Because of the growing anthropogenous impact on Earth climate and ozone layer, studies of gaseous composition of the atmosphere is one of the central problems of modern atmospheric physics and chemistry. In the last decades the significant progress in understanding of features of spatial-temporal distribution of minor gas components (MGC) of the atmosphere has been achieved. The important role in such progress was played by the development of global satellite and ground-based observation systems, and the three-dimensional transport-chemistry modeling of the atmosphere. A body of information on atmospheric MGCs has been obtained by means of various satellite methods of measurements. Unique feature of satellite methods is a possibility of performing global observations with good frequency. It is especially important when studying the general circulation of the atmosphere, the climate and his changes and for numerical weather forecasts.

In essence, remote measurements are inverse problems of atmospheric optics. The most part of those are ill-posed in cllasic sense. Those are reduced to Fredgolm equations of the first kind and needs for applying special regularization algorithms. Af present there are many such algorithms differing by using various a priori information and methods of the regulization of the solution of integral equations. Simulation studies of errors of remote measurements and principal factors defining those are most important in preparation of satellite experiments.

In our laboratory, algorithms and software based on various methods of solving the inverse problems of atmospheric optics (multiple linear regression, artificial neural networks (ANN), and also physics-mathematical iteration algorithm based on statistical regularization method) have been developed for interpreting IRFS-2 and MTVZA satellite measurements. Numerical experiments and calculations of error matrices allow to estimating potential errors of the retrieval of various parameters (atmospheric temperature and humidity profiles, total content of water vapor and MGC, cloud liquid water content, near-surface wind speed, land and sea temperature).

Potential errors of temperature profile retrieval from IRFS-2 data using various the above methods are shown in Fig. 1.1. As it is seen, the iteration algorithm and ANN bring to aproximately indentical errors of 1–2 K. Fig. 1.2 shows similar errors for retrieving the humidity profiles. As shown the Figure, the ANN algorithm has some advantages in solving this non-linear inverse problem. Errors of the relative humidity retrieval using the ANN algorithm are 10–15%. Table in Fig. 1.3 demonstrates numerical estimates of retrieval errors for a number atmospheric gases, a priori variability of these gases and a number of freedom degrees (independent parameters) in outgoing radiation measurements by IRFS-2.

Results of modeling the land temperature and emissivity retrieval using IRFS-2 data showed the prospects of using multi-spectral measurements in the 8–12 um transparence window. Examples of retrieving the emissivity of soil and rough sandstone are given in Fig. 1.4.

Numerical estimates of potential errors for retrieving a number of atmospheric and surface parameters from MTVZA data are presented in Fig. 1.5.

Interpretation software developed in our laboratory and intended for processing IRFS-2 and MTVZA data is continuously perfected in process of obtaining operational data from a satellite board.

These studies are carried out in cooperation with scientists of SRC "Planeta".

**Principal papers**

1. Kostsov V.S., 2015: General approach to the formulation and solution of the multi-parameter inverse problems of atmospheric remote sensing with measurements and constraints of different types. *Int. Journ. Rem. Sensing*, **36**, 11, 2973–3004.

2. Polyakov A.V., Yu.M. Timofeev, Ya.A. Virolainen, 2014: Using artificial neural networks in the temperature and humidity sounding of the atmosphere. *Izvestiya, Atmospheric and Oceanic Physics*, **50**, 3, 330–336.

3. **Ïîëÿêîâ À.Â.**, 2014: Using artificial neural networks in retrieving vertical profiles of atmospheric parameters. *Atmospheric and Ocean Optics*, **27**, 1, 34–39 (in Russian) **.**

4. Polyakov Alexander, Yurii M. Timofeyev & Yana Virolainen, 2014: Comparison of different techniques in atmospheric temperature-humidity sensing from space. *Int. J. Rem. Sens.*, **35**, 15, 5899–5912.

5. **Polyakov A.V., Yu.M. Timofeev, V.S. Kostsov**, 2012: Satellite temperature sounding of the atmosphere under cloudiness. *Earth Res. from Space*, 5, 37–42 (in Russian).

4. Polyakov A.V., Yu.M. Timofeev, A.B. Uspensky, 2011: Possibilities for Determining Temperature and Emissivity of the Land Surface from Data of Satellite IR Sounders with High Spectral Resolution (IRFS-2). *Izvestiya, Atmospheric and Oceanic Physics*, **47**, 9, 1092–1096.

5. **Polyakov A.V., Yu.M. Timofeyev, A.B. Uspensky**, 2010: Possibilities of determination of the ozone and trace gases using the high spectral resolution data from IR satellite sensor IRFS-2. *Earth Res. from Space*, 3, 3–11 (in Russian).

6. **Virolainen Ya.A., Timofeev Yu.M., Polyakov A.V., Uspensky A.B.**, 2010: Optimal parameterization of outgoing thermal radiance spectra (satellite experiment with IKFS-2 spectrometer). *Atmospheric and Ocean Optics*, **23**, 2, 143–148 (in Russian).