Search Results
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
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
An angular dependence model (ADM) describes the anisotropy in the reflectance field. ADMs are a key element in determining the top-of-the-atmosphere (TOA) albedos and radiative fluxes. This study utilizes 1-yr satellite data from the Scanner for Radiation Budget (ScaRaB) for overcast scenes to examine the variation of ADMs with cloud properties. Using ScaRaB shortwave (SW) overcast radiance measurements, an SW mean overcast ADM, similar to the Earth Radiation Budget Experiment (ERBE) ADM, was generated. Differences between the ScaRaB and ERBE overcast ADMs lead to biases of ∼0.01–0.04 in mean albedos inferred from specific angular bins. The largest biases are in the backward scattering direction. Overcast ADMs for the visible (VIS) wavelength were also generated using ScaRaB VIS measurements. They are very similar to, but a little smaller at large viewing angles and a little larger at nadir, than the SW overcast ADMs. To evaluate the effect of cloud properties on ADMs, ScaRaB overcast observations were further classified into thin, thick, warm, and cold cloud categories to generate four subsets of ADMs. The resulting ADMs for thin and thick clouds show opposite trends and deviate significantly from the overall mean ADM by more than 10%. Deviations from the mean ADM were also noted for the ADMs developed for warm water clouds and cold ice clouds. These deviations were attributed to the different scattering phase functions of water and ice particles and were compared with results from model simulations. Use of a single mean overcast ADM results in albedo biases of 0.01–0.04, relative to the use of specific ADMs for particular cloud types. The biases reduced to ∼0.005 when averaged over all cloud types and viewing geometry.
Abstract
An angular dependence model (ADM) describes the anisotropy in the reflectance field. ADMs are a key element in determining the top-of-the-atmosphere (TOA) albedos and radiative fluxes. This study utilizes 1-yr satellite data from the Scanner for Radiation Budget (ScaRaB) for overcast scenes to examine the variation of ADMs with cloud properties. Using ScaRaB shortwave (SW) overcast radiance measurements, an SW mean overcast ADM, similar to the Earth Radiation Budget Experiment (ERBE) ADM, was generated. Differences between the ScaRaB and ERBE overcast ADMs lead to biases of ∼0.01–0.04 in mean albedos inferred from specific angular bins. The largest biases are in the backward scattering direction. Overcast ADMs for the visible (VIS) wavelength were also generated using ScaRaB VIS measurements. They are very similar to, but a little smaller at large viewing angles and a little larger at nadir, than the SW overcast ADMs. To evaluate the effect of cloud properties on ADMs, ScaRaB overcast observations were further classified into thin, thick, warm, and cold cloud categories to generate four subsets of ADMs. The resulting ADMs for thin and thick clouds show opposite trends and deviate significantly from the overall mean ADM by more than 10%. Deviations from the mean ADM were also noted for the ADMs developed for warm water clouds and cold ice clouds. These deviations were attributed to the different scattering phase functions of water and ice particles and were compared with results from model simulations. Use of a single mean overcast ADM results in albedo biases of 0.01–0.04, relative to the use of specific ADMs for particular cloud types. The biases reduced to ∼0.005 when averaged over all cloud types and viewing geometry.
