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. Scientists are working in collaboration with other agencies to develop new technologies to:
In an effort funded by NASA, RAL 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 decision making in air quality management. The system is being set up over the USA but can be easily applied to any part of the world.
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). GSI is used to assimilate retrievals of aerosol optical depth from the NASA Aqua/Terra Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instruments and possibly retrieval of carbon monoxide from the NASA/Terra Measurements Of Pollution In The Troposphere (MOPITT) and the EUMETSAT/MetOp Infrared Atmospheric Sounding Interferometer (IASI). Surface observations of PM2.5 (and possibly of ground-level ozone) from the AIRNow network, the Interagency Monitoring of Protected Visual Environments (IMPROVE) stations, and the Clean Air Status and Trends Network (CASTNET) will also be assimilated. 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. This effort is led by NCAR, in collaboration with NOAA, CU Boulder, and the University of Maryland.
A first prototype of the GSI/CMAQ system, analog ensemble probabilistic predictions of ground-level zone and surface PM2.5, and the corresponding gridded predictions will be will be implemented and tested with the NSF/NCAR/State of Colorado Front Range Air Pollution and Photochemistry Experiment (FRAPPÉ) and the NASA Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ). Both field campaigns took place in the summer of 2014. The comprehensive suite of measurements will be used to assess the accuracy of the proposed forecasting product.
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Air Quality Forecasting
