0000000000414168
AUTHOR
Johanna Tamminen
Markov Chain Monte Carlo Methods for High Dimensional Inversion in Remote Sensing
SummaryWe discuss the inversion of the gas profiles (ozone, NO3, NO2, aerosols and neutral density) in the upper atmosphere from the spectral occultation measurements. The data are produced by the ‘Global ozone monitoring of occultation of stars’ instrument on board the Envisat satellite that was launched in March 2002. The instrument measures the attenuation of light spectra at various horizontal paths from about 100 km down to 10–20 km. The new feature is that these data allow the inversion of the gas concentration height profiles. A short introduction is given to the present operational data management procedure with examples of the first real data inversion. Several solution options for…
Componentwise adaptation for high dimensional MCMC
We introduce a new adaptive MCMC algorithm, based on the traditional single component Metropolis-Hastings algorithm and on our earlier adaptive Metropolis algorithm (AM). In the new algorithm the adaption is performed component by component. The chain is no more Markovian, but it remains ergodic. The algorithm is demonstrated to work well in varying test cases up to 1000 dimensions.
Retrieval of atmospheric CH4profiles from Fourier transform infrared data using dimension reduction and MCMC
We introduce an inversion method that uses dimension reduction for the retrieval of atmospheric methane (CH4) profiles. Uncertainty analysis is performed using the Markov chain Monte Carlo (MCMC) statistical estimation. These techniques are used to retrieve CH4 profiles from the ground-based spectral measurements by the Fourier Transform Spectrometer (FTS) instrument at Sodankyla (67.4 degrees N, 26.6 degrees E), Northern Finland. The Sodankyla FTS is part of the Total Carbon Column Observing Network (TCCON), a global network that observes solar spectra in near-infrared wavelengths. The high spectral resolution of the data provides approximately 3 degrees of freedom about the vertical struc…