An integrated package for subgrid convection, clouds and precipitation compatible with meso‐gamma scales

Gerard, Luc

doi:10.1002/qj.58

Cited by 101 publications

(84 citation statements)

References 26 publications

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“…The key concept behind the package lies in the precipitation and cloud scheme called Modular Multiscale Microphysics and Transport (3MT) developed by Gerard and Geleyn (2005), Gerard (2007), and Gerard et al (2009). With mesh sizes mostly below the Rossby radius of deformation for convective phenomena, the parameterization schemes must take into consideration that the return current from updrafts is happening in a multitude of grid boxes.…”

Section: A Experimental Designmentioning

confidence: 99%

“…The multiscale performance of 3MT has been validated in a numerical weather prediction context up to a spatial resolution of 4 km (see Gerard et al 2009). The ALARO-0 model utilizes 1) the Action de Recherche Petite Echelle Grande Echell (ARPEGE) Calcul Radiatif avec Nebulosite (ACRANEB) scheme for radiation (Ritter and Geleyn 1992, recast in a Net Exchanged Rate framework), 2) a semi-Lagrangian horizontal diffusion scheme (SLHD) (Vana et al 2008), 3) some pseudoprognostic turbulent kinetic energy (pTKE) scheme (i.e., a Louis-type scheme for stability dependencies, but with memory, advection, and autodiffusion of the overall intensity of turbulence), and 4) a statistical sedimentation scheme for precipitation within a prognostic-type scheme for microphysics ).…”

Section: A Experimental Designmentioning

confidence: 99%

“…Since the 1990s the model has been widely used by the numerical weather prediction (NWP) community and, more recently, in regional climate modeling (e.g., Radu et al 2008;Skalak et al 2008). Furthermore, the model uses a diagnostic-type deep convection and microphysics parameterization based on Bougeault (1985) with upgrades from Gerard and Geleyn (2005). The new physical parameterizations within the ALARO-0 model, as proposed by Gerard et al (2009), were specifically designed to be used from mesoscale to the convection-permitting scales (so-called gray-zone scales) and are centered around an improved convection and cloud scheme.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the model uses a diagnostic-type deep convection and microphysics parameterization based on Bougeault (1985) with upgrades from Gerard and Geleyn (2005). The new physical parameterizations within the ALARO-0 model, as proposed by Gerard et al (2009), were specifically designed to be used from mesoscale to the convection-permitting scales (so-called gray-zone scales) and are centered around an improved convection and cloud scheme. For this study we use the version of the ALARO-0 model that was adopted for the operational applications in the Royal Meteorological Institute (RMI) of Belgium in 2010.…”

Section: Introductionmentioning

confidence: 99%

“…Since then this model has undergone systematic verification with respect to observations at 7-km resolution. Gerard et al (2009) tested the new parameterizations within the ALARO-0 model in a 1-day case study over Belgium, which was characterized by heavy convective precipitation. From this study an improvement of ALARO-0 at varying horizontal scales has been demonstrated.…”

Section: Introductionmentioning

confidence: 99%

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Multiscale Performance of the ALARO-0 Model for Simulating Extreme Summer Precipitation Climatology in Belgium

et al. 2013

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Daily summer precipitation over Belgium from the Aire Limitee Adaptation Dynamique Developpement International (ALADIN) model and a version of the model that has been updated with physical parameterizations, the so-called ALARO-0 model [ALADIN and AROME (Application de la Recherche a l'Operationnel a Meso-Echelle) combined model, first baseline version released in 1998], are compared with respect to station observations for the period 1961-90. The 40-yr European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-40) is dynamically downscaled using both models on a horizontal resolution of 40 km, followed by a one-way nesting on high spatial resolutions of 10 and 4 km. This setup allows us to explore the relative importance of spatial resolution versus parameterization formulation on the model skill to correctly simulate extreme daily precipitation. Model performances are assessed through standard statistical errors and density, frequency, and quantile distributions as well as extreme value analysis, using the peak-over-threshold method and generalized Pareto distribution. The 40-km simulations of ALADIN and ALARO-0 show similar results, both reproducing the observations reasonably well. For the high-resolution simulations, ALARO-0 at both 10 and 4 km is in better agreement with the observations than ALADIN. The ALADIN model consistently produces too high precipitation rates. The findings demonstrate that the new parameterizations within the ALARO-0 model are responsible for a correct simulation of extreme summer precipitation at various horizontal resolutions. Moreover, this study shows that ALARO-0 is a good candidate model for regional climate modeling.

