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- Author or Editor: J.-Y. Ku x
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Abstract
This study investigates the potential utility of the application of a photochemical modeling system in providing simultaneous forecasts of ozone (O3) and fine particulate matter (PM2.5) over New York State. To this end, daily simulations from the Community Multiscale Air Quality (CMAQ) model for three extended time periods during 2004 and 2005 have been performed, and predictions were compared with observations of ozone and total and speciated PM2.5. Model performance for 8-h daily maximum O3 was found to be similar to other forecasting systems and to be better than that for the 24-h-averaged total PM2.5. Both pollutants exhibited no seasonal differences in model performance. CMAQ simulations successfully captured the urban–rural and seasonal differences evident in observed total and speciated PM2.5 concentrations. However, total PM2.5 mass was strongly overestimated in the New York City metropolitan area, and further analysis of speciated observations and model predictions showed that most of this overprediction stems from organic aerosols and crustal material. An analysis of hourly speciated data measured in Bronx County, New York, suggests that a combination of uncertainties in vertical mixing, magnitude, and temporal allocation of emissions and deposition processes are all possible contributors to this overprediction in the complex urban area. Categorical evaluation of CMAQ simulations in terms of exceeding two different threshold levels of the air quality index (AQI) again indicates better performance for ozone than PM2.5 and better performance for lower exceedance thresholds. In most regions of New York State, the routine air quality forecasts based on observed concentrations and expert judgment show slightly better agreement with the observed distributions of AQI categories than do CMAQ simulations. However, CMAQ shows skill similar to these routine forecasts in terms of capturing the AQI tendency, that is, in predicting changes in air quality conditions. Overall, the results presented in this study reveal that additional research and development is needed to improve CMAQ simulations of PM2.5 concentrations over New York State, especially for the New York City metropolitan area. On the other hand, because CMAQ simulations capture urban–rural concentration gradients and day-to-day fluctuations in observed air quality despite systematic overpredictions in some areas, it would be useful to develop tools that combine CMAQ’s predictive capability in terms of spatial concentration gradients and AQI tendencies with real-time observations of ambient pollutant levels to generate forecasts with higher temporal and spatial resolutions (e.g., county level) than those of techniques based exclusively on monitoring data.
Abstract
This study investigates the potential utility of the application of a photochemical modeling system in providing simultaneous forecasts of ozone (O3) and fine particulate matter (PM2.5) over New York State. To this end, daily simulations from the Community Multiscale Air Quality (CMAQ) model for three extended time periods during 2004 and 2005 have been performed, and predictions were compared with observations of ozone and total and speciated PM2.5. Model performance for 8-h daily maximum O3 was found to be similar to other forecasting systems and to be better than that for the 24-h-averaged total PM2.5. Both pollutants exhibited no seasonal differences in model performance. CMAQ simulations successfully captured the urban–rural and seasonal differences evident in observed total and speciated PM2.5 concentrations. However, total PM2.5 mass was strongly overestimated in the New York City metropolitan area, and further analysis of speciated observations and model predictions showed that most of this overprediction stems from organic aerosols and crustal material. An analysis of hourly speciated data measured in Bronx County, New York, suggests that a combination of uncertainties in vertical mixing, magnitude, and temporal allocation of emissions and deposition processes are all possible contributors to this overprediction in the complex urban area. Categorical evaluation of CMAQ simulations in terms of exceeding two different threshold levels of the air quality index (AQI) again indicates better performance for ozone than PM2.5 and better performance for lower exceedance thresholds. In most regions of New York State, the routine air quality forecasts based on observed concentrations and expert judgment show slightly better agreement with the observed distributions of AQI categories than do CMAQ simulations. However, CMAQ shows skill similar to these routine forecasts in terms of capturing the AQI tendency, that is, in predicting changes in air quality conditions. Overall, the results presented in this study reveal that additional research and development is needed to improve CMAQ simulations of PM2.5 concentrations over New York State, especially for the New York City metropolitan area. On the other hand, because CMAQ simulations capture urban–rural concentration gradients and day-to-day fluctuations in observed air quality despite systematic overpredictions in some areas, it would be useful to develop tools that combine CMAQ’s predictive capability in terms of spatial concentration gradients and AQI tendencies with real-time observations of ambient pollutant levels to generate forecasts with higher temporal and spatial resolutions (e.g., county level) than those of techniques based exclusively on monitoring data.
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.
