Tracking Clouds: Assessing the representation of mesoscale cloud patterns in the EUREC4A model data

We compare satellite data from the EUREC⁴A campaign (observed by the Advanced Baseline Image onboard the GOES-16 satellite) and model output from ICON-LEM tailored for the EUREC⁴A campaign [1]. All datasets are located east of Barbados in the Caribbean Sea. We build on previous cloud tracking analyses for the GOES satellite dataset [2].

We use a cloud tracking algorithm [2] to find lifetime and cloud size distributions. The trajectories can be classified into the four mesoscale cloud patterns “sugar” to “fish” based on C³ONTEXT data which makes it possible to investigate how well these patterns are represented in the model data. We compare the distributions of cloud sizes, lifetimes and the average development of cloud size over the cloud’s lifetimes.

Cloud modelling is a very important tool for climate research. However, it is not an easy task to validate model data and assess a model’s performance. The cloud tracking enables us to provide a unique quality assessment of the model data. Lifetime information is interesting because it encompasses multiple dynamic scales from micro to planetary regimes, while cloud size and cloud cover are important factors for the radiative properties of the clouds in a region and characterise the clouds’ general behavior.

 

[1] Schulz, Hauke & Stevens, Bjorn (2023) “Evaluating Large-Domain, Hecto-Meter, Large-Eddy Simulations of Trade-Wind Clouds Using EUREC4A Data” in Journal of Advances in Modeling Earth Systems, doi: 10.1029/2023MS003648

[2] Seelig et al. (2023) “Do optically denser trade-wind cumuli live longer?”, in Geophysical Research Letters, doi: 10.1029/2023GL103339