One day in the life of clouds 

In 1870, Prof. Paey,  President of the Anthropological Society of Cuba, underlined that no one can ignore that studying clouds is one of the most practical needs of meteorology (1). More than 150 years later, the long-term stability of the Earth's atmosphere and climate (2) is recognized as sensitive to cloud dynamics (3), especially cloud thinning, relating it directly to climate change (4). The critical conclusion, documented in numerous studies (5), is that climate change is also a health crisis (6). The general panorama and the need to classify the clouds (7) to create a reliable Library for Machine Learning. Graph Geometric Algebra networks for graph representation learning (8) can become the decisive moment for cloud studies and modeling passing from classification to physics-informed Turing-like patterns recognition inside the diurnal variations of clouds and corresponding humidity profiles of the atmosphere. Multifractal and p-adic forecasting (9) of Big Data patterning is envisaged as the New Science of Complexity based on the physics of atmosphere, clouds, and climate (10, 11). Based on the physics-informed approach, we focus on original numbers systems and their multiscale pattering, fusing, and unifying Big Geo Data inside the probability-embedded medium, introducing the new methodology for Turning-type patterning quantifier of cloud system multiscale structure complexity extracted from physics-informed and statistics-informed raw data and images with moving space-temporal boundaries. Muuk'il Kaab (MIK) agile, bio-inspired (bees-type) software was designed and calibrated multiscale images from smartphones to high-precision photo cameras on clouds. This contribution shows more than ten years of testing as a new Metacomplexity Universal Quantitative Attribute (MCUQA) for complex pattern recognition, measurement, multiscale visualization, and skeletonization. Our research aims to optimize the fusion of multidimensional multiphysical raw data sets by the same nature-inspired bee-type software through data visualization, image analytics, virtualization, and the unification and forecasting of physics-informed measures with number theory.Keywords: Big Data; data fusion; algebra of images; physics-informed 3D signals visualization; networks images geometrization; Complexity quantitative attributes; thermodynamic, multifractal, and p-adic forecasting.References:Poey, F. New classification of clouds. 1870, Nature 2:382-385. Henderson-Sellers, A. Clouds and the long-term stability of the Earth's atmosphere and climate. Nature, 1979, 279260786-260788. Bony, S., Stevens, B., Frierson; D.M.W., Jakob, Ch., Kageyama, M., Pincus, R., Shepherd; T.G., Sherwood, S.C., Siebesma, A.P., Sobel, A.,M. and Webb, M. Clouds, circulation and climate sensitivity. Nature Geoscience, 2015, 261- 268. Sokol, A., Wall, C., & Hartmann, D.L. Greater climate sensitivity implied by anvil cloud thinning. Nature Geoscience, 2024, 17, 398-403. What happens when climate and mental health crises collide? Nature, 628, 235. Wong, C. Climate change is also the health crisis: These graphics explain Why. Nature, 624, 14-16. Schirber, M. Nobel prize: Complexity, from atoms to atmospheres. 2021, Physics 14, 141. Zhong, J., Cao, W. Graph Geometric Algebra networks for graph representation learning. 2025, Nature, Scientific Reports, 15, 170. Dubrulle, B. 2022. Multifractality, Universality and Singularity in Turbulence. 2022. Fractal and Fractional, 6, 613. Mason, B.J. Physics of clouds and precipitation. 1954. Nature, 20, 957-959. Bracco, A., Brajard, J., Dijkstra, H.A., Hassanzadeh, P.,, Ch. 2025. Machine learning for the physics of climate. Nature Reviews Physics, 7, 6-20.