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Updated: Jul 25, 2025

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

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Relative humidity prediction with covariates and error correction based on SARIMA-EG-ECM model.

Jiajun Guo¹, Liang Zhang¹, Ruqiang Guo¹

¹College of Science, Northwest A and F University, Yangling, Shaanxi 712100 China.

Modeling Earth Systems and Environment

|June 26, 2023

Summary

Predicting relative humidity (RH) is crucial for various sectors. A new hybrid model, SARIMA-EG-ECM (SEE), effectively forecasts RH by analyzing long-term and short-term relationships with meteorological variables.

Keywords:

Cointegration model Error correction model Multiplicative seasonality model Relative humidity

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Area of Science:

Meteorology and Climatology
Environmental Science

Background:

Relative humidity (RH) is a key atmospheric variable with significant implications for weather forecasting, climate studies, agriculture, and public health.
Accurate RH prediction is essential for informed decision-making across various critical sectors.

Purpose of the Study:

To investigate the influence of covariates and error correction on relative humidity (RH) prediction.
To propose and evaluate a novel hybrid model for enhanced RH forecasting.

Main Methods:

Developed a hybrid Seasonal Autoregressive Integrated Moving Average (SARIMA) with Engle-Granger (EG) cointegration and Error Correction Model (ECM), termed SARIMA-EG-ECM (SEE).
Applied the SEE model to meteorological data from Hailun Agricultural Ecology Experimental Station, China.
Utilized meteorological variables like air temperature (TEMP), dew point temperature (DEWP), precipitation (PRCP), atmospheric pressure (ATMO), sea-level pressure (SLP), and soil temperature (40ST) as covariates.

Main Results:

Established a long-term equilibrium relationship (cointegration) between RH and TEMP, DEWP, PRCP, ATMO, SLP, and 40ST.
Identified significant short-term impacts of DEWP, ATMO, and SLP fluctuations on RH fluctuations via the ECM.
The SEE model demonstrated slightly decreased performance with extended forecast horizons (6-12 months) but outperformed SARIMA and Long Short-Term Memory (LSTM) models.

Conclusions:

The SEE model provides a robust framework for RH prediction by integrating long-term equilibrium and short-term dynamic relationships.
The findings highlight the importance of considering multiple meteorological covariates and error correction mechanisms for accurate RH forecasting.
The proposed hybrid approach offers a superior alternative to traditional time series and deep learning models for RH prediction in meteorological applications.