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- namelistfc : copy this file to 'fort.4' to run the experiment (modify as required)
- NODE.001_01 : this is the model output file as run at ECMWF. If your run fails, it may be useful to compare with this file.
Suggested sensitivity experiments
As ERA-Interim is an improved analysis, forecasts from these starting initial conditions will not reproduce the actual forecast of the storm. For that, the model should be run with operational data.
The IFS is highly tuned to give the best forecast over a range of initial conditions. However, it is instructive to try some sensitivity experiments to understand the role of various physical and dynamical processes.
- What's the impact of the different 'lead times' on the forecast of the storm (i.e. starting from different dates)?
- What's the difference and why between forecasts started with the operational analysis of the time and the ERA-Interim analysis?
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Sensitivity experiments
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- What's the impact of resolution on the forecast of the storm?
Reduce the timestep of the model - does this improve or worsen the forecast?
Reduce the gravity wave drag - how does this affect the forecast in the upper and lower levels?
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Edit the source code to half the gravity wave drag coefficient File: ifs/phys_ec/sugwd.F90, change:
to:
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Increase the precipitation auto conversion rate
Expand Edit the source code to increase the auto conversion rate by 20%
File: ifs/phys_ec/sucldp.F90, change:
Code Block line 123: RKCONV=1._JPRB/6000._JPRB ! 1/autoconversion time scale (s)
to:
Code Block line 123: ! RKCONV=1._JPRB/6000._JPRB ! 1/autoconversion time scale (s) line 124: RKCONV=1.2_JPRB/6000._JPRB ! default scaled by 20%: 1/autoconversion time scale (s)
Further reading
This article in a recent ECMWF Newsletter has a description of student projects at the University of Stockholm using the Lothar storm case study.
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