Igor G. Zurbenko

Space and Time Scales in Ambient Ozone Data

Abstract 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...

Detecting and tracking changes in ozone air quality

This paper presents a statistical method for filtering out or moderating the influence of meteorological fluctuations on ozone concentrations. Use of this technique in examining trends in ambient ozone air quality is demonstrated with ozone data from a monitoring location in New Jersey. The results indicate that this method can detect changes in ozone air quality due to changes in emissions in the presence of meteorological fluctuations. This method can be useful in examining the effectiveness of regulatory initiatives in improving ozone air quality.This paper presents a statistical method for filtering out or moderating the influence of meteorological fluctuations on ozone concentrations. Use of this technique in examining trends in ambient ozone air quality is demonstrated with ozone data from a monitoring location in New Jersey. The results indicate that this method can detect changes in ozone air quality due to changes in emissions in the presence of meteorological fluctuations. This method can be useful in examining the effectiveness of regulatory initiatives in improving ozone air quality.

Separating Different Scales of Motion in Time Series of Meteorological Variables

Abstract The removal of synoptic and seasonal signals from time series of meteorological variables leaves datasets amenable to the study of trends, climate change, and the reasons for such trends and changes. In this paper, four techniques for separating different scales of motion are examined and their effectiveness compared. These techniques are PEST, anomalies, wavelet transform, and the Kolmogorov–Zurbenko (KZ) filter. It is shown that PEST and anomalies do not cleanly separate the synoptic and seasonal signals from the data as well as the other two methods. The KZ filter method is shown to have the same level of accuracy as the wavelet transform method. However, the KZ filter method can be applied to datasets with missing observations and is much easier to use than the wavelet transform method.

Moderating the Influence of Meteorological Conditions on Ambient Ozone Concentrations

Because ambient ozone concentrations are so strongly influenced by stochastic and seasonal variations, it is difficult to assess the effectiveness of regulatory controls in improving ambient ozone air quality. The purpose of this paper is to present a method for moderating the influence of meteorological fluctuations on ambient ozone levels. Techniques presented here account for temperature and other meteorological variables that affect ambient ozone concentrations. To this end, we have examined the correlation between several meteorological variables and ozone concentrations. In addition, we have evaluated trends in ozone time series after removing the effects of these variables on ozone concentrations. The results indicate that inclusion of two meteorological variables strengthens the relationship between ozone and meteorological effects. Moreover, the meteorologically-independent ozone time series at one of the locations studied had a significant trend that was not detected in temperature-independent ozone concentrations.

Evaluating the Effectiveness of Ozone Management Efforts in the Presence of Meteorological Variability

It is difficult to assess the effectiveness of regulatory programs in improving ozone air quality in the presence of meteorological fluctuations. In this paper, techniques are presented that improve upon previous methods for moderating the effects of meteorology on ozone concentrations. This approach entails the use of the relations between ozone and meteorological variables to construct meteorologically adjusted ozone time series. To this end, the effectiveness and usefulness of various methods for separating time series of ozone and meteorological data into long-term (climate- and policy-related), seasonal (solar-induced), and short-term (weather-related) components are examined. Correlations between baseline components (sum of long-term and seasonal variations) of ozone and meteorological variables are then investigated independently of correlations between short-term components (weather effects) of ozone and meteorological variables. This allows us to account for the effects of the dominant meteorological variables on each time scale embedded in time series of ozone data. Ozone time series that are devoid of seasonal and climatic variations as well as weather-related fluctuations can then be constructed to detect and track changes in ozone due to the emission control policies implemented. The results of this study reveal that the combination of solar radiation and specific humidity performs best in filtering the seasonal and climatic variations from the baseline component of the ozone data. The combination of temperature and dew point depression performs best in moderating the weather-related effects on the short-term component of ozone data. This method is able to explain about 65% of the variance in ozone data through meteorological variables at several locations examined here.

Detecting Discontinuities in Time Series of Upper-Air Data: Development and Demonstration of an Adaptive Filter Technique

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 appli...

Interpreting the Information in Ozone Observations and Model Predictions Relevant to Regulatory Policies in the Eastern United States

Abstract To study the underlying forcing mechanisms that distinguish the days with high ozone concentrations from average or nonepisodic days, the observed and model–predicted ozone time series are spectrally decomposed into different temporal components; the modeled values are based on the results of a three–month simulation with the Urban Airshed Model–Variable Grid Version photochemical modeling system. The ozone power spectrum is represented as the sum of four temporal components, ranging from the intraday timescale to the multiweek timescale. The results reveal that only those components that contain fluctuations with periods equal to or greater than one day carry the information that distinguishes ozone episode days from nonepisodic days. Which of the longer–term fluctuations is dominant in a particular episode varies from episode to episode. However, the magnitude of the intraday fluctuations is nearly invariant in time. The promulgation of the 8–h standard for ozone further emphasizes the importan...

Assessing the impact of the acid deposition control program

Abstract A goal of the acidic deposition control program in the United States has been to link emissions control policies, such as those mandated under Title IV of the US Clean Air Act Amendments (CAAA) of 1990, to improvements in air and water quality. Recently, several researchers have reported trends in the time series of pollutant data in an effort to evaluate the effectiveness of the CAAA in reducing the acidic deposition problem. It is well known that pollutant concentrations are highly influenced by meteorological and climatic variations. Also, spatial and temporal inhomogeneities in time series of pollutant concentrations, induced by differences in the data collection, reduction, and reporting practices, can significantly affect the trend estimates. We present a method to discern breaks or discontinuities in the time series of pollutants stemming from emission reductions in the presence of meteorological and climatological variability. Using data from a few sites, this paper illustrates that linear trend estimates of concentrations of SO2, aerosol SO42−, and precipitation-weighted SO42− and NO3− can be biased because of such complex features embedded in pollutant time series.

Construction of high-resolution wavelets

Abstract An iterative fast Fourier transform algorithm for the robust estimation of higher-order spectra is introduced and examined. The estimation displays very strong resistance to noise and can be applied to nonstationary processes and random fields. A wavelet adaptive procedure is constructed for image recognition processing. Several applications are discussed.

Semi-Adaptive Nonparametric Spectral Estimation

Abstract When dominant information is available about a process, its corresponding spectral density will exhibit a variable order of smoothness. In such situations, calculating a nonparametric spectral estimate with a fixed smoothing parameter will lead to biased spectral estimates. We propose a simple method that allows the bandwidth of the spectral window in the smoothed periodogram running average procedure to vary in order to compensate for the possibly changing order of smoothness of the underlying spectral density. At each point of estimation, our approach is to extend the bandwidth until the squared variation of the periodogram within reaches a prespecified level. Our method may be particularly benevolent when the process is nonstationary with close spectral lines, although it may be implemented in any situation that warrants adaptive scatterplot “smoothing,” such as in those cases where sharp peaks are considered to be information and not error. We illustrate our method on an observed and simulate...