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Zhang, J.L., Campbell, J.R., Reid, J.S., Westphal, D.L., Baker, N.L., Campbell, W.F. and Hyer, E.J. (2011) Evaluating the Impact of Assimilating CALIOP-Derived Aerosol Extinction Profiles on a Global Mass Transport Model. Geophysical Research Letters, 38, Article ID: L14801.
http://dx.doi.org/10.1029/2011gl047737

has been cited by the following article:

• TITLE: Assessment of WRF/Chem Simulated Vertical Distributions of Particulate Matter from the 2009 Minto Flats South Wildfire in Interior Alaska by CALIPSO Total Backscatter and Depolarization Measurements

  AUTHORS: James Michael Madden, Nicole Mölders, Kenneth Sassen

  KEYWORDS: CALIPSO, MODIS, WRF/Chem, Wildfire-Smoke Distribution, Interior Alaska

  JOURNAL NAME: Open Journal of Air Pollution, Vol.4 No.3, August 28, 2015

  ABSTRACT: This feasibility study examined whether total backscatter and depolarization measurements from Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) in combination with sparse surface meteorological data and other information permitted qualitative assessment of simulated vertical and horizontal distributions of aerosols from wildfires over Interior Alaska. Comparisons between co-located WRF/Chem cross-sections and CALIPSO curtains showed temporal and spatial differences in smoke-plume height above ground, vertical and horizontal extension. Simple estimates of contributions of errors and processes elucidated that the different spatial and temporal resolution of model grid-cells and the lidar scan could provide offsets of the magnitude found in the comparison. The overestimation of 10 m wind speeds by on average 1.33 m·sǃ contributed to the offset. Energy estimates suggested that the energy needed for permafrost thawing may contribute to discrepancies between simulated and CALIPSO indicated plume height. A sensitivity study with lower emission rates showed similar features. The study demonstrated that use of CALIPSO data in combination with data from other sources than air-quality networks could serve for identification of potential model shortcomings by assessment of magnitudes of error and process impacts.