Mesoscale modeling of smoke transport from equatorial Southeast Asian Maritime Continent to the Philippines: First comparison of ensemble analysis with in situ observations

Atmospheric transport of smoke from equatorial Southeast Asian Maritime Continent (Indonesia, Singapore, and Malaysia) to the Philippines was recently verified by the first‐ever measurement of aerosol composition in the region of the Sulu Sea from a research vessel named Vasco. However, numerical mo...

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Published in:Atmosphere
Main Authors: Ge, Cui, Wang, Jun, Reid, Jeffrey S., Posselt, Derek J., Xian, Peng, Hyer, Edward
Format: Article in Journal/Newspaper
Language:unknown
Published: Cambridge Univ. Press 2017
Subjects:
smoke in maritime content
smoke transport
ensemble modeling
cumulus schemes
the Philippines
Atmospheric and Oceanic Sciences
Science
Aerosol Robotic Network
Online Access:http://hdl.handle.net/2027.42/137624
https://doi.org/10.1002/2016JD026241
id ftumdeepblue:oai:deepblue.lib.umich.edu:2027.42/137624
record_format openpolar
institution Open Polar
collection University of Michigan: Deep Blue
op_collection_id ftumdeepblue
language unknown
topic smoke in maritime content
smoke transport
ensemble modeling
cumulus schemes
the Philippines
Atmospheric and Oceanic Sciences
Science
spellingShingle smoke in maritime content
smoke transport
ensemble modeling
cumulus schemes
the Philippines
Atmospheric and Oceanic Sciences
Science
Ge, Cui
Wang, Jun
Reid, Jeffrey S.
Posselt, Derek J.
Xian, Peng
Hyer, Edward
Mesoscale modeling of smoke transport from equatorial Southeast Asian Maritime Continent to the Philippines: First comparison of ensemble analysis with in situ observations
topic_facet smoke in maritime content
smoke transport
ensemble modeling
cumulus schemes
the Philippines
Atmospheric and Oceanic Sciences
Science
description Atmospheric transport of smoke from equatorial Southeast Asian Maritime Continent (Indonesia, Singapore, and Malaysia) to the Philippines was recently verified by the first‐ever measurement of aerosol composition in the region of the Sulu Sea from a research vessel named Vasco. However, numerical modeling of such transport can have large uncertainties due to the lack of observations for parameterization schemes and for describing fire emission and meteorology in this region. These uncertainties are analyzed here, for the first time, with an ensemble of 24 Weather Research and Forecasting model with Chemistry (WRF‐Chem) simulations. The ensemble reproduces the time series of observed surface nonsea‐salt PM2.5 concentrations observed from the Vasco vessel during 17–30 September 2011 and overall agrees with satellite (Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) and Moderate Resolution Imaging Spectroradiometer (MODIS)) and Aerosol Robotic Network (AERONET) data. The difference of meteorology between National Centers for Environmental Prediction (NCEP’s) Final (FNL) and European Center for Medium range Weather Forecasting (ECMWF’s) ERA renders the biggest spread in the ensemble (up to 20 μg m−3 or 200% in surface PM2.5), with FNL showing systematically superior results. The second biggest uncertainty is from fire emissions; the 2 day maximum Fire Locating and Modelling of Burning Emissions (FLAMBE) emission is superior than the instantaneous one. While Grell‐Devenyi (G3) and Betts‐Miller‐Janjić cumulus schemes only produce a difference of 3 μg m−3 of surface PM2.5 over the Sulu Sea, the ensemble mean agrees best with Climate Prediction Center (CPC) MORPHing (CMORPH)’s spatial distribution of precipitation. Simulation with FNL‐G3, 2 day maximum FLAMBE, and 800 m injection height outperforms other ensemble members. Finally, the global transport model (Navy Aerosol Analysis and Prediction System (NAAPS)) outperforms all WRF‐Chem simulations in describing smoke transport on 20 September ...
format Article in Journal/Newspaper
author Ge, Cui
Wang, Jun
Reid, Jeffrey S.
Posselt, Derek J.
Xian, Peng
Hyer, Edward
author_facet Ge, Cui
Wang, Jun
Reid, Jeffrey S.
Posselt, Derek J.
