Research on correction algorithm for temperature and precipitation in Southwest China based on NCEP CFSv2

doi:10.11755/j.issn.1006-7639-2026-03-0499

Abstract

Abstract:

To address the substantial sub-seasonal forecast biases in temperature and precipitation over Southwest China produced by the NCEP CFSv2 model, this study uses station observations and model data from 1999 to 2024 to probabilistically correct the model’s daily mean temperature and precipitation using a nonparametric percentile mapping method. The results indicate that although the model can capture the approximately normal distribution of daily mean temperature, it generally overestimates the probability of low daily mean temperatures and light precipitation and underestimates the probability of high daily mean temperatures. After correction, the biases in the predicted probabilities across different magnitudes of daily mean temperature and precipitation are effectively reduced. The spatial biases in climatological mean temperature and precipitation, as well as the interannual root-mean-square errors, are significantly decreased, whereas the improvement in interannual correlation coefficients is limited. The correction enhances the predictive skill of daily mean temperature at daily, pentad, dekadal, and monthly time scales across various forecast lead times. For daily precipitation, the correction shows some instability at daily scale; however, its predictive skill improves to varying degrees with increasing verification time scales, effectively extending the model’s forecast lead time.

Key words: NCEP CFSv2, probabilistic correction, verification, Southwest China

摘要：

针对美国国家环境预报中心（National Centers for Environmental Prediction，NCEP）开发的第二代气候预报系统（Climate Forecast System Version 2，CFSv2）对西南地区次季节气温和降水预测存在较大系统偏差的问题，利用1999—2024年西南地区气象台观测资料和模式预测资料，采用非参数百分位映射法对模式日平均气温和降水量进行概率订正，并评估其在不同超前时效和不同时间尺度的预测结果。结果表明：模式能够较好模拟日平均气温近似正态分布特征，但普遍高估较小量级日平均气温和降水量发生概率，低估较大量级日平均气温概率。订正后，不同量级日平均气温和降水量的概率预测偏差得到有效改善，多年平均气温和降水量的空间分布误差及年际变化均方根误差显著降低，但对年际变化相关系数的提升有限。订正后，不同超前时效下日、候、旬和月尺度日平均气温预测技巧均有提高。对于日降水量，订正在日尺度上的效果存在一定不稳定性，但随着检验时间尺度增加，其预测技巧得到不同程度提升，并能够有效延长模式预测时效。

关键词: NCEP CFSv2, 概率订正, 检验, 西南地区

CLC Number:

P456

WU Yao, TANG Hongyu, ZHU Haonan, WEI Linxiao, HE Huigen. Research on correction algorithm for temperature and precipitation in Southwest China based on NCEP CFSv2[J]. Journal of Arid Meteorology, 2026, 44(3): 499-511.

吴遥, 唐红玉, 朱浩楠, 魏麟骁, 何慧根. 基于NCEP CFSv2的西南地区气温和降水订正算法研究[J]. 干旱气象, 2026, 44(3): 499-511.

Figures/Tables 12

Fig.1 Probability density distributions of daily mean temperature observations， forecasts， and bias-corrected forecasts in each month over Southwest China during 1999-2023

Fig.2 Spatial distributions of multi-year mean and bias of daily mean temperature observations， forecasts， and bias-corrected forecasts averaged over lead times of 1-44 days in Southwest China during 1999-2023 （Unit： ℃）

Fig.3 Multi-year mean monthly variations of spatial correlation coefficients of daily mean temperature forecasts and bias-corrected forecasts against observations and their differences for lead times of 1-44 days at different temporal scales over Southwest China during 1999-2023

Fig.4 Multi-year mean monthly variations of MAE of daily mean temperature forecasts and bias-corrected forecasts against observations and their differences for lead times of 1-44 days at different temporal scales over Southwest China during 1999-2023 （Unit： ℃）

Fig.5 Spatial distribution of interannual correlation coefficients and RMSE （Unit： ℃） of daily mean temperature forecasts and bias-corrected forecasts against observations averaged for lead times of 1-44 days over Southwest China during 1999-2023

Fig.6 Probability density distributions of daily precipitation observations， forecasts， and bias-corrected forecasts in each month over Southwest China during 1999-2023

Fig.7 Spatial distributions of multi-year mean and bias of daily precipitation observations， forecasts， and bias-corrected forecasts averaged over lead times of 1-44 days in Southwest China during 1999-2023 （Unit： mm）

Fig.8 Multi-year mean monthly variations of spatial correlation coefficients of daily precipitation forecasts and bias-corrected forecasts against observations and their differences for lead times of 1-44 days at different temporal scales over Southwest China during 1999-2023

Fig.9 Multi-year mean monthly variations of MAE of daily precipitation forecasts and bias-corrected forecasts against observations and their differences for lead times of 1-44 days at different temporal scales over Southwest China during 1999-2023 （Unit： mm）

Fig.10 Spatial distribution of interannual correlation coefficients and RMSE （Unit： mm） of daily precipitation forecasts and bias-corrected forecasts against observations averaged for lead times of 1-44 days over Southwest China during 1999-2023

Fig.11 Monthly variations of spatial correlation coefficients and MAE （Unit： °C） of daily mean temperature forecasts and bias-corrected forecasts against observations and their differences for lead times of 1-44 days at daily scale over Southwest China in 2024

Fig.12 Monthly variations of spatial correlation coefficients and MAE （Unit： mm） of daily precipitation forecasts and bias-corrected forecasts against observations and their differences for lead times of 1-44 days at daily scale over Southwest China in 2024

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