2024 , Volume 29, № 4, p.41-54

This paper presents a method for estimating methane emissions using satellite data and forecasts by a transport-diffusion model. The algorithm is based on calculating the optimal parameter estimate based on observational data and forecast over a given time interval. In this case, the Gaussian estimation is used, the algorithm is a special case of the deterministic version of the ensemble Kalman filter. The average values in these subareas are calculated over seven days period. A methodology for estimating zones with maximum changes in emissions is proposed and the behavior of these estimates over time for different seasons is studied. The proposed methodology allows effective assessing for areas which are appropriate for a detailed study of possible methane sources. An algorithm is also discussed which emissions have been estimated by calculating the analysis values of the LETKF algorithm and estimating the average values over a given period for subregions of equal area. In this case, both model forecast data and satellite data are used. The algorithm has the property of locality. The analysis stage of the ensemble Kalman filter algorithm is considered; the efficient local LETKF algorithm is implemented. A variant of the algorithm is considered, in which, at the analysis stage, the forecast results of the MOZART-4 model at given points in time and AIRS satellite data on methane concentration are used. The MOZART-4 model is a numerical atmospheric model designed to estimate the distribution and transport of chemicals. The AIRS infrared spectrometer is a key tool for measuring the vertical profile of various indicators of the state of the Earth’s atmosphere, including methane content. The AIRS system on NASA’s Aqua satellite provides highly accurate infrared data on the Earth’s thermal radiation. Its ability to record spectra in more than 2000 channels allows for the precise measurements needed to study the dynamics of atmospheric processes. Values of the obtained methane emissions are used to estimate the spatiotemporal distribution of methane emissions under given conditions. The algorithm under consideration is significantly less labor-intensive than the ensemble Kalman filter algorithm, and at the same time allows evaluating trends in emission variations and the areas where these changes are greatest. The obtained estimates can be used both independently and as a first step in the information and computing system for data assimilation that is currently being developed.