fractional_kernel_right_vector_of_mole_fraction_of_methane_in_air 1 [UUUU] The averaging kernel matrix is rather storage intensive and in order to ensure their availability for each individual retrieval, a storage efficient format is needed. In Schneider et al. (2022) a singular vector decomposition his proposed. This allows a storage efficient provision of the averaging kernels in form of the number of leading singular vectors (the rank), the left leading singular vectors, the leading singular values, and the right leading singular vectors. Weber (2019) documents the respective storage efficiency.
Change Date: 29 Jan 2024, 4:12 p.m.
right_singular_vector_of_remote_sensing_averaging_kernel_of_logarithm_of_mole_fraction_of_methane_in_air 1 [UUUU] The averaging kernel matrix is rather storage intensive and in order to ensure their availability for each individual retrieval, a storage efficient format is needed. In Schneider et al. (2022) a singular vector decomposition his proposed. This allows a storage efficient provision of the averaging kernels in form of the number of leading singular vectors (the rank), the left leading singular vectors, the leading singular values, and the right leading singular vectors. Weber (2019) documents the respective storage efficiency.
Change Date: 29 Jan 2024, 4:14 p.m.
right_singular_vector_of_remote_sensing_averaging_kernel_of_logarithm_of_mole_fraction_of_methane_in_air 1 [UUUU]P07 id: 3AWXTX3S Right singular vectors of the matrix representing the logarithmic scale remote sensing averaging kernels (Weber 2019; Schneider et al., 2022) of the methane mole fractions obtained by a remote sensing observation (changes of methane in the retrieved atmosphere relative to the changes of methane in the true atmosphere, Rodgers 2000; Keppens et al., 2015).