Dynamic Downscaling Using the RegCM Model for Different Initializations Using CFSv2 Data

Abstract The main objective of this study was to evaluate the regional climate forecasts of precipitation over Brazil during the winter season of 2018 through the RegCM4.7 model. It was used with different initializations, both spatial and in five specific areas. To emit alerts of possible below/above climate normal anomalies is necessary the verification these model abilities in predicting precipitation. The RegCM4.7 model was used with Global Climate Forecast System Version 2 global model data. The forecast quality was evaluated qualitatively and quantitatively, comparing its results with Climate Prediction Center (CPC) analysis data. The RegCM4.7 model was able to predict precipitation consistently a few months in advance for the june, july and august quarter (JJA), with minor mistakes over the northeastern and southeastern Brazil. However, the biggest errors were identified over the northern and southern regions. Prediction correlations were less than 0.8 during every experiments and subdomains, except for the Northeast region that presented the highest correlation values. In general, it stands out that RegCM4.7 was able to predict the spatial distribution of precipitation in advance over every domain, but with a tendency to underestimate what was observed.摘要 本研究的核心目标为，基于区域气候模式RegCM4.7（RegCM4.7），评估2018年南半球冬季（6月、7月、8月，简称JJA）巴西境内降水的区域气候预报能力。该模式采用差异化初始化方案，涵盖空间全局初始化及五个特定区域的分区初始化。为能够针对气候距平偏高或偏低的情况发布预警，有必要验证该模式在降水预报方面的实际性能。本研究采用全球气候预报系统第2版（Global Climate Forecast System Version 2）的全球模式数据驱动RegCM4.7。通过定性与定量两种方式评估预报质量，并将模式结果与气候预测中心（Climate Prediction Center, CPC）的分析数据进行对比。结果表明，RegCM4.7可提前数月对JJA时段的巴西降水实现稳定预报，仅在巴西东北部与东南部地区出现较小误差。不过，该模式在北部与南部区域的预报误差相对最大。所有试验及子区域的预报相关系数均低于0.8，仅东北部地区的相关系数为所有区域中最高。总体而言，RegCM4.7可提前预报各子区域的降水空间分布特征，但存在对实测降水值低估的系统性倾向。