初始条件和气象预报对南美洲西部热带地区亚季节到季节水文预报技能的贡献。

G. C. Recalde-Coronel, Benjamin Zaitchik, William Pan, Yifan Zhou, Hamada Badr
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摘要

亚季节到季节(S2S)时间尺度的水文预测可以帮助农业和水电等依赖气候的部门改进决策。在此,我们介绍了南美洲西部热带地区(WTSA)的 S2S 水文预报系统(S2S-HFS)。该系统使用美国宇航局戈达德地球观测系统的全球 S2S 气象预报系统(GEOS-S2S),结合广义模拟回归降尺度算法和美国宇航局土地信息系统(LIS)。在这项实施研究中,我们利用 2002-2017 年的集合后报评估了系统对澳大利亚秋季(3 月-4 月-5 月)三个月水文预报的性能。结果表明,S2S-HFS 在预测月降水量方面的技能一般领先一个月,在预测蒸散量方面领先两个月,在预测土壤含水量方面领先三个月。后报性能较好的生态区位于沿海低地或亚马逊低地森林。我们进行了专门分析,以了解影响该地区的两个重要远缘联系在 S2S-HFS 中的体现情况:厄尔尼诺南方涛动(ENSO)和南极涛动(AAO)。我们发现,在厄尔尼诺/南方涛动(ENSO)的正态阶段和南极涛动(AAO)的负态阶段,所有变量在一个月前的预报技能都会增强。总之,这项研究表明,S2S-HFS 对 WTSA 的许多生态区域都有一定的预报能力,尤其是对土壤水分等长记忆变量。然而,降水预报的技能在预报初始化后迅速衰减,这一现象与世界大部分地区的 S2S 气象预报一致。
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Contributions of initial conditions and meteorological forecast to subseasonal-to-seasonal hydrological forecast skill in Western Tropical South America.
Hydrological predictions at sub-seasonal to seasonal (S2S) timescales can support improved decision-making in climate-dependent sectors like agriculture and hydropower. Here, we present an S2S hydrological forecasting system (S2S-HFS) for western tropical South America (WTSA). The system uses the global NASA Goddard Earth Observing System S2S meteorological forecast system (GEOS-S2S) in combination with the Generalized Analog Regression Downscaling algorithm and the NASA Land Information System (LIS). In this implementation study, we evaluate system performance for three-month hydrological forecasts for the austral autumn season (March–April–May) using ensemble hindcasts for 2002-2017. Results indicate that the S2S-HFS generally offers skill in predictions of monthly precipitation up to one month lead, evapotranspiration up to 2 months lead, and soil moisture content up to three months lead. Ecoregions with better hindcast performance are located either in the coastal lowlands or in the Amazon lowland forest. We perform dedicated analysis to understand how two important teleconnections affecting the region are represented in the S2S-HFS: the El Niño Southern Oscillation (ENSO) and the Antarctic Oscillation (AAO). We find that forecast skill for all variables at one month lead is enhanced during the positive phase of ENSO and the negative phase of AAO. Overall, this study indicates that there is meaningful skill in the S2S-HFS for many ecoregions in WTSA, particularly for long memory variables such as soil moisture. The skill of the precipitation forecast, however, decays rapidly after forecast initialization, a phenomenon that is consistent with S2S meteorological forecasts over much of the world.
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