|
To be done.
TODO: Add maps.
On 27 April 2014 7pm local time (00UTC 28 April), tornadoes hit towns north and west of Little Rock, Arkansas.
|
TODO: note area of interest (show WV image?)
|
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.
Not all of the suggested experiments are applicable to both cases, indicated in brackets.
Turn off deep convection (both)
Do this by editing
|
Impact of the improved diurnal cycle of convection. (Africa only)
In this sensitivity experiment, look at the timing of convective and precipitation events by changing how the model parametrizes the diurnal cycle.
|
Increase the precipitation auto conversion rate - what impact does this have? (both)
Edit the source code to increase the auto conversion rate by 20% File: ifs/phys_ec/sucldp.F90, change:
to:
|
Impact of the convective time scale adjustment (both)
An optimization factor in the parametrization is used for tuning the diurnal cycle. This can be altered by changing a value in the model namelist.
To change the timescale: - Edit the fort.4 file - Find the namelist NAMCUMF, parameter RTAUA. - The default value is RTAUA=1. - Run two sensitivity experiments with values of RTAUA = 0.33 and 3. The ratio between the actual cloud base mass flux and the unit (initial) cloud base mass flux:
Look at the amplitude of precipitation. |
Sensitivity to entrainment rate (both)
To change the entrainment rate: - Edit the fort.4 file - Find the namelist block NAMCUMF, parameter ENTRORG - The default value is ENTRORG= 1.75E-3 ENTRORG= 5.8E-4 reduced by factor 3 (mostly shallow convection regime) ENTRORG= 5.25E-3 multiplied by factor 3 (mostly deep convection regime) Look at the cloud top height, precipitation and eventually changes in temperature and moisture fields with respect to the reference. Note also this is having less impact with the diurnal cycle activated. |
The forecasting system at ECMWF makes use of "ensembles" of forecasts to account for errors in the initial state. In reality, the forecast depends on the initial state in a much more complex way than just the model resolution or starting date. At ECMWF many initial states are created for the same starting time by use of "singular vectors" and "ensemble data assimilation" techniques which change the vertical structure of the initial perturbations.
As further reading and an extension of this case study, research how these methods work.
We are pleased to acknowledge the input from Peter Bechtold, Filip Vana, Sandor Kertesz in preparing the material for the OpenIFS user workshop in Stockholm 2014, from which the material on this page is derived.
<script type="text/javascript" src="https://software.ecmwf.int/issues/s/en_UKet2vtj/787/12/1.2.5/_/download/batch/com.atlassian.jira.collector.plugin.jira-issue-collector-plugin:issuecollector/com.atlassian.jira.collector.plugin.jira-issue-collector-plugin:issuecollector.js?collectorId=5fd84ec6"></script> |