• Created by Unknown User (nagc), last modified on Jan 05, 2016

You are viewing an old version of this page. View the current version.

Compare with Current View Page History

« Previous Version 12 Next »

Introduction

Two cases (US Tornado case and Africa case)  - different types of convection

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).

African diurnal deep convection (Central Africa)

 

On this page...

Initial conditions

 

Case study: deep convection

On 27 April 7pm local time (00UTC 28 April), tornadoes hit towns north and west of Little Rock, Arkansas.

Key questions and tasks using the control forecast


  1. Understand the weather situation resulting in tornadoes
  2. Evaluate the control forecast and compare it to the analysis and observations
  3. What is the area of threat according to the control forecast?
  4. How does the convective adjustment process takes place and and what is the role of large scale forcing (why and where it happens)?

 

Case study: diurnal variation of convection

 

 

 

Key questions and tasks using the control forecast

 

  1. Understand the weather situation over Africa.
  2. What difference and why does diurnal variation of convection make?
  3. Describe the phase and amplitude of (scaling) of the precipitation with the different experiments with respect to surface heat fluxes.
  4. What is it that scales precipitation flux? 
    Compare buoyancy flux with enthalpy. Think about the effect of local heating combined with atmospheric moisture.
    What is the role of large scale in this case?
  5. How important is the correct diurnal cycle of precipitation and radiation for 2m temperature and dewpoint forecast?
  6. Compare differences between Central Africa and other areas (e.g. Amazonia)

 

 


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

 

 

 


 

  • No labels