Theoretical Predictability Limits of Spatially Anisotropic Multifractal Processes: Implications for Weather Prediction. Issue 7 (12th July 2019) - Ramanathan, Arun; Satyanarayana, A.N.V.; Mandal, M.
Abstract: A correlation spectrum‐based approach is used to express the theoretical predictability limits of multifractal processes as an analytical function of their anisotropy parameters. This spatially anisotropic power law function is then used to investigate the general impact of anisotropy on the predictability of atmospheric fields in the weather regime. The investigation reveals that (i) vertical stratification of a field increases and decreases its super and subsphero‐scale predictability limits, respectively; (ii) trivial horizontal anisotropy slightly improves predictability at all scales; and (iii) horizontal anisotropy together with vertical stratification significantly enhances its predictability over almost the entire scale range. Applying these general results to the case of horizontal wind fields suggests that the interplay between spatial‐anisotropy and atmospheric predictability could account for improvements in forecast skill, commonly observed during the occurrence of rotating thunderstorms and breaks in the Indian summer monsoon. Plain Language Summary: Quantifying theoretical atmospheric predictability limits is necessary to understand the possibility of making reliable weather predictions. Since atmospheric fields are multifractal and frequently anisotropic with roundish structures near the sphero‐scale, this study expresses the predictability limits via their multifractal and anisotropy parameters for theoretically investigating how spatial anisotropy of a filed impacts its predictability. The investigation shows that horizontal anisotropy moderately increases predictability at all scales, whereas vertical stratification diminishes predictability at scales roughly smaller than the sphero‐scale while enhancing it at larger scales; horizontal anisotropy with vertical stratification, on the other hand, further improves predictability. The spatial anisotropy of horizontal winds seems to be responsible for the extended predictability of organized thunderstorms and monsoon breaks. Key Points: The theoretical predictability limits of atmospheric fields follow spatially anisotropic scaling laws Vertical stratification of these fields increases their supersphero‐scale predictability, while trivial horizontal anisotropy slightly improves predictability at all scales Spatial anisotropy of horizontal wind fields seems to play a crucial role in the extended predictability of supercell thunderstorms and monsoon breaks