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Comparing causal techniques for rainfall variability analysis using causality algorithms in Iran.

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Causal analysis effectively predicted rainfall

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Area of Science:

  • Environmental Science
  • Atmospheric Science
  • Quantitative Analysis

Background:

  • Causal analysis (CA) offers robust quantitative mechanisms for climatic predictions.
  • Understanding rainfall's causal patterns is crucial for environmental and atmospheric studies.

Purpose of the Study:

  • To investigate causal patterns in rainfall's effect on climate in Iran.
  • To predict causal relationships of climatic variables using advanced causal models.
  • To evaluate the efficacy of different causal modeling techniques.

Main Methods:

  • Utilized climatic data from 170 Iranian stations (1975-2014).
  • Employed causal hybrid techniques (CHT) and other causal models (FGT, SGT, TGT).
  • Estimated models using partial least squares algorithms (PSA) and mechanical equations modeling algorithms (MEMA).
  • Assessed model quality with goodness of fit indices (AFI, CFI, PFI).

Main Results:

  • Causal hybrid techniques (CHT) best predicted climatic spatiotemporal effect variability (SEV).
  • Winter rainfall showed the highest total effect (0.98), while summer rainfall had the lowest (0.1).
  • SEV for winter rainfall's total effect ranged from 80% to 98% across Iran.
  • Autumn rainfall's total effect on annual rainfall varied between 60% and 74%.

Conclusions:

  • CHT is a superior method for predicting rainfall's spatiotemporal effect variability.
  • Rainfall's causal effects on climate vary significantly by season and region in Iran.
  • CA holds significant potential for applications in atmospheric and environmental science.