An object‐based technique was utilized to identify hydrometeor size‐sorting signatures at lower levels in the convective regions of 10 mesoscale convective systems (MCSs) during the 2015 Plains Elevated Convection at Night (PECAN) field campaign. Composite statistical analysis indicates that the magnitudes of size‐sorting signatures (the separation distances between rain diameter maxima and concentration maxima) are nonlinearly correlated to the echo‐top height, rain mass beneath the melting level, and precipitation rates at higher percentiles. To explore this correlation, the weather forecasting and research model was used to simulate the 20 June 2015 MCS during PECAN. Statistical analysis of the model outputs indicates more active riming growth and quicker graupel fallout at warmer temperatures near areas with larger separation distances. While updraft intensity above the melting level was also positively correlated to separation distances, this correlation was only statistically significant within certain temperature ranges. Additional analyses reveal that the higher intense precipitation potential near signatures with large separation distances could be attributed to precipitation production from the melted graupel. Finally, spatial correspondence between graupel distribution at the melting level and rain distribution at the lowest model level illustrates the critical role of graupel sedimentation and sorting in creating size‐sorting signatures in MCSs during the PECAN field experiment.
Robust feature selection is vital for creating reliable and interpretable machine-learning (ML) models. When designing statistical prediction models in cases where domain knowledge is limited and underlying interactions are unknown, choosing the optimal set of features is often difficult. To mitigate this issue, we introduce a multidata (M) causal feature selection approach that simultaneously processes an ensemble of time series datasets and produces a single set of causal drivers. This approach uses the causal discovery algorithms PC $ {}_1 $ or PCMCI that are implemented in the Tigramite Python package. These algorithms utilize conditional independence tests to infer parts of the causal graph. Our causal feature selection approach filters out causally spurious links before passing the remaining causal features as inputs to ML models (multiple linear regression and random forest) that predict the targets. We apply our framework to the statistical intensity prediction of Western Pacific tropical cyclones (TCs), for which it is often difficult to accurately choose drivers and their dimensionality reduction (time lags, vertical levels, and area-averaging). Using more stringent significance thresholds in the conditional independence tests helps eliminate spurious causal relationships, thus helping the ML model generalize better to unseen TC cases. M-PC $ {}_1 $ with a reduced number of features outperforms M-PCMCI, noncausal ML, and other feature selection methods (lagged correlation and random), even slightly outperforming feature selection based on explainable artificial intelligence. The optimal causal drivers obtained from our causal feature selection help improve our understanding of underlying relationships and suggest new potential drivers of TC intensification.
<p>Quantifiable assessment of how different physical processes promote tropical cyclone (TC) development is paramount in improving basic understanding of TC genesis and TC intensification forecasts. This assessment can be made via Eulerian budgets or by linearizing the equations of motion. For instance, the Sawyer-Eliassen equation gives the secondary circulation driven by a steady thermodynamic forcing. However, existing diagnostic frameworks often make implicit assumptions such as axisymmetry and temporally-averaged forcing, precluding discussions on how spatially heterogeneous or transient forcing may affect TC intensity.&#160;</p><p>In this work, we combine principal component analysis with multiple linear regression to build a linear framework that predicts the evolution of three-dimensional wind fields at different forecast windows, based on current heating and wind conditions. We apply this model to ensembles of WRF simulations on Hurricane Maria (2017) and Typhoon Haiyan (2013). Uniquely, the simulations include cloud radiative feedback denial experiments, which enables us to quantify the extent to which radiative processes drive TC intensification. Given their simplicity, our models are reasonably accurate, with coefficients of determination exceeding 0.8 for forecast windows longer than six hours. The linear nature of our model allows us to cleanly decompose the contributions of different physical processes to three-dimensional TC kinematic changes. Using radiative heating as an example, preliminary results suggest that this heating creates outward-propagating diurnal variability in wind perturbations during critical intensification periods of Hurricane Maria. These wind perturbations resemble a shallow lower-tropospheric secondary circulation; implications of this circulation to TC intensification are explored.&#160;</p><p>More generally, our framework can map thermodynamic forcing to kinematic changes without relying on axisymmetric assumptions, which opens the door to data-driven discovery of the leading physical pathways to TC intensification.</p>
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