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Showing posts from February, 2025

Extreme Rainfall-Producing Echo Training Processes During Two Landfalling Typhoons in East China.

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  Echo training represents the primary mechanism through which rain bands precipitate extreme rainfall events. This study employed a combination of observational data and ERA5 reanalysis data to examine two instances of “echo training” that occurred following the landfall of typhoons Soudelor (2015, Process 1) and Fitow (2013, Process 2) in China. The findings indicate notable differences in the environmental background, characteristics, and the organization of convective rainbands between these two “echo training” processes. During Process 1, a well-developed convective system accompanied by a deeper boundary layer convergence between the cold pool and the easterly flow is observed. The presence of baroclinic structures permits the uplift of warm and humid air from the ocean that facilitated by the solenoidal term. During this period, the dispersion queues on both sides of the rainband contributed to the strengthening of convection and enabled convective cells to traverse the leng...

Application of Random Forest for Identification of an Appropriate Model for Predicting Meteorological Drought.

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This research aims to find the best model for predicting the Standardized Precipitation Index (SPI) and the Standardized Precipitation and Evapotranspiration Index (SPEI) in the future. The study estimates SPI and SPEI at different time scales, ranging from 1 to 48 months. To predict drought, Random Forest (RF) models are used based on lag times of 1–12 months for the estimated drought indices (SPI and SPEI). Accuracy and error metrics like Nash–Sutcliffe efficiency (NSE), root-mean-square error (RMSE), producer accuracy (PA), user accuracy (UA), and Choen’s kappa are used to assess the models. Read the research Paper!