Summary of the Final Report of Research Results1) Title of the proposed research
Wildfire detection and the estimation of methane emission ratio and gas emission rates
2) Principal Investigator (PI) and Co-Investigators (Co-Is)
PI:
Dr. Nakako Kobayashi
Co-I:
Dr. Gen Inoue
Dr. Heonsook Kim3) PI’s affiliation
Research Institute for Humanity and Nature
Now at Nagoya University4) Summary of the Final Report of Research Results
Biomass fire is associated with gas emissions consisted of CO2, CO, CH4, H2, hydrocarbons, andparticulates. These gases injected into the atmosphere work to increase radiative forcing, which accelerates the global warming. Burned biomass is estimated from the area submitted to the fire, theabove-ground biomass, and burning efficiency. Because of conspicuous dominance in the gases, it isrelatively easy to estimate CO2 emission. However, CH4 emission is rich in variety depending onburning condition mainly determined by fuel composition and wetness. Our final aim was to derive
CH4 emission ratios from the GOSAT data for various vegetation types, and to improve an estimation accuracy of emission amount from the wildfires in the world with the use of the ratios. The
emission ratio is represented as a ratio of concentration (or mass) excess in smoke plume from its
ambient background level for CH4 to that for CO2, and typically provides an important measure ofemission amount.
Although accuracy of FTS data are intensively examined by various researchers and assured to be
1-2% for values averaged in some extent of area and time, we need to know the intrinsic error in asingle scan data. We used the FTS data (V01.XX) and examined its variance in central Australia(latitude between 18.8 and 25.3 north and longitude between 123.8 and 131.5) and in the Southern
Hemisphere winter of 2009. The place and season were selected so that an effect of complex topography, vegetation cover, and artificial emission could be minimized. The average of the XCH4 data
(total number n=251) was 1.742 ppm and the standard deviation was 0.011 ppm, corresponding to ca.
0.7% of the average.

Figure 1.

Distribution of XCH4 over European Russia on 31 July 2010. Points indicate XCH4 (ppm) and thecolor gradation (blue-red) is proportional to its magnitude. Background is true color image obtained from CAI,and tiny red plots indicate hot spots of wildfire. Gray arrows represent wind speed and direction at 850hPa.

In investigating the ability for detection of the concentration excess, we focused on the case of massive wildfire near Moscow which continued for more than two months in the summer of 2010. In theevent, large emission amount of CH4 was predicted because the most intensive fires occurred in disturbed wetland area. Fist FTS L2 data were plotted on CAI image with the help of GIS. In addition,
fire hotspots information generated from MODIS data and wind fields obtained from NCEP/NCAR
reanalysis were superimposed on the image to infer the location of fire plumes easily. Figure 1
shows an example of one day. Here we can see no systematic enhancement in downwind XCH4 values and several outliers of unrealistically low level (1.57-1.69 ppm). The low values suggest thewrong estimates of total column amount (CLA) of dry air. Comparing retrieved atmospheric pressure to a priori value from GPV dataset, difference of several tens or more than 100 hPa was foundfrequently. Retrieved values are generally overestimated, which inevitably brings underestimation ofVMRs. Dense aerosol concentration in fire plumes may have resulted in large error in the retrieval.
For this reason, we applied a simple correction method applied to SCIAMACHY data followingFrankenberg et al. (2005). This method uses CLACO2 as a proxy for CLAdry_air. The corrected XCH4
is represented as follows:
XCH 4 ' =CLACH 4
CLACO2

XCO2 '(1)where XCO2’ is actual XCO2, which is not affected by the error mentioned above. Here, we used theglobal annual mean XCO2, which is assumed to be 370 ppm. Note that XCH4’ indicates relativeFigure 2. The same as Figure 1, but for XCH4’. Relatively high values are highlighted by a yellow circle.

value to the XCO2’ value, and now the absolute value has little meaning. By replacing XCH4 by
XCH4’ in Figure 1, outlier disappeared and some were above average of neighborhood (more than1.77 ppm) on the path of a smoke plume expected from wind vectors (Figure 2). The concentration
excess was corresponding to 2-3 times of the single-scan standard deviation observed in Australia,
and the credibility would be high in light of the consistency with visible smoke distribution. Furthermore, because CO2 concentration is likely to have increased as well, the 370 ppm of XCO2’would be smaller than in reality. Thus, XCH4’ would have been underestimated, not overestimated.
To decrease such uncertainties, we will need to estimate more realistic value of XCO2’ with numerical simulation combining atmospheric circulation model with surface CO2 emission and vegetation
activity such as WRF-chem, VPRM, and STILT.
This study aimed to construct a dataset of methane emission rates in wildfire by use of GOSAT
data, and to estimate methane emission amount more precisely than before using a mesoscale atmospheric model with the rates. But we went no further than investigation of data accuracy and ofability for plume detection as described above. This is mainly due to insufficient ability and time ofPI, but also due to the data scarcity around fires after being selected according to strict criteria. We
hope that our information can be any of help in further improvement of GOSAT product and forstudies aimed at wildfire research.
5) List of publications relating to the proposed research
None.