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Precipitation Gravimetry01:03

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Precipitation gravimetry is based on converting an analyte into a sparingly soluble precipitate, which is separated by filtration and weighed. An ideal precipitate should be pure, insoluble, of known composition, and easily filtered from the reaction mixture.
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Classification of precipitation types in Poland using machine learning and threshold temperature methods.

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Accurately classifying precipitation types like rain and snow is crucial for hydrology. Machine learning, specifically Random Forest, proved most effective, achieving high accuracy in differentiating rainfall from snowfall.

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Area of Science:

  • Hydrology and meteorology
  • Climatology and atmospheric science

Background:

  • Precipitation phase (rain, snow, sleet) significantly impacts hydrology and surface runoff.
  • Limited availability and incompleteness of precipitation phase data from weather stations pose a challenge.

Purpose of the Study:

  • To classify precipitation into rainfall and snowfall using data from 40 Polish meteorological stations (1966-2020).
  • To compare the accuracy of machine learning (Random Forest) against temperature-based methods for precipitation classification.

Main Methods:

  • Utilized data from 40 meteorological stations in Poland (1966-2020).
  • Employed three methods for rainfall/snowfall classification: Random Forest (RF), daily mean threshold air temperature, and daily wet bulb threshold temperature.
  • Investigated the impact of water vapor pressure and mean wet bulb temperature on RF model accuracy.

Main Results:

  • The Random Forest (RF) method achieved the highest classification accuracy (0.90-1.00).
  • Temperature-based methods (mean wet bulb and mean air temperature) showed similar accuracy (0.86-1.00).
  • Optimized threshold temperatures were determined for each station; water vapor pressure improved RF accuracy, while removing mean wet bulb temperature enhanced it.

Conclusions:

  • The Random Forest model offers superior accuracy for differentiating rainfall and snowfall compared to temperature-based methods.
  • Water vapor pressure is a significant factor in precipitation classification models.
  • Further research is needed to understand station-specific variations in classification effectiveness.