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- Author or Editor: T. P. Ackerman x
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Abstract
The accuracy needed in cirrus cloud scattering and microphysical properties is quantified such that the radiative effect on climate can he determined. Our ability to compute and observe these properties to within needed accuracies is assessed, with the greatest attention given to those properties that most affect the fluxes.
Model calculations indicate that computing net longwave fluxes at the surface to within ±5% requires that cloud temperature be known to within as little as ±3 K in cold climates for extinction optical depths greater than two. Such accuracy could be more difficult to obtain than that needed in the values of scattering parameters. For a baseline case (defined in text), computing net shortwave fluxes at the surface to within ±5% requires accuracies in cloud ice water content that, when the optical depth is greater than 1.25, are beyond the accuracies of current measurements. Similarly, surface shortwave flux computations require accuracies in the asymmetry parameter that are beyond our current abilities when the optical depth is greater than four. Unless simplifications are discovered, the scattering properties needed to compute cirrus cloud fluxes cannot be obtained explicitly with existing scattering algorithms because the range of crystal sizes is too great and crystal shapes are too varied to be treated computationally. Thus, bulk cirrus scattering properties might be better obtained by inverting cirrus cloud fluxes and radiances. Finally, typical aircraft broadband flux measurements are not sufficiently accurate to provide a convincing validation of calculations. In light of these findings we recommend a reexamination of the methodology used in field programs such as FIRE and suggest a complementary approach.
Abstract
The accuracy needed in cirrus cloud scattering and microphysical properties is quantified such that the radiative effect on climate can he determined. Our ability to compute and observe these properties to within needed accuracies is assessed, with the greatest attention given to those properties that most affect the fluxes.
Model calculations indicate that computing net longwave fluxes at the surface to within ±5% requires that cloud temperature be known to within as little as ±3 K in cold climates for extinction optical depths greater than two. Such accuracy could be more difficult to obtain than that needed in the values of scattering parameters. For a baseline case (defined in text), computing net shortwave fluxes at the surface to within ±5% requires accuracies in cloud ice water content that, when the optical depth is greater than 1.25, are beyond the accuracies of current measurements. Similarly, surface shortwave flux computations require accuracies in the asymmetry parameter that are beyond our current abilities when the optical depth is greater than four. Unless simplifications are discovered, the scattering properties needed to compute cirrus cloud fluxes cannot be obtained explicitly with existing scattering algorithms because the range of crystal sizes is too great and crystal shapes are too varied to be treated computationally. Thus, bulk cirrus scattering properties might be better obtained by inverting cirrus cloud fluxes and radiances. Finally, typical aircraft broadband flux measurements are not sufficiently accurate to provide a convincing validation of calculations. In light of these findings we recommend a reexamination of the methodology used in field programs such as FIRE and suggest a complementary approach.
Abstract
Several space agencies are presently considering missions with active instruments (radar, lidar), which are able to document cloud stratification and cloud microphysical properties on the global scale. The objective of this paper is to develop an algorithm to derive as much information as possible from a single-frequency, nadir-looking cloud radar operating from an airborne or spaceborne platform. It is impossible to derive all parameters of interest in the radiative budget of a cloud from only the radar reflectivity profile, unless some a priori knowledge of cloud processes is introduced in the formulation of the algorithm itself. The a priori knowledge considered here (total concentration of particles invariant with altitude, adiabatic liquid water content) does not apply to all cloud types but only to warm stratiform clouds where entrainment is weak.
The algorithm concept and inversion procedure, including a stable scheme for correcting the radar reflectivity for attenuation, are first described. A test of the algorithm is then performed using numerical simulations in order to investigate the sensitivity of the retrieval to measurement noise, degradation of the range resolution, shape of the cloud droplet distribution, and presence of entrainment. In the realistic conditions of an airborne experiment, the retrieval of cloud base h b , total number concentration of particles N T , profiles of the liquid water content, and effective radius r e can be performed with good accuracy (provided the entrainment coefficient is below 1 km−1). With the sampling characteristics of a spaceborne radar, retrievals of the cloud base and liquid water content remain reasonably accurate, but the estimates of N T and r e are degraded to a level where they become meaningless.
A test of the algorithm is performed using a dataset from the zenith-pointing ground-based 94-GHz radar of The Pennsylvania State University, obtained during the Continental Stratus Experiment. The algorithm is found to be successful in 43% of cases. An attempt of evaluation of the retrieval is made by comparison with ceilometer data. Most failure cases are probably due to the presence of drizzle.
