Tracking Clouds: Comparing Geostationary Satellite Observations and Model Data in the EUREC4A domain

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. Since cloud model data can not be expected to be an exact match of corresponding satellite data, there is no immediate method of comparison available.

We use a cloud tracking algorithm [1] to find the lifetime and cloud size distributions of the cloud datasets. This 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.

Here we compare satellite data from the EUREC⁴A campaign [2] (observed by the Advanced Baseline Image onboard the GOES-16 satellite) and model output from ICON-LEM tailored for the EUREC⁴A campaign [3], where two resolutions are available. All datasets are located east of Barbados in the Caribbean Sea. We build on previous cloud tracking analyses for the GOES satellite dataset [1].

For the comparison between the three datasets, we first show the temporal development of cloud cover and number of clouds as an overview for the datasets. Secondly, we show the distributions of clouds lifetimes and sizes for all trajectories. The linear regression exponent for the logarithmic cloud size distribution can be expected to be around -2 on the global scale [4], which all three datasets come close to. However for this region, we would expect small clouds to have a bigger influence compared to the global view. This effect can be observed in the model data which have slightly more negative exponents for both resolutions. Thirdly, we show the average development of cloud size over the lifetime of the tracked clouds as a further metric for evaluating how well the model can represent the cloud-development processes.

References

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

[2] EUREC⁴A campaign: www.eurec4a.eu

[3] 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

[4] Wood, Robert & Field, Paul (2011) “The Distribution of Cloud Horizontal Sizes”, in J. Climate, doi: 10.1175/2011JCLI4056.1