skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Assimilating compact phase space retrievals of atmospheric composition with WRF-Chem/DART: a regional chemical transport/ensemble Kalman filter data assimilation system

Abstract

This study introduces the Weather Research and Forecasting Model with chemistry/Data Assimilation Research Testbed (WRF-Chem/DART) chemical transport forecasting/data assimilation system together with the assimilation of ''compact phase space retrievals'' of satellite-derived atmospheric composition products. WRF-Chem is a state-of-the-art chemical transport model. DART is a flexible software environment for researching ensemble data assimilation with different assimilation and forecast model options. DART's primary assimilation tool is the ensemble adjustment Kalman filter. WRF-Chem/DART is applied to the assimilation of Terra/Measurement of Pollution in the Troposphere (MOPITT) carbon monoxide (CO) trace gas retrieval profiles. Those CO observations are first assimilated as quasi-optimal retrievals (QORs). Our results show that assimilation of the CO retrievals (i) reduced WRF-Chem's CO bias in retrieval and state space, and (ii) improved the CO forecast skill by reducing the Root Mean Square Error (RMSE) and increasing the Coefficient of Determination ($$R^2$$). Those CO forecast improvements were significant at the 95 % level. Trace gas retrieval data sets contain (i) large amounts of data with limited information content per observation, (ii) error covariance cross-correlations, and (iii) contributions from the retrieval prior profile that should be removed before assimilation. Those characteristics present challenges to the assimilation of retrievals. This paper addresses those challenges by introducing the assimilationmore » of compact phase space retrievals (CPSRs). CPSRs are obtained by preprocessing retrieval data sets with an algorithm that (i) compresses the retrieval data, (ii) diagonalizes the error covariance, and (iii) removes the retrieval prior profile contribution. Most modern ensemble assimilation algorithms can efficiently assimilate CPSRs. Our results show that assimilation of MOPITT CO CPSRs reduced the number of observations (and assimilation computation costs) by ~ 35 %, while providing CO forecast improvements comparable to or better than with the assimilation of MOPITT CO QORs.« less

Authors:
 [1];  [2];  [1];  [1];  [1]
  1. National Center for Atmospheric Research, Boulder, CO (United States)
  2. Univ. of Arizona, Tucson, AZ (United States)
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1480715
Grant/Contract Number:  
AC02-05CH11231
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Geoscientific Model Development (Online)
Additional Journal Information:
Journal Volume: 9; Journal Issue: 3; Journal ID: ISSN 1991-9603
Publisher:
European Geosciences Union
Country of Publication:
United States
Language:
English
Subject:
58 GEOSCIENCES

Citation Formats

Mizzi, Arthur P., Arellano, Jr., Avelino F., Edwards, David P., Anderson, Jeffrey L., and Pfister, Gabriele G. Assimilating compact phase space retrievals of atmospheric composition with WRF-Chem/DART: a regional chemical transport/ensemble Kalman filter data assimilation system. United States: N. p., 2016. Web. doi:10.5194/gmd-9-965-2016.
Mizzi, Arthur P., Arellano, Jr., Avelino F., Edwards, David P., Anderson, Jeffrey L., & Pfister, Gabriele G. Assimilating compact phase space retrievals of atmospheric composition with WRF-Chem/DART: a regional chemical transport/ensemble Kalman filter data assimilation system. United States. https://doi.org/10.5194/gmd-9-965-2016
Mizzi, Arthur P., Arellano, Jr., Avelino F., Edwards, David P., Anderson, Jeffrey L., and Pfister, Gabriele G. Fri . "Assimilating compact phase space retrievals of atmospheric composition with WRF-Chem/DART: a regional chemical transport/ensemble Kalman filter data assimilation system". United States. https://doi.org/10.5194/gmd-9-965-2016. https://www.osti.gov/servlets/purl/1480715.
@article{osti_1480715,
title = {Assimilating compact phase space retrievals of atmospheric composition with WRF-Chem/DART: a regional chemical transport/ensemble Kalman filter data assimilation system},
author = {Mizzi, Arthur P. and Arellano, Jr., Avelino F. and Edwards, David P. and Anderson, Jeffrey L. and Pfister, Gabriele G.},
abstractNote = {This study introduces the Weather Research and Forecasting Model with chemistry/Data Assimilation Research Testbed (WRF-Chem/DART) chemical transport forecasting/data assimilation system together with the assimilation of ''compact phase space retrievals'' of satellite-derived atmospheric composition products. WRF-Chem is a state-of-the-art chemical transport model. DART is a flexible software environment for researching ensemble data assimilation with different assimilation and forecast model options. DART's primary assimilation tool is the ensemble adjustment Kalman filter. WRF-Chem/DART is applied to the assimilation of Terra/Measurement of Pollution in the Troposphere (MOPITT) carbon monoxide (CO) trace gas retrieval profiles. Those CO observations are first assimilated as quasi-optimal retrievals (QORs). Our results show that assimilation of the CO retrievals (i) reduced WRF-Chem's CO bias in retrieval and state space, and (ii) improved the CO forecast skill by reducing the Root Mean Square Error (RMSE) and increasing the Coefficient of Determination ($R^2$). Those CO forecast improvements were significant at the 95 % level. Trace gas retrieval data sets contain (i) large amounts of data with limited information content per observation, (ii) error covariance cross-correlations, and (iii) contributions from the retrieval prior profile that should be removed before assimilation. Those characteristics present challenges to the assimilation of retrievals. This paper addresses those challenges by introducing the assimilation of compact phase space retrievals (CPSRs). CPSRs are obtained by preprocessing retrieval data sets with an algorithm that (i) compresses the retrieval data, (ii) diagonalizes the error covariance, and (iii) removes the retrieval prior profile contribution. Most modern ensemble assimilation algorithms can efficiently assimilate CPSRs. Our results show that assimilation of MOPITT CO CPSRs reduced the number of observations (and assimilation computation costs) by ~ 35 %, while providing CO forecast improvements comparable to or better than with the assimilation of MOPITT CO QORs.},
doi = {10.5194/gmd-9-965-2016},
url = {https://www.osti.gov/biblio/1480715}, journal = {Geoscientific Model Development (Online)},
issn = {1991-9603},
number = 3,
volume = 9,
place = {United States},
year = {2016},
month = {3}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record