Abstract
Several space agencies are presently considering missions with active instruments (radar, lidar), which are able to document cloud stratification and cloud microphysical properties on the global scale. The objective of this paper is to develop an algorithm to derive as much information as possible from a single-frequency, nadir-looking cloud radar operating from an airborne or spaceborne platform. It is impossible to derive all parameters of interest in the radiative budget of a cloud from only the radar reflectivity profile, unless some a priori knowledge of cloud processes is introduced in the formulation of the algorithm itself. The a priori knowledge considered here (total concentration of particles invariant with altitude, adiabatic liquid water content) does not apply to all cloud types but only to warm stratiform clouds where entrainment is weak.
The algorithm concept and inversion procedure, including a stable scheme for correcting the radar reflectivity for attenuation, are first described. A test of the algorithm is then performed using numerical simulations in order to investigate the sensitivity of the retrieval to measurement noise, degradation of the range resolution, shape of the cloud droplet distribution, and presence of entrainment. In the realistic conditions of an airborne experiment, the retrieval of cloud base h b , total number concentration of particles N T , profiles of the liquid water content, and effective radius r e can be performed with good accuracy (provided the entrainment coefficient is below 1 km−1). With the sampling characteristics of a spaceborne radar, retrievals of the cloud base and liquid water content remain reasonably accurate, but the estimates of N T and r e are degraded to a level where they become meaningless.
A test of the algorithm is performed using a dataset from the zenith-pointing ground-based 94-GHz radar of The Pennsylvania State University, obtained during the Continental Stratus Experiment. The algorithm is found to be successful in 43% of cases. An attempt of evaluation of the retrieval is made by comparison with ceilometer data. Most failure cases are probably due to the presence of drizzle.
Abstract
Abstract
Abstract
A dual-channel retrieval technique involving the water vapor band at 6.5 μm and the window region at 10.5 gm has been developed to infer the temperature and emissivity of tropical anvils. This technique has been applied to data obtained from the ER-2 narrow field-of-view radiometers during two flights in the field observation of the Stratosphere-Troposphere Exchange Project (STEP) near Damn, Australia, January-February 1987. The retrieved cloud temperatures are between 190 and 240 K, while the cloud emissivities derived from the retrieval algorithm range from about 0.2 to 1. Moreover, the visible optical depths have been obtained from the cloud emissivity through a theoretical parameterization with values of 0.5-10. A significant portion of tropical cirrus clouds are found to have optical depths greater than about 6. Because of the parameterization, the present technique is unable to precisely determine the optical depth values for optically thick cirrus clouds.
Abstract
A dual-channel retrieval technique involving the water vapor band at 6.5 μm and the window region at 10.5 gm has been developed to infer the temperature and emissivity of tropical anvils. This technique has been applied to data obtained from the ER-2 narrow field-of-view radiometers during two flights in the field observation of the Stratosphere-Troposphere Exchange Project (STEP) near Damn, Australia, January-February 1987. The retrieved cloud temperatures are between 190 and 240 K, while the cloud emissivities derived from the retrieval algorithm range from about 0.2 to 1. Moreover, the visible optical depths have been obtained from the cloud emissivity through a theoretical parameterization with values of 0.5-10. A significant portion of tropical cirrus clouds are found to have optical depths greater than about 6. Because of the parameterization, the present technique is unable to precisely determine the optical depth values for optically thick cirrus clouds.
Abstract
Algorithms for deriving winds from profiler range-gated spectra currently rely on consensus averaging to remove outliers from the subhourly velocity estimates. For persistent ground clutter in the echo return that is stronger than the atmospheric component, consensus averaging of the spectral peak power densities fails because the peak power density is derived from the ground clutter and not the atmosphere. To negate the deleterious effects of persistent ground clutter, as well as to attempt to improve performance during periods of poor signal-to-noise ratio, an algorithm was developed that uses the local maxima in power density in each spectrum to build multiple profiles of possible radial velocity estimates from the first to last range tale. To build profiles of radial velocity estimates from a set of spectra, the spectra are smoothed, the local power density maxima are identified, chains are formed across range gates by connecting those local power density maxima that satisfy a continuity constraint, and finally profiles are built from a combination of chains by maximizing an energy function based on continuity, gate separation, and summed power density. Features based on power density and power density after half-plane subtraction are then constructed for each profile and a backpropagation neural network is subsequently used to classify the profile most likely reflecting the atmospheric state. It was found that use of this technique significantly reduced ground clutter contamination in the horizontal beam velocity estimates and improved performance at low signal-to-noise ratios for all velocity estimates.
