High-Resolution Space–Time Ozone Modeling for Assessing Trends

High Resolution Space-Time Ozone Modeling for Assessing Trends.

The assessment of air pollution regulatory programs designed to improve ground level ozone concentrations is a topic of considerable interest to environmental managers. To aid this assessment, it is necessary to model the space-time behavior of ozone for predicting summaries of ozone across spatial domains of interest and for the detection of long-term trends at monitoring sites. These trends, ...

Modeling and assessing climatic trends

Climate studies often fit linear trends to data. In many cases simplifying assumptions such as independent errors and constant variance are used. We review a variety of approaches to estimating linear trends, and illustrate with US temperature data how oversimplified assumptions may lead to false significance. We outline a variety of methods to fit nonlinear trend models. Using the Berkeley Ear...

High - Resolution Ozone Imager ( HIROIG ) Final Report

Spatial-Temporal Trend Modeling for Ozone Concentration in Tehran City

 Fitting a suitable covariance function for the correlation structure of spatial-temporal data requires de-trending the data. In this article, some potential models for spatial-temporal trend are presented. Eventually the best model will be announced for de-trending tropospheric ozone concentration data for the city of Tehran (Capital city of Iran). By using the selected trend model, some ...

Global tropospheric ozone modeling: Quantifying errors due to grid resolution

[1] Ozone production in global chemical models is dependent on model resolution because ozone chemistry is inherently nonlinear, the timescales for chemical production are short, and precursors are artificially distributed over the spatial scale of the model grid. In this study we examine the sensitivity of ozone, its precursors, and its production to resolution by running a global chemical tra...