Methods for Mesoscale Convective Systems Detection and Tracking:a Survey

Patil, Vidya; Das, Subrata Kumar; Phadke, Anuradha C.

doi:10.1109/icccnt45670.2019.8944656

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…UH has been used to define severe weather indices (Sobash et al 2016;Gallo et al 2019;Sobash et al 2020) and can be combined with reflectivity fields to recognize severe thunderstorms in a "member viewer" approach (Roberts et al 2019). MCS detection algorithms have been also used on observed data (Patil et al 2019). Concerning BEs, an automated detection based on computer vision with skeletonization and shape matching approaches has been developed by Kamani et al (2016).…”

Section: Introductionmentioning

confidence: 99%

Detection of Bow Echoes in Kilometer-Scale Forecasts Using a Convolutional Neural Network

Mounier

Raynaud

Rottner

et al. 2022

Artificial Intelligence for the Earth Systems

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Bow echoes (BEs) are bow-shaped lines of convective cells that are often associated with swaths of damaging straight-line winds and small tornadoes. This paper describes a convolutional neural network (CNN) able to detect BEs directly from French kilometer-scale model outputs in order to facilitate and accelerate the operational forecasting of BEs. The detections are only based on the maximum pseudoreflectivity field predictor (“pseudo” because it is expressed in mm h−1 and not in dBZ). A preprocessing of the training database is carried out in order to reduce imbalance issues between the two classes (inside or outside bow echoes). A CNN sensitivity analysis against a set of hyperparameters is done. The selected CNN configuration has a hit rate of 86% and a false alarm rate of 39%. The strengths and weaknesses of this CNN are then emphasized with an object-oriented evaluation. The BE largest pseudoreflectivities are correctly detected by the CNN, which tends to underestimate the size of BEs. Detected BE objects have wind gusts similar to the hand-labeled BE. Most of the time, false alarm objects and missed objects are rather small (e.g., <1500 km2). Based on a cooperation with forecasters, synthesis plots are proposed that summarize the BE detections in French kilometer-scale models. A subjective evaluation of the CNN performances is also reported. The overall positive feedback from forecasters is in good agreement with the object-oriented evaluation. Forecasters perceive these products as relevant and potentially useful to handle the large amount of available data from numerical weather prediction models.

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Section: Introductionmentioning

confidence: 99%

Detection of Bow Echoes in Kilometer-Scale Forecasts Using a Convolutional Neural Network

Mounier

Raynaud

Rottner

et al. 2022

Artificial Intelligence for the Earth Systems

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Mesoscale Convective Systems in Central Africa: Characteristics of the Associated Seasonal and Diurnal Cycle of Observed Surface Meteorological Parameters

Tikeng,

Pokam,

Washington

2024

Advances in Meteorology

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This study examines mesoscale convective systems (MCSs) in relation to 1‐min automatic weather station data for 2 years over the city of Yaounde. The focus is on characterising the atmospheric variability associated with MCS activity while distinguishing the days with and without MCS activities. This paper aims to determine the diurnal cycles of occurrence frequencies and percentages of rainfall, relative humidity, dew point temperature, solar radiation, temperature, and wind speed for days with and without MCSs. There are more than 623 MCS events during the study period (over 150 events per rainy season). The link between MCS activity and regional‐scale circulation and atmospheric instability is investigated. The diurnal cycle of the number of MCSs shows a maximum in the afternoon (around 1,600–2,200 LT), a morning minimum (around 0700–1,300 LT), and substantial activity during the night. Surface relative humidity is 5% lower on non‐MCS days, surface dew point 2% higher on MCS days between 0700 and 1800 hr, and solar radiation higher on MCS days between 0500 and 1000 hr. The percentage of rainfall associated with MCSs can exceed 60% on an annual scale and up to 80% on a seasonal scale. MCS activity is associated with instability in the lower troposphere, and this convective instability is maximal during the peak of the MCS activity.

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Methods for Mesoscale Convective Systems Detection and Tracking:a Survey

Cited by 2 publications

References 13 publications

Detection of Bow Echoes in Kilometer-Scale Forecasts Using a Convolutional Neural Network

Detection of Bow Echoes in Kilometer-Scale Forecasts Using a Convolutional Neural Network

Mesoscale Convective Systems in Central Africa: Characteristics of the Associated Seasonal and Diurnal Cycle of Observed Surface Meteorological Parameters

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