Exploratory Precipitation Metrics: Spatiotemporal Characteristics, Process-Oriented, and Phenomena-Based

Abstract

Precipitation sustains life and supports human activities, making its prediction one of the most societally relevant challenges in weather and climate modeling. Limitations in modeling precipitation underscore the need for diagnostics and metrics to evaluate precipitation in simulations and predictions. While routine use of basic metrics is important for documenting model skill, more sophisticated diagnostics and metrics aimed at connecting model biases to their sources and revealing precipitation characteristics relevant to how model precipitation is used are critical for improving models and their uses. This paper illustrates examples of exploratory diagnostics and metrics including: (1) spatiotemporal characteristics such as diurnal variability, probability of extremes, duration of dry spells, spectral characteristics, and spatiotemporal coherence of precipitation; (2) process-oriented metrics based on the rainfall-moisture coupling and temperature-water vapor environments of precipitation; and (3) phenomena-based metrics focusing on precipitation associated with weather phenomena including low pressure systems, mesoscale convective systems, frontal systems, and atmospheric rivers. Together, these diagnostics and metrics delineate the multifaceted and multiscale nature of precipitation, its relations with the environments, and its generation mechanisms. The metrics are applied to historical simulations from the Coupled Model Intercomparison Project Phase 5 and Phase 6. Models exhibit diverse skill as measured by the suite of metrics, with very few models consistently ranked as top or bottom performers compared to other models in multiple metrics. Analysis of model skill across metrics and models suggests possible relationships among subsets of metrics, motivating the need for more systematic analysis to understand model biases for informing model development.

Precipitation‐Moisture Coupling Over Tropical Oceans: Sequential Roles of Shallow, Deep, and Mesoscale Convective Systems

“…For example, the cyclical coevolution of MCS and the large‐scale moisture may provide additional statistical information to improve the parameterizations of MCS. In addition, the cyclical coevolution between various tropical convection and moisture can also be used as a process‐oriented metric to reflect the relationships between tropical rainfall and the thermodynamic environments, which provide information on the ability of models to reproduce the observed relationships and the potential contributions of large‐scale biases in the environments to the precipitation biases (e.g., W20; Leung et al., 2022).…”

Machine Learning‐Based Detection of Weather Fronts and Associated Extreme Precipitation in Historical and Future Climates

“…This result also points to the importance of other sources of extreme precipitation over the U.S. (Huang et al, 2018), and the difficulty of disentangling total and extreme precipitation from converging sources (Blázquez & Solman, 2019;Kunkel & Champion, 2019;Kunkel et al, 2012). It is also important to note that model projections of frontal precipitation may be influenced by compensating errors in frequency and intensity, as shown in Catto, Jakob, and Nicholls (2015) evaluating wintertime frontal precipitation in CMIP5 models and Leung et al (2022) with CMIP6 models. However Leung et al (2022) noted that CMIP6 models on average appear to have smaller errors, indicating some potential improvement in model biases.…”

“…However Leung et al. (2022) noted that CMIP6 models on average appear to have smaller errors, indicating some potential improvement in model biases.…”

“…Looking across CMIP6, Akinsanola, Kooperman, and Srivastava et al (2020) found that several individual models, including CESM2, exhibited a summertime dry bias over the eastern U.S. and a wintertime wet bias over the western U.S., which they linked to biases in orographic and convective processes. Leung et al (2022) found that CMIP6 models were able to well represent the spatial patterns of daily precipitation and duration of dry spells, implying these models would also be able to capture precipitation extremes. Continued model improvements in resolution (Bador et al, 2020;Wehner et al, 2014), calibration (Yang et al, 2012) and process representation such as convective precipitation (Harding et al, 2013) and atmospheric circulation (Priestley & Catto, 2022;Shepherd, 2014) may help address these biases and aid in the assessment of extreme precipitation associated with fronts.…”

Conditions for Convective Deep Inflow

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Accurate simulation and forecasting of weather and climate depends on adequate representations of deep convection in general circulation models (GCMs). This remains a challenging subject (Kuo et al, 2020;Leung et al, 2022;Yano & Plant, 2020) even with recent advances in cloud-resolving models (CRMs) and machine learning (Bretherton et al, 2022;Wing et al, 2020). Challenges arise especially in regards to organized convection, such as mesoscale convective systems (MCSs; Moncrieff et al, 2012;Yano & Moncrieff, 2016) that account for a significant fraction of precipitation (Nesbitt et al, 2006).

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