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Abstract
We consider an approximation that is useful for solving the diffuse transmission and reflection problem when the phase function has a sharp forward peak. In this approximation the forward peak is truncated and compensated for by changing the volume scattering coefficient. It provides values for reflected and transmitted fluxes and intensities that are accurate to better than 1% for most angles and for widely differing values of the absorption and total optical depth.
Abstract
We consider an approximation that is useful for solving the diffuse transmission and reflection problem when the phase function has a sharp forward peak. In this approximation the forward peak is truncated and compensated for by changing the volume scattering coefficient. It provides values for reflected and transmitted fluxes and intensities that are accurate to better than 1% for most angles and for widely differing values of the absorption and total optical depth.
Abstract
Theoretical models of the Venus cloud layer are compared with observations in the U, B and V spectral regions. It is found that the models are sensitive to the detailed scattering properties of the particles. A model of a terrestrial type cloud containing spherical water droplets or ice particles with radii distributed around 4 μ provides good agreement with the observed phase curve of Venus, superior to that obtained in previously published calculations. There is a small disagreement with the observations at low phase angles, suggesting the particles may have a slightly higher index of refraction than for water. However, observations are sparse and uncertain at these angles and improved data are needed to resolve this point. The comparison with observations leads to the following conclusions: the particles in the cloud layer must be of micron-size or larger, and are highly transparent; highly reflective but opaque particles are excluded; and scattering properties of the cloud particles on Venus resemble those of water droplets, ice particles, or particles of transparent minerals such as quartz.
Abstract
Theoretical models of the Venus cloud layer are compared with observations in the U, B and V spectral regions. It is found that the models are sensitive to the detailed scattering properties of the particles. A model of a terrestrial type cloud containing spherical water droplets or ice particles with radii distributed around 4 μ provides good agreement with the observed phase curve of Venus, superior to that obtained in previously published calculations. There is a small disagreement with the observations at low phase angles, suggesting the particles may have a slightly higher index of refraction than for water. However, observations are sparse and uncertain at these angles and improved data are needed to resolve this point. The comparison with observations leads to the following conclusions: the particles in the cloud layer must be of micron-size or larger, and are highly transparent; highly reflective but opaque particles are excluded; and scattering properties of the cloud particles on Venus resemble those of water droplets, ice particles, or particles of transparent minerals such as quartz.
A simple method of finding the percentage frequency that areas near a target will be affected by radioactive fallout of a critical dose is presented. The target location, the climatological period, the fission yield, and a critical dose or dose rate must be specified. If the distribution of the actual effective winds is not available, the assumption of a circular normal distribution of the effective wind is made. This distribution can be obtained by C. E. P. Brooks' method or, if the points are numerous as in describing an areal frequency pattern, by a mechanical method described herein.
A simple method of finding the percentage frequency that areas near a target will be affected by radioactive fallout of a critical dose is presented. The target location, the climatological period, the fission yield, and a critical dose or dose rate must be specified. If the distribution of the actual effective winds is not available, the assumption of a circular normal distribution of the effective wind is made. This distribution can be obtained by C. E. P. Brooks' method or, if the points are numerous as in describing an areal frequency pattern, by a mechanical method described herein.
Abstract
A method is described for analyzing the feedback and synergistic contributions of temperature, water vapor, cloud cover, surface albedo and CO2 to the change in the radiation balance at the top of the atmosphere due to a perturbation in an annual-averaged zonal atmospheric climate model. The method is illustrated through analysis of a doubled CO2 experiment with the Lawrence Livermore. National Laboratory Statistical Dynamical Model (LLNL SDM). The method provides insight into the sensitivity of the model to feedback changes in individual parameters and how each parameter influences the effects of the others.
