Abstract
Theoretical predictability measures of turbulent atmospheric flows are essential in estimating how realistic the current storm-scale strategic forecast skill expectations are. Atmospheric predictability studies in the past have usually neglected intermittency and anisotropy, which are typical features of atmospheric flows, rendering their application to the storm-scale weather regime ineffective. Furthermore, these studies are frequently limited to second-order statistical measures, which do not contain information about the rarer, more severe, and, therefore, more important (from a forecasting and mitigation perspective) weather events. Here we overcome these rather severe limitations by proposing an analytical expression for the theoretical predictability limits of anisotropic multifractal fields based on higher-order autocorrelation functions. The predictability limits are dependent on the order of statistical moment (q) and are smaller for larger q. Since higher-order statistical measures take into account rarer events, such more extreme phenomena are less predictable. While spatial anisotropy of the fields seems to increase their predictability limits (making them larger than the commonly expected eddy turnover times), the ratio of anisotropic to isotropic predictability limits is independent of q. Our results indicate that reliable storm-scale weather forecasting with around 3 to 5 hours lead time is theoretically possible.
Highlights
Theoretical predictability measures of turbulent atmospheric flows are essential in estimating how realistic the current storm-scale strategic forecast skill expectations are
As far as the field of aviation weather forecasting is concerned, knowledge of the location and intensity of hazardous convective weather about 2 to 6 hours in advance is vital for air traffic planning with minimal weather delays or diversions[14]
Determining the theoretical predictability limits of the storm-scale atmosphere is crucial in knowing if the shortcomings of current strategic mesoscale forecasts are just artifacts of the forecasting techniques used or if we have reached the intrinsic storm-scale atmospheric predictability limit
Summary
Theoretical predictability measures of turbulent atmospheric flows are essential in estimating how realistic the current storm-scale strategic forecast skill expectations are. Atmospheric predictability studies in the past have usually neglected intermittency and anisotropy, which are typical features of atmospheric flows, rendering their application to the storm-scale weather regime ineffective. These studies are frequently limited to second-order statistical measures, which do not contain information about the rarer, more severe, and, more important (from a forecasting and mitigation perspective) weather events. We overcome these rather severe limitations by proposing an analytical expression for the theoretical predictability limits of anisotropic multifractal fields based on higher-order autocorrelation functions. In this study an approach based on scaling laws that seem to be ubiquitous in nature[31,32,33,34] is used as explained in the Results section to obtain predictability estimates of the storm-scale atmospheric regime that are discussed in detail followed by a brief conclusion in the Discussion section
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