Xian, Peng
Hyer, Edward
author_sort Ge, Cui
title Mesoscale modeling of smoke transport from equatorial Southeast Asian Maritime Continent to the Philippines: First comparison of ensemble analysis with in situ observations
title_short Mesoscale modeling of smoke transport from equatorial Southeast Asian Maritime Continent to the Philippines: First comparison of ensemble analysis with in situ observations
title_full Mesoscale modeling of smoke transport from equatorial Southeast Asian Maritime Continent to the Philippines: First comparison of ensemble analysis with in situ observations
title_fullStr Mesoscale modeling of smoke transport from equatorial Southeast Asian Maritime Continent to the Philippines: First comparison of ensemble analysis with in situ observations
title_full_unstemmed Mesoscale modeling of smoke transport from equatorial Southeast Asian Maritime Continent to the Philippines: First comparison of ensemble analysis with in situ observations
title_sort mesoscale modeling of smoke transport from equatorial southeast asian maritime continent to the philippines: first comparison of ensemble analysis with in situ observations
publisher Cambridge Univ. Press
publishDate 2017
url http://hdl.handle.net/2027.42/137624
https://doi.org/10.1002/2016JD026241
genre Aerosol Robotic Network
genre_facet Aerosol Robotic Network
op_relation Ge, Cui; Wang, Jun; Reid, Jeffrey S.; Posselt, Derek J.; Xian, Peng; Hyer, Edward (2017). "Mesoscale modeling of smoke transport from equatorial Southeast Asian Maritime Continent to the Philippines: First comparison of ensemble analysis with in situ observations." Journal of Geophysical Research: Atmospheres 122(10): 5380-5398.
2169-897X
2169-8996
http://hdl.handle.net/2027.42/137624
doi:10.1002/2016JD026241
Journal of Geophysical Research: Atmospheres
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spelling ftumdeepblue:oai:deepblue.lib.umich.edu:2027.42/137624 2023-08-20T03:59:13+02:00 Mesoscale modeling of smoke transport from equatorial Southeast Asian Maritime Continent to the Philippines: First comparison of ensemble analysis with in situ observations Ge, Cui Wang, Jun Reid, Jeffrey S. Posselt, Derek J. Xian, Peng Hyer, Edward 2017-05-27 application/pdf http://hdl.handle.net/2027.42/137624 https://doi.org/10.1002/2016JD026241 unknown Cambridge Univ. Press Wiley Periodicals, Inc. Ge, Cui; Wang, Jun; Reid, Jeffrey S.; Posselt, Derek J.; Xian, Peng; Hyer, Edward (2017). "Mesoscale modeling of smoke transport from equatorial Southeast Asian Maritime Continent to the Philippines: First comparison of ensemble analysis with in situ observations." Journal of Geophysical Research: Atmospheres 122(10): 5380-5398. 2169-897X 2169-8996 http://hdl.handle.net/2027.42/137624 doi:10.1002/2016JD026241 Journal of Geophysical Research: Atmospheres Reid, J. S., et al. ( 2009 ), Global monitoring and forecasting of biomass‐burning smoke: Description of and lessons from the fire locating and modeling of burning emissions (FLAMBE) program, IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens., 2, 144 – 162, doi:10.1109/jstars.2009.2027443. Lynch, P., et al. ( 2016 ), An 11‐year global gridded aerosol optical thickness reanalysis (v1. 0) for atmospheric and climate sciences, Geosci. Model Dev., 9, 1489. Mallet, V., and B. Sportisse ( 2006 ), Uncertainty in a chemistry‐transport model due to physical parameterizations and numerical approximations: An ensemble approach applied to ozone modeling, J. Geophys. Res., 111, D01302, doi:10.1029/2005JD006149. McKeen, S., et al. ( 2007 ), Evaluation of several PM 2.5 forecast models using data collected during the ICARTT/NEAQS 2004 field study, J. Geophys. Res., 112, D10S20, doi:10.1029/2006JD007608. National Centers for Environmental Prediction/National Weather Service/NOAA/U.S. Department of Commerce ( 2000 ), NCEP FNL operational model global tropospheric