Error in running CMOD5.N with Python

[raavikant]

Hi,

As per my knowledge, it seems like your code is trying to iteratively estimate the wind speed (V) using the cmod5n_inverse function based on observed sigma0 values, incidence angles, and azimuth angles (phi). However, you mentioned that the results are not as expected, and the values seem to be repeating.

Influence of SAR Incidence direction and Angle on the classification result

Is there anyone to kindly explain how the look direction change (Desc. vs Asce.) with different Incidence Angle may lead to different classification results? e.g., Ascending: Incidence Angle (near-far): 38.75-41.20 obtained on 10 Aug. e.g., Descending: Incidence Angle (near-far): 46.3-48.2 obtained on 13 Aug From the same area. The Asce. image covers a larger area Objective: Water binary classification Thanks

You may try a few potential issues and suggestions to help you debug:

1. Initial Guess: Your initial guess for wind speed is set to 10 m/s for all observations. Depending on the problem, this may or may not be a suitable initial guess. You might want to experiment with different initial values.
2. Convergence Criteria: The loop in your cmod5n_inverse function iterates for a fixed number of times (10 iterations). In practice, you may want to implement a convergence criterion to stop the iteration when the change in V becomes very small, indicating convergence.
3. Step Size: The step size for adjusting V decreases by half in each iteration. If the change is too small or too large, it might lead to convergence issues. You can experiment with different step sizes or adapt the step size dynamically based on the convergence behavior.
4. Data Scaling: Ensure that the input data (sigma0_obs, incidence, phi) is correctly scaled and compatible with the assumptions made in the cmod5n_forward function. Any mismatch in units or assumptions could lead to convergence problems.
5. Debugging Output: Your code includes debugging print statements, which is a good practice. Review the output of these print statements to understand how the variables (V, step, sigma0_calc) change in each iteration and whether they behave as expected.
6. Check cmod5n_forward Function: Ensure that the cmod5n_forward function is implemented correctly and returns meaningful results. Check the formulas and constants used in this function to verify if they match the expected behavior.
7. Input Data: Double-check that the input data (sigma0_obs, incidence, phi) corresponds to the problem you are trying to solve. Any discrepancies in the input data can lead to unexpected results.
8. Initial Conditions: Make sure that the initial conditions and parameters for the problem are set correctly. For example, check if the constants (C) used in the cmod5n_forward function are appropriate for your specific application. You may also get assistance from an individual, having Python or mlops certification.

By addressing these points and experimenting with different initial guesses and convergence criteria, you should be able to debug and improve the performance of your wind speed estimation code.

Thanks