A Novel Ensemble System for PM2.5 Probabilistic Predictions over the US
Abstract
The National Air Quality (AQ) Forecasting Capability (NAQFC) at the National Oceanic and Atmospheric Administration (NOAA) produces forecasts of ozone, particulate matter, and other pollutants so that advance notice and warning can be issued to help individuals and communities limit the exposure and reduce health problems caused by air pollution. The current NAQFC, based on the U.S. Environmental Protection Agency (EPA) Community Multi-scale AQ (CMAQ) modeling system, provides only deterministic AQ forecasts and does not quantify the uncertainty associated with the predictions, which could be large given the chaotic nature of atmosphere and nonlinearity in atmospheric chemistry. We explore and evaluate an ensemble prediction system of PM2.5 with a novel design. The ensemble is generated by perturbing three key aspects of PM2.5 modeling namely the meteorology, emissions, and CMAQ secondary organic aerosol (SOA) formulation. The combination of different meteorological, emissions, and SOA formation perturbations results in a large number of ensemble members which could be computationally unfeasible for NAQFC operations. We have addressed this limitation by down selecting the combination perturbations which preserve the most part of the skill and quality of the original ensemble. The proposed ensemble significantly improves the accuracy of operational PM2.5 predictions while providing a reliable quantification of the PM2.5 prediction uncertainty.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2019
- Bibcode:
- 2019AGUFM.A23Q2928A
- Keywords:
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- 0345 Pollution: urban and regional;
- ATMOSPHERIC COMPOSITION AND STRUCTURE;
- 0365 Troposphere: composition and chemistry;
- ATMOSPHERIC COMPOSITION AND STRUCTURE;
- 0368 Troposphere: constituent transport and chemistry;
- ATMOSPHERIC COMPOSITION AND STRUCTURE