Evaluation of modeled surface ozone biases as a function of cloud cover fraction
A regional air-quality forecast system's model of surface ozone variability based on cloud coverage is evaluated using satellite-observed cloud fraction (CF) information and a surface air-quality monitoring system. We compared CF and daily maximum ozone from the National Oceanic and Atmospheric...
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Series: | Geoscientific Model Development |
Online Access: | http://www.geosci-model-dev.net/8/2959/2015/gmd-8-2959-2015.pdf |
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doaj-453d1980d095470eb6eeb0ee44660d442020-11-24T23:10:25ZengCopernicus PublicationsGeoscientific Model Development1991-959X1991-96032015-09-01892959296510.5194/gmd-8-2959-2015Evaluation of modeled surface ozone biases as a function of cloud cover fractionH. C. Kim0P. Lee1F. Ngan2Y. Tang3H. L. Yoo4L. Pan5NOAA Air Resources Laboratory (ARL), NOAA center for Weather and Climate Prediction, 5830 University Research Court, College Park, MD 20740, USANOAA Air Resources Laboratory (ARL), NOAA center for Weather and Climate Prediction, 5830 University Research Court, College Park, MD 20740, USANOAA Air Resources Laboratory (ARL), NOAA center for Weather and Climate Prediction, 5830 University Research Court, College Park, MD 20740, USANOAA Air Resources Laboratory (ARL), NOAA center for Weather and Climate Prediction, 5830 University Research Court, College Park, MD 20740, USANOAA Air Resources Laboratory (ARL), NOAA center for Weather and Climate Prediction, 5830 University Research Court, College Park, MD 20740, USANOAA Air Resources Laboratory (ARL), NOAA center for Weather and Climate Prediction, 5830 University Research Court, College Park, MD 20740, USAA regional air-quality forecast system's model of surface ozone variability based on cloud coverage is evaluated using satellite-observed cloud fraction (CF) information and a surface air-quality monitoring system. We compared CF and daily maximum ozone from the National Oceanic and Atmospheric Administration's National Air Quality Forecast Capability (NOAA NAQFC) with CFs from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the US Environmental Protection Agency's AirNow surface ozone measurements during May to October 2014. We found that observed surface ozone shows a negative correlation with the MODIS CFs, showing around 1 ppb decrease for 10 % MODIS CF change over the contiguous United States, while the correlation of modeled surface ozone with the model CFs is much weaker, showing only −0.5 ppb per 10 % NAQFC CF change. Further, daytime CF differences between MODIS and NAQFC are correlated with modeled surface-ozone biases between AirNow and NAQFC, showing −1.05 ppb per 10 % CF change, implying that spatial and temporal misplacement of the modeled cloud field might have biased modeled surface ozone level. Current NAQFC cloud fields seem to have fewer CFs compared to MODIS cloud fields (mean NAQFC CF = 0.38 and mean MODIS CF = 0.55), contributing up to 35 % of surface-ozone bias in the current NAQFC system.http://www.geosci-model-dev.net/8/2959/2015/gmd-8-2959-2015.pdf |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
H. C. Kim P. Lee F. Ngan Y. Tang H. L. Yoo L. Pan |
spellingShingle |
H. C. Kim P. Lee F. Ngan Y. Tang H. L. Yoo L. Pan Evaluation of modeled surface ozone biases as a function of cloud cover fraction Geoscientific Model Development |
author_facet |
H. C. Kim P. Lee F. Ngan Y. Tang H. L. Yoo L. Pan |
author_sort |
H. C. Kim |
title |
Evaluation of modeled surface ozone biases as a function of cloud cover fraction |
title_short |
Evaluation of modeled surface ozone biases as a function of cloud cover fraction |
title_full |
Evaluation of modeled surface ozone biases as a function of cloud cover fraction |
title_fullStr |
Evaluation of modeled surface ozone biases as a function of cloud cover fraction |
title_full_unstemmed |
Evaluation of modeled surface ozone biases as a function of cloud cover fraction |
title_sort |
evaluation of modeled surface ozone biases as a function of cloud cover fraction |
publisher |
Copernicus Publications |
series |
Geoscientific Model Development |
issn |
1991-959X 1991-9603 |
publishDate |
2015-09-01 |
description |
A regional air-quality forecast system's model of surface ozone variability
based on cloud coverage is evaluated using satellite-observed cloud fraction
(CF) information and a surface air-quality monitoring system. We compared CF
and daily maximum ozone from the National Oceanic and Atmospheric
Administration's National Air Quality Forecast Capability (NOAA NAQFC) with
CFs from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the
US Environmental Protection Agency's AirNow surface ozone measurements
during May to October 2014. We found that observed surface ozone shows a
negative correlation with the MODIS CFs, showing around 1 ppb decrease for
10 % MODIS CF change over the contiguous United States, while the
correlation of modeled surface ozone with the model CFs is much weaker,
showing only −0.5 ppb per 10 % NAQFC CF change. Further, daytime CF
differences between MODIS and NAQFC are correlated with modeled
surface-ozone biases between AirNow and NAQFC, showing −1.05 ppb per 10 %
CF change, implying that spatial and temporal misplacement of the modeled
cloud field might have biased modeled surface ozone level. Current NAQFC
cloud fields seem to have fewer CFs compared to MODIS cloud fields (mean
NAQFC CF = 0.38 and mean MODIS CF = 0.55), contributing up to 35 % of
surface-ozone bias in the current NAQFC system. |
url |
http://www.geosci-model-dev.net/8/2959/2015/gmd-8-2959-2015.pdf |
work_keys_str_mv |
AT hckim evaluationofmodeledsurfaceozonebiasesasafunctionofcloudcoverfraction AT plee evaluationofmodeledsurfaceozonebiasesasafunctionofcloudcoverfraction AT fngan evaluationofmodeledsurfaceozonebiasesasafunctionofcloudcoverfraction AT ytang evaluationofmodeledsurfaceozonebiasesasafunctionofcloudcoverfraction AT hlyoo evaluationofmodeledsurfaceozonebiasesasafunctionofcloudcoverfraction AT lpan evaluationofmodeledsurfaceozonebiasesasafunctionofcloudcoverfraction |
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