Fractal Analysis of Clouds in DYAMOND Summer Simulations (revised)

Data accompanying "The Fractal Nature of Clouds in Global Storm-Resolving Models", by H. M. Christensen and O. Driver, submitted to Geophysical Research Letters.

Summary

We compute the fractal dimension of clouds in the DYAMOND Summer simulations: https://www.esiwace.eu/services/dyamond/summer
This is compared to the dimension computed using the Himawari 8 satellite.

The simulations span 1 August--10 September 2016. We use data between 25^oS-25^oN, 80-200^oE. A binary cloud field is defined for the model simulations using outgoing long wave radiation using a given threshold. For Himawari observations we use the derived Cloud Top Temperature product, with a consistent threshold: see paper for details. Any pixel with outgoing long wave radiation or cloud top temperature below these values is defined as 'cloudy'.

Available model and satellite derived data

[model identifier]_clouds_230.csv

Contains sets of Area-Perimeter data couplets for each selected timestamp in the DYAMOND simulation indicated by [model identifier], using the 230 K cloud top temperature threshold.

[model identifier]_dims_threshold.csv

Contains the fractal dimension measured for each selected timestamp in the DYAMOND simulation indicated by [model identifier], as a function of threshold. This is the Area-Perimeter fractal dimension, \(P \propto A^{D/2}\). This can be obtained as the gradient of the regression line through the logarithm of the data in the 'clouds' files, multiplied by two. Data are provided for the following thresholds: 200, 210, 220, 230, 240, 250, 260 K.

Since the satellite fields are only available during daylight hours, we provide and analyse the data at 0200, 0300, and 0400 UTC for both satellite and model data (or the closest available timestamp to these times for each model).

Acknowledgements

H.M.C. was funded by Natural Environment Research Council grant number NE/P018238/1.

DYAMOND data management was provided by the German Climate Computing Center (DKRZ) and supported through the projects ESiWACE and ESiWACE2. The projects ESiWACE and ESiWACE2 have received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreements No 675191 and 823988. This work used resources of the Deutsches Klimarechenzentrum (DKRZ) granted by its Scientific Steering Committee (WLA) under project IDs bk1040 and bb1153.