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中国农学通报

中国农学通报 ›› 2026, Vol. 42 ›› Issue (8): 174-182.doi: 10.11924/j.issn.1000-6850.casb2025-0723

• 资源·环境·生态·土壤·气象 • 上一篇    下一篇

BCC-CPSv3模式对内蒙古气温和降水的多尺度预测技巧评估

张保龙1,2(), 拓砚军1,2, 张舒昊1,2, 王刚1,2, 于亮亮1,2()   

  1. 1 内蒙古巴彦淖尔市气象局， 内蒙古巴彦淖尔 015000
    2 中国气象局乌梁素海湿地生态气象野外科学试验基地， 内蒙古巴彦淖尔 015000
  • 收稿日期:2025-08-31 修回日期:2025-12-25 出版日期:2026-04-25 发布日期:2026-04-23
  • 通讯作者:
    于亮亮，男，1989年出生，内蒙古呼伦贝尔人，高级工程师，学士，研究方向为遥感与应用气象、农业气象。通讯地址：015000 内蒙古巴彦淖尔市临河区新华西街北金沙路东 巴彦淖尔市气象局，E-mail：。
  • 作者简介:

    张保龙，男，1985年出生，宁夏石嘴山人，高级工程师，学士，研究方向为气候与气候变化、农气气象。通讯地址：015000 内蒙古巴彦淖尔市临河区新华西街北金沙路东 巴彦淖尔市气象局，E-mail：。

  • 基金资助:
    中国气象局创新发展专项资助(CXFZ2025J148); 内蒙古自治区自然科学基金面上项目(2025MS04054); 内蒙古自治区气象局科研项目(nmqxkxsy202409); 内蒙古自治区气象局科技创新项目(nmqxkjcx202629); 巴彦淖尔市科技计划项目(K202510)

Assessment of Multi-Scale Prediction Skills of BCC-CPSv3 Model for Temperature and Precipitation in Inner Mongolia

ZHANG Baolong1,2(), TUO Yanjun1,2, ZHANG Shuhao1,2, WANG Gang1,2, YU Liangliang1,2()   

  1. 1 Bayannur Meteorological Bureau, Bayannur, Inner Mongolia 015000
    2 Wuliangsuhai Wetland Ecological Meteorology Field Scientific Experiment Base, China Meteorological Administration, Bayannur, Inner Mongolia 015000
  • Received:2025-08-31 Revised:2025-12-25 Published:2026-04-25 Online:2026-04-23

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摘要：

本研究基于2023—2024年观测资料与BCC-CPSv3模式数据，采用“分区—分季—分时效”的多尺度评估框架，结合偏差、均方根误差(RMSE)、时间相关系数(TCC)、和空间异常相关系数(ACC)等指标，以及对数变换技术，系统评估了该模式在内蒙古地区气温和降水预测方面的技巧与误差特征。结果表明：气温预测存在明显的季节性偏差：夏季东部农田区存在显著暖偏差(+3.1℃)，春季西部沙漠区则表现为冷偏差(-2.2℃)。冬季气温预测技巧(TCC=0.43)显著高于夏季(TCC=0.28)，空间分布呈现“东高西低”的特征，这与下垫面差异有关。降水预测主要受极端降水事件（日降水>20 mm）的影响，春季西部存在正偏差(+0.64 mm)，夏季东部为负偏差(-0.24 mm)。对数变换能够有效降低极端值造成的误差（可剥离58%的误差）。降水预测技巧以冬季最高，并随预报时效逐渐衰减（每旬-0.04），其他季节则呈现断崖式下降，空间分布为“西高东低”。气温与降水预测技巧的反向空间差异反映了不同的控制机制：气温预测主要依赖大尺度环流的稳定性（如冬季蒙古高压主导），而降水预测则受到对流性降水突发性的制约（夏季中小尺度系统影响）。研究结果明确了模式优化方向：气温预测需修正东部农田地区的潜热参数，降水预测则需调整夏季对流触发阈值以及春季西风水汽的模拟。本研究为内蒙古地区春旱、夏涝的防灾减灾工作及以及区域气候模式的改进提供了重要的科学依据。

关键词: BCC-CPSv3, 内蒙古, 预测技巧, 对数变换, 误差特征, 气温与降水预测

Abstract:

Based on observations and BCC-CPSv3 model data from 2023-2024, this study employs a multidimensional evaluation framework of 'region-season-time' integrating metrics such as bias, root mean square error (RMSE), time correlation coefficient (TCC), and spatial anomaly correlation coefficient (ACC), and log transformation to systematically analyze the model's multi-scale prediction skills and error characteristics for temperature and precipitation in Inner Mongolia. Key findings include: Temperature predictions exhibit significant seasonal biases (a warm bias of +3.1℃ in summer over eastern farmland areas and a cold bias of -2.2℃ in spring over western deserts). Prediction skill (TCC) is markedly higher in winter (TCC=0.43) than in summer (TCC=0.28), with spatial heterogeneity (higher in east, lower in west) driven by underlying surface variations. Precipitation predictions are dominated by extreme values (daily precipitation >20 mm), showing a spring positive bias (+0.64 mm) in the west and a summer negative bias (-0.24 mm) in the east. Log transformation effectively isolates 58% of extreme-value-induced errors. Prediction skill peaks in winter with gradual decay (-0.04 per ten-day period), while other season's exhibit abrupt declines. Spatial distribution follows a 'higher in west, lower in east' pattern. Mechanistic analysis reveals opposing spatial patterns between temperature and precipitation skills, temperature depends on large-scale circulation stability (e.g., winter Mongolian High dominance), whereas precipitation is constrained by convective intermittency (summer meso-microscale systems). The study identifies targeted optimization pathways: adjusting latent heat parameters for eastern farmland to correct temperature biases; modifying convective triggering thresholds in summer and westerly moisture simulations in spring to improve precipitation predictions. These findings provide critical insights for mitigating spring droughts, summer floods, and advancing regional model refinement in Inner Mongolia.

Key words: BCC-CPSv3, Inner Mongolia, prediction skill, log transformation, error characteristics, temperature and precipitation prediction

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