Analogue-dynamical Prediction of Numerical Model Errors in the Mid-Lower Reaches of the Yangtze River

TL;DR

This paper introduces an analogue error correction scheme using principal component analysis to improve precipitation forecasts in the Yangtze River's mid-lower reaches, demonstrating superior performance over traditional methods.

Contribution

The study develops a novel analogue error correction method based on circulation characteristics and PCA, enhancing numerical model precipitation predictions.

Findings

AEC outperforms SEC in correction accuracy

Error correction significantly improves forecast skill

Method effectively captures circulation-related errors

Abstract

A new prediction error correction scheme based on 74 circulation characteristics data provided by Weather Diagnostic Forecasting Division of National Climate Center, which is designed to develop the Operational Numerical Forecast Model (ONFM) of the National Climate Center of China, and the skill level of the precipitation prediction for rainy season in the midlower reaches (MLR) of the Yangtze River by ONFM is obviously raised. The approach use principal component(PC) analysis to prediction error of ONFM. And we used different factors to correct the different PCs of the error of precipitation field. The comparative study results indicate that the effectiveness of the new analogue error correction (AEC) scheme is better than system error correction (SEC) scheme.