GFS-Wave Monthly Satellite Verification
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General Information

These pages show a comparison of two WAVEWATCH III^® implementations:

1. GFSv16-Wave
2. Multigrid WW3 (Multi_1)

comparing model Significant Wave Height with Global Ocean L4 Significant Wave Height from NRT Satellite Measurements generated using EU Copernicus Marine Services information.

WorkFlow Description

1. Convert all model GRiB files to netcdf.
2. Extract the model nowcast/forecasts for all four daily cycles, synchronized to valid time.
3. Interpolate the model grid to the satellite track using a nearest neighbors algorithm.
4. Compute monthly descriptive statistics, retaining spatial information.
5. Plot monthly RMSE and Bias fields

Statistics Definitions

Bias

The mean difference between the model and observations, measures the tendency of the model process to over- or under-estimate the value of a parameter. Smaller absolute bias values indicate better agreement between measured and calculated values. Positive bias means overprediction, negative means underprediction.

        diff = model_data - observations
        bias = diff.mean()       
        

Root-Mean-Square Error (RMS Error)

Also called the root-mean-squared deviation, it's a measure of the differences between the observed and predicted values. Smaller RMSE values indicate better agreement between measured and calculated values.

        rmse=(diff**2).mean()**0.5