analyses, April 1997 through June 2007, Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory, Boulder, Colo., doi:10.5065/D6FB50XD. Nasrollahi, N., A. AghaKouchak, J. Li, X. Gao, K. Hsu, and S. Sorooshian ( 2012 ), Assessing the impacts of different WRF precipitation physics in hurricane simulations, Weather Forecasting, 27, 1003 – 1016. O’Neill, N. T. ( 2003 ), Spectral discrimination of coarse and fine mode optical depth, J. Geophys. Res., 108 ( D17 ), 4559, doi:10.1029/2002JD002975. Olivier, J. G., Bouwman, A., Berdowski, J., Veldt, C., Bloos, J., Visschedijk, A., Zandveld, P., and Haverlag, J. ( 1996 ), Description of EDGAR version 2.0: A set of global emission inventories of greenhouse gases and ozone‐depleting substances for all anthropogenic and most natural sources on a per country basis and on 1 degree × 1 degree grid. Reid, J., P. Xian, E. Hyer, M. Flatau, E. Ramirez, F. Turk, C. Sampson, C. Zhang, E. Fukada, and E. Maloney ( 2012 ), Multi‐scale meteorological conceptual analysis of observed active fire hotspot activity and smoke optical depth in the Maritime Continent, Atmos. Chem. Phys., 12, 2117 – 2147. Reid, J. S., et al. ( 2013 ), Observing and understanding the Southeast Asian aerosol system by remote sensing: An initial review and analysis for the Seven Southeast Asian Studies (7SEAS) program, Atmos. Res., 122, 403 – 468, doi:10.1016/j.atmosres.2012.06.005. Reid, J. S., et al. ( 2015 ), Observations of the temporal variability in aerosol properties and their relationships to meteorology in the summer monsoonal South China Sea/East Sea: The scale‐dependent role of monsoonal flows, the Madden–Julian Oscillation, tropical cyclones, squall lines and cold pools, Atmos. Chem. Phys., 15, 1745 – 1768, doi:10.5194/acp‐15‐1745‐2015. Rosenfeld, D., and W. Woodley ( 2000 ), Deep convective clouds with sustained supercooled liquid water down to −37.5°C, Nature, 405, 440 – 442, doi:10.1038/35013030. Rubin, J. I., J. S. Reid, J. A. Hansen, J. L. Anderson, T. J. Hoar, C. A. Reynolds, W. R. Sessions, and D. L. Westphal ( 2016 ), Development of the Ensemble Navy Aerosol Analysis Prediction System (ENAAPS) and its application of the Data Assimilation Research Testbed (DART) in support of aerosol forecasting, Atmos. Chem. Phys., 16, 3927. Schell, B., I. J. Ackermann, H. Hass, F. S. Binkowski, and A. Ebel ( 2001 ), Modeling the formation of secondary organic aerosol within a comprehensive air quality model system, J. Geophys. Res., 106, 28,275 – 28,293, doi:10.1029/2001JD000384. Shi, Y., J. Zhang, J. S. Reid, B. Liu, and E. J. Hyer ( 2014 ), Critical evaluation of cloud contamination in the MISR aerosol products using MODIS cloud mask products, Atmos. Meas. Tech., 7, 1791 – 1801, doi:10.5194/amt-7-1791-2014. Taylor, K. E. ( 2001 ), Summarizing multiple aspects of model performance in a single diagram, J. Geophys. Res., 106 ( D7 ), 7183 – 7192. Van Der Werf, G. R., J. T. Randerson, L. Giglio, N. Gobron, and A. Dolman ( 2008 ), Climate controls on the variability of fires in the tropics and subtropics, Global Biogeochem. Cycles, 22, GB3028, doi:10.1029/2007GB003122. Wang, J., C. Ge, Z. Yang, E. J. Hyer, J. S. Reid, B.‐N. Chew, M. Mahmud, Y. Zhang, and M. Zhang ( 2013 ), Mesoscale modeling of smoke transport over the Southeast Asian Maritime Continent: Interplay of sea breeze, trade wind, typhoon, and topography, Atmos. Res., 122, 486 – 503, doi:10.1016/j.atmosres.2012.05.009. Wiedinmyer, C., S. Akagi, R. J. Yokelson, L. Emmons, J. Al‐Saadi, J. Orlando, and A. Soja ( 2011 ), The Fire INventory from NCAR (FINN): A high resolution global model to estimate the emissions from open burning, Geosci. Model Dev., 4, 625. Winker, D. M., et al. ( 2010 ), The CALIPSO mission: A global 3D view of aerosols and clouds, Bull. Am. Meteorol. Soc., 91, 1211 – 1229, doi:10.1175/2010BAMS3009.1. Xian, P., J. S. Reid, J. F. Turk, E. J. Hyer, and D. L. Westphal ( 2009 ), Impact of modeled versus satellite measured tropical precipitation on regional smoke optical thickness in an aerosol transport model, Geophys. Res. Lett., 36, L16805, doi:10.1029/2009GL038823. Xian, P., J. S. Reid, S. A. Atwood, R. S. Johnson, E. J. Hyer, D. L. Westphal, and W. Sessions ( 2013 ), Smoke aerosol transport patterns over the Maritime Continent, Atmos. Res., 122, 469 – 485. Yang, Q., W. Gustafson Jr., J. D. Fast, H. Wang, R. C. Easter, H. Morrison, Y.