Measuring Convective Organization

Organized systems of deep convective clouds are often associated with high-impact weather and changes in such systems may have implications for climate sensitivity. This has motivated the derivation of many organization indices that attempt to measure the level of deep convective aggregation in models and observations. Here we conduct a comprehensive review of existing methodologies and highlight some of their drawbacks, such as only measuring organization in a relative sense, being biased toward particular spatial scales, or being very sensitive to the details of the calculation algorithm. One widely used metric, Iorg, uses statistics of nearest-neighbor distances between convective storms to address the first of these concerns, but we show here that it is insensitive to organization beyond the meso-b scale and very contingent on the details of the implementation. We thus introduce a new and complementary metric, Lorg, based on all-pair convective storm distances, which is also an absolute metric that can discern regular, random, and clustered cloud scenes. It is linearly sensitive to spatial scale in most applications and robust to the implementation methodology. We also derive a discrete form suited to gridded data and provide corrections to account for cyclic boundary conditions and finite, open boundary domains of nonequal aspect ratios. We demonstrate the use of the metric with idealized synthetic configurations, as well as model output and satellite rainfall retrievals in the tropics. We claim that this new metric usefully supplements the existing family of indices that can help to understand convective organization across spatial scales.

Automated summary

While many organization indices have been derived to measure the level of deep convective aggregation, they have drawbacks such as being biased towards certain spatial scales or being very sensitive to calculation algorithms. The new metric, Lorg, offers an absolute measurement that distinguishes regular, random, and clustered cloud scenes, and is linearly sensitive to spatial scales in most applications. This metric provides a useful supplement to existing indices for understanding convective organization across spatial scales.