An Investigation Into the Variant‐Weight Multi‐Model Ensemble Forecasting Technique Based on the Analyses of Model Systematic Errors Calculation and Elimination. Issue 10 (20th October 2022)

• Record Type: Journal Article
• Authors: Liang, Zhaoming; Wei, Xiaomin; Sun, Xiaogong
• Is Part Of: Earth and space science. Volume 9:Issue 10(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-10-20
• Subjects: variant‐weight multi‐model ensemble -- systematic errors calculation and elimination -- optimal time length -- surface temperature forecast; Space sciences -- Periodicals; Geophysics -- Periodicals; 500.5
• DOI: 10.1029/2022EA002547
• Languages: English
• ISSNs: 2333-5084

Abstract

Abstract: Eliminating the systematic errors of member models is a key step before the variant‐weight multi‐model ensemble forecasting. However, how to reasonably calculate model systematic errors is a problem worthy study. Taking surface temperature as the subject investigated, this study explores this problem through comparative analyses of a series of variant‐weight multi‐model ensemble forecasting experiments. The results showed that eliminating the systematic errors of member models dramatically improves the ensemble forecasting outcomes. The calculation of model systematic errors based on the optimal time lengths regarding the spatiotemporal distribution characteristics of model forecasting errors is a reasonable and effective approach. The surface temperature forecasts with model systematic errors calculated with the optimal time lengths and those beyond the optimal time lengths exhibit higher accuracies. Owing to the approximately uniform distribution of temperature, no significant difference happens between the accuracies of the surface temperature forecasts with model systematic errors calculated using the average and quantile methods. In addition, the updating of the models participating into the ensemble forecasting would weaken the representativeness of the optimal time lengths for model systematic errors calculation that are obtained based on historical period data, and thus, weaken the forecasting skill. The research results provide some train of thought for enhancing the variant‐weight multi‐model ensemble forecasting accuracy. Plain Language Summary: It is significant to calculate and eliminate systematic errors of member models for improving the variant‐weight multi‐model ensemble forecasting. However, how to reasonably calculate the model systematical errors is a problem worth study. This paper, setting surface temperatures as the subject investigated, explored this problem through a series of comparative analyses of variant‐weight multi‐model ensemble forecasting experiments. In addition, the issues about how to choose the method for systematic errors calculation as well as judge the potential impact of the variability of spatiotemporal distribution characteristics on the variant‐weight multi‐model ensemble forecasting are discussed. The research results might provide some train of thought for enhancing the variant‐weight multi‐model ensemble forecasting accuracy. Key Points: Eliminating the systematic errors of member models significantly improves the variant‐weight multi‐model ensemble forecasting skill The improvement of forecasting skill is dependent on the determination of the optimal time lengths for model systematic errors calculation Determining the optimal time length based on the spatiotemporal distribution characteristics of forecasting errors is an effective way