Introduction
This page describes two studies of different convective cases; one over N. America associated with formation of severe tornadoes, the other over central Africa.
Both cases are studied starting the forecast from the same date/time (initial conditions).
The N.America case has strong large scale forcing whereas the central African case is driven by the diurnal cycle.
US Tornado convection case (Arkansas)
On the 27 April 7pm local time (00UTC 28 April), tornadoes hit towns north and west of Little Rock, Arkansas killing approx 17 people. (http://edition.cnn.com/2014/04/28/us/severe-weather/index.html?hpt=hp_c2). On the evening on the 28 April fatal tornadoes occurred over Mississippi (http://www.bbc.co.uk/news/world-us-canada-27199071).
More information can also be found on the ECMWF Severe Event Catalogue 201404 - Convection - Arkansas U.S.
African diurnal deep convection (Central Africa)
Initial conditions
Key questions to address with the control forecast
Case study: N.Amerca deep convection
On 27 April 2014 7pm local time (00UTC 28 April), tornadoes hit towns north and west of Little Rock, Arkansas.
Case study: diurnal variation of convection
Sensitivity experiments
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 convection (i.e. starting from different dates)? Note that this only makes sense for the North American convection
- What's the impact of resolution on the forecast of the convection?
- Does reducing the model timestep improve or worsen the forecast?
Impact of the improved diurnal cycle of convection
Namelist block NAMCUMF, parameter RCAPDCYCL
RCAPDCYCL = 2 (default) activates the diurnal cycle using sub-cloud CAPE,
RCAPDCYCL = 1 diurnal cycle using surface sensible heat flux,
RCAPDCYCL = 0 reverts the code to a setting before the diurnal cycle for convection was
implemented.
Look at the timing of convective and precipitation events.
Optimization factor for the time scale adjustment:
Namelist block NAMCUMF, parameter RTAUA
RTAUA=1. default value (set it to 0.33 and 3.)
The ratio between the actual cloud base mass flux and the unit (initial) cloud base mass flux:
\[ \frac{M_{base}}{M^*_{base}} = \frac{PCAPE - PCAPE_{bl}}{\tau} \]Look at the amplitude of precipitation.
Sensitivity to entrainment rate:
Namelist block NAMCUMF, parameter ENTRORG
ENTRORG= 1.75E-3 default setting
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.
Further reading
201404 - Convection - Arkansas U.S
Comments
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.
Acknowledgements