While the laser radar systems have high performance at short ranges and low altitudes, the atmospheric effects have been the major constraints of detection and parameter estimation of laser pulses at long ranges and high altitudes. The turbulence which depends on different atmospheric states is hard to quantify due to the wavelength dependent effects of various conditions at different layers of the atmosphere. The turbulence may also be caused by interaction of the atmosphere with other objects, such as the vortex flow due to the aerodynamics of the air targets, or the nonlinear propagation characteristic of the high energy laser pulses. These adverse effects of the atmosphere have been limiting the usefulness of the laser radar systems for a wide range of applications. If the atmosphere is considered as a nonlinear media with nonuniform index of refraction, then it can be thought of as a nonlinear distributed lens under diffraction limited conditions. In this paper, a neural network modeling of the ionosphere layer is presented and the laser pulse is characterized by a set of input features. The transient CO2 laser pulses is simulated to transmit through the atmosphere to a satellite-borne receiver. The satellite receiver model is composed of three stages, i.e., the filtering and processing of the ionospheric propagated waveform, the envelope extraction and channel simulation, and the detection and parameter estimation. The received signal is then evaluated against the background noise through Monte Carlo simulations.