A New Organization Metric for Synoptic Scale Tropical Convective Aggregation

Jin, Daeho; Oreopoulos, Lazaros; Lee, Dongmin; Tan, Jackson; Kim, Kyu‐myong

doi:10.1029/2022jd036665

Cited by 3 publications

(6 citation statements)

References 47 publications

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“…Condition (1) states that the consequences on the indices must be small when one grid box is added as a new convective object. The consequences of adding a single convective grid box are also shown by Retsch et al (2020) and Jin et al (2022). However, only a few cases have been examined.…”

Section: First Category: Noise-safenessmentioning

confidence: 69%

“…This article focuses on a domain of 10 • x10 • because it is comparable to the domain sizes used in cloud-resolving model studies (Tompkins, 2001;Bretherton et al, 2005;Muller and Held, 2012;Wing and Emanuel, 2014;Holloway and Woolnough, 2016). In recent years, several observational studies tried to quantify the convective organization on the entire tropics (Xu et al, 2019;Bony et al, 2020;Jin et al, 2022;Bläckberg and Singh, 2022;Stubenrauch et al, 2023), hence a similar assessment should be performed using the tropics as a domain. Such a domain is far from being a square, thus attention should be given to the influence of domain shape and size.…”

Section: Discussionmentioning

confidence: 99%

“…Its concept reproduces the gravitational potential and it is determined from the distance between the centers of the objects and the radii of equalarea circles A scalar value is associated with each unique connection between pairs of objects, and the arithmetic average is computed over all the pairs. Recently, a modification of COP called Area-based COP (ABCOP), was proposed by Jin et al (2022) in order to improve the dependency on the object's area and number. Both COP and ABCOP are positive, dimensionless and higher values represent higher degrees of organization.…”

Section: Indices Of Convective Organizationmentioning

confidence: 99%

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Assessment of object-based indices to identify convective organization

Mandorli,

Stubenrauch

2023

Preprint

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Abstract. The emerging field of convective organization has attracted significant attention due to its potential implications for weather and climate. Numerous indices have been developed to identify organization of convection, serving as essential tools for advancing our understanding in this area. Because of the large number of convective indices, many results on organization are still uncertain and different studies have shown diverging results. The present analysis studies and compares nine object-based indices in order to evaluate their ability to quantify organization. The analysis begins by establishing a set of criteria expected for convective organization and subsequently subjecting the indices to assessment against these benchmarks. The criteria are organized into three categories. The first category tests the robustness of the indices against noise. The second category evaluates their sensitivity to the size and position of the convective objects. The third category assesses their dependency on the specific characteristics of the dataset in use. Among the indices scrutinized, none fulfill all the desired conditions, and some conditions are only marginally satisfied. Therefore, we developed a new index, called Organization Index based on Distance and Relative Area (OIDRA), as an example of a well-behaving index. The unmet conditions and differences between indices can explain the discord between different organization studies. The results come down to a guideline that will help to advance our description of deep convective organization.

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Section: First Category: Noise-safenessmentioning

confidence: 69%

Section: Discussionmentioning

confidence: 99%

Section: Indices Of Convective Organizationmentioning

confidence: 99%

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Assessment of object-based indices to identify convective organization

Mandorli,

Stubenrauch

2023

Preprint

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“…(2017); the morphological index of convective aggregation (Kadoya & Masunaga, 2018); the area‐based convective organization potential method of Jin et al. (2022), and the shape‐based BLW method of Beucler et al. (2020).…”

