Toward reduced transport errors in a high resolution urban CO2 inversion system
We present a high-resolution atmospheric inversion system combining a Lagrangian Particle Dispersion Model (LPDM) and the Weather Research and Forecasting model (WRF), and test the impact of assimilating meteorological observation on transport accuracy. A Four Dimensional Data Assimilation (FDDA) te...
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doaj-a4247709416744128bfdea013fc072d12020-11-24T22:25:22ZengBioOneElementa: Science of the Anthropocene2325-10262017-05-01510.1525/elementa.133161Toward reduced transport errors in a high resolution urban CO2 inversion systemAijun Deng0Thomas Lauvaux1Kenneth J. Davis2Brian J. Gaudet3Natasha Miles4Scott J. Richardson5Kai Wu6Daniel P. Sarmiento7R. Michael Hardesty8Timothy A. Bonin9W. Alan Brewer10Kevin R. Gurney11Utopus Insights, Inc., New YorkThe Pennsylvania State UniversityThe Pennsylvania State University, PennsylvaniaThe Pennsylvania State University, PennsylvaniaThe Pennsylvania State University, PennsylvaniaThe Pennsylvania State University, PennsylvaniaThe Pennsylvania State University, PennsylvaniaThe Pennsylvania State University, PennsylvaniaCooperative Institute for Research in Environmental Sciences, University of Colorado/NOAA, Chemical Sciences Division, ColoradoCooperative Institute for Research in Environmental Sciences, University of Colorado/NOAA, Chemical Sciences Division, ColoradoCooperative Institute for Research in Environmental Sciences, University of Colorado/NOAA, Chemical Sciences Division, ColoradoArizona State University, ArizonaWe present a high-resolution atmospheric inversion system combining a Lagrangian Particle Dispersion Model (LPDM) and the Weather Research and Forecasting model (WRF), and test the impact of assimilating meteorological observation on transport accuracy. A Four Dimensional Data Assimilation (FDDA) technique continuously assimilates meteorological observations from various observing systems into the transport modeling system, and is coupled to the high resolution CO2 emission product Hestia to simulate the atmospheric mole fractions of CO2. For the Indianapolis Flux Experiment (INFLUX) project, we evaluated the impact of assimilating different meteorological observation systems on the linearized adjoint solutions and the CO2 inverse fluxes estimated using observed CO2 mole fractions from 11 out of 12 communications towers over Indianapolis for the Sep.-Nov. 2013 period. While assimilating WMO surface measurements improved the simulated wind speed and direction, their impact on the planetary boundary layer (PBL) was limited. Simulated PBL wind statistics improved significantly when assimilating upper-air observations from the commercial airline program Aircraft Communications Addressing and Reporting System (ACARS) and continuous ground-based Doppler lidar wind observations. Wind direction mean absolute error (MAE) decreased from 26 to 14 degrees and the wind speed MAE decreased from 2.0 to 1.2 m s–1, while the bias remains small in all configurations (< 6 degrees and 0.2 m s–1). Wind speed MAE and ME are larger in daytime than in nighttime. PBL depth MAE is reduced by ~10%, with little bias reduction. The inverse results indicate that the spatial distribution of CO2 inverse fluxes were affected by the model performance while the overall flux estimates changed little across WRF simulations when aggregated over the entire domain. Our results show that PBL wind observations are a potent tool for increasing the precision of urban meteorological reanalyses, but that the impact on inverse flux estimates is dependent on the specific urban environment.https://www.elementascience.org/articles/133Greenhouse gas, Transport, Weather and Research Forecasting Model (WRF), Inversion |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Aijun Deng Thomas Lauvaux Kenneth J. Davis Brian J. Gaudet Natasha Miles Scott J. Richardson Kai Wu Daniel P. Sarmiento R. Michael Hardesty Timothy A. Bonin W. Alan Brewer Kevin R. Gurney |
spellingShingle |
Aijun Deng Thomas Lauvaux Kenneth J. Davis Brian J. Gaudet Natasha Miles Scott J. Richardson Kai Wu Daniel P. Sarmiento R. Michael Hardesty Timothy A. Bonin W. Alan Brewer Kevin R. Gurney Toward reduced transport errors in a high resolution urban CO2 inversion system Elementa: Science of the Anthropocene Greenhouse gas, Transport, Weather and Research Forecasting Model (WRF), Inversion |
author_facet |
Aijun Deng Thomas Lauvaux Kenneth J. Davis Brian J. Gaudet Natasha Miles Scott J. Richardson Kai Wu Daniel P. Sarmiento R. Michael Hardesty Timothy A. Bonin W. Alan Brewer Kevin R. Gurney |
author_sort |
Aijun Deng |
title |
Toward reduced transport errors in a high resolution urban CO2 inversion system |
title_short |
Toward reduced transport errors in a high resolution urban CO2 inversion system |
title_full |
Toward reduced transport errors in a high resolution urban CO2 inversion system |
title_fullStr |
Toward reduced transport errors in a high resolution urban CO2 inversion system |
title_full_unstemmed |
Toward reduced transport errors in a high resolution urban CO2 inversion system |
title_sort |
toward reduced transport errors in a high resolution urban co2 inversion system |
publisher |
BioOne |
series |
Elementa: Science of the Anthropocene |
issn |
2325-1026 |
publishDate |
2017-05-01 |
description |
We present a high-resolution atmospheric inversion system combining a Lagrangian Particle Dispersion Model (LPDM) and the Weather Research and Forecasting model (WRF), and test the impact of assimilating meteorological observation on transport accuracy. A Four Dimensional Data Assimilation (FDDA) technique continuously assimilates meteorological observations from various observing systems into the transport modeling system, and is coupled to the high resolution CO2 emission product Hestia to simulate the atmospheric mole fractions of CO2. For the Indianapolis Flux Experiment (INFLUX) project, we evaluated the impact of assimilating different meteorological observation systems on the linearized adjoint solutions and the CO2 inverse fluxes estimated using observed CO2 mole fractions from 11 out of 12 communications towers over Indianapolis for the Sep.-Nov. 2013 period. While assimilating WMO surface measurements improved the simulated wind speed and direction, their impact on the planetary boundary layer (PBL) was limited. Simulated PBL wind statistics improved significantly when assimilating upper-air observations from the commercial airline program Aircraft Communications Addressing and Reporting System (ACARS) and continuous ground-based Doppler lidar wind observations. Wind direction mean absolute error (MAE) decreased from 26 to 14 degrees and the wind speed MAE decreased from 2.0 to 1.2 m s–1, while the bias remains small in all configurations (< 6 degrees and 0.2 m s–1). Wind speed MAE and ME are larger in daytime than in nighttime. PBL depth MAE is reduced by ~10%, with little bias reduction. The inverse results indicate that the spatial distribution of CO2 inverse fluxes were affected by the model performance while the overall flux estimates changed little across WRF simulations when aggregated over the entire domain. Our results show that PBL wind observations are a potent tool for increasing the precision of urban meteorological reanalyses, but that the impact on inverse flux estimates is dependent on the specific urban environment. |
topic |
Greenhouse gas, Transport, Weather and Research Forecasting Model (WRF), Inversion |
url |
https://www.elementascience.org/articles/133 |
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