‐N. Lee, E. G. Chapman, S. Spak, and M. Mena‐Carrasco ( 2011 ), Assessing regional scale predictions of aerosols, marine stratocumulus, and their interactions during VOCALS‐REx using WRF‐Chem, Atmos. Chem. Phys., 11, 11,951 – 11,975. Zhang, J., and J. S. Reid ( 2006 ), MODIS aerosol product analysis for data assimilation: Assessment of level 2 aerosol optical thickness retrievals, J. Geophys. Res., 111, 22207, doi:10.1029/2005JD006898. Zhang, Q., D. G. Streets, G. R. Carmichael, K. He, H. Huo, A. Kannari, Z. Klimont, I. Park, S. Reddy, and J. Fu ( 2009 ), Asian emissions in 2006 for the NASA INTEX‐B mission, Atmos. Chem. Phys., 9, 5131 – 5153. Ackermann, I. J., H. Hass, M. Memmesheimer, A. Ebel, F. S. Binkowski, and U. 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IndexNoFollow smoke in maritime content smoke transport ensemble modeling cumulus schemes the Philippines Atmospheric and Oceanic Sciences Science Article 2017 ftumdeepblue https://doi.org/10.1002/2016JD02624110.1109/jstars.2009.202744310.1029/2005JD00614910.1029/2006JD00760810.5065/D6FB50XD10.1029/2002JD00297510.1016/j.atmosres.2012.06.00510.5194/acp‐15‐1745‐201510.1038/3501303010.1029/2001JD00038410.5194/amt-7-1791-201410. 2023-07-31T20:52:26Z Atmospheric transport of smoke from equatorial Southeast Asian Maritime Continent (Indonesia, Singapore, and Malaysia) to the Philippines was recently verified by the first‐ever measurement of aerosol composition in the region of the Sulu Sea from a research vessel named Vasco. However, numerical modeling of such transport can have large uncertainties due to the lack of observations for parameterization schemes and for describing fire emission and meteorology in this region. These uncertainties are analyzed here, for the first time, with an ensemble of 24 Weather Research and Forecasting model with Chemistry (WRF‐Chem) simulations. The ensemble reproduces the time series of observed surface nonsea‐salt PM2.5 concentrations observed from the Vasco vessel during 17–30 September 2011 and overall agrees with satellite (Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) and Moderate Resolution Imaging Spectroradiometer (MODIS)) and Aerosol Robotic Network (AERONET) data. The difference of meteorology between National Centers for Environmental Prediction (NCEP’s) Final (FNL) and European Center for Medium range Weather Forecasting (ECMWF’s) ERA renders the biggest spread in the ensemble (up to 20 μg m−3 or 200% in surface PM2.5), with FNL showing systematically superior results. The second biggest uncertainty is from fire emissions; the 2 day maximum Fire Locating and Modelling of Burning Emissions (FLAMBE) emission is superior than the instantaneous one. While Grell‐Devenyi (G3) and Betts‐Miller‐Janjić cumulus schemes only produce a difference of 3 μg m−3 of surface PM2.5 over the Sulu Sea, the ensemble mean agrees best with Climate Prediction Center (CPC) MORPHing (CMORPH)’s spatial distribution of precipitation. Simulation with FNL‐G3, 2 day maximum FLAMBE, and 800 m injection height outperforms other ensemble members. Finally, the global transport model (Navy Aerosol Analysis and Prediction System (NAAPS)) outperforms all WRF‐Chem simulations in describing smoke transport on 20 September ... Article in Journal/Newspaper Aerosol Robotic Network University of Michigan: Deep Blue Atmosphere 8 10 201

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