Cloud computing

Steven Jonathan Böing with his virtual clouds in de background. (Photo: Tomas van Dijk)

Name: Steven Jonathan Böing (27)
Nationality: Dutch
Supervisors: Prof. Harm Jonker, Prof. Pier Siebesma (TU Delft/KNMI)
Subject: High Resolution Modeling of Deep Cumulus Convection

“My research is about deep convection. You can think of the tallest and widest clouds that occur in the atmosphere - say several kilometres wide and 10 kilometres high. What we are trying to investigate involves two issues that have a large effect on the formation and the development of these clouds.

The first half of my PhD has been about the influence of the layer below the cloud. The results were surprising! What we discovered is that it is not so much the magnitude of the extremes that determines whether a cloud will rise and become a very tall cloud, rather it is mainly the fact that the bigger clouds are already wider at cloud base.
Implementing this in a weather model is a process that takes quite some time. This work is in a very early stage. One of the things that have happened is that people just put in one very simple model for the organization of clouds in a weather model.
Weather models typically look at a scale of ten kilometres or more, which is coarser than individual clouds. We run computer simulations with a resolution of about 100 meters, and time steps of a few seconds, wherein you can actually see the individual clouds develop. Our simulations can also be thought of as a virtual laboratory for clouds.

Deep convective clouds are the clouds that occur in the tropics, also referred to as ‘cloud number 9’; the cumulonimbus cloud. This cloud is associated with heavy rainfall and wind gusts, so it’s very important to have the behaviour of these clouds modelled accurately in a weather model.
One of the funny things is that this often goes wrong in weather models. Weather models typically have a bias, a tendency to give this type of convection too early in the day. We’re trying to see if we can develop a model with which we can actually account for the gradual development of the clouds.

The second thing I’m looking at is the influence of the atmosphere above this layer, wherein the clouds form. For example, when the atmosphere is relatively dry around the cloud, it cannot become very tall, because it loses its moisture very rapidly. The influence of the air surrounding the cloud is much easier to implement than that of the air below. This makes it exciting, because sometimes with a very small tweak in defining the parameters, you can really improve the performance of a weather model.”