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For this task, use the metview icons in the row labelled 'Oper forecast'
oper_rmse.mv : this plots the root-mean-square-error growth curves for the operational HRES forecast for the different lead times.
oper_to_an_runs.mv : this plots the same parameter from the different forecasts for the same verifying time. Use this to understand how the forecasts differed, particularly for the later forecasts closer to the event. oper_to_an_diff.mv : this plots a single parameter as a difference between the operational HIRES forecast and the ECMWF analysis. Use this to understand the forecast errors from the different lead times.
Parameters & map appearance. These macros have the same choice of parameters to plot and same choice of |
Use the metview macros to plot different days and compare to analysis and plot forecast differences.
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Each team should look at the forecast from all 4 starting dates and each team member should see the RMSE curves. Start by looking at the RMS error curves for the 4 different starting dates using MSLP (mean-sea-level pressure) and WGUST10 wind parameters (wind gust at 10m: WGUST10 and wind-speed at 850hPa : SPEED850) and the two geographical regions: use . Use the As a team, discuss what plots & parameters to use to address the questions above given what you see in the error growth curves. A starting point is to Then look at the difference between forecast and analysis to understand the error in the forecast, particularly the starting formation and final error. Team members can look at particular dates and choose particular variables for team discussion. |
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- Sources of forecast uncertainty: initial analysis and model error.
- Initial analysis uncertainty: sampled by use of Singular Vectors (SV) and Ensemble Data Assimilation (EDA).
- Model uncertainty: sampled by use of stochastic processes. In IFS this means Stochastically Perturbed Physical Tendencies (SPPT) and the spectral backscatter scheme (SKEB)
- Singular Vectors: a way of representing the fastest growing modes.
- Ensemble mean : this gives the average of all the ensemble members. Where the spread is high, small scale features can be smoothed out in the ensemble mean.
- Ensemble spread: this is
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- gives the standard deviation of the ensemble members and represents how different the members are from the ensemble mean
Objective: understand the forecast uncertainty
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