Abstract
Algorithms for deriving winds from profiler range-gated spectra currently rely on consensus averaging to remove outliers from the subhourly velocity estimates. For persistent ground clutter in the echo return that is stronger than the atmospheric component, consensus averaging of the spectral peak power densities fails because the peak power density is derived from the ground clutter and not the atmosphere. To negate the deleterious effects of persistent ground clutter, as well as to attempt to improve performance during periods of poor signal-to-noise ratio, an algorithm was developed that uses the local maxima in power density in each spectrum to build multiple profiles of possible radial velocity estimates from the first to last range tale. To build profiles of radial velocity estimates from a set of spectra, the spectra are smoothed, the local power density maxima are identified, chains are formed across range gates by connecting those local power density maxima that satisfy a continuity constraint, and finally profiles are built from a combination of chains by maximizing an energy function based on continuity, gate separation, and summed power density. Features based on power density and power density after half-plane subtraction are then constructed for each profile and a backpropagation neural network is subsequently used to classify the profile most likely reflecting the atmospheric state. It was found that use of this technique significantly reduced ground clutter contamination in the horizontal beam velocity estimates and improved performance at low signal-to-noise ratios for all velocity estimates.
Abstract
Cirrus clouds that formed on 26 November and 6 December 1991 during the First International Satellite Cloud Climatology Project Regional Experiment (FIRE) II, which took place over the Kansas region. are studied because of significant dynamic activity in the micro (<1 km) and meso γ (<25 km) scales within the cloud. Observations are obtained from the NCAR King Air, NOAA Doppler, and PSU conventional radar. For this reason coherent structures (e.g., cells, vortex) that transfer significant heat, moisture, and turbulence are analyzed using aircraft and radar observations. Aircraft data is collected at 20 Hz, and calculations are made at two different scales. Scale separation is chosen at about 1 km. A coherence analysis technique is used to specify the correlation between temperature and vertical velocity w fluctuations. A swirling coefficient, indicating spirality, is calculated to better understand cloud dynamics. Sensible heat, latent heat, and radiative fluxes are compared with each other in two scales. Results showed that dynamic activity, including w about ±1.5 m s−1, and mean sensible heat fluxes (SHFs) and latent heat fluxes (LHFs) ∼10 W m−2 is estimated to be much larger for the 26 November case compared to the 6 December case. The swirling coefficient is estimated to be larger in upper and lower levels compared to those in middle levels for both days. Individual values of SHFs and LHFs are also found to be comparable with those of FIRE I. The size of coherent structures is estimated from aircraft and radar measurements to be about 0.5 and 3.5 km.
Abstract
Cirrus clouds that formed on 26 November and 6 December 1991 during the First International Satellite Cloud Climatology Project Regional Experiment (FIRE) II, which took place over the Kansas region. are studied because of significant dynamic activity in the micro (<1 km) and meso γ (<25 km) scales within the cloud. Observations are obtained from the NCAR King Air, NOAA Doppler, and PSU conventional radar. For this reason coherent structures (e.g., cells, vortex) that transfer significant heat, moisture, and turbulence are analyzed using aircraft and radar observations. Aircraft data is collected at 20 Hz, and calculations are made at two different scales. Scale separation is chosen at about 1 km. A coherence analysis technique is used to specify the correlation between temperature and vertical velocity w fluctuations. A swirling coefficient, indicating spirality, is calculated to better understand cloud dynamics. Sensible heat, latent heat, and radiative fluxes are compared with each other in two scales. Results showed that dynamic activity, including w about ±1.5 m s−1, and mean sensible heat fluxes (SHFs) and latent heat fluxes (LHFs) ∼10 W m−2 is estimated to be much larger for the 26 November case compared to the 6 December case. The swirling coefficient is estimated to be larger in upper and lower levels compared to those in middle levels for both days. Individual values of SHFs and LHFs are also found to be comparable with those of FIRE I. The size of coherent structures is estimated from aircraft and radar measurements to be about 0.5 and 3.5 km.
Abstract
Satellite meteorology is a relatively new branch of the atmospheric sciences. The field emerged in the late 1950s during the Cold War and built on the advances in rocketry after World War II. In less than 70 years, satellite observations have transformed the way scientists observe and study Earth. This paper discusses some of the key advances in our understanding of the energy and water cycles, weather forecasting, and atmospheric composition enabled by satellite observations. While progress truly has been an international achievement, in accord with a monograph observing the centennial of the American Meteorological Society, as well as limited space, the emphasis of this chapter is on the U.S. satellite effort.
Abstract
Satellite meteorology is a relatively new branch of the atmospheric sciences. The field emerged in the late 1950s during the Cold War and built on the advances in rocketry after World War II. In less than 70 years, satellite observations have transformed the way scientists observe and study Earth. This paper discusses some of the key advances in our understanding of the energy and water cycles, weather forecasting, and atmospheric composition enabled by satellite observations. While progress truly has been an international achievement, in accord with a monograph observing the centennial of the American Meteorological Society, as well as limited space, the emphasis of this chapter is on the U.S. satellite effort.
