Regional air quality forecasting using spatiotemporal deep learning

2021 ◽  
Vol 283 ◽  
pp. 125341
Author(s):  
S Abirami ◽  
P Chitra
Atmosphere ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 302
Author(s):  
Rajesh Kumar ◽  
Piyush Bhardwaj ◽  
Gabriele Pfister ◽  
Carl Drews ◽  
Shawn Honomichl ◽  
...  

This paper describes a quasi-operational regional air quality forecasting system for the contiguous United States (CONUS) developed at the National Center for Atmospheric Research (NCAR) to support air quality decision-making, field campaign planning, early identification of model errors and biases, and support the atmospheric science community in their research. This system aims to complement the operational air quality forecasts produced by the National Oceanic and Atmospheric Administration (NOAA), not to replace them. A publicly available information dissemination system has been established that displays various air quality products, including a near-real-time evaluation of the model forecasts. Here, we report the performance of our air quality forecasting system in simulating meteorology and fine particulate matter (PM2.5) for the first year after our system started, i.e., 1 June 2019 to 31 May 2020. Our system shows excellent skill in capturing hourly to daily variations in temperature, surface pressure, relative humidity, water vapor mixing ratios, and wind direction but shows relatively larger errors in wind speed. The model also captures the seasonal cycle of surface PM2.5 very well in different regions and for different types of sites (urban, suburban, and rural) in the CONUS with a mean bias smaller than 1 µg m−3. The skill of the air quality forecasts remains fairly stable between the first and second days of the forecasts. Our air quality forecast products are publicly available at a NCAR webpage. We invite the community to use our forecasting products for their research, as input for urban scale (<4 km), air quality forecasts, or the co-development of customized products, just to name a few applications.


2021 ◽  
Vol 12 (5) ◽  
pp. 101045
Author(s):  
Chi-Yeh Lin ◽  
Yue-Shan Chang ◽  
Satheesh Abimannan

SOLA ◽  
2007 ◽  
Vol 3 ◽  
pp. 81-84 ◽  
Author(s):  
Masayuki Takigawa ◽  
Masanori Niwano ◽  
Hajime Akimoto ◽  
Masaaki Takahashi

2012 ◽  
Vol 12 (12) ◽  
pp. 5603-5615 ◽  
Author(s):  
F. L. Herron-Thorpe ◽  
G. H. Mount ◽  
L. K. Emmons ◽  
B. K. Lamb ◽  
S. H. Chung ◽  
...  

Abstract. Results from a regional air quality forecast model, AIRPACT-3, were compared to AIRS carbon monoxide column densities for the spring of 2010 over the Pacific Northwest. AIRPACT-3 column densities showed high correlation (R > 0.9) but were significantly biased (~25%) with consistent under-predictions for spring months when there is significant transport from Asia. The AIRPACT-3 CO bias relative to AIRS was eliminated by incorporating dynamic boundary conditions derived from NCAR's MOZART forecasts with assimilated MOPITT carbon monoxide. Changes in ozone-related boundary conditions derived from MOZART forecasts are also discussed and found to affect background levels by ± 10 ppb but not found to significantly affect peak ozone surface concentrations.


2012 ◽  
Vol 12 (2) ◽  
pp. 3695-3730
Author(s):  
F. L. Herron-Thorpe ◽  
G. H. Mount ◽  
L. K. Emmons ◽  
B. K. Lamb ◽  
S. H. Chung ◽  
...  

Abstract. Results from a regional air quality forecast model, AIRPACT-3, were compared to AIRS carbon monoxide column densities for the spring of 2010 over the Pacific Northwest. AIRPACT-3 column densities showed high correlation (R>0.9) but were significantly biased (~25 %) with significant under-predictions for spring months with significant transport from Asia. The AIRPACT-3 CO bias relative to AIRS was eliminated by incorporating dynamic boundary conditions derived from NCAR's MOZART forecasts with assimilated MOPITT carbon monoxide. Changes in ozone-related boundary conditions derived from MOZART forecasts are also discussed and found to affect background levels by ±10 ppb but not found to significantly affect peak ozone surface concentrations.


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