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- Author or Editor: Y. J. Rao x
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Abstract
Using the data obtained from the Advanced Very High Resolution Radiometer (AVHRR) 3.7-µm and 10.9-µm channels, a retrieval scheme has been developed to simultaneously infer cirrus cloud optical depth and mean effective ice crystal size based on the theory of radiative transfer and parameterizations. A numerical scheme is further developed to remove the solar component in the 3.7-µm radiance for applications to daytime satellite data. This scheme is based on the correlation between the 3.7-µm (solar) and 0.63-µm reflectances. Validation of the algorithm has been performed by using various datasets that were collected during the FIRE-II IFO (Nov-Dec 1991) at Coffeyville, Kansas. We have focused on the 26 November and 5 December cases. The retrieval analysis over a 0.5°×1.0° area is performed around Coffeyville for each case based on AVHRR-HRPT data. For validation the authors analyze the photomicrograph data collected by the balloonborne replicator, determine the microphysical and optical properties of the sampled cirrus clouds, and derive their position at the satellite overpass based on sounding data. It is demonstrated that the retrieved cirrus cloud temperature, mean effective ice crystal size, and optical depth closely match the observed values. Further, the retrieved cirrus cloud properties are applied to the computation of surface radiative fluxes using a radiative transfer program that involves a consistent representation of cirrus cloud fields. The computed values are compared with the data measured from ground-based radiometers, and it is shown that the computed downward surface IR and solar fluxes are within 5 and 10 W m−2 of the measured values, respectively, near the time of satellite overpass.
Abstract
Using the data obtained from the Advanced Very High Resolution Radiometer (AVHRR) 3.7-µm and 10.9-µm channels, a retrieval scheme has been developed to simultaneously infer cirrus cloud optical depth and mean effective ice crystal size based on the theory of radiative transfer and parameterizations. A numerical scheme is further developed to remove the solar component in the 3.7-µm radiance for applications to daytime satellite data. This scheme is based on the correlation between the 3.7-µm (solar) and 0.63-µm reflectances. Validation of the algorithm has been performed by using various datasets that were collected during the FIRE-II IFO (Nov-Dec 1991) at Coffeyville, Kansas. We have focused on the 26 November and 5 December cases. The retrieval analysis over a 0.5°×1.0° area is performed around Coffeyville for each case based on AVHRR-HRPT data. For validation the authors analyze the photomicrograph data collected by the balloonborne replicator, determine the microphysical and optical properties of the sampled cirrus clouds, and derive their position at the satellite overpass based on sounding data. It is demonstrated that the retrieved cirrus cloud temperature, mean effective ice crystal size, and optical depth closely match the observed values. Further, the retrieved cirrus cloud properties are applied to the computation of surface radiative fluxes using a radiative transfer program that involves a consistent representation of cirrus cloud fields. The computed values are compared with the data measured from ground-based radiometers, and it is shown that the computed downward surface IR and solar fluxes are within 5 and 10 W m−2 of the measured values, respectively, near the time of satellite overpass.
This paper describes the characteristic space and time scales in time series of ambient ozone data. The authors discuss the need and a methodology for cleanly separating the various scales of motion embedded in ozone time series data, namely, short-term (weather related) variations, seasonal (solar induced) variations, and long-term (climate–policy related) trends, in order to provide a better understanding of the underlying physical processes that affect ambient ozone levels. Spatial and temporal information in ozone time series data, obscure prior to separation, is clearly displayed by simple laws afterward. In addition, process changes due to policy or climate changes may be very small and invisible unless they are separated from weather and seasonality. Successful analysis of the ozone problem, therefore, requires a careful separation of seasonal and synoptic components.
The authors show that baseline ozone retains global information on the scale of more than 2 months in time and about 300 km in space. The short-term ozone component, attributable to short-term weather and precursor emission fluctuations, is highly correlated in space, retaining 50% of the short-term information at distances ranging from 350 to 400 km; in time, short-term ozone resembles a Markov process with 1-day lag correlations ranging from 0.2 to 0.5. The correlation structure of short-term ozone permits highly accurate predictions of ozone concentrations up to distances of about 600 km from a given monitor. These results clearly demonstrate that ozone is a regional-scale problem.
This paper describes the characteristic space and time scales in time series of ambient ozone data. The authors discuss the need and a methodology for cleanly separating the various scales of motion embedded in ozone time series data, namely, short-term (weather related) variations, seasonal (solar induced) variations, and long-term (climate–policy related) trends, in order to provide a better understanding of the underlying physical processes that affect ambient ozone levels. Spatial and temporal information in ozone time series data, obscure prior to separation, is clearly displayed by simple laws afterward. In addition, process changes due to policy or climate changes may be very small and invisible unless they are separated from weather and seasonality. Successful analysis of the ozone problem, therefore, requires a careful separation of seasonal and synoptic components.