Abstract
A method is described for analyzing the feedback and synergistic contributions of temperature, water vapor, cloud cover, surface albedo and CO2 to the change in the radiation balance at the top of the atmosphere due to a perturbation in an annual-averaged zonal atmospheric climate model. The method is illustrated through analysis of a doubled CO2 experiment with the Lawrence Livermore. National Laboratory Statistical Dynamical Model (LLNL SDM). The method provides insight into the sensitivity of the model to feedback changes in individual parameters and how each parameter influences the effects of the others.
Abstract
Profile measurements of atmospheric water vapor, cirrus clouds, and carbon dioxide using the Raman Airborne Spectroscopic lidar (RASL) during ground-based, upward-looking tests are presented here. These measurements improve upon any previously demonstrated using Raman lidar. Daytime boundary layer profiling of water vapor mixing ratio up to an altitude of approximately 4 km under moist, midsummer conditions is performed with less than 5% random error using temporal and spatial resolution of 2 min and 60–210 m, respectively. Daytime cirrus cloud optical depth and extinction-to-backscatter ratio measurements are made using a 1-min average. The potential to simultaneously profile carbon dioxide and water vapor mixing ratio through the boundary layer and extending into the free troposphere during the nighttime is also demonstrated.
Abstract
Profile measurements of atmospheric water vapor, cirrus clouds, and carbon dioxide using the Raman Airborne Spectroscopic lidar (RASL) during ground-based, upward-looking tests are presented here. These measurements improve upon any previously demonstrated using Raman lidar. Daytime boundary layer profiling of water vapor mixing ratio up to an altitude of approximately 4 km under moist, midsummer conditions is performed with less than 5% random error using temporal and spatial resolution of 2 min and 60–210 m, respectively. Daytime cirrus cloud optical depth and extinction-to-backscatter ratio measurements are made using a 1-min average. The potential to simultaneously profile carbon dioxide and water vapor mixing ratio through the boundary layer and extending into the free troposphere during the nighttime is also demonstrated.
Abstract
By automatically tracking the sun, a four-channel solar radiometer was used to continuously measure optical depth and atmospheric water vapor. The design of this autotracking solar radiometer is presented to allow construction by the reader. A technique for calculating the precipitable water from the ratio of a water band to a nearby nonabsorbing band is discussed. Studies of the temporal variability of precipitable water and atmospheric optical depth at 0.610, 0.8730 and 1.04 μm are presented. There was good correlation between the optical depth measured using the autotracker and visibility determined from nearby National Weather Service Station data. However, much more temporal structure was evident in the autotracker data than in the visibility data. Cirrus clouds caused large changes in optical depth over short time periods. They appear to be the largest deleterious atmospheric effect over agricultural areas that are remote from urban pollution sources. Cirrus clouds also caused anomalously low estimates of precipitable water.
Abstract
By automatically tracking the sun, a four-channel solar radiometer was used to continuously measure optical depth and atmospheric water vapor. The design of this autotracking solar radiometer is presented to allow construction by the reader. A technique for calculating the precipitable water from the ratio of a water band to a nearby nonabsorbing band is discussed. Studies of the temporal variability of precipitable water and atmospheric optical depth at 0.610, 0.8730 and 1.04 μm are presented. There was good correlation between the optical depth measured using the autotracker and visibility determined from nearby National Weather Service Station data. However, much more temporal structure was evident in the autotracker data than in the visibility data. Cirrus clouds caused large changes in optical depth over short time periods. They appear to be the largest deleterious atmospheric effect over agricultural areas that are remote from urban pollution sources. Cirrus clouds also caused anomalously low estimates of precipitable water.
