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- Author or Editor: J. R. Porter x
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Abstract
It is shown that genetic inversions can be used to recover lognormal aerosol size distributions from multiangle optical scattering cross-section data measured by a polar nephelometer at a wavelength of 0.532 μm. The inversions can also be used to recover the absolute calibration factor of the polar nephelometer. The method is demonstrated by applying it to polar nephelometer data measured during the Shoreline Environment Aerosol Study (SEAS) at Bellows Beach on the island of Oahu, Hawaii. Also, the inverted size distributions are compared with those inferred from direct measurements by particle sizers during SEAS. At 0.532 μm, the polar nephelometer data are dominated by the effect of coarse-mode hydrated sea salt. Although the inversion was unable to place constraints on the accumulation-mode size distribution, the modeled size distribution provides a good description of optical scattering at wavelengths of 0.532 μm and above.
Abstract
It is shown that genetic inversions can be used to recover lognormal aerosol size distributions from multiangle optical scattering cross-section data measured by a polar nephelometer at a wavelength of 0.532 μm. The inversions can also be used to recover the absolute calibration factor of the polar nephelometer. The method is demonstrated by applying it to polar nephelometer data measured during the Shoreline Environment Aerosol Study (SEAS) at Bellows Beach on the island of Oahu, Hawaii. Also, the inverted size distributions are compared with those inferred from direct measurements by particle sizers during SEAS. At 0.532 μm, the polar nephelometer data are dominated by the effect of coarse-mode hydrated sea salt. Although the inversion was unable to place constraints on the accumulation-mode size distribution, the modeled size distribution provides a good description of optical scattering at wavelengths of 0.532 μm and above.
A systematic and objective approach was used to optimize the siting of the individual radars forming the Next Generation Weather Radar (NEXRAD) network. Prime consideration was given to meteorological factors, in conjunction with the user agencies' needs and the population distribution. The latter was assessed by a novel technique using weather satellite photographs showing urban illumination at night. Priority coverage areas were identified for population centers based on the expected paths of storms and their travel speeds. Radar viewing of the priority coverage areas down to low altitudes is needed so that approaching storms can be detected and warnings issued as early as possible. Other siting criteria taken into account included consideration of terrain features and local obstructions, locations of airways and civilian and military airports, electromagnetic interference, and integration of NEXRAD data into the national weather system.
The methodology for selecting the network is described. Environmental impacts and costs of site acquisition and preparation were also involved in the study, but are not discussed in this paper.
A systematic and objective approach was used to optimize the siting of the individual radars forming the Next Generation Weather Radar (NEXRAD) network. Prime consideration was given to meteorological factors, in conjunction with the user agencies' needs and the population distribution. The latter was assessed by a novel technique using weather satellite photographs showing urban illumination at night. Priority coverage areas were identified for population centers based on the expected paths of storms and their travel speeds. Radar viewing of the priority coverage areas down to low altitudes is needed so that approaching storms can be detected and warnings issued as early as possible. Other siting criteria taken into account included consideration of terrain features and local obstructions, locations of airways and civilian and military airports, electromagnetic interference, and integration of NEXRAD data into the national weather system.
The methodology for selecting the network is described. Environmental impacts and costs of site acquisition and preparation were also involved in the study, but are not discussed in this paper.
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
The characteristics of a small, lightweight portable lidar system for measuring aerosol (Mie) scatter at wavelengths of 1064 and 532 nm are described. It uses a 20-Hz Nd:YAG pulsed laser as a source and a 12.7-cm-diameter telescope as a receiver. By using a minimal number of commercially available components, the cost of construction has been reduced. The lidar has a useable range of 60–3000 m for clean marine conditions. Its performance has been demonstrated using measurements of tropospheric aerosols on the island of Hawaii.
Abstract
The characteristics of a small, lightweight portable lidar system for measuring aerosol (Mie) scatter at wavelengths of 1064 and 532 nm are described. It uses a 20-Hz Nd:YAG pulsed laser as a source and a 12.7-cm-diameter telescope as a receiver. By using a minimal number of commercially available components, the cost of construction has been reduced. The lidar has a useable range of 60–3000 m for clean marine conditions. Its performance has been demonstrated using measurements of tropospheric aerosols on the island of Hawaii.
Abstract
Scanning lidar measurements were carried out during the Shoreline Environment Aerosol Study (SEAS) experiment (19–30 April 2000) to characterize the aerosol scattering fields in the coastal marine boundary layer at Bellows Beach on the southeast side of Oahu, Hawaii. The sea salt was found to be well mixed throughout the mixed layer, although the depth of the trade wind mixed layer was found to vary significantly over short timescales. As expected, the frequency distribution of aerosol scatter had a lognormal distribution, with the exception of regions downwind of breaking waves, where the frequency distribution was bimodal. A spatial statistical study revealed that the island-blocking effects cause low-level clouds to develop as they approach the island, with enhanced drizzle near the coastline reaching all the way to the surface. The spray from waves breaking on an outer reef was found to be intermittent and contained to heights of 20 m (on average) for the average wind speed of 7 m s−1. Sea-salt concentrations and fluxes from the breaking waves were estimated from the lidar measurements and found to be within the range of values reported by other investigators.
Abstract
Scanning lidar measurements were carried out during the Shoreline Environment Aerosol Study (SEAS) experiment (19–30 April 2000) to characterize the aerosol scattering fields in the coastal marine boundary layer at Bellows Beach on the southeast side of Oahu, Hawaii. The sea salt was found to be well mixed throughout the mixed layer, although the depth of the trade wind mixed layer was found to vary significantly over short timescales. As expected, the frequency distribution of aerosol scatter had a lognormal distribution, with the exception of regions downwind of breaking waves, where the frequency distribution was bimodal. A spatial statistical study revealed that the island-blocking effects cause low-level clouds to develop as they approach the island, with enhanced drizzle near the coastline reaching all the way to the surface. The spray from waves breaking on an outer reef was found to be intermittent and contained to heights of 20 m (on average) for the average wind speed of 7 m s−1. Sea-salt concentrations and fluxes from the breaking waves were estimated from the lidar measurements and found to be within the range of values reported by other investigators.
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.
