Ozone and PM2.5


Figure 1. Air pollution across the world
Figure 1. Air pollution across the world

Air pollution is estimated to cause about 3.7 million premature deaths worldwide and destroy enough crops to feed millions of people every year, and is thus a major global environmental risk to both our health and food security.

NCAR has more than two decades of experience in developing advanced community models that are widely used for both air quality prediction and research.

The National Center for Atmospheric Research (NCAR) works in collaboration with other agencies to develop new technologies that allow us to:

  • Forecast air quality for cities and rural areas days in advance.

  • Project impact of future changes in human activities and climate on air quality.

  • Quantify cross-border transport of air pollution.

  • Quantify regional transport of air pollutants within a country.

  • Assess societal impacts of air pollution

  • Improve emission estimates.

FY17 Accomplishments

In an effort funded by NASA, NCAR and its partners are developing a new capability to produce 48-hour detailed forecasts of ground level ozone and fine particulate matter. The new forecasting capability combines satellite and in-situ observations with state-of-the-art modeling capabilities. It will generate more detailed, probabilistic air quality forecasts compared to the current forecasts, which provide just a single-value prediction and do not specify the uncertainty associated with the prediction. Just as a weather forecast, for example, might warn of a 80% chance of rain in the afternoon, new air quality forecasts might warn of a 80% chance of high ozone levels during certain times of the day while the current forecasts only tell whether ozone will be high or low. Such detailed forecasts will significantly enhance the decision-making activity in air quality management. The system is being set up over the USA but can be easily applied to any part of the world.

Fine particulate matter predictions over the US.
Figure 2.Fine particulate matter predictions over the US.

The first objective of the ongoing project is to improve the initialization of the National Oceanic and Atmospheric Administration (NOAA) / National Centers for Environmental Prediction (NCEP) operational air quality system, which is based on the Community Multiscale Air Quality (CMAQ) model, through chemical data assimilation of satellite retrieval products with the Community Gridpoint Statistical Interpolation (GSI) system (Fig. 1). We are using GSI to assimilate retrievals of aerosol optical depth from the NASA Aqua/Terra Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instruments. The second objective is to improve the CMAQ deterministic predictions and reliably quantify their uncertainty with analog-based post-processing methods applied to the CMAQ deterministic predictions. The third objective is the extrapolation of deterministic and probabilistic point-based predictions to a two-dimensional grid over the U.S. with a Barnes-type iterative objective analysis scheme. The proposed effort is led by NCAR, in collaboration with NOAA, CU Boulder, and the University of Maryland. Currently, NOAA/NCEP is running operationally in real-time the deterministic analog-based correction for the prediction of ground level ozone and surface PM2.5.

FY18 Plans

Complete the development of the GSI/CMAQ system, and the implementation of analog-based ensemble probabilistic predictions of ground-level zone and surface PM2.5 for NOAA/NCEP.