Exploring a graph theory based algorithm for automated identification and characterization of large mesoscale convective systems in satellite datasets

Whitehall, Kim; Mattmann, Chris A.; Jenkins, Gregory S.; Rwebangira, Mugizi Robert; Demoz, Belay; Waliser, Duane E.; Kim, Jinwon; Goodale, C. E.; Hart, Andrew F.; Ramírez, Paul; Joyce, Michael; Boustani, Maziyar; Zimdars, Paul; Loikith, Paul C.; Lee, Huikyo

doi:10.1007/s12145-014-0181-3

Cited by 30 publications

(22 citation statements)

References 33 publications

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“…The data span a period of five years from 1 April 2010 to 30 September 2014, and only include warm seasons (April-September) each year. Severe thunderstorms are common features in the study area, so understanding their life cycle characteristics is significant for weather forecasting, disaster management, and hydrological management [31].…”

Section: Lightning Datamentioning

confidence: 99%

Life Cycle Characteristics of Warm-Season Severe Thunderstorms in Central United States from 2010 to 2014

Liu

2016

Climate

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Abstract:Weather monitoring systems, such as Doppler radars, collect a high volume of measurements with fine spatial and temporal resolutions that provide opportunities to study many convective weather events. This study examines the spatial and temporal characteristics of severe thunderstorm life cycles in central United States mainly covering Kansas, Oklahoma, and northern Texas during the warm seasons from 2010 to 2014. Thunderstorms are identified using radar reflectivity and cloud-to-ground lightning data and are tracked using a directed graph model that can represent the whole life cycle of a thunderstorm. Thunderstorms were stored in a GIS database with a number of additional thunderstorm attributes. Spatial and temporal characteristics of the thunderstorms were analyzed, including the yearly total number of thunderstorms, their monthly distribution, durations, initiation time, termination time, movement speed and direction, and the spatial distributions of thunderstorm tracks, initiations, and terminations. Results revealed that thunderstorms were most frequent across the eastern part of the study area, especially at the borders between Kansas, Missouri, Oklahoma, and Arkansas. Finally, thunderstorm occurrence is linked to land cover, including a comparison of thunderstorms between urban and surrounding rural areas. Results demonstrated that thunderstorms would favor forests and urban areas. This study demonstrates that advanced GIS representations and analyses for spatiotemporal events provide effective research tools to meteorological studies.

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Section: Lightning Datamentioning

confidence: 99%

Life Cycle Characteristics of Warm-Season Severe Thunderstorms in Central United States from 2010 to 2014

Liu

2016

Climate

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“…According to Harada et al (2016), the JRA-55 has higher quality in the representation of daily precipitation in the tropics. The method for identifying MCS in this study is a modified GTG algorithm (Whitehall et al 2015). Application of the GTG algorithm is a new method which has been used in the earlier studies (Nuryanto et al 2017a;Putri et al 2017Putri et al , 2018, and have the advantage of anticipating a problem relating to the complex life cycle of the MCSs (Whitehall et al 2015), such as the merging of multiple convective cells as identifying by Mathon et al (2002).…”

Section: Methodsmentioning

confidence: 99%

“…In this paper, we address the influence of atmospheric conditions on two different MCS events, its life cycle, and related maturity processes of MCS supporting heavy rainfall over the GJ area. The evidence of MCS events that occurred in the GJ area on January 2013 was investigated using modified "Grab 'em, Tag 'em, Graph 'em" (GTG) algorithm (Whitehall et al 2015;Nuryanto et al 2017a;Putri et al 2017Putri et al , 2018. A construction theory from the documented previous studies and further investigation of certain atmospheric condition analysis during the MCS events are presented.…”