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Section: A Experimental Designmentioning

confidence: 99%

Section: A Experimental Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multiscale Performance of the ALARO-0 Model for Simulating Extreme Summer Precipitation Climatology in Belgium

et al. 2013

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How does the air-sea coupling frequency affect convection during the MJO passage?

Zhao

Nasuno²

2020

Preprint

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The importance of air-sea coupling in the simulation and prediction of the Madden-Julian Oscillation (MJO) has been well established. However, it remains unclear how air-sea coupling modulates the convection and related oceanic features on the subdaily scale. Based on a regional cloud-permitting coupled model, we evaluated the impact of the air-sea coupling on the convection during the convectively active phase of the MJO by varying the coupling frequency. The model successfully reproduced the atmospheric and oceanic variations observed by satellite and in situ measurements but with some quantitative biases. According to the sensitivity experiments, we found that stronger convection was mainly caused by the higher sea surface temperatures (SSTs) generated in high-frequency coupled experiments, especially when the coupling frequency was 1 hr or shorter. A lower coupling frequency would generate the phase lags in the diurnal cycle of SST and related turbulent heat fluxes. Our analyses further demonstrated that the phase-lagged diurnal cycle of SST suppressed deep convection through a decrease in daytime moistening in the lower troposphere. Meanwhile, in the upper ocean, the high-frequency air-sea coupling helped maintain the shallower mixed and isothermal layers by diurnal heating and cooling at the sea surface, which led to a higher mean SST. In contrast, the low-frequency coupled experiments underestimated the SST and therefore convective activities. Overall, our results demonstrated that high-frequency air-sea coupling (1 hr or shorter) could improve the reproducibility of the intensity and temporal variation in both diurnal convection and upper ocean processes. Plain Language SummaryThe Madden-Julian Oscillation (MJO) is one of the most important sources of atmospheric variability in tropical regions; however, even the modern numerical models could not well reproduce the MJO. We believe that the underestimation of air-sea coupling may cause some parts of such biases in simulating/predicting the MJO. Therefore, our study is aimed to uncover the impact of the air-sea coupling on convection and the related oceanic feature during the MJO. By varying the air-sea coupling frequency, our results showed that the 1-hr or higher-frequency coupled experiments had better performance due to the well-reproduced sea surface temperature, while suppressed convection was found in low-frequency coupled experiments. In general, our study suggested that the high-frequency air-sea coupling could improve the reproducibility of both convection and upper ocean features during the MJO.

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Stochastic Deep Learning parameterization of Ocean Momentum Forcing

Guillaumin

Zanna

2021

Preprint

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Coupled climate simulations that span several hundred years cannot be run at a high-enough spatial resolution to resolve mesoscale ocean dynamics. These mesoscale dynamics backscatter to macroscales. Recently, several studies have considered Deep Learning to parameterize subgrid forcing within macroscale ocean equations using data from idealized simulations. In this manuscript, we present a stochastic Deep Learning parameterization that is trained on data generated by CM2.6, a highresolution state-of-the-art coupled climate model with nominal resolution 1/10°. We train a Convolutional Neural Network for the subgrid momentum forcing using macroscale surface velocities from a few selected subdomains. At each location and each time step of the coarse grid, rather than predicting a single number, we predict the mean and standard deviation of a Gaussian probability distribution. This approach requires training our neural network to minimize a negative log-likelihood loss function rather than the Mean Square Error, which has been the standard in applications of Deep Learning to the problem of parameterizations. Each prediction of the mean subgrid forcing can be associated with an uncertainty estimate and can form the basis for a stochastic subgrid parameterization. Offline tests show that our parameterization generalizes well to the global oceans, and a climate with increased CO2 levels, without further training. We test our stochastic parameterization in an idealized shallow water model. The implementation is stable and improves some statistics of the flow. Our work demonstrates the potential of combining Deep Learning tools with a probabilistic approach in parameterizing unresolved ocean dynamics.

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An integrated package for subgrid convection, clouds and precipitation compatible with meso‐gamma scales

Cited by 101 publications

References 26 publications

Multiscale Performance of the ALARO-0 Model for Simulating Extreme Summer Precipitation Climatology in Belgium

Multiscale Performance of the ALARO-0 Model for Simulating Extreme Summer Precipitation Climatology in Belgium

How does the air-sea coupling frequency affect convection during the MJO passage?

Stochastic Deep Learning parameterization of Ocean Momentum Forcing

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