Application of M5 Model Tree in Passive Remote Sensing of Thin Ice Cloud Microphysical Properties in Terahertz Region

This article presents a new method for retrieving the Ice Water Path (IWP), the median volume equivalent sphere diameter (Dme) of thin ice clouds (IWP < 100 g/m2, Dme < 80 μm) in the Terahertz band. The upwelling brightness temperature depressions caused by the ice clouds at 325.15, 448.0, 664.0 and 874.0 GHz channels are simulated by the Atmospheric Radiative Transfer Simulator (ARTS). The simulated forward radiative transfer models are taken as historical data for the M5 model tree algorithm to construct a set of piecewise functions which represent the relation of simulated brightness temperature depressions and IWP. The inversion results are optimized by an empirical relation of the IWP and the Dme for thin ice clouds which is summarized by previous studies. We inverse IWP and Dme with the simulated brightness temperature and analyze the inversion performance of selected channels. The 874.4 ± 6.0 GHz channel provides the most accurate results, because of the strong brightness temperature response to the change of IWP in the forward radiative transfer model. In order to improve the thin ice clouds IWP and Dme retrieval accuracy at the middle-high frequency channels in Terahertz band, a dual-channel inversion method was proposed that combines the 448.0 ± 3.0 GHz and 664.0 ± 4.2 GHz channel. The error analysis shows that the results of the 874.4 ± 6.0 GHz channel and the dual-channel inversion are reliable, and the IWP inversion results meet the error requirement range proposed by previous studies.

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Structural Reliability Analysis of Corroded Pipeline made in X60 Steel Based on M5 Model Tree Algorithm and Monte Carlo Simulation

Procedia Structural Integrity ◽

10.1016/j.prostr.2018.12.349 ◽

2018 ◽

Vol 13 ◽

pp. 1670-1675 ◽

Cited By ~ 1

Author(s):

Mohamed Seghier ◽

Behrooz Keshtegar ◽

José Correia ◽

Abílio De Jesus ◽

G. Lesiuk

Keyword(s):

Monte Carlo Simulation ◽

Monte Carlo ◽

Reliability Analysis ◽

Structural Reliability ◽

M5 Model Tree ◽

Tree Algorithm ◽

Structural Reliability Analysis ◽

Model Tree ◽

M5 Model ◽

Made In

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Downscaling of precipitation on a lake basin: evaluation of rule and decision tree induction algorithms

Hydrology Research ◽

10.2166/nh.2012.040 ◽

2012 ◽

Vol 43 (3) ◽

pp. 215-230 ◽

Cited By ~ 22

Author(s):

Manish Kumar Goyal ◽

C. S. P. Ojha

Keyword(s):

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Lake Basin ◽

Monthly Precipitation ◽

M5 Model Tree ◽

Reanalysis Dataset ◽

Model Tree ◽

Basin Scale ◽

M5 Model

We investigate the performance of existing state-of-the-art rule induction and tree algorithms, namely Single Conjunctive Rule Learner, Decision Table, M5 Model Tree, Decision Stump and REPTree. Downscaling models are developed using these algorithms to obtain projections of mean monthly precipitation to lake-basin scale in an arid region in India. The effectiveness of these algorithms is evaluated through application to downscale the predictand for the Lake Pichola region in Rajasthan state in India, which is considered to be a climatically sensitive region. The predictor variables are extracted from (1) the National Centre for Environmental Prediction (NCEP) reanalysis dataset for the period 1948–2000 and (2) the simulations from the third-generation Canadian Coupled Global Climate Model (CGCM3) for emission scenarios A1B, A2, B1 and COMMIT for the period 2001–2100. M5 Model Tree algorithm was found to yield better performance among all other learning techniques explored in the present study. The precipitation is projected to increase in future for A2 and A1B scenarios, whereas it is least for B1 and COMMIT scenarios using predictors.

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