D. N. Mukhin

Prognosis of Qualitative Behavior of a Dynamic System by the Observed Chaotic Time Series

An approach to the long-term prognosis of qualitative behavior of a dynamic system (DS) is proposed, which is based on the nonlinear-dynamical analysis of a weakly nonstationary chaotic time series (TS). A method for constructing prognostic models using the observed evolution of a single dynamic variable is described, which employs the proposed approach for prediction of bifurcations of low-dimensional DSs. The method is applied to analyze the TS generated by the Roessler system and the system of equations modeling photochemical processes in the mesosphere. The analysis is performed for a TS calculated in the case of a slow variation in the control parameter of the system. The duration of the “observed” TS is limited such that the system demonstrates only one, chaotic, type of behavior without any bifurcations during the observed TS. The proposed algorithm allows us to predict correctly the bifurcation sequences for both systems at times much longer than the duration of the observed TS, to point out the expected instants of specific bifurcation transitions and accuracy of determining these instants, as well as to calculate the probabilities to observe the predicted regimes of the systems behavior at the time of interest.

Retrieval of vertical stratosphere temperature profile from ground-based measurements of atmospheric self-radiation spectrum in millimeter-range lines of molecular oxygen

The retrieval technique of the vertical stratosphere temperature profile from ground-based measurements of the atmospheric self-radiation spectrum in a millimeter-wave range is proposed. This technique is based on the Bayesian approach to solving an inverse problem and includes assumptions on the measurement noise, as well as some prior information about the retrieved profile. The possibility of temperature retrieval in the range of 20–55 km with 10 K accuracy is demonstrated.

BAYESIAN APPROACH TO RETRIEVING A VERTICAL OZONE PROFILE FROM RADIOMETRIC MEASUREMENT DATA

We propose a technique for retrieving a vertical profile of the atmospheric ozone number density from ground-based radiometry data. The technique is based on the Bayesian approach to solving inverse problems and permits one, by allowing for measurement noise and using certain a priori information on the retrieved profile, to construct the probability distribution of the ozone number density in the entire altitude range being monitored. Using the proposed technique, we compare the retrieval results obtained for various (both well-known and suggested for the first time in this paper) methods of approximation and regularization of retrieved profiles. Model examples demonstrate that the proposed technique is capable of retrieving ozone-profile disturbances which accompany the formation of ozone holes.