Abstract
Two datasets have been combined to demonstrate how the availability of more comprehensive datasets could serve to elucidate the shortwave radiative impact of clouds on both the atmospheric column and the surface. These datasets consist of two measurements of net downward shortwave radiation: one of near-surface measurements made at the Boulder Atmospheric Observatory tower, and the other of collocated top-of-the-atmosphere measurements from the Earth Radiation Budget Experiment. Output from the European Centre for Medium-Range Weather Forecasts General Circulation Model also has been used as an aid in interpreting the data, while the data have in turn been employed to validate the model's shortwave radiation code as it pertains to cloud radiation properties. Combined, the datasets and model demonstrate a strategy for determining under what conditions the shortwave radiative impact of clouds leads to a heating or cooling of the atmospheric column. The datasets also show, in terms of a linear slope-offset algorithm for retrieving the net downward shortwave radiation at the surface from satellite measurements, that the clouds present during this study produced a modest negative bias in the retrieved surface flux relative to that inferred from a clear-sky algorithm.
Abstract
Two datasets have been combined to demonstrate how the availability of more comprehensive datasets could serve to elucidate the shortwave radiative impact of clouds on both the atmospheric column and the surface. These datasets consist of two measurements of net downward shortwave radiation: one of near-surface measurements made at the Boulder Atmospheric Observatory tower, and the other of collocated top-of-the-atmosphere measurements from the Earth Radiation Budget Experiment. Output from the European Centre for Medium-Range Weather Forecasts General Circulation Model also has been used as an aid in interpreting the data, while the data have in turn been employed to validate the model's shortwave radiation code as it pertains to cloud radiation properties. Combined, the datasets and model demonstrate a strategy for determining under what conditions the shortwave radiative impact of clouds leads to a heating or cooling of the atmospheric column. The datasets also show, in terms of a linear slope-offset algorithm for retrieving the net downward shortwave radiation at the surface from satellite measurements, that the clouds present during this study produced a modest negative bias in the retrieved surface flux relative to that inferred from a clear-sky algorithm.
To significantly improve the simulation of climate by general circulation models (GCMs), systematic errors in representations of relevant processes must first be identified, and then reduced. This endeavor demands that the GCM parameterizations of unresolved processes, in particular, should be tested over a wide range of time scales, not just in climate simulations. Thus, a numerical weather prediction (NWP) methodology for evaluating model parameterizations and gaining insights into their behavior may prove useful, provided that suitable adaptations are made for implementation in climate GCMs. This method entails the generation of short-range weather forecasts by a realistically initialized climate GCM, and the application of six hourly NWP analyses and observations of parameterized variables to evaluate these forecasts. The behavior of the parameterizations in such a weather-forecasting framework can provide insights on how these schemes might be improved, and modified parameterizations then can be tested in the same framework.
To further this method for evaluating and analyzing parameterizations in climate GCMs, the U.S. Department of Energy is funding a joint venture of its Climate Change Prediction Program (CCPP) and Atmospheric Radiation Measurement (ARM) Program: the CCPP-ARM Parameterization Testbed (CAPT). This article elaborates the scientific rationale for CAPT, discusses technical aspects of its methodology, and presents examples of its implementation in a representative climate GCM.
To significantly improve the simulation of climate by general circulation models (GCMs), systematic errors in representations of relevant processes must first be identified, and then reduced. This endeavor demands that the GCM parameterizations of unresolved processes, in particular, should be tested over a wide range of time scales, not just in climate simulations. Thus, a numerical weather prediction (NWP) methodology for evaluating model parameterizations and gaining insights into their behavior may prove useful, provided that suitable adaptations are made for implementation in climate GCMs. This method entails the generation of short-range weather forecasts by a realistically initialized climate GCM, and the application of six hourly NWP analyses and observations of parameterized variables to evaluate these forecasts. The behavior of the parameterizations in such a weather-forecasting framework can provide insights on how these schemes might be improved, and modified parameterizations then can be tested in the same framework.
To further this method for evaluating and analyzing parameterizations in climate GCMs, the U.S. Department of Energy is funding a joint venture of its Climate Change Prediction Program (CCPP) and Atmospheric Radiation Measurement (ARM) Program: the CCPP-ARM Parameterization Testbed (CAPT). This article elaborates the scientific rationale for CAPT, discusses technical aspects of its methodology, and presents examples of its implementation in a representative climate GCM.