The authors show that baseline ozone retains global information on the scale of more than 2 months in time and about 300 km in space. The short-term ozone component, attributable to short-term weather and precursor emission fluctuations, is highly correlated in space, retaining 50% of the short-term information at distances ranging from 350 to 400 km; in time, short-term ozone resembles a Markov process with 1-day lag correlations ranging from 0.2 to 0.5. The correlation structure of short-term ozone permits highly accurate predictions of ozone concentrations up to distances of about 600 km from a given monitor. These results clearly demonstrate that ozone is a regional-scale problem.
Abstract
Recognizing the need for a long-term database to address the problem of global climate change, the National Climatic Data Center has embarked on a project called the Comprehensive Aerological Reference Data Set to create an upper-air database consisting of radiosondes, pibals, surface reports, and station histories for the Northern and Southern Hemispheres. Unfortunately, these data contain systematic errors caused by changes in instruments, data acquisition procedures, etc. It is essential that systematic errors be identified and/or removed before these data can be used confidently in the context of greenhouse-gas-induced climate modification.
The purpose of this paper is to illustrate the use of an adaptive moving average filter in detecting systematic biases and to compare its performance with the Schwarz criterion, a parametric method. The advantage of the adaptive filter over traditional parametric methods is that it is less affected by seasonal patterns and trends. The filter has been applied to upper-air relative humidity and temperature data. The accuracy of locating the time at which a bias is introduced ranges from about 600 days for changes of 0.1 standard deviations to about 20 days for changes of 0.5 standard deviations.
Abstract
Recognizing the need for a long-term database to address the problem of global climate change, the National Climatic Data Center has embarked on a project called the Comprehensive Aerological Reference Data Set to create an upper-air database consisting of radiosondes, pibals, surface reports, and station histories for the Northern and Southern Hemispheres. Unfortunately, these data contain systematic errors caused by changes in instruments, data acquisition procedures, etc. It is essential that systematic errors be identified and/or removed before these data can be used confidently in the context of greenhouse-gas-induced climate modification.
The purpose of this paper is to illustrate the use of an adaptive moving average filter in detecting systematic biases and to compare its performance with the Schwarz criterion, a parametric method. The advantage of the adaptive filter over traditional parametric methods is that it is less affected by seasonal patterns and trends. The filter has been applied to upper-air relative humidity and temperature data. The accuracy of locating the time at which a bias is introduced ranges from about 600 days for changes of 0.1 standard deviations to about 20 days for changes of 0.5 standard deviations.
Abstract
Lidar profiling of atmospheric aerosols and clouds in the lower atmosphere has been in progress at the Indian Institute of Tropical Meteorology (IITM), Pune (18°32′N, 73°52′E, 559 m MSL), India, for more than two decades. To enlarge the scope of these studies, an eye-safe new portable dual polarization micropulse lidar (DPMPL) has been developed and installed at this station. The system utilizes a diode-pumped solid-state (DPSS) neodymium–yttrium–aluminum–garnet (Nd:YAG) laser second harmonic, with either parallel polarization or alternate parallel and perpendicular polarization, as a transmitter and a Schmidt–Cassegrain telescope, with a high-speed detection and data acquisition and processing system, as a receiver. This online system in real-time mode provides backscatter intensity profiles up to about 75 km at every minute in both parallel and perpendicular polarization channels, corresponding to each state of polarization of the transmitted laser radiation. Thus, this versatile lidar system is expected to play a vital role not only in atmospheric aerosol and cloud physics research and environmental monitoring but also in weather and climate modeling studies of the impact of radiative forcing on the earth–atmosphere radiation balance and hydrological cycle. This paper provides a detailed description of Asia’s only lidar facility and presents initial observations of space–time variations of boundary layer structure from experiments carried out during winter 2005/06.
Abstract
Lidar profiling of atmospheric aerosols and clouds in the lower atmosphere has been in progress at the Indian Institute of Tropical Meteorology (IITM), Pune (18°32′N, 73°52′E, 559 m MSL), India, for more than two decades. To enlarge the scope of these studies, an eye-safe new portable dual polarization micropulse lidar (DPMPL) has been developed and installed at this station. The system utilizes a diode-pumped solid-state (DPSS) neodymium–yttrium–aluminum–garnet (Nd:YAG) laser second harmonic, with either parallel polarization or alternate parallel and perpendicular polarization, as a transmitter and a Schmidt–Cassegrain telescope, with a high-speed detection and data acquisition and processing system, as a receiver. This online system in real-time mode provides backscatter intensity profiles up to about 75 km at every minute in both parallel and perpendicular polarization channels, corresponding to each state of polarization of the transmitted laser radiation. Thus, this versatile lidar system is expected to play a vital role not only in atmospheric aerosol and cloud physics research and environmental monitoring but also in weather and climate modeling studies of the impact of radiative forcing on the earth–atmosphere radiation balance and hydrological cycle. This paper provides a detailed description of Asia’s only lidar facility and presents initial observations of space–time variations of boundary layer structure from experiments carried out during winter 2005/06.
