the world BSRN (WMO) networks. It is found that surface CRF is much more susceptible to the variability in background conditions than TOA CRF. Selection of overly clear sky conditions often leads to significant overestimation of surface CRF, but TOA CRF remains intact or only slightly affected. As a result, the ratio of CRF at the surface and TOA is prone to overestimation. With careful treatments of these effects, the CRF ratio turns out to vary mostly between 0.9 and 1.1, implying approximately the same magnitude of atmospheric absorption under clear-sky and cloudy-sky conditions.
Abstract
The concept of cloud radiative forcing (CRF) has been widely employed in studying the effects of clouds on the earth’s radiation budget and climate. CRF denotes, in principle, the net influence of cloud alone on the radiation budget of a system. In practice, however, observational determination of CRF is fraught with uncertainties due to factors other than cloud that induce changes in atmospheric background conditions. The most notable variables include aerosol, water vapor, and the data sampling scheme. The impact of these factors on the derivation of CRF and cloud absorption is investigated here by means of modeling and analysis of multiple datasets. Improved estimation of CRF is attempted at the top of the atmosphere (TOA) and at the surface from spatially and temporally collocated ground and satellite measurements for broadband shortwave fluxes. Satellite data employed include pixel measurements from ERBE (1988–90), ScaRaB (1994–95), and CERES (1998), as well as surface data acquired across the Canadian radiation network, the ARM Central Facility site in Oklahoma, the US/NOAA SURFRAD networks, and the world BSRN (WMO) networks. It is found that surface CRF is much more susceptible to the variability in background conditions than TOA CRF. Selection of overly clear sky conditions often leads to significant overestimation of surface CRF, but TOA CRF remains intact or only slightly affected. As a result, the ratio of CRF at the surface and TOA is prone to overestimation. With careful treatments of these effects, the CRF ratio turns out to vary mostly between 0.9 and 1.1, implying approximately the same magnitude of atmospheric absorption under clear-sky and cloudy-sky conditions.
Abstract
The frequent occurrence of high cirrus overlapping low water cloud poses a major challenge in retrieving their optical properties from spaceborne sensors. This paper presents a novel retrieval method that takes full advantage of the satellite data from the Moderate Resolution Imaging Spectroradiometer (MODIS). The main objectives are identification of overlapped high cirrus and low water clouds and determination of their individual optical depths, top heights, and emissivities. The overlapped high cloud top is determined from the MODIS CO2-slicing retrieval and the underlying low cloud top is determined from the neighboring MODIS pixels that are identified as single-layer low clouds. The algorithm applies a dual-layer cloud radiative transfer model using initial cloud properties derived from the MODIS CO2-slicing channels and the visible (0.65 μm) and infrared (11 μm) window channels. An automated iterative procedure follows by adjusting the high cirrus and low water cloud optical depths until computed radiances from the dual-layer model match with observed radiances from both the visible and infrared channels. The algorithm is valid for both single-layer and dual-layer clouds with the cirrus optical depth <∼4 (emissivity <∼0.85). For more than two-layer clouds, its validity depends on the thickness of the upper-layer cloud. A preliminary validation is conducted by comparing against ground-based active remote sensing data. Pixel-by-pixel retrievals and error analyses are presented. It is demonstrated that retrievals based on a single-layer assumption can result in systematic biases in the retrieved cloud top and optical properties for overlapped clouds. Such biases can be removed or lessened considerably by applying the new algorithm.
Abstract
The frequent occurrence of high cirrus overlapping low water cloud poses a major challenge in retrieving their optical properties from spaceborne sensors. This paper presents a novel retrieval method that takes full advantage of the satellite data from the Moderate Resolution Imaging Spectroradiometer (MODIS). The main objectives are identification of overlapped high cirrus and low water clouds and determination of their individual optical depths, top heights, and emissivities. The overlapped high cloud top is determined from the MODIS CO2-slicing retrieval and the underlying low cloud top is determined from the neighboring MODIS pixels that are identified as single-layer low clouds. The algorithm applies a dual-layer cloud radiative transfer model using initial cloud properties derived from the MODIS CO2-slicing channels and the visible (0.65 μm) and infrared (11 μm) window channels. An automated iterative procedure follows by adjusting the high cirrus and low water cloud optical depths until computed radiances from the dual-layer model match with observed radiances from both the visible and infrared channels. The algorithm is valid for both single-layer and dual-layer clouds with the cirrus optical depth <∼4 (emissivity <∼0.85). For more than two-layer clouds, its validity depends on the thickness of the upper-layer cloud. A preliminary validation is conducted by comparing against ground-based active remote sensing data. Pixel-by-pixel retrievals and error analyses are presented. It is demonstrated that retrievals based on a single-layer assumption can result in systematic biases in the retrieved cloud top and optical properties for overlapped clouds. Such biases can be removed or lessened considerably by applying the new algorithm.
Abstract
This study investigates and accounts for the influence of various ice cloud parameters on the retrieval of the surface solar radiation budget (SSRB) from reflected flux at the top of the atmosphere (TOA). The optical properties of ice clouds depend on ice crystal shape, size distribution, water content, and the vertical profiles of geometric and microphysical structure. As a result, the relationship between the SSRB and TOA-reflected flux for an ice cloud atmosphere is more complex and differs from that for water cloud and cloudless atmospheres. The sensitivities of the relationship between the SSRB and TOA-reflected flux are examined with respect to various ice cloud parameters. Uncertainties in the retrieval of the SSRB due to inadequate knowledge of various ice cloud parameters are evaluated thoroughly. The uncertainty study is concerned with both pure ice clouds and multiphase clouds (ice cloud above water cloud). According to the magnitudes of errors in the SSRB retrieval caused by different input variables, parameterized correction terms were introduced. If the input variables are known accurately, errors in the retrieval of the SSRB under a wide range of ice cloud conditions are expected to diminish substantially, to less than 10 W m−2 for 91% of the simulated ice cloud cases. In comparison, the same accuracy may be attained for only 19% of the retrievals for the same ice cloud cases using the retrieval algorithm designed for non-ice-cloud conditions.
Abstract
This study investigates and accounts for the influence of various ice cloud parameters on the retrieval of the surface solar radiation budget (SSRB) from reflected flux at the top of the atmosphere (TOA). The optical properties of ice clouds depend on ice crystal shape, size distribution, water content, and the vertical profiles of geometric and microphysical structure. As a result, the relationship between the SSRB and TOA-reflected flux for an ice cloud atmosphere is more complex and differs from that for water cloud and cloudless atmospheres. The sensitivities of the relationship between the SSRB and TOA-reflected flux are examined with respect to various ice cloud parameters. Uncertainties in the retrieval of the SSRB due to inadequate knowledge of various ice cloud parameters are evaluated thoroughly. The uncertainty study is concerned with both pure ice clouds and multiphase clouds (ice cloud above water cloud). According to the magnitudes of errors in the SSRB retrieval caused by different input variables, parameterized correction terms were introduced. If the input variables are known accurately, errors in the retrieval of the SSRB under a wide range of ice cloud conditions are expected to diminish substantially, to less than 10 W m−2 for 91% of the simulated ice cloud cases. In comparison, the same accuracy may be attained for only 19% of the retrievals for the same ice cloud cases using the retrieval algorithm designed for non-ice-cloud conditions.
