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Precipitation and Co-precipitation

Precipitation and Co-precipitation

Precipitation and coprecipitation methods can be used to separate a mixture of ions in a solution. In qualitative inorganic analysis, ions that form sparingly soluble precipitates with the same reagent are separated based on the differences in solubility products. For example, consider the separation of Cu(II) and Fe(II) ions by precipitation as insoluble sulfides. First, copper(II) sulfide is precipitated by the addition of acidic H2S, where the dissociation of H2S is suppressed. Adding H2S...

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A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

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Forecast of Winter Precipitation Type Based on Machine Learning Method.

Zhang Lang¹, Qiuzi Han Wen², Bo Yu³

¹Chongqing Research Institute of Big Data, Peking University, Chongqing 400000, China.

Entropy (Basel, Switzerland)

|January 21, 2023

Summary

This summary is machine-generated.

This study introduces a novel machine learning method for winter precipitation-type prediction, outperforming existing models. The developed model accurately forecasts precipitation types using meteorological data, enhancing climate prediction capabilities.

Keywords:

machine learning model output statistics winter precipitation-type prediction

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Area of Science:

Meteorology
Climatology
Machine Learning

Background:

Winter precipitation-type prediction is complex due to intricate physical mechanisms and numerical modeling challenges.
Accurate prediction is crucial for various applications, including weather forecasting and climate impact assessments.

Purpose of the Study:

To develop and evaluate a novel machine learning-based method for winter precipitation-type prediction.
To compare the performance of the new method against established forecast products.

Main Methods:

Utilized the LightGBM machine learning algorithm for precipitation-type prediction.
Employed in situ precipitation-type observations from 32 stations in northern China (1997-2018) as labels.
Selected features from ERA5 reanalysis meteorological data, analyzing feature importance and temporal offsets.

Main Results:

The machine learning precipitation-type (MLPT) model achieved an overall accuracy (ACC) of 0.83 and a Heidke skill score (HSS) of 0.69.
MLPT performance surpassed the ECMWF precipitation-type (ECPT) forecast products (ACC=0.78, HSS=0.59).
Model accuracy was notably higher for stations below 800 m elevation.

Conclusions:

The developed machine learning approach offers a feasible and accurate method for predicting winter precipitation types using analysis data.
The study highlights the importance of proper validation data sampling and acknowledges the impact of extreme climate conditions on prediction accuracy.
Feature importance analysis revealed that surrounding area data with a -12h time offset significantly influences ground precipitation types.