The environment of aggregated deep convection

2019

J Adv Model Earth Syst

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This study investigates the role of interactive sea surface temperature (SST) in the early development of aggregated convection using a vector vorticity equation cloud‐resolving model coupled to a slab ocean. The simulations are initialized by a mock Walker circulation driven by initial SST gradient in the elongated x axis, with an average of 300 K and sinusoidal variation of amplitude ranging from 1.5 to 3 K. According to large‐scale perturbation strength, which is caused by SST variation, the results can be divided into two groups. Under weak perturbation, convection‐SST feedback efficiently eliminates SST gradient and moisture anomaly. The large‐scale environment is homogenized within 2 days. Even though SST in the group with stronger perturbation undergoes a similar process, significant moist static energy (MSE) advection in the boundary produces enough moisture difference to introduce virtual temperature effect and aggregation is triggered. Once dry zone starts to expand, radiative and convective effects regenerate SST gradient, which intensifies circulation and accelerates the process. We further show that the evolution of aggregation or not is captured by the trend of MSE‐EIS (estimated inversion strength) variance. The results highlight the boundary layer processes on the formation of aggregated convection in the tropics.

Section: The Modelmentioning

confidence: 99%

The Role of Interactive SST in the Cloud‐Resolving Simulations of Aggregated Convection

2019

J Adv Model Earth Syst

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“…These coastal regions are the hotspots where favorable environments for aggregated convection occur frequently, as identified in Fig. 13 of Tsai and Wu (2017) using a CWV > 45 mm and a critical vertical wind shear > 2 m s −1 (100 hPa) −1 within low-level layers (1000 -850 hPa). In the observational analysis of Hamada et al (2014), extreme rainfall over these regions is contributed mainly by convection systems that are both large in size and strong in intensity.…”

Section: Summary and Discussionmentioning

confidence: 99%

“…The NOAH land surface model (Chen et al 1996;Chen and Dudhia 2001) is implemented as the bottom boundary for surface fluxes (Wu et al 2019), coupled with the radiation para meterization using the rapid radiative transfer model for GCMs (Iacono et al 2008). The model has also been used to study the convective aggregation under different vertical wind shear and column moisture conditions (Tsai and Wu 2017), convection organization with heterogeneous land surface fluxes (Wu et al 2015), the transition of stratocumulus clouds (Tsai and Wu 2016), the precipitation hotspots caused by the afternoon thunderstorm in Taipei basin (Kuo and Wu 2019), and the design of unified parameterization for deep convection (Arakawa and Wu 2013;Wu and Arakawa 2014).…”

Section: Vector Vorticity Equation Cloud-resolving Model (Vvm)mentioning

confidence: 99%

“…The details of the land-ocean configuration of the simulated domain in this study are introduced in Section 4. The object-based analysis is also applied to the simulated cloud field, following the method in Tsai and Wu (2017). The grids with the sum of the cloud water and cloud ice mixing ratio > 10 −5 kg kg −1 are defined as "cloudy", and the spatially contiguous cloudy grids are connected to identify the 3D cloud objects in the simulations.…”

Section: Vector Vorticity Equation Cloud-resolving Model (Vvm)mentioning

confidence: 99%

“…The moisture development is considered part of the dynamical systems in which the interactions between convection, environmental water vapor, and atmospheric motions are important in understanding the multi-scale variabilities in the tropics (Yu and Neelin 1994;Raymond and Torres 1998;Sobel and Bretherton 2003;Fuchs 2007, 2009;Sugiyama 2009). Using idealized cloud-resolving model (CRM) simulations, Tsai and Wu (2017) further identified the environment for an increasing probability of the development of large aggregated convection when the environmental column relative humidity (CRH) is greater than 80 % without (and 67 % with) environmental wind shear. Their results also suggested that, after the critical CRH is met, the degree of aggregation is enhanced with the increase of CRH.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Role of Coastal Convection to Moisture Buildup during the South China Sea Summer Monsoon Onset

Journal of the Meteorological Society of Japan

Tsai

et al. 2019

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In this study, the climatological characteristics of object-based precipitation systems (OPSs) and moisture development are analyzed over the South China Sea (SCS) during the sharp transition of the summer monsoon onset. The satellite-observed statistics of the OPSs showed that over the 20-day pre-onset period, OPSs of small (< 100 km) to medium size (100-300 km) are active over the lands surrounding the SCS. The pre-onset composite mean shows a basin-scale (~ 1000 km) local circulation with anomalous subsidence over the ocean, and ocean convection is mostly suppressed. Over the 20-day post-onset period, large (> 300 km) OPSs develop over the coastal ocean and contribute to over 60 % of the total precipitation. The number of large OPSs observed significantly increases along with the sharp moisture buildup within 10 days after the onset. The moisture budget suggests that the local contribution from convective vertical mixing is the major moisture source during the first pentad after the onset. The relationship between moisture buildup and convection organization is then examined using a set of idealized cloud-resolving model (CRM) experiments, with a land-ocean configuration approximating the SCS basin. The CRM appropriately represents the observed development of coastal convection. In the noshear environment, a strong basin-scale circulation is formed, which suppresses the ocean moisture development. When large-scale vertical wind shear is imposed to represent the changes of large-scale circulation during the onset pentad, organized convection systems are increased over the coastal ocean and propagate toward the open ocean, accompanied by fast ocean moistening within 5-10 days.

A deep learning framework for analyzing cloud characteristics of aggregated convection using cloud‐resolving model simulations

Atmospheric Science Letters

2023

Self Cite

This study introduces a framework to extract the high-dimensional nonlinear relationships among state variables for aggregated convection. The prototype of such a framework is developed that applies the convolutional neural network models (CNN models) to retrieve the cloud characteristics from cloudresolving model (CRM) simulations. CNN model prediction factors are hidden in the high dimensional weighted parameters in each neural network layer.Therefore, we can dig out relevant physics processes by iterating the CNN models' training process and eliminating the features with the physics explanation we can provide at a given stage. Within a few iterations, explainable nonlinear relationships among variables can be provided. We identified that the averaged cloud water path (CWP), the maximum value of CWP in each cloud, and the cloud coverage rate are essential for identifying aggregation. Furthermore, by analyzing the encoded channels of the CNN model, we found a strong relationship between aggregation, cloud peripherals, and fractal dimensions. The results suggest that the important nonlinear cloud characteristics for identifying the aggregation can be captured with the proper adjustment and limitation of the input data to the CNN models. Our framework provides a possibility that we can explore the high dimensional relationship between the physics process with the assistance of the CNN model.