A review on regional convection‐permitting climate modeling: Demonstrations, prospects, and challenges

By Andreas F. Prein, W. Langhans, Giorgia Fosser, A. Ferrone, Nikolina Ban, K. Goergen, Michael Keller, M. Tölle, Oliver Gutjahr, F. Feser and 5 more. Reviews of Geophysics, 2015

1,236 citations recorded by the source corpus.

Regional climate modeling using convection‐permitting models (CPMs; horizontal grid spacing <4 km) emerges as a promising framework to provide more reliable climate information on regional to local scales compared to traditionally used large‐scale models (LSMs; horizontal grid spacing >10 km). CPMs no longer rely on convection parameterization schemes, which had been identified as a major source of errors and uncertainties in LSMs. Moreover, CPMs allow for a more accurate representation of surface and orography fields. The drawback of CPMs is the high demand on computational resources. For this reason, first CPM climate simulations only appeared a decade ago. In this study, we aim to provide a common basis for CPM climate simulations by giving a holistic review of the topic. The most important components in CPMs such as physical parameterizations and dynamical formulations are discussed critically. An overview of weaknesses and an outlook on required future developments is provided. Most importantly, this review presents the consolidated outcome of studies that addressed the added value of CPM climate simulations compared to LSMs. Improvements are evident mostly for climate statistics related to deep convection, mountainous regions, or extreme events. The climate change signals of CPM simulations suggest an increase in flash floods, changes in hail storm characteristics, and reductions in the snowpack over mountains. In conclusion, CPMs are a very promising tool for future climate research. However, coordinated modeling programs are crucially needed to advance parameterizations of unresolved physics and to assess the full potential of CPMs.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/df9feb3803cb224422efe346df0930331bef61f7

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Citations in the corpus

Large‐eddy simulations over Germany using ICON: a comprehensive evaluation (2017, background): “, 2015), now regularly allowing users to switch off parametrizations for deep convection (Baldauf et al., 2011; Holloway et al., 2012, 2014; Prein et al., 2015; Clark et al., 2016).”
Daily evaluation of 26 precipitation datasets using Stage-IV gauge-radar data for the CONUS (2018, neutral): “…consistently performed better in winter than in summer This is because predictable large-scale stratiform systems dominate in winter (Adler et al., 2001; Ebert et al., 2007; Coifﬁer, 2011), whereas unpredictable small-scale convective cells dominate in summer (Arakawa, 2004; Prein et al., 2015) –”
Finite basis physics-informed neural networks (FBPINNs): a scalable domain decomposition approach for solving differential equations (2021, background): “Indeed, many areas of science rely on our ability to accurately solve these problems, from modelling the climate to understanding fundamental particle physics [Giorgi, 2019, Prein et al., 2015, Agostinelli et al., 2003].”
Structure and Evolution of Mesoscale Convective Systems: Sensitivity to Cloud Microphysics in Convection‐Permitting Simulations Over the United States (2018, methodology): “Similar to Prein et al. (2017) who analyzed MCS precipitation in a 13-year WRF convection permitting climate simulation, we find that the simulated warm season mean patterns of MCS precipitation frequency and amount are in general agreement with observations (Figure 5). MCSs contribute to approximat”
Confronting the Challenge of Modeling Cloud and Precipitation Microphysics (2020, methodology): “Convection‐permitting model configurations have also been used recently for regional climate modeling (e.g.,Hohenegger et al., 2008; Kendon et al., 2012; Prein et al., 2015; Rasmussen et al., 2011, 2017; Wakazuki et al., 2008), and it is anticipated that global convection‐permittingweather and clima”
Convection‐permitting modeling with regional climate models: Latest developments and next steps (2021, methodology): “CPRCMs essentially extend the RCM concept to scales where deep convection can be explicitly simulated (Prein et al., 2015).”
Toward Convective-Scale Prediction within the Next Generation Global Prediction System (2019, methodology): “The FV3 in fvGFS uses the “vertically Lagrangian” dynamics of Lin (2004) extended with the nonhydrostatic pressure gradient computation of Lin (1997) and a semi-implicit solver for vertically propagating sound waves, discretized on the cubedsphere grid of Putman and Lin (2007).”