author = "Miler, John B. and Basu, Sourish and Trudeau, Michael and Gatti, 
                         Luciana Vanni and Domingues, Lucas Gatti and Deeter, Merritt N. 
                         and Muller, Jean-Francois",
          affiliation = "{NOAA/ESRL Global Monitoring Division} and {NOAA/ESRL Global 
                         Monitoring Division} and {NOAA/ESRL Global Monitoring Division} 
                         and {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and {National 
                         Center for Atmospheric Research} and {Belgian Institute for Space 
                title = "Estimating emissions of Amazonian biomass burning using in situ 
                         and satellite measurements of atmospheric carbon monoxide",
                 year = "2019",
         organization = "AGU Fall Meeting",
             abstract = "Fire is a significant mechanism by which carbon leaves the forest 
                         and savanna biomes of the Amazon Basin. Most often, these carbon 
                         emissions are quantified using bottom-up inventory approaches that 
                         are partly constrained by satellite detection of fire hot spots or 
                         burned area, but also rely on highly uncertain parameters such as 
                         fuel loading and combustion completeness. As a complement to 
                         bottom-up emissions estimates, here we will present, independent, 
                         top-down estimates of carbon emissions from fires based on 
                         measurements of atmospheric carbon monoxide (CO). CO is emitted 
                         from fires, which allows its use, within an atmospheric inverse 
                         model, to determine total carbon emissions given knowledge of 
                         emission ratios for different vegetation types. In order to 
                         isolate the impact of Amazonian fires on atmospheric CO 
                         concentrations, a global CO modeling framework has been developed 
                         that describes all the sources and sinks of atmospheric CO. Most 
                         importantly for the Amazonian atmosphere, we have included a 
                         description of the production of CO resulting from oxidation of 
                         non-methane volatile organic compounds (NMVOCs) constrained by 
                         satellite observations of formaldehyde. We have conducted three 
                         separate inversions spanning 2010-2017 using different types of 
                         atmospheric CO data as constraints on fire emissions: a) in situ 
                         CO data from the NOAA Global Greenhouse Gas Reference Network and 
                         especially from INPE bi-weekly aircraft vertical profiles from the 
                         surface to 4.5 km above sea level; b) satellite CO data from the 
                         IASI instrument aboard the MetOp A satellite; and c) satellite CO 
                         data from the MOPITT sensor flying aboard NASAs Terra. We will 
                         also use the highly accurate (but sparse in time and space) INPE 
                         in situ vertical profiles to assess the potentially biased (but 
                         spatially and temporally dense) satellite CO data streams. 
                         Preliminary comparisons of in situ CO data with those from MOPITT 
                         show low bias, suggesting that the emissions derived from (at 
                         least) the MOPITT-based inversions should be robust. For all of 
                         our inversely-derived C emissions, we will consider the impact of 
                         emissions ratio uncertainties, and with that perspective, we will 
                         compare our top-down C emissions with those from inventories such 
                         as GFED and GFAS.",
  conference-location = "San Francisco, CA",
      conference-year = "09-13 dec.",
             language = "en",
           targetfile = "miller_estimating.pdf",
        urlaccessdate = "2022, Jan. 26"