Citation Metrics:
Cited by: 9 works
Citation information provided by
Web of Science

Save / Share:

Works referenced in this record:

Regional scale ozone data assimilation using an ensemble Kalman filter and the CHIMERE chemical transport model
journal, January 2014


The Fire INventory from NCAR (FINN): a high resolution global model to estimate the emissions from open burning
journal, January 2011


Assimilation of IASI satellite CO fields into a global chemistry transport model for validation against aircraft measurements
journal, January 2012


Using 3DVAR data assimilation system to improve ozone simulations in the Mexico City basin
journal, January 2008


Description and evaluation of the Model for Ozone and Related chemical Tracers, version 4 (MOZART-4)
journal, January 2010


Quantifying the contribution of inflow on surface ozone over California during summer 2008: CONTRIBUTION OF INFLOW ON SURFACE OZONE
journal, November 2013


A review of operational, regional-scale, chemical weather forecasting models in Europe
journal, January 2012


Observations of near-surface carbon monoxide from space using MOPITT multispectral retrievals
journal, January 2010


Online simulations of global aerosol distributions in the NASA GEOS-4 model and comparisons to satellite and ground-based aerosol optical depth
journal, January 2010


Experiments with the assimilation of fine aerosols using an ensemble Kalman filter: EnKF ASSIMILATION OF FINE AEROSOLS
journal, November 2012


CO source contribution analysis for California during ARCTAS-CARB
journal, January 2011


Evaluation of MOPITT retrievals of lower-tropospheric carbon monoxide over the United States: RETRIEVALS OF LOWER-TROPOSPHERIC CO
journal, July 2012


An Ensemble Adjustment Kalman Filter for Data Assimilation
journal, December 2001


A Local Least Squares Framework for Ensemble Filtering
journal, April 2003


The Data Assimilation Research Testbed: A Community Facility
journal, September 2009


Validation of MOPITT Version 5 thermal-infrared, near-infrared, and multispectral carbon monoxide profile retrievals for 2000-2011: VALIDATION OF MOPITT VERSION 5 RETRIEVALS
journal, June 2013


Fully coupled “online” chemistry within the WRF model
journal, December 2005


Efficient methods to assimilate remotely sensed data based on information content
journal, July 1998


On the Equivalence between Radiance and Retrieval Assimilation
journal, January 2012


Use of the Information Content in Satellite Measurements for an Efficient Interface to Data Assimilation
journal, July 2008


Information Content in Remote Sensing
journal, January 1974


    Works referencing / citing this record:

    Importance of Bias Correction in Data Assimilation of Multiple Observations Over Eastern China Using WRF‐Chem/DART
    journal, January 2020


    Integration of satellite remote sensing data in ecosystem modelling at local scales: Practices and trends
    journal, June 2018


    TCCON and NDACC XCO measurements: difference, discussion and application
    journal, January 2019


    Ensemble forecasts of air quality in eastern China – Part 2: Evaluation of the MarcoPolo–Panda prediction system, version 1
    journal, January 2019