Section: Introductionmentioning

confidence: 99%

“…A quantitative metric for the degree of aggregation is essential for a thorough analysis, and several approaches have been developed in recent years, each of which focuses on particular characteristics of aggregated convection. Some of these methods include the Simple Convective Aggregation Index (SCAI) of Tobin et al (2012), which is based on the numbers of convective clusters within a region along with the average distances between clusters; the subsidence fraction method of Coppin and Bony (2015); an approach based on the spatial variance of moist static energy (Wing & Emanuel, 2014); the convective organization potential (COP) of White et al (2017); the morphological index of convective aggregation (Kadoya & Masunaga, 2018); the area-based convective organization potential method of Jin et al (2022), and the shape-based BLW method of Beucler et al (2020). Tompkins and Semie (2017) developed the I org index for use in numerical models, based on statistical comparisons with a pure random process.…”

mentioning

confidence: 99%

The Influence of Convective Aggregation on the Stable Isotopic Composition of Water Vapor

Galewsky,

Schneider,

Diekmann

et al. 2023

AGU Advances

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Remote sensing datasets of water vapor isotopic composition are used along with objective measures of convective aggregation to better understand the impact of convective aggregation on the atmospheric hydrologic cycle in the global tropics (30°N to 30°S) for the period 2015–2020. When convection is unaggregated, vertical velocity profiles are top‐heavy, mixing ratios increase and water vapor δD decreases as the mean precipitation rate increases, consistent with partial hydrometeor evaporation below anvils into a relatively humid atmospheric column. Aggregated convection is associated with bottom‐heavy vertical velocity profiles and a positive correlation between mixing ratio and δD, a result that is consistent with isotopic enrichment from detrainment of shallow convection near the observation level. Intermediate degrees of aggregation do not display significant variation in δD with mixing ratio or precipitation rate. Convective aggregation provides a useful paradigm for understanding the relationships between mixing ratio and isotopic composition across a range of convective settings. The results presented here may have utility for a variety of applications including the interpretation of paleoclimate archives and the evaluation of numerical simulations of convection.

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Convective organization and 3D structure of tropical cloud systems deduced from synergistic A-Train observations and machine learning

Stubenrauch¹,

Mandorli²,

Lemaitre³

2023

Atmos. Chem. Phys.

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Abstract. We are building a 3D description of upper tropospheric (UT) cloud systems in order to study the relation between convection and cirrus anvils. For this purpose we used cloud data from the Atmospheric InfraRed Sounder and the Infrared Atmospheric Sounding Interferometer and atmospheric and surface properties from the meteorological reanalyses ERA-Interim and machine learning techniques. The different artificial neural network models were trained on collocated radar–lidar data from the A-Train in order to add cloud top height, cloud vertical extent and cloud layering, as well as a rain intensity classification to describe the UT cloud systems. The latter has an accuracy of about 65 % to 70 % and allows us to build objects of strong precipitation, used to identify convective organization. This rain intensity classification is more efficient to detect large latent heating than cold cloud temperature. In combination with a cloud system analysis, we found that deeper convection leads to larger heavy rain areas and a larger detrainment, with a slightly smaller thick anvil emissivity. This kind of analysis can be used for a process-oriented evaluation of convective precipitation parameterizations in climate models. Furthermore, we have shown the usefulness of our data to investigate tropical convective organization metrics. A comparison of different tropical convective organization indices and proxies to define convective areas has revealed that all indices show a similar annual cycle in convective organization, in phase with convective core height and anvil detrainment. The geographical patterns and magnitudes in radiative heating rate interannual changes with respect to one specific convective organization index (Iorg) for the period 2008 to 2018 are similar to the ones related to the El Niño–Southern Oscillation. However, since the interannual anomalies of the convective organization indices are very small and noisy, it was impossible to find a coherent relationship with those of other tropical mean variables such as surface temperature, thin cirrus area or subsidence area.

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A New Organization Metric for Synoptic Scale Tropical Convective Aggregation

Cited by 3 publications

References 47 publications

Assessment of object-based indices to identify convective organization

Assessment of object-based indices to identify convective organization

The Influence of Convective Aggregation on the Stable Isotopic Composition of Water Vapor

Convective organization and 3D structure of tropical cloud systems deduced from synergistic A-Train observations and machine learning

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