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HRES data is provided at the same resolution as the operational model, in order to give the best representation of the Hurricane and cut-off low interations. This may mean that some plotting will be slow.
Fields available
The fields (parameters) available in the analyses are available in the forecast data.
A new field is total precipitation : tp.
Questions to consider
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For this exercise, you will use the metview icons in the row labelled 'HRES forecast' as shown above. hres_rmse.mv : this plots the root-mean-square-error growth curves for the operational HRES forecast compared to the ECMWF analyses.
hres_to_an_diff.mv : this plots a single parameter as a difference map between the operational HRES forecast and the ECMWF analysis. Use this to understand the forecast errors.
Parameters & map appearance. These macros have the same choice of parameters to plot and same choice of |
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Forecast performance
Task 1: Forecast error
In this task, we'll look at the difference between the forecast and the analysis by using "root-mean-square error" (RMSE) curves as a way of summarising the performance of the forecast. Root-mean square error curves are a standard measure to determine forecast error compared to the analysis and several of the exercises will use them. The RMSE is computed by taking the square-root of the mean of the forecast difference between the HRES and analyses.
Using the hres_rmse.mv icon, right-click, select 'Edit' and plot the RMSE curves for MSLP (mean-sea-level pressure). Repeat for the 10m wind-gust parameter wgust10RMSE curve for z500. RMSE of the 500hPa geopotential is a standard measure for assessing forecast model performance at ECMWF (for more information see: http://www.ecmwf.int/en/forecasts/quality-our-forecasts).
Repeat for the mean-sea-level pressure mslp.
Repeat for both geographical regions: mapType=0 1 (Atlantic) and mapType=1.
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2 (France).
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1. What do the RMSE curves show? 2. Why are the curves different between the two regions? |
Task 2: Compare forecast to analysis
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