Fractal Analysis of Clouds in DYAMOND Summer Simulations

<strong>Data accompanying "<em>The Fractal Nature of Clouds in Global Storm-Resolving Models</em>", by H. M. Christensen and O. Driver, submitted to Geophysical Research Letters.</strong> <strong>Summary</strong> We compute the fractal dimension of clouds in the DYAMOND Summer simulations: https://www.esiwace.eu/services/dyamond/summer<br> This is compared to the dimension computed using Himawari 8. The simulations span 1 August--10 September 2016. We use data between 25^oS-25^oN, 80-180^oE. A binary cloud field is defined for the model simulations using outgoing long wave radiation with a threshold of 132 W/m². For Himawari observations we use the derived Cloud Top Temperature product, with a threshold of 215 K. Any pixel with outgoing long wave radiation or cloud top temperature below these values is defined as 'cloudy'. <strong>Available model derived data</strong> [model identifier]_clouds_[threshold]_NOREGRID.csv Contains sets of Area-Perimeter data couplets for each saved timestamp in the DYAMOND simulation indicated by [model identifier] [model identifier]_dims_[threshold]_NOREGRID.csv Contains the fractal dimension measured for each saved timestamp in the DYAMOND simulation indicated by [model identifier]. 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. <strong>Available satellite derived data</strong> As for the model data, except that the satellite fields are only available during daylight hours. We therefore provide the data between 0200 and 0400 UTC inclusive. <strong>Acknowledgements</strong> 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.