The application discloses a method for
atmospheric ozone vertical profile inversion based on a differentiable
radiative transfer physical constraint, and comprises the following steps: collecting
atmospheric ozone data and auxiliary data to construct a
data set; constructing a logarithmic residual inversion
network model based on a logarithmic space residual learning mechanism; embedding a differentiable forward
simulation operator at the end of the logarithmic residual inversion
network model; introducing a physical constraint
loss function; training the model through a preheating-fine-tuning two-stage training method based on the
data set, and realizing the inversion of the
atmospheric ozone vertical profile based on the trained model. The application effectively solves the problem of neural network gradient disappearance or weight imbalance caused by the large concentration span (from ppb level to ppm level) of
ozone in the vertical direction.