Section: Introductionmentioning

confidence: 99%

Characteristics of two mesoscale convective systems (MCSs) over the Greater Jakarta: case of heavy rainfall period 15–18 January 2013

et al. 2019

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Two different mesoscale convective system (MCS) events that produced the heavy rainfall over the Greater Jakarta (GJ) during 15-18 January 2013 period were investigated. The purpose of the present study is to analyze the atmospheric conditions of two different MCSs during the heavy rainfall. Data consist of 3 hourly rainfalls of meteorological stations, infrared satellite, sounding, 6-hourly surface wind and reanalysis data. The first MCS was developed at 16:00 LT on 14 January 2013 over the eastern coast of Sumatra covered an area of 249,732 km 2 at maximum size, with about 16 h durations. The next MCS was developed at 22:00 LT on 16 January 2013 over the northern coast of the GJ in 9 h of duration, and maximum covered area around 55,829 km 2 . A warmer and moist air was observed on the low-level layer in the evening of 16 January 2013 (prior of second MCS), in comparison to 14 January 2013 event. Combination of both the surface strong wind perturbation and equivalent potential temperature in the second MCS might be contributed to heavy rainfall over the GJ than the first one. which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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“…SciSpark is a parallel analytical engine for science data that uses the highly scalable MapReduce computing paradigm for in-memory computing on a cluster. SciSpark has been successfully applied to testing several RCMES use cases, such as mesoscale convective system (MCS) characterization (Whitehall et al, 2015) and the probability density function clustering of surface temperature . We will apply and test SciSpark to analyses of high-resolution datasets and publish new versions of RCMES with parallel-capable examples.…”

Section: Summary and Future Development Plansmentioning

confidence: 99%

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Lee¹

2018

Preprint

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Regional Climate Model Evaluation System powered by Apache Open Climate Workbench v1.3.0: an enabling tool for facilitating regional climate studies, Geosci. Model Dev., 11,[4435][4436][4437][4438][4439][4440][4441][4442][4443][4444][4445][4446][4447][4448][4449] Abstract. The Regional Climate Model Evaluation System (RCMES) is an enabling tool of the National Aeronautics and Space Administration to support the United States National Climate Assessment. As a comprehensive system for evaluating climate models on regional and continental scales using observational datasets from a variety of sources, RCMES is designed to yield information on the performance of climate models and guide their improvement. Here, we present a user-oriented document describing the latest version of RCMES, its development process, and future plans for improvements. The main objective of RCMES is to facilitate the climate model evaluation process at regional scales. RCMES provides a framework for performing systematic evaluations of climate simulations, such as those from the Coordinated Regional Climate Downscaling Experiment (CORDEX), using in situ observations, as well as satellite and reanalysis data products. The main components of RCMES are (1) a database of observations widely used for climate model evaluation, (2) various data loaders to import climate models and observations on local file systems and Earth System Grid Federation (ESGF) nodes, (3) a versatile processor to subset and regrid the loaded datasets, (4) performance metrics designed to assess and quantify model skill, (5) plotting routines to visualize the performance metrics, (6) a toolkit for statistically downscaling climate model simulations, and (7) two installation packages to maximize convenience of users without Python skills. RCMES website is maintained up to date with a brief explanation of these components. Although there are other open-source software (OSS) toolkits that facilitate analysis and evaluation of climate models, there is a need for climate scientists to participate in the development and customization of OSS to study regional climate change. To establish infrastructure and to ensure software sustainability, development of RCMES is an open, publicly accessible process enabled by leveraging the Apache Software Foundation's OSS library, Apache Open Climate Workbench (OCW). The OCW software that powers RCMES includes a Python OSS library for common climate model evaluation tasks as well as a set of user-friendly interfaces for quickly configuring a model evaluation task. OCW also allows users to build their own climate data analysis tools, such as the statistical downscaling toolkit provided as a part of RCMES.

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Exploring a graph theory based algorithm for automated identification and characterization of large mesoscale convective systems in satellite datasets

Cited by 30 publications

References 33 publications

Life Cycle Characteristics of Warm-Season Severe Thunderstorms in Central United States from 2010 to 2014

Life Cycle Characteristics of Warm-Season Severe Thunderstorms in Central United States from 2010 to 2014

Characteristics of two mesoscale convective systems (MCSs) over the Greater Jakarta: case of heavy rainfall period 15–18 January 2013

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