Biogenic isoprene emissions driven by regional weather predictions using different initialization methods: case studies during the SEAC<sup>4</sup>RS and DISCOVER-AQ airborne campaigns
Land and atmospheric initial conditions of the Weather Research and Forecasting (WRF) model are often interpolated from a different model output. We perform case studies during NASA's SEAC<sup>4</sup>RS and DISCOVER-AQ Houston airborne campaigns, demonstrating that using land in...
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doaj-51a65782d75d4d97976bfda9dff469e72020-11-25T00:37:37ZengCopernicus PublicationsGeoscientific Model Development1991-959X1991-96032017-08-01103085310410.5194/gmd-10-3085-2017Biogenic isoprene emissions driven by regional weather predictions using different initialization methods: case studies during the SEAC<sup>4</sup>RS and DISCOVER-AQ airborne campaignsM. Huang0M. Huang1G. R. Carmichael2J. H. Crawford3A. Wisthaler4A. Wisthaler5X. Zhan6C. R. Hain7C. R. Hain8P. Lee9A. B. Guenther10George Mason University, Fairfax, VA, USAUniversity of Maryland, College Park, MD, USAUniversity of Iowa, Iowa City, IA, USANASA Langley Research Center, Hampton, VA, USAUniversity of Oslo, Oslo, NorwayUniversity of Innsbruck, Innsbruck, AustriaNOAA National Environmental Satellite, Data, and Information Service, College Park, MD, USAUniversity of Maryland, College Park, MD, USAnow at: NASA Marshall Space Flight Center, Huntsville, AL, USANOAA Air Resources Laboratory, College Park, MD, USAUniversity of California, Irvine, CA, USALand and atmospheric initial conditions of the Weather Research and Forecasting (WRF) model are often interpolated from a different model output. We perform case studies during NASA's SEAC<sup>4</sup>RS and DISCOVER-AQ Houston airborne campaigns, demonstrating that using land initial conditions directly downscaled from a coarser resolution dataset led to significant positive biases in the coupled NASA-Unified WRF (NUWRF, version 7) surface and near-surface air temperature and planetary boundary layer height (PBLH) around the Missouri Ozarks and Houston, Texas, as well as poorly partitioned latent and sensible heat fluxes. Replacing land initial conditions with the output from a long-term offline Land Information System (LIS) simulation can effectively reduce the positive biases in NUWRF surface air temperature by ∼ 2 °C. We also show that the LIS land initialization can modify surface air temperature errors almost 10 times as effectively as applying a different atmospheric initialization method. The LIS-NUWRF-based isoprene emission calculations by the Model of Emissions of Gases and Aerosols from Nature (MEGAN, version 2.1) are at least 20 % lower than those computed using the coarser resolution data-initialized NUWRF run, and are closer to aircraft-observation-derived emissions. Higher resolution MEGAN calculations are prone to amplified discrepancies with aircraft-observation-derived emissions on small scales. This is possibly a result of some limitations of MEGAN's parameterization and uncertainty in its inputs on small scales, as well as the representation error and the neglect of horizontal transport in deriving emissions from aircraft data. This study emphasizes the importance of proper land initialization to the coupled atmospheric weather modeling and the follow-on emission modeling. We anticipate it to also be critical to accurately representing other processes included in air quality modeling and chemical data assimilation. Having more confidence in the weather inputs is also beneficial for determining and quantifying the other sources of uncertainties (e.g., parameterization, other input data) of the models that they drive.https://www.geosci-model-dev.net/10/3085/2017/gmd-10-3085-2017.pdf |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
M. Huang M. Huang G. R. Carmichael J. H. Crawford A. Wisthaler A. Wisthaler X. Zhan C. R. Hain C. R. Hain P. Lee A. B. Guenther |
spellingShingle |
M. Huang M. Huang G. R. Carmichael J. H. Crawford A. Wisthaler A. Wisthaler X. Zhan C. R. Hain C. R. Hain P. Lee A. B. Guenther Biogenic isoprene emissions driven by regional weather predictions using different initialization methods: case studies during the SEAC<sup>4</sup>RS and DISCOVER-AQ airborne campaigns Geoscientific Model Development |
author_facet |
M. Huang M. Huang G. R. Carmichael J. H. Crawford A. Wisthaler A. Wisthaler X. Zhan C. R. Hain C. R. Hain P. Lee A. B. Guenther |
author_sort |
M. Huang |
title |
Biogenic isoprene emissions driven by regional weather predictions using different initialization methods: case studies during the SEAC<sup>4</sup>RS and DISCOVER-AQ airborne campaigns |
title_short |
Biogenic isoprene emissions driven by regional weather predictions using different initialization methods: case studies during the SEAC<sup>4</sup>RS and DISCOVER-AQ airborne campaigns |
title_full |
Biogenic isoprene emissions driven by regional weather predictions using different initialization methods: case studies during the SEAC<sup>4</sup>RS and DISCOVER-AQ airborne campaigns |
title_fullStr |
Biogenic isoprene emissions driven by regional weather predictions using different initialization methods: case studies during the SEAC<sup>4</sup>RS and DISCOVER-AQ airborne campaigns |
title_full_unstemmed |
Biogenic isoprene emissions driven by regional weather predictions using different initialization methods: case studies during the SEAC<sup>4</sup>RS and DISCOVER-AQ airborne campaigns |
title_sort |
biogenic isoprene emissions driven by regional weather predictions using different initialization methods: case studies during the seac<sup>4</sup>rs and discover-aq airborne campaigns |
publisher |
Copernicus Publications |
series |
Geoscientific Model Development |
issn |
1991-959X 1991-9603 |
publishDate |
2017-08-01 |
description |
Land and atmospheric initial conditions of the Weather
Research and Forecasting (WRF) model are often interpolated from a different
model output. We perform case studies during NASA's SEAC<sup>4</sup>RS and
DISCOVER-AQ Houston airborne campaigns, demonstrating that using land
initial conditions directly downscaled from a coarser resolution dataset led
to significant positive biases in the coupled NASA-Unified WRF (NUWRF,
version 7) surface and near-surface air temperature and planetary boundary layer
height (PBLH) around the Missouri Ozarks and Houston, Texas, as well as
poorly partitioned latent and sensible heat fluxes. Replacing land initial
conditions with the output from a long-term offline Land Information System (LIS) simulation can effectively reduce the positive biases in NUWRF surface
air temperature by ∼ 2 °C. We also show that the LIS
land initialization can modify surface air temperature errors almost 10
times as effectively as applying a different atmospheric initialization
method. The LIS-NUWRF-based isoprene emission calculations by the Model of
Emissions of Gases and Aerosols from Nature (MEGAN, version 2.1) are at
least 20 % lower than those computed using the coarser resolution
data-initialized NUWRF run, and are closer to aircraft-observation-derived
emissions. Higher resolution MEGAN calculations are prone to amplified
discrepancies with aircraft-observation-derived emissions on small scales.
This is possibly a result of some limitations of MEGAN's
parameterization and uncertainty in its inputs on small scales, as well as the
representation error and the neglect of horizontal transport in deriving
emissions from aircraft data. This study emphasizes the importance of proper
land initialization to the coupled atmospheric weather modeling and the
follow-on emission modeling. We anticipate it to also be critical to
accurately representing other processes included in air quality modeling and
chemical data assimilation. Having more confidence in the weather inputs is
also beneficial for determining and quantifying the other sources of
uncertainties (e.g., parameterization, other input data) of the models that
they drive. |
url |
https://www.geosci-model-dev.net/10/3085/2017/gmd-10-3085-2